Tokenization Strategies for Single-Cell RNA Sequencing Data in Foundation Models: A Comprehensive Guide

Hazel Turner Nov 27, 2025 349

Single-cell foundation models (scFMs) are transforming biomedical research by enabling large-scale analysis of cellular heterogeneity.

Tokenization Strategies for Single-Cell RNA Sequencing Data in Foundation Models: A Comprehensive Guide

Abstract

Single-cell foundation models (scFMs) are transforming biomedical research by enabling large-scale analysis of cellular heterogeneity. Tokenization—the process of converting raw scRNA-seq data into model-processable units—is a critical yet challenging step that directly impacts model performance on tasks like cell type annotation, batch integration, and drug sensitivity prediction. This article provides a comprehensive overview of tokenization strategies for scRNA-seq data in scFMs, covering foundational concepts, methodological approaches, troubleshooting guidelines, and validation frameworks. Drawing from the latest research and benchmarking studies, we offer practical insights for researchers and drug development professionals seeking to implement scFMs effectively, highlighting how optimal tokenization strategies can enhance biological discovery and clinical applications.

Understanding Tokenization: The Bridge Between Single-Cell Biology and Foundation Models

Defining Tokenization in the Context of Single-Cell Genomics

Tokenization serves as the critical first step in processing single-cell RNA sequencing (scRNA-seq) data for analysis with foundation models (scFMs), bridging the gap between biological complexity and computational analysis. In natural language processing (NLP), tokenization converts raw text into discrete units (tokens) that models can process. Similarly, for single-cell genomics, tokenization transforms gene expression profiles from individual cells into structured sequences that transformer-based architectures can interpret [1]. This process enables researchers to apply advanced deep learning techniques to explore cellular heterogeneity and gene regulatory networks at unprecedented resolution [2] [1].

The fundamental challenge in single-cell data tokenization stems from the non-sequential nature of genomic data. Unlike words in a sentence, genes in a cell have no inherent ordering, requiring researchers to impose artificial sequences that preserve biological meaning while enabling computational efficiency [1]. This technical guide examines current tokenization strategies within the broader thesis that effective tokenization methodologies are paramount for advancing single-cell foundation models in research and therapeutic development.

Fundamental Concepts and Biological Background

Single-Cell RNA Sequencing Basics

Single-cell RNA sequencing (scRNA-seq) has revolutionized genomics by enabling researchers to measure gene expression at the resolution of individual cells, unlike traditional bulk RNA sequencing which only provides population averages [2]. This technology captures the fundamental unit of biological organization, revealing cellular heterogeneity within tissues that was previously obscured [3] [2]. The typical scRNA-seq workflow involves cell isolation, library preparation, sequencing, and computational analysis, generating complex datasets where each cell is represented by expression levels of thousands of genes [3].

From Bulk to Single-Cell Resolution

Bulk RNA sequencing averages expression across thousands to millions of cells, masking differences between individual cells. In contrast, scRNA-seq preserves cellular heterogeneity, allowing identification of rare cell populations, transitional states, and complex cellular hierarchies [2]. This resolution is particularly valuable for understanding tumor microenvironments, developmental biology, and immune system complexity, where cellular diversity drives functional outcomes [2].

The Data Structure of scRNA-seq

A typical scRNA-seq dataset consists of a gene-cell matrix with rows representing genes (features) and columns representing individual cells (observations) [3]. The values in this matrix represent molecular counts, which are notably sparse due to both biological and technical factors, including dropout events where genes are detected in some cells but not others despite being expressed [4]. This high-dimensional sparsity presents unique challenges for analysis and interpretation that tokenization strategies must address.

Tokenization Methodologies for scRNA-seq Data

Core Principle: Analogizing Biological Data to Language

In single-cell foundation models, the tokenization process establishes a conceptual analogy between genomics and natural language: cells represent documents, genes represent vocabulary, and expression patterns represent sentences [1]. This framework allows researchers to leverage advanced NLP architectures for biological discovery. As noted in a recent Nature review, "In these scFMs, individual cells are treated analogously to sentences, and genes or other genomic features along with their values are treated as words or tokens" [1].

Primary Tokenization Strategies

Table 1: Comparison of Primary Tokenization Strategies for scRNA-seq Data

Strategy Method Description Advantages Limitations
Expression Ranking Genes are ordered by expression level within each cell to create a deterministic sequence [1] Provides consistent ordering; captures most highly expressed genes May overlook co-expression patterns of moderately expressed genes
Expression Binning Continuous expression values are discretized into bins, with each bin representing a token category [1] [5] Handles continuous nature of expression data; reduces dimensionality May lose subtle expression differences; introduces arbitrary bin boundaries
Binary Tokenization Genes are represented as present or absent based on detection thresholds [4] Reduces technical noise; simplifies model input Loses quantitative expression information
Hybrid Embedding Combines gene identity embeddings with expression value embeddings [5] Preserves both gene identity and expression level information Increases model complexity and computational requirements
The Tokenization Workflow

The tokenization process follows a structured pipeline to convert raw gene expression data into model-ready tokens:

TokenizationWorkflow RawData Raw Expression Matrix QC Quality Control & Normalization RawData->QC GeneSelection Gene Selection (Optional) QC->GeneSelection TokenizationMethod Tokenization Strategy Application GeneSelection->TokenizationMethod InputRepresentation Model Input Representation TokenizationMethod->InputRepresentation

Figure 1: The sequential workflow for tokenizing scRNA-seq data, from raw counts to model input representation.

Advanced Tokenization Considerations
Incorporating Biological Prior Knowledge

Advanced tokenization approaches integrate biological context through gene metadata inclusion, such as chromosomal location, pathway membership, or protein-protein interaction data [1]. For example, some models prepend special tokens representing cell type or experimental conditions, enabling the model to learn context-dependent gene interactions [1] [5].

Multi-Modal Tokenization

With the rise of multi-omics technologies, tokenization strategies have expanded to incorporate diverse data types including chromatin accessibility (scATAC-seq), spatial coordinates, and protein abundance [1]. This requires modality-specific tokens that allow the model to distinguish between data types while learning integrated representations [1].

Integration with Single-Cell Foundation Model Architectures

Transformer Architectures for Single-Cell Data

Single-cell foundation models predominantly utilize transformer architectures, which employ self-attention mechanisms to weight the importance of different genes when making predictions [1] [4]. These architectures come in several variants:

  • Encoder-only models (e.g., BERT-style): Use bidirectional attention to learn from all genes simultaneously, ideal for classification tasks like cell type annotation [1] [5]
  • Decoder-only models (e.g., GPT-style): Employ masked self-attention that iteratively predicts genes conditioned on known values, suited for generation tasks [1]
  • Encoder-decoder models: Combine both approaches for complex tasks requiring both understanding and generation [1]
Comprehensive scFM Architecture

scFMArchitecture Input Single-Cell Expression Matrix Tokenization Tokenization Module Input->Tokenization Embedding Token Embedding + Positional Encoding Tokenization->Embedding Transformer Transformer Layers with Self-Attention Embedding->Transformer Output Task-Specific Output Heads Transformer->Output

Figure 2: End-to-end architecture of single-cell foundation models showing tokenization's role.

Positional Encoding Strategies

Since gene expression data lacks natural ordering, positional encoding provides artificial sequence information to the model. Common approaches include:

  • Expression-based positioning: Genes are positioned in sequences according to expression magnitude [1]
  • Learnable positional embeddings: Each position in the sequence receives a trainable embedding vector [4]
  • Biological prior positioning: Genes are ordered according to chromosomal location or functional relationships [1]

Experimental Validation and Performance Metrics

Benchmarking Tokenization Strategies

Table 2: Performance Comparison of Tokenization Methods on Cell Type Annotation

Model Tokenization Approach Accuracy F1-Score Computational Efficiency
scBERT [5] Gene embedding + expression binning 85.1% (NeurIPS) 0.815 Moderate
scGPT [1] Expression ranking + value normalization 84.3% (Benchmark) 0.801 High requirements
scSFUT [4] Fixed-window sub-vector segmentation 86.7% (Cross-species) 0.839 High efficiency
ACTINN [4] Traditional feature selection 80.1% (Benchmark) 0.745 High efficiency
Seurat [5] Reference mapping 80.1% (NeurIPS) 0.640 Moderate
Case Study: scBERT Validation on NeurIPS Dataset
Experimental Protocol

A comprehensive evaluation of scBERT was conducted on the NeurIPS dataset, comprising single-cell multi-omics data from mobilized peripheral CD34+ hematopoietic stem and progenitor cells (HSPCs) [5]. The experimental workflow followed these steps:

  • Data Acquisition: Gene expression data was obtained from the NeurIPS 2022 Kaggle competition, generated using 10X Chromium Single Cell Multiome ATAC + Gene Expression technology [5]
  • Cell Population: The dataset encompassed seven distinct cell types: B-cell progenitor (BP, n=262), erythrocyte progenitor (EryP, n=3,402), haematopoietic stem cell (HSC, n=10,757), mast cell progenitor (MasP, n=2,175), megakaryocyte progenitor (MkP, n=3,394), monocyte progenitor (MoP, n=258), and neutrophil progenitor (NeuP, n=3,663) [5]
  • Data Splitting: The dataset was divided with 70% for training and 30% for testing, with the training subset further split 80:20 for training and validation [5]
  • Model Training: scBERT was fine-tuned using the established protocol with a learning rate of 5e-5 and batch size of 32 for 50 epochs [5]
  • Performance Assessment: Model predictions were compared against ground truth annotations using accuracy, F1-score, and confusion matrix analysis [5]
Results and Interpretation

scBERT achieved a validation accuracy of 85.1%, outperforming Seurat (80.1%) on the same dataset [5]. On held-out test data, scBERT maintained strong performance with 83.97% accuracy compared to Seurat's 81.6% [5]. The statistical significance of this improvement was confirmed with a p-value of 0.0004 from a paired t-test [5].

Notably, the model demonstrated robust performance despite significant class imbalance in the dataset, with HSC cells representing 10,757 observations compared to only 258 MoP cells [5]. This highlights the resilience of properly tokenized transformer models to real-world data distribution challenges.

Table 3: Essential Research Resources for scRNA-seq Tokenization and Analysis

Resource Category Specific Tools/Platforms Primary Function Application in Tokenization
Data Repositories CZ CELLxGENE [1], PanglaoDB [5], Human Cell Atlas [1] Provide curated single-cell datasets Source of diverse training data for tokenizer development
Processing Tools Scanpy [5], Seurat [5] Quality control, normalization, and preprocessing Prepare raw data for tokenization through filtering and normalization
Foundation Models scBERT [5], scGPT [1], scSFUT [4] Pretrained models for single-cell analysis Implement various tokenization strategies for specific analytical tasks
Gene Reference Ensembl Gene Database [1], Gene Ontology [1] Standardized gene annotations Provide biological context for gene token embedding
Benchmark Datasets Zheng68k [5], MacParland [5], NeurIPS Multiome [5] Standardized evaluation datasets Enable comparative assessment of tokenization methodologies

Challenges and Future Directions

Current Limitations in Tokenization Practices

Despite significant advances, current tokenization approaches face several challenges:

  • Sequence arbitrariness: Artificial gene ordering lacks biological justification and may introduce bias [1]
  • Batch effects: Technical variations between experiments can confound biological signals [1]
  • Computational intensity: Processing millions of cells with full gene complements demands substantial resources [4]
  • Cross-species generalization: Models trained on human data may not transfer effectively to other organisms [4]
Emerging Innovations

Promising research directions aim to address these limitations through:

  • Biological attention mechanisms: Incorporating known gene-gene interactions to guide model attention [1]
  • Adaptive tokenization: Dynamically adjusting tokenization strategy based on data characteristics [4]
  • Multi-resolution approaches: Combining gene-level, pathway-level, and cell-level tokens [1]
  • Transferable representations: Developing tokenization schemes that generalize across technologies and species [4]

Tokenization represents a fundamental preprocessing step that translates continuous, high-dimensional scRNA-seq data into structured representations amenable to analysis by single-cell foundation models. As the field progresses toward increasingly integrated multi-omic assays and larger-scale cellular atlases, sophisticated tokenization strategies will play an ever more critical role in unlocking biological insights. The development of biologically informed, computationally efficient tokenization methods remains an active area of research with significant potential to advance both basic science and therapeutic development.

Tokenization serves as the foundational bridge that transforms the complex, high-dimensional language of biology into a structured format that artificial intelligence models can comprehend and process. In the context of single-cell RNA sequencing (scRNA-seq) data and single-cell foundation models (scFMs), effective tokenization strategies are paramount for capturing cellular heterogeneity, gene-gene interactions, and regulatory networks. This technical guide examines current tokenization methodologies, their computational implementations, and their impact on downstream biological discovery. We provide a comprehensive framework for researchers seeking to implement robust tokenization pipelines that preserve biological signal while enabling scalable machine learning applications in drug development and basic research.

Single-cell RNA sequencing has revolutionized our understanding of cellular heterogeneity, revealing striking differences in gene expression between individual cells that were previously masked in bulk sequencing approaches. The transcriptome of each cell represents a complex, high-dimensional molecular signature of its identity, state, and function [6]. However, this biological complexity presents substantial computational challenges: scRNA-seq data is characterized by extreme sparsity, technical noise, high dimensionality, and dropout events where transcripts fail to be detected even when present in the cell [7].

Single-cell foundation models (scFMs) represent a promising approach to deciphering this complexity, leveraging transformer architectures originally developed for natural language processing (NLP) [1]. The core premise is intuitive: if we can represent biological data in a format that AI can understand, we can uncover patterns beyond human analytical capacity. In this framework, tokenization—the process of converting raw gene expression data into discrete, machine-readable units—becomes the critical first step that determines what patterns the model can and cannot learn [1] [8].

Without effective tokenization, even the most sophisticated neural network architectures struggle to extract meaningful biological signals from the sparse, noisy matrices that characterize scRNA-seq data. This whitepaper examines how tokenization strategies enable researchers to transform cellular heterogeneity into machine-readable data, facilitating discoveries in cell development, disease mechanisms, and therapeutic interventions.

The Computational Challenge: From Gene Expression to Token Sequences

Fundamental Data Characteristics

ScRNA-seq data presents several unique computational challenges that tokenization must address. The data is typically represented as a matrix with cells as rows and genes as columns, with each entry representing the expression count of a particular gene in a particular cell. This structure exhibits:

  • High dimensionality: Tens of thousands of genes measured per cell [6]
  • Extreme sparsity: Typically 80-95% zero values due to biological and technical factors [7]
  • Technical noise: Amplification bias, batch effects, and dropout events [7]
  • Non-sequential nature: Unlike natural language, genes have no inherent ordering [1]

The Tokenization Solution Space

Tokenization strategies for scRNA-seq data must transform this challenging data structure into sequential token representations compatible with transformer architectures. The following table summarizes the primary approaches and their characteristics:

Table 1: Comparative Analysis of Tokenization Strategies for scRNA-seq Data

Strategy Core Methodology Advantages Limitations Representative Models
Gene-level Tokenization Each gene represents a unique token ID Direct biological interpretability; Preserves gene identity Requires fixed gene vocabulary; Cannot handle unseen genes scBERT, scGPT, Geneformer
Expression-based Ranking Genes ordered by expression magnitude within each cell Creates deterministic sequences from non-sequential data Arbitrary ordering may not reflect biological relationships scGPT, TOSICA
Binning Approaches Expression values discretized into bins Captures expression level information beyond presence/absence Introduces ordinality assumptions; Information loss scBERT
Hybrid Methods Combines gene identity with expression information Richer representation of transcriptional state Increased computational complexity scSFUT
Dynamic Token Adaptation Modifies token embeddings based on external data (e.g., DNA sequence) Enables multi-modal integration; Context-aware representations Requires additional data processing Bio-DTA

Tokenization Architectures and Implementation Frameworks

Core Tokenization Workflow

The tokenization process typically follows a structured pipeline that transforms raw count data into model-ready token sequences. The following diagram illustrates this generalized workflow:

G cluster_legend Processing Stages RawData Raw scRNA-seq Matrix QC Quality Control & Normalization RawData->QC GeneSelection Gene Selection/ Filtering QC->GeneSelection TokenDefinition Token Definition Strategy GeneSelection->TokenDefinition SequenceConstruction Sequence Construction TokenDefinition->SequenceConstruction Embedding Token Embedding SequenceConstruction->Embedding ModelInput Model Input Embedding->ModelInput LegendStart Input/Output LegendProcess Processing Step

Advanced Tokenization Methodologies

Expression-Based Ranking and Sequencing

A predominant strategy for overcoming the non-sequential nature of gene expression data involves creating an artificial sequence by ranking genes based on their expression values. In this approach, each cell is treated as a "sentence" where genes are ordered from highest to lowest expression, creating a deterministic sequence that captures the most biologically relevant signals [1]. Models such as scGPT and Geneformer employ variations of this method, typically selecting the top 1,000-2,000 highly variable genes based on expression magnitude [9].

The ranking process follows this protocol:

  • Library size normalization: Normalize counts per cell to account for varying sequencing depths
  • Log transformation: Apply log(1+x) transformation to stabilize variance
  • Gene ranking: Sort genes by expression value in descending order
  • Sequence truncation: Select top N genes (typically 1,000-2,000) based on computational constraints
  • Token ID assignment: Map each gene to a unique token identifier in the model's vocabulary
Bin-Based Tokenization

An alternative approach, implemented in models like scBERT, discretizes expression values into bins or categories [8]. This method represents both gene identity and expression level information:

  • Expression value discretization: Categorize expression values into bins (e.g., low, medium, high)
  • Composite tokens: Create tokens that represent gene-bin combinations (e.g., "GENEAhigh")
  • Sequence construction: Order tokens based on biological knowledge or expression levels

This approach preserves more quantitative information about expression levels but increases the vocabulary size and requires careful handling of expression value normalization across cells and datasets.

Dynamic Token Adaptation

Recent advances in multi-modal single-cell foundation models have introduced dynamic token adaptation (DTA), which modifies token embeddings based on external data sources [9]. Bio-DTA implements this approach by:

  • DNA sequence processing: Generating embeddings from DNA sequences around transcriptional start sites using Enformer
  • Adapter projection: Mapping DNA sequence embeddings to the token embedding space via a multilayer perceptron
  • Contextual integration: Using these dynamic embeddings as input to transformer encoders alongside standard gene tokens

This approach enables the model to learn connections between genetic variation and gene expression patterns, providing a more comprehensive view of cellular function.

Experimental Protocols and Implementation Guidelines

Standardized Tokenization Protocol for scRNA-seq Data

Based on current best practices across multiple scFMs, we recommend the following detailed protocol for tokenizing scRNA-seq data:

Input Requirements:

  • Raw or normalized count matrix (cells × genes)
  • Gene identifiers (e.g., ENSEMBL IDs, gene symbols)
  • Metadata (e.g., batch information, sample conditions)

Processing Steps:

  • Quality Control and Filtering

    • Filter cells with fewer than 200 detected genes
    • Filter cells with high mitochondrial content (>5-20%)
    • Remove genes expressed in fewer than 3 cells [8]
    • Apply doublet detection algorithms (e.g., DoubletFinder) [10]

  • Normalization

    • Apply library size normalization (e.g., 10,000 counts per cell)
    • Log-transform using log(1+x) [10]
    • Optionally regress out technical covariates (e.g., batch effects)

  • Gene Selection

    • Identify highly variable genes using Seurat v3 or SCANPY workflows
    • Select top 1,000-2,000 genes for computational efficiency [9]

  • Token Sequence Construction

    • For each cell, sort selected genes by normalized expression values
    • Truncate to top N genes (N determined by model constraints)
    • Map each gene to its corresponding token ID

  • Special Tokens and Metadata Integration

    • Add [CLS] token for cell-level representation [1]
    • Include special tokens for batch information or experimental conditions
    • Append [PAD] tokens to maintain consistent sequence length

Validation and Quality Assessment

To ensure tokenization preserves biological signal, implement the following quality checks:

  • Reconstruction accuracy: Measure ability to reconstruct original expression values from token sequences
  • Batch effect monitoring: Assess whether batch information leaks into token representations
  • Biological fidelity: Verify that known cell type markers remain discriminative after tokenization

Table 2: Research Reagent Solutions for Tokenization Implementation

Reagent/Resource Function Implementation Examples
CellRanger Processing raw sequencing data to count matrices 10x Genomics pipeline for initial data generation
SCANPY/Seurat Quality control, normalization, and gene selection Standard preprocessing workflows in Python/R
HVG Selection Identifying highly variable genes for token reduction Seurat v3, SCANPY highlyvariablegenes()
Tokenizer Libraries Mapping genes to token IDs with vocabulary management Hugging Face Tokenizers, custom implementations
UMAP/t-SNE Visual validation of tokenization quality Projection of token embeddings to 2D space
Batch Correction Removing technical artifacts pre-tokenization Combat, Harmony, Scanorama

Technical Innovations in Tokenization Architectures

The scSFUT Approach: Scale-Free and Unbiased Tokenization

The Single-Cell Scale-Free and Unbiased Transformer (scSFUT) introduces a novel tokenization approach that processes full-length gene vectors without requiring gene selection [8]. This method addresses key limitations in existing approaches:

  • Gene Embedding Algorithm:

    • Segments each cell's gene expression vector into fixed-size windows
    • Applies 1D-convolution to capture local gene-gene correlations
    • Generates token sequences that preserve global gene context

  • Bias-Free Attention Mechanism:

    • Implements precision-preserving attention computation
    • Avoids the low-rank approximations that introduce performance bias in models like scBERT and xTrimoGene [8]

  • End-to-End Trainable Architecture:

    • Jointly optimizes tokenization and model objectives
    • Enables learning of token representations specific to annotation tasks

Multi-Modal Token Integration

Advanced scFMs are increasingly incorporating multiple data modalities through specialized tokenization strategies. The sciRED framework demonstrates how factor analysis can guide tokenization for improved interpretability [11]. This approach:

  • Decomposes variation into technical and biological components
  • Guides token selection based on factors with strong biological signatures
  • Enables residual tokenization that focuses on unexplained biological variation

The following diagram illustrates how multi-modal tokenization integrates diverse data sources:

G DNA DNA Sequence Data TokenAdapter Dynamic Token Adapter DNA->TokenAdapter RNA scRNA-seq Expression RNA->TokenAdapter ATAC scATAC-seq Accessibility ATAC->TokenAdapter Protein Protein Abundance Protein->TokenAdapter MultiModalTokens Multi-Modal Token Sequence TokenAdapter->MultiModalTokens Transformer Transformer Encoder MultiModalTokens->Transformer LatentRep Integrated Latent Representation Transformer->LatentRep

Impact on Biological Discovery and Therapeutic Applications

Effective tokenization strategies have enabled significant advances in biological discovery and drug development applications:

Enhanced Cell Type Annotation

Optimized tokenization enables more precise and automated cell type identification, a fundamental task in single-cell analysis. Models leveraging sophisticated tokenization strategies demonstrate:

  • Cross-species generalization: Ability to annotate cell types across different organisms [8]
  • Rare cell detection: Improved identification of rare populations through preservation of subtle expression patterns [11]
  • Continuous state identification: Capture of transitional cell states through fine-grained token representations

Disease Mechanism Elucidation

In rheumatoid arthritis research, latent factor models guided by appropriate tokenization identified novel disease-associated pathways:

  • OSMR signaling signature in synovial fibroblasts [12]
  • MERTK-mediated efferocytic signature in synovial monocytes [12]

These discoveries were enabled by tokenization approaches that preserved subtle expression patterns in specific cellular subpopulations that might be lost with aggressive gene filtering.

Toxicological Applications

In toxicology, tokenization strategies that maintain sensitivity to dose-dependent changes have revealed:

  • Cell type-specific responses to chemical exposures [10]
  • Alterations in cell type proportions following toxicant exposure [10]
  • Pathway-specific perturbations that inform mechanistic toxicology

Future Directions and Emerging Challenges

As single-cell technologies continue to evolve, tokenization strategies must adapt to several emerging challenges and opportunities:

Scaling Constraints and Solutions

The exponential growth in single-cell dataset sizes presents ongoing challenges for tokenization:

  • Memory limitations with millions of cells and tens of thousands of genes
  • Computational efficiency requirements for processing large-scale atlases
  • Distributed tokenization approaches for multi-institutional datasets

Multi-Modal Integration

Future tokenization strategies must seamlessly integrate diverse data types:

  • Spatial transcriptomics incorporating positional information
  • Epigenetic data from scATAC-seq and methylation profiling
  • Protein abundance from CITE-seq and related technologies
  • Time-series and perturbation data capturing dynamic responses

Interpretability and Biological Validation

As tokenization strategies become more complex, maintaining interpretability is crucial:

  • Benchmarking standards for evaluating tokenization quality
  • Biological validation frameworks connecting token representations to known biology
  • Visualization tools for exploring token-level contributions to model predictions

Tokenization represents the critical interface between biological complexity and computational analysis in single-cell genomics. By transforming high-dimensional, sparse gene expression data into structured token sequences, researchers can leverage the full power of modern foundation models to unravel cellular heterogeneity, disease mechanisms, and therapeutic opportunities. The continuing evolution of tokenization strategies—from simple gene ranking to dynamic, multi-modal approaches—will undoubtedly drive further advances in both basic biology and translational applications. As the field progresses, developing standardized, validated, and interpretable tokenization pipelines will be essential for ensuring that biological insights keep pace with technological capabilities.

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the investigation of cellular heterogeneity at an unprecedented resolution. However, the analysis of scRNA-seq data is fraught with computational challenges that stem from its inherent properties. This technical guide examines three core challenges—sparsity, high dimensionality, and the non-sequential nature of the data—within the specific context of developing tokenization strategies for single-cell foundation models (scFMs). As the field moves toward analyzing millions of cells and integrating multi-omic modalities, addressing these challenges becomes paramount for unlocking the full potential of single-cell genomics. The emergence of scFMs, which treat cells as "sentences" and genes as "words," offers a promising framework for unified analysis but requires specialized approaches to handle the unique structure of single-cell data.

The Challenge of Data Sparsity

Understanding scRNA-seq Sparsity

Sparsity in scRNA-seq data refers to the abundance of zero counts, which can exceed 90% of all measurements in a dataset. These zeros represent a mixture of biological and technical factors: true absence of transcripts (biological zeros) and failure to detect present transcripts due to limited sequencing depth (technical zeros or "dropouts") [13]. The sparsity challenge has intensified as technological advances have enabled the sequencing of exponentially more cells. Analysis of 56 datasets published between 2015 and 2021 revealed a clear trend: as the number of cells per dataset increases, the detection rate (fraction of non-zero values) decreases [13]. This inverse relationship means that newer, larger datasets are becoming progressively sparser, presenting substantial analytical difficulties.

Consequences of Sparsity

The preponderance of zeros in scRNA-seq data creates significant problems for conventional analysis methods. Standard count distribution models (e.g., Poisson) do not account for this excess of zeros, leading to biased inferences [13]. Sparsity can obscure true biological signals, particularly for rare cell types and lowly-expressed genes, potentially leading to their misclassification or complete omission from analyses [14]. Furthermore, traditional analytical approaches that rely on count-based metrics may become less informative as sparsity increases, necessitating alternative computational frameworks.

Binarization as a Strategy for Sparse Data

Table 1: Performance Comparison of Count-Based vs. Binary-Based Analysis Methods

Analysis Task Count-Based Approach Binary-Based Approach Performance Comparison
Dimensionality Reduction PCA on normalized counts PCA on binarized data Highly similar UMAP visualizations (r ≥ 0.73 correlation) [13]
Data Integration Harmony on count-based PCA Harmony on binary-based PCA Improved mixing for binary representation (LISI: 1.18 vs. 1.12) [13]
Cell Type Identification scPred/SingleR on counts scPred/SingleR on binarized data Highly similar performance (median F1-score ~0.93) [13]
Differential Expression Pseudobulk with mean expression Pseudobulk with detection rate Strong correlation (Spearman's ρ ≥ 0.99) [13]

Interestingly, the very sparsity that complicates analysis also presents opportunities. With the increasing prevalence of zeros, a binary representation (where zero counts remain zero and non-zero counts become one) can capture most of the biological signal while offering substantial computational advantages [13]. Research has demonstrated that the correlation between normalized expression counts and their binarized counterparts is remarkably strong (point-biserial correlation p = 0.93 on average across ~1.5 million cells), indicating that binarization preserves the essential biological information [13]. This strong correlation is primarily explained by the detection rate and the variance of non-zero counts, with sparser datasets showing higher correlations between count and binary representations.

Specialized methods have been developed to leverage binarized data. For instance, scBFA is a dimensionality reduction method specifically designed for binarized scRNA-seq data that demonstrates improved visualization and classification of cell identity [13]. Similarly, Binary Differential Analysis (BDA) enables differential expression analysis from binarized data, faithfully capturing biological variation across cell types and conditions [13]. These approaches highlight how embracing sparsity through appropriate computational strategies can yield robust biological insights while offering computational efficiency.

The Challenge of High Dimensionality

The Dimensionality Problem

A typical scRNA-seq dataset measures expression levels of thousands of genes across thousands to millions of cells, creating a high-dimensional space where each gene represents a dimension. This high dimensionality presents multiple analytical challenges, including increased computational demands, the "curse of dimensionality" where distance metrics become less meaningful, and difficulty in visualizing the underlying structure of the data [14]. The problem is exacerbated by technical noise and skewed distributions that can obscure true biological signals.

Dimensionality Reduction Strategies

Table 2: Dimensionality Reduction Methods for scRNA-seq Data

Method Type Key Mechanism Advantages Limitations
PCA Linear Identifies orthogonal axes of maximum variance Computationally efficient, preserves global structure Assumes linear relationships, sensitive to outliers
t-SNE Non-linear Minimizes KL divergence between high-/low-dim distributions Effective at preserving local structure Poor preservation of global structure, results sensitive to parameters
UMAP Non-linear Minimizes cross-entropy between high-/low-dim distributions Better global structure preservation than t-SNE Cluster distances may not reflect true biological differences
scLENS Non-linear RMT-based noise filtering with L2 normalization Data-driven dimension determination, handles sparsity well Relatively new method, less widely adopted
supCPM Supervised non-linear Capacity-adjusted distance with cluster label guidance Preserves global structure, tracks cluster variance Requires accurate cluster labels as input

Dimensionality reduction (DR) methods are essential for navigating high-dimensional scRNA-seq data. These techniques project the data into a lower-dimensional space while attempting to preserve important biological relationships. Principal Component Analysis (PCA) identifies orthogonal axes of maximum variance in the data and is widely used for initial exploration [15]. Non-linear methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have become standards for visualization, with UMAP particularly valued for preserving both local and global relationships [15].

However, conventional DR methods have limitations. Most require subjective user decisions to set thresholds that differentiate signal from noise, introducing potential bias and reducing reproducibility [14]. Methods like t-SNE and UMAP may not optimally preserve global geometric structure, potentially resulting in misleading visualizations where clusters appear close in the embedded space despite being distant in the original high-dimensional space [16].

Advanced Solutions for Dimensionality Reduction

Recent methodological advances address these limitations through automated, data-driven approaches. scLENS (single-cell Low-dimension Embedding using effective Noise Subtraction) incorporates random matrix theory (RMT)-based noise filtering to automatically identify biologically meaningful signals without subjective user input [14]. This method first applies L2 normalization after log normalization to prevent signal distortion caused by variations in total gene counts between cells, then uses RMT to distinguish true biological signals from random noise, and finally applies a signal robustness test to filter out low-quality signals caused by dropouts.

Supervised approaches represent another advancement. supCPM (supervised Capacity Preserved Mapping) incorporates cluster label information to guide dimensionality reduction, addressing the crowding issue common in other methods while preserving global geometric structure and tracking cluster variance [16]. This method uses a capacity-adjusted distance metric that accounts for differences in intrinsic dimensionality across the data, enabling more faithful visualizations that maintain both local and global relationships.

The Non-Sequential Nature of scRNA-seq Data

The Sequence Problem for Foundation Models

The successful application of transformer architectures from natural language processing to single-cell biology presents a fundamental challenge: unlike words in a sentence, genes in a cell have no inherent ordering [17]. This non-sequential nature contradicts the basic assumption of transformer models, which process input as ordered sequences where position carries meaningful information. Developing effective tokenization strategies that impose a meaningful sequence on gene expression data is therefore crucial for the development of effective single-cell foundation models (scFMs).

Tokenization Strategies for scFMs

Table 3: Tokenization Strategies for Single-Cell Foundation Models

Strategy Description Advantages Challenges
Expression Ranking Orders genes by expression level within each cell Deterministic, emphasizes highly expressed genes May overlook important low-expression genes
Expression Binning Groups genes into bins based on expression values Reduces sensitivity to small expression variations Requires careful bin definition
Gene Identifier Sequencing Uses fixed gene ordering (e.g., alphabetical, genomic position) Consistent across cells, simple to implement May not reflect biological relationships
Metadata Incorporation Includes gene or cell metadata as special tokens Provides additional biological context Increases model complexity
Modality Indicators Adds tokens indicating data modality (RNA, ATAC, etc.) Enables multi-omic integration Requires harmonization across data types

Several tokenization strategies have emerged to address the non-sequential nature of scRNA-seq data for foundation models. A common approach involves imposing an artificial ordering based on expression levels, such as ranking genes within each cell by their expression values and feeding the ordered list as the "sentence" for the model [17]. This provides a deterministic sequence that emphasizes highly expressed genes. Alternative approaches partition genes into bins based on expression values or simply use normalized counts without complex ranking schemes [17].

Beyond basic tokenization, scFMs often incorporate special tokens to enrich the input representation. These may include tokens representing cell identity and metadata, modality indicators for multi-omic data, and gene metadata such as gene ontology terms or chromosomal locations [17]. Positional encoding schemes are then adapted to represent the relative order or rank of each gene in the cell, enabling the transformer architecture to process the artificially sequenced data.

Implications for Model Architecture and Performance

The tokenization strategy directly impacts model performance and interpretability. While some models report robustness to technical biases without incorporating batch-specific tokens, others explicitly include batch information as special tokens to account for technical variation [17]. The resulting latent embeddings from scFMs capture gene-gene and cell-cell relationships, enabling various downstream tasks including cell type annotation, data correction, and simulation of cellular responses to perturbations.

Integrated Experimental Framework

A Combined Protocol for Addressing scRNA-seq Challenges

This section outlines an integrated experimental protocol that simultaneously addresses sparsity, high dimensionality, and the non-sequential nature of scRNA-seq data within the context of scFM development.

Step 1: Data Preprocessing and Sparsity Management

  • Begin with quality control to remove low-quality cells and genes.
  • Apply L2 normalization following log normalization to prevent signal distortion caused by variations in sequencing depth [14]. This step ensures that cell vector lengths are uniform, addressing artifacts that can arise from differences in total gene counts.
  • Consider binarization for extremely sparse datasets or when computational efficiency is paramount, particularly for large-scale studies with >100,000 cells [13].

Step 2: Automated Dimensionality Reduction

  • Implement RMT-based noise filtering to automatically determine signal dimensions without subjective user input [14].
  • Calculate the cell similarity matrix from normalized data and perform eigenvalue decomposition.
  • Fit eigenvalues to the Marchenko-Pastur distribution to distinguish biological signals from random noise, using the Tracy-Widom distribution to establish significance thresholds.
  • Apply a signal robustness test through binary sparse perturbation to remove low-quality signals caused by dropouts.

Step 3: Tokenization for Foundation Models

  • For scFM development, select an appropriate tokenization strategy based on data characteristics and analytical goals.
  • For cell-type identification tasks, expression-based ranking often provides effective sequencing.
  • For multi-omic integration, incorporate modality-specific tokens and consider metadata enrichment.
  • Convert tokens to embedding vectors with positional encoding that reflects the chosen sequencing strategy.

Step 4: Validation and Interpretation

  • Validate the resulting representations through downstream tasks including clustering, visualization, and differential expression analysis.
  • Compare binary and count-based representations to ensure essential biological signals are preserved.
  • Assess embedding quality using silhouette scores, clustering concordance, and biological consistency of identified patterns.

G cluster_0 Sparsity Solutions cluster_1 Dimensionality Solutions cluster_2 Tokenization Solutions scRNA-seq Data scRNA-seq Data Preprocessing & Sparsity Management Preprocessing & Sparsity Management scRNA-seq Data->Preprocessing & Sparsity Management Automated Dimensionality Reduction Automated Dimensionality Reduction Preprocessing & Sparsity Management->Automated Dimensionality Reduction L2 Normalization L2 Normalization Preprocessing & Sparsity Management->L2 Normalization Binarization Binarization Preprocessing & Sparsity Management->Binarization Tokenization for scFMs Tokenization for scFMs Automated Dimensionality Reduction->Tokenization for scFMs RMT Noise Filtering RMT Noise Filtering Automated Dimensionality Reduction->RMT Noise Filtering Automated Dimension Detection Automated Dimension Detection Automated Dimensionality Reduction->Automated Dimension Detection Validation & Interpretation Validation & Interpretation Tokenization for scFMs->Validation & Interpretation Expression Ranking Expression Ranking Tokenization for scFMs->Expression Ranking Metadata Enrichment Metadata Enrichment Tokenization for scFMs->Metadata Enrichment Biological Insights Biological Insights Validation & Interpretation->Biological Insights Signal Robustness Test Signal Robustness Test Validation & Interpretation->Signal Robustness Test Capacity Preserved Mapping Capacity Preserved Mapping Multi-omic Integration Multi-omic Integration

Experimental Workflow for Addressing scRNA-seq Challenges

Essential Research Reagents and Computational Tools

Table 4: Essential Research Reagents and Tools for scRNA-seq Analysis

Category Item Function/Purpose
Computational Frameworks Seurat, Scanpy Comprehensive scRNA-seq analysis platforms providing preprocessing, normalization, and basic dimensionality reduction [14]
Dimensionality Reduction scLENS Automated dimensionality reduction with RMT-based noise filtering and signal robustness testing [14]
Dimensionality Reduction supCPM Supervised visualization preserving global structure and cluster variance [16]
Binarization Methods scBFA Dimensionality reduction specifically designed for binarized scRNA-seq data [13]
Foundation Models scBERT, GeneFormer Transformer-based models for single-cell data analysis requiring specialized tokenization [17]
Data Resources CZ CELLxGENE, Human Cell Atlas Curated single-cell data repositories providing standardized datasets for model training and validation [17]
Integration Tools Harmony Batch effect correction and data integration capable of processing both count and binary representations [13]

The core challenges of sparsity, high dimensionality, and non-sequential nature in scRNA-seq data represent significant but surmountable obstacles in the development of single-cell foundation models. Strategic approaches including binarization for sparse data, automated dimensionality reduction, and innovative tokenization strategies provide powerful solutions that not only address these challenges but also leverage them to extract meaningful biological insights. As single-cell technologies continue to evolve, producing ever-larger and more complex datasets, the computational frameworks outlined in this guide will become increasingly essential for unlocking the full potential of single-cell genomics in basic research and therapeutic development.

The rapid advancement of single-cell RNA sequencing (scRNA-seq) technologies has fundamentally transformed our ability to listen to the intricate conversations occurring within biological systems. This technological revolution provides an unprecedented view of cellular heterogeneity, enabling researchers to decompose tissues into their constituent cell types and states with remarkable resolution. As the scale and complexity of scRNA-seq datasets grow, the field increasingly borrows conceptual frameworks and computational techniques from other domains dealing with high-dimensional, sequential data. Among the most powerful of these borrowed paradigms is the analogy between natural language and cellular biology, where cells can be viewed as sentences and their constituent genes or genomic features as words [18].

This analogy forms the foundational premise for developing single-cell Foundation Models (scFMs)—large-scale neural networks pre-trained on massive corpora of single-cell data. Just as modern large language models (LLMs) learn the statistical relationships between words in vast text collections, scFMs aim to learn the fundamental "grammar" and "syntax" of cellular identity and function. The process of tokenization, which converts raw genetic features into model-readable numerical representations, stands as the critical first step in this analytical pipeline, directly influencing all downstream tasks from cell type annotation to perturbation response prediction [18]. This whitepaper explores the theoretical underpinnings, methodological considerations, and practical implementations of tokenization strategies for scRNA-seq data within scFM research, providing technical guidance for researchers and drug development professionals working at this interdisciplinary frontier.

The Core Analogy: Deconstructing the Linguistic Framework

Semantic Units in Biological Context

The linguistic analogy for single-cell data transforms our conceptual approach to cellular analysis. In this framework:

  • Vocabulary (Genes/Features): The complete set of genes measurable by a platform constitutes the model's vocabulary, typically numbering 20,000-30,000 for full-length scRNA-seq. Each gene represents a discrete semantic unit, analogous to a word in a language.
  • Sentences (Cells): Individual cells represent sentences formed from the vocabulary. The expression levels of genes within a cell create a unique "statement" describing that cell's current molecular state.
  • Documents (Samples/Tissues): Collections of cells from related biological contexts (e.g., a tissue sample, patient, or experimental condition) form documents comprising multiple cellular "sentences."
  • Corpus (Reference Atlases): Large-scale integrated datasets, such as the Human Cell Atlas, serve as the training corpus, containing billions of cellular "sentences" across diverse tissues, donors, and conditions [19].

This structural analogy enables the application of NLP techniques to biological data. However, important distinctions exist: biological "sentences" (cells) lack the explicit sequential ordering of linguistic sentences, and gene-gene relationships form complex, non-linear networks rather than simple linear dependencies.

Tokenization Strategies for Genetic Vocabulary

Tokenization converts the continuous, high-dimensional space of gene expression into discrete tokens suitable for model input. Current approaches in scFM research include:

Table 1: Tokenization Strategies for scRNA-seq Data in scFM Development

Strategy Mechanism Advantages Limitations Example Applications
Gene-based Tokenization Each gene represents a unique token ID Simple implementation, preserves gene identity Fixed vocabulary size, poor handling of novel genes scGPT, scFoundation [18]
Binned Expression Tokenization Expression values discretized into bins (e.g., low/medium/high) Captures expression magnitude, ordinal relationships Increased sequence length, bin boundaries arbitrary Geneformer
Hybrid Tokenization Combines gene ID + expression level tokens Rich representation of both identity and quantity Complex implementation, longer sequences -
Feature-based Tokenization Uses highly variable genes (HVGs) as vocabulary Reduced dimensionality, computational efficiency Potential information loss, selection method critical Seurat, Scanpy [19]

The choice of tokenization strategy profoundly impacts model performance. Gene-based tokenization maintains biological interpretability but faces challenges with the curse of dimensionality. Conversely, feature selection methods (e.g., highly variable gene selection) reduce computational burden but may discard biologically relevant information if not carefully implemented [19].

Benchmarking Tokenization: Insights from Feature Selection Studies

The Critical Role of Feature Selection

Feature selection serves as the biological equivalent of vocabulary pruning in NLP, identifying the most informative genes for downstream analysis. Recent benchmarking studies demonstrate that feature selection methods significantly impact the performance of scRNA-seq data integration and query mapping—key tasks for scFM development [19].

Comprehensive evaluations reveal that using highly variable genes (HVGs) consistently produces high-quality integrations, validating common practice in the field. However, the specific implementation details—including the number of features selected, batch-aware selection criteria, and integration method interactions—require careful consideration [19]. Studies assessing over 20 feature selection methods using metrics spanning five performance categories (batch effect removal, biological conservation, query mapping, label transfer, and unseen population detection) provide quantitative frameworks for evaluating tokenization strategies.

Quantitative Performance Comparisons

Table 2: Benchmarking Metrics for Feature Selection and Tokenization Strategies

Metric Category Key Metrics High-Performing Approaches Performance Range
Integration (Batch Correction) Batch PCR, CMS, iLISI Highly variable features (2000-3000 genes) 30-50% improvement over random features [19]
Integration (Biology Conservation) Isolated Label F1, bNMI, cLISI Batch-aware HVG selection 25-40% better biological preservation [19]
Query Mapping Cell Distance, Label Distance, mLISI Lineage-specific feature selection Mapping accuracy: 60-85% [19]
Label Transfer F1 (Macro/Micro/Rarity) Integration-specific feature selection F1 scores: 0.7-0.9 [19]
Unseen Population Detection Milo, Unseen Cell Distance Larger feature sets (3000-5000 genes) Detection precision: 45-75% [19]

These benchmarks reveal several critical insights for tokenization in scFMs. First, the number of selected features significantly impacts performance, with 2,000-3,000 features often representing a sweet spot between information content and noise reduction. Second, batch-aware feature selection methods—which account for technical variation across datasets—consistently outperform batch-agnostic approaches. Third, the optimal tokenization strategy depends on the specific downstream task, suggesting that scFMs may benefit from task-specific tokenization approaches [19].

Experimental Protocols for Tokenization Benchmarking

Data Acquisition and Preprocessing

Robust evaluation of tokenization strategies requires standardized data processing pipelines. The following protocol outlines the essential steps for preparing scRNA-seq data for tokenization benchmarking:

  • Data Collection: Obtain raw count matrices from public repositories (e.g., GEO, ArrayExpress) or process FASTQ files using established pipelines. For 10X Genomics data, use Cell Ranger (cellranger count) or alternative pseudo-alignment methods (e.g., alevin, kallisto-bustools) [20].
  • Quality Control: Filter cells based on quality metrics using tools like scuttle:

  • Normalization: Perform library size normalization and variance stabilization. The scran package provides effective methods for multi-batch data:

  • Data Integration: For multi-sample datasets, apply integration methods such as scVI, Harmony, or Seurat's CCA to remove batch effects while preserving biological variation [19] [21].

Feature Selection Methodologies

Different feature selection approaches directly correspond to alternative tokenization strategies for scFMs:

  • Highly Variable Gene Selection: Identify genes with higher-than-expected variability across cells using the Seurat (implemented in Scanpy) or scran methods:

  • Batch-Aware Feature Selection: Extend HVG selection to multi-batch datasets by selecting features that are variable across batches:

  • Lineage-Specific Feature Selection: Identify features associated with specific differentiation trajectories using pseudotime methods (e.g., Slingshot, Monocle3) or marker gene detection.

Performance Evaluation Framework

Comprehensive benchmarking requires multiple metric categories assessed through the following protocol:

  • Integration Quality:
    • Calculate batch correction metrics (Batch ASW, iLISI, Batch PCR) using the scib package [19].
    • Evaluate biological conservation using metrics (cLISI, ARI, NMI) on known cell type labels.
  • Query Mapping Accuracy:
    • Split datasets into reference and query subsets.
    • Map query cells to reference using integration method.
    • Calculate mapping confidence scores (kNN purity, mapping entropy).
  • Downstream Task Performance:
    • Assess cell type classification accuracy (F1-score, balanced accuracy).
    • Evaluate rare cell population detection (precision-recall curves).
    • Test differential expression consistency across batches.

Visualizing Tokenization Workflows in scFM Development

The following diagram illustrates the complete tokenization and modeling pipeline for single-cell foundation models, highlighting the critical role of feature selection as the biological equivalent of vocabulary construction in NLP.

tokenization_workflow cluster_data_prep Data Preparation Phase cluster_tokenization Tokenization Phase cluster_modeling Modeling Phase raw_data Raw scRNA-seq Data (Cell × Gene Matrix) preprocessing Data Preprocessing (QC, Normalization, Batch Correction) raw_data->preprocessing feature_selection Feature Selection/Tokenization (HVG, Batch-Aware, Lineage-Specific) preprocessing->feature_selection token_representation Token Representation (Gene ID, Binned Expression, Hybrid) feature_selection->token_representation gene_word Gene = Word feature_selection->gene_word model_input Model Input Sequence (Cell as Sequence of Gene Tokens) token_representation->model_input scfm_training scFM Pre-training (Masked Token Prediction, Contrastive Learning) model_input->scfm_training cell_sentence Cell = Sentence model_input->cell_sentence downstream_tasks Downstream Tasks (Cell Type Annotation, Perturbation Prediction, ...) scfm_training->downstream_tasks

Tokenization Workflow for Single-Cell Foundation Models

This workflow highlights how raw single-cell data undergoes progressive transformation into tokenized representations suitable for foundation model training. The feature selection/tokenization step serves as the critical bridge between biological measurements and computational modeling, directly determining which aspects of cellular identity are preserved for downstream analysis.

Successful implementation of tokenization strategies for scFM development requires both wet-lab reagents for data generation and computational tools for analysis. The following table details essential resources in the researcher's toolkit.

Table 3: Research Reagent Solutions for scRNA-seq and scFM Development

Category Item Specification/Function Application in scFM Development
Wet-Lab Reagents 10X Genomics Chromium Chip Microfluidic device for single-cell partitioning High-throughput single-cell library preparation for training data generation
Reverse Transcriptase Master Mix Converts RNA to cDNA with cell barcoding Creates uniquely labeled transcriptomes for cell-specific "sentence" construction
Nucleotide Unique Molecular Identifiers (UMIs) Molecular barcodes for transcript counting Enables accurate digital gene expression quantification for token values
Poly(dT) Magnetic Beads mRNA capture via poly-A tail selection Isolates protein-coding genes for vocabulary definition
Computational Tools Cell Ranger (10X) Processing pipeline for droplet-based data Generates initial count matrices from raw sequencing data [20]
Scanpy/Seurat Python/R toolkits for single-cell analysis Implements feature selection, normalization, and preliminary analysis [19]
scVI/scANVI Deep generative models for single-cell data Performs batch correction and generates integrated embeddings [19] [21]
scGPT/scFoundation Foundation models for single-cell biology Implements transformer architectures pretrained on massive single-cell datasets [18]
Reference Data Human Cell Atlas Comprehensive reference of all human cells Provides training "corpus" for generalizable scFMs [19]
Tabula Sapiens/Sapiens/Muris Cross-species cell atlases Enables comparative biology and cross-species model transfer

This toolkit enables the complete pipeline from experimental data generation through computational analysis and model development. The wet-lab reagents ensure high-quality input data, while the computational tools implement the tokenization strategies and model architectures that bring the biological analogy to life.

Emerging Challenges and Opportunities

As single-cell foundation models evolve, several frontiers in tokenization strategy demand attention:

  • Multi-modal Tokenization: Current approaches primarily focus on gene expression data, but emerging multi-omics technologies (simultaneous measurement of gene expression, chromatin accessibility, protein abundance, etc.) require integrated tokenization schemes that can represent diverse data types within a unified embedding space.
  • Dynamic Vocabulary Adaptation: Fixed vocabularies based on static gene sets struggle to incorporate newly discovered genes or handle cross-species applications. Future tokenization approaches may benefit from hierarchical or compositional representations that can adapt to expanding biological knowledge.
  • Spatial Context Integration: Spatial transcriptomics technologies add geographical coordinates to gene expression measurements, creating an additional dimension beyond the "sentence" analogy that requires novel tokenization strategies incorporating spatial relationships.
  • Perturbation Modeling: Current benchmarking reveals limitations in predicting cellular responses to perturbations [18]. Improved tokenization strategies that better capture gene regulatory relationships may enhance perturbation prediction accuracy.

The analogy between biological systems and natural language—cells as sentences, genes as words—provides a powerful conceptual framework and practical methodology for advancing single-cell computational biology. Tokenization strategies derived from this analogy serve as the critical bridge connecting raw biological measurements to sophisticated foundation models capable of decoding cellular identity, function, and response.

Benchmark studies consistently demonstrate that feature selection methods significantly impact downstream analysis performance, with highly variable gene selection emerging as a robust approach for biological tokenization [19]. However, optimal implementation requires careful consideration of dataset-specific factors including batch effects, cellular heterogeneity, and analytical objectives.

As the field progresses toward increasingly comprehensive single-cell atlases and more sophisticated foundation models, the development of refined tokenization strategies will remain essential for maximizing model performance and biological insight. By thoughtfully applying and extending the linguistic analogy, researchers can continue to advance our ability to "read" and interpret the fundamental language of biology, with profound implications for basic research and therapeutic development.

Within the research on single-cell foundation models (scFMs), tokenization strategies form the critical bridge that transforms raw single-cell RNA-sequencing (scRNA-seq) data into a structured input that deep learning models can process. The concept is borrowed directly from Natural Language Processing (NLP), where it has been a foundational step for transformer-based models. In NLP, tokenization converts unstructured text into discrete units (tokens), enabling models like BERT to learn complex linguistic patterns. Similarly, in single-cell biology, tokenization aims to convert gene expression profiles into a 'language' that models can understand, treating cells as documents and genes as words to decipher the underlying biological grammar [1] [17].

However, the application of NLP-style tokenization to biological data is not a simple one-to-one mapping. ScRNA-seq data possesses unique characteristics—such as its non-sequential nature and high-dimensional sparsity—that create significant challenges and necessitate method adaptations. This guide provides an in-depth technical examination of the parallels and critical differences between tokenization in NLP and its application in scFMs, framing the discussion within the broader thesis of developing effective tokenization strategies for scRNA-seq data. It is intended for researchers, scientists, and drug development professionals who need to understand the core computational techniques driving innovations in single-cell analysis.

Fundamental Parallels with NLP Tokenization

The development of tokenization methods for scFMs draws heavily from established NLP principles. The core analogy treats a single cell as a sentence or document and its constituent genes as individual words. This conceptual parallel allows model architects to leverage the powerful transformer architecture for biological discovery [1] [17].

Table 1: Core Conceptual Parallels Between NLP and scFM Tokenization

Aspect NLP Tokenization scFM Tokenization Functional Purpose
Basic Unit Words/Subwords Genes/Genomic Features Define fundamental semantic building blocks for the model [1].
Composite Structure Sentences/Documents Individual Cells Create a structured context from individual units for pattern learning [1] [17].
Model Architecture Transformer Transformer (BERT, GPT variants) Process token sequences to capture long-range dependencies and complex relationships [8] [22].
Pretraining Task Masked Language Modeling Masked Gene/Token Modeling Learn robust, context-aware representations through self-supervised learning [8] [22].

A key parallel lies in the self-supervised pretraining objective. Inspired by masked language modeling in NLP, where random words in a sentence are masked and predicted, scFMs like scBERT and scGPT employ a mask-then-reconstruct proxy task. By masking a portion of the input gene tokens and training the model to recover them based on the remaining context, the model learns the complex gene-gene co-expression relationships and underlying regulatory grammar from vast amounts of unlabeled scRNA-seq data [8] [22]. This process enables the model to develop a general understanding of cellular biology before being fine-tuned for specific downstream tasks like cell type annotation or perturbation response prediction.

Critical Differences and Technical Challenges

Despite the conceptual parallels, fundamental differences between natural language and genomic data necessitate significant adaptations in tokenization strategies.

The Non-Sequential Nature of Gene Expression

A paramount difference is the lack of a natural sequence in gene expression data. In a sentence, word order is semantically critical; however, genes within a cell have no inherent biological ordering. This presents a fundamental challenge for transformer models, which inherently process sequential data. To overcome this, scFMs impose an artificial sequence. Common strategies, as utilized by models like scBERT and Geneformer, include ranking genes by their expression value within each cell, effectively creating a "sentence" of genes from highest to lowest expresser [1] [17]. Other approaches involve partitioning genes into bins based on expression levels. This imposed order, while computationally necessary, is biologically arbitrary and represents a key divergence from NLP.

High Dimensionality and Sparsity

ScRNA-seq data is characterized by its extremely high dimensionality (tens of thousands of genes) and pronounced sparsity, largely due to dropout events where genes are measured as unexpressed even when present. This creates a scenario vastly different from the dense, lower-vocabulary setting of most NLP tasks. Naively representing each gene as a token leads to computational intractability and difficulties in model learning. To address this, the field has developed specialized techniques. The scSFUT model, for instance, introduces a gene embedding algorithm that uses sequential tokenization with a fixed window size and 1D-convolution. This method segments high-dimensional cell samples into information-dense sub-vectors, expanding the attention receptive field while maintaining manageable computational loads [8]. This approach seeks to learn directly from the full gene length without relying on pre-filtering steps like Highly Variable Gene (HVG) selection, which can introduce bias and lead to biological information loss [8].

Incorporating Biological Metadata

The "vocabulary" of scFMs is more complex than in NLP. While a word token in NLP is a discrete entity, a gene token in an scFM often needs to encapsulate more than just an identifier. To enrich the biological context and improve model generalization, advanced tokenization schemes incorporate special tokens for metadata. This can include tokens for cell-level context (e.g., tissue of origin, donor), experimental batch information to correct for technical artifacts, and even multi-omic modalities when integrating data from assays like scATAC-seq [1] [17]. Furthermore, some models explore incorporating gene metadata, such as Gene Ontology terms or chromosomal location, directly into the token embeddings to provide a richer prior of biological function [1].

Table 2: Key Technical Challenges in scFM Tokenization vs. NLP

Technical Challenge Manifestation in NLP Manifestation in scFMs Proposed/Current Solutions
Input Sequence Natural word order. No inherent gene order. Impose order by expression value ranking or binning [1] [17].
Input Sparsity Dense token embeddings. Highly sparse expression vectors (many zeros). Specialized embedding layers; modeling techniques robust to dropouts [8].
Data Structure Sequential, contextual. Non-sequential, co-expressive. Use of attention mechanisms to model gene-gene interactions without relying on position [8] [22].
Scalability Large but finite vocabulary. Very high dimensionality (~20-30k genes/cell). Gene embedding with compression (e.g., scSFUT's windowing); HVG selection (common but lossy) [8].
Generalization Across dialects, languages. Across species, tissues, platforms. Incorporation of species/tissue tokens; training on massively diverse datasets (e.g., CELLxGENE) [1].

TokenizationWorkflow NLPStart Input: Unstructured Text NLPTokenize Tokenization Process NLPStart->NLPTokenize NLPTokens Output: Word/Subword Tokens (Sequential Order) NLPTokenize->NLPTokens scStart Input: scRNA-seq Count Matrix scOrder Impose Artificial Sequence (e.g., Rank by Expression) scStart->scOrder scEnrich Enrich with Metadata Tokens (e.g., Batch, Species) scOrder->scEnrich scTokens Output: Gene Tokens + Metadata (With Expression Value) scEnrich->scTokens

Figure 1: A comparative workflow of tokenization in NLP versus single-cell foundation models, highlighting the key additional steps required for biological data.

Experimental Protocols and Methodologies

Evaluating the efficacy of a tokenization strategy is integral to scFM development. The following section outlines standard experimental protocols for benchmarking these methods.

Benchmarking Cell Type Annotation

Objective: To assess how effectively a tokenization scheme enables an scFM to accurately annotate cell types in a hold-out dataset. Protocol:

  • Pretraining: A foundation model (e.g., scBERT, scSFUT) is pretrained on a large, diverse corpus of scRNA-seq data (e.g., from CELLxGENE or PanglaoDB) using a self-supervised task like masked gene reconstruction [8] [22].
  • Fine-tuning: The pretrained model is fine-tuned on a smaller, labeled reference dataset where cell types are known. This step adapts the general model to the specific annotation task.
  • Validation: The fine-tuned model is used to predict cell types on a completely unseen test dataset. Performance is quantified using metrics such as Accuracy, Macro F1-score (which is crucial for imbalanced cell type distributions), and Cohen's Kappa [8] [22].
  • Comparative Analysis: Performance is compared against baseline methods, which may include other scFMs with different tokenization approaches, autoencoder-based models (e.g., ACTINN), and traditional supervised learning methods.

Cross-Species and Cross-Tissue Generalization

Objective: To evaluate the robustness and generalizability of the tokenization and model when applied to data from different species or tissues not seen during training. Protocol:

  • Model Training: An scFM is pretrained and/or fine-tuned on datasets primarily from one species (e.g., human).
  • Zero-Shot or Few-Shot Transfer: The model is directly applied (zero-shot) or minimally adapted (few-shot) to datasets from a different species (e.g., mouse) or a novel tissue.
  • Evaluation: Annotation accuracy or the quality of learned cell embeddings is measured on the out-of-distribution data. Successful tokenization strategies will enable the model to align the latent representations of biologically similar cell types across the technical and biological divides [8]. Models like scSFUT specifically highlight performance on cross-species datasets as a key benchmark [8].

In Silico Perturbation Prediction

Objective: To test the model's capacity to predict cellular responses to genetic or chemical perturbations, a task of high value for drug discovery. Protocol:

  • Baseline Modeling: A model is fine-tuned to represent a specific cellular state (e.g., a disease state like RUNX1-familial platelet disorder) [23].
  • Open-Loop Prediction: The model performs in silico perturbation (ISP) by manipulating input tokens (e.g., setting a gene's expression to zero to simulate knockout) and predicting the resulting cell state.
  • Closed-Loop Validation: As proposed in recent work, the model's predictions are experimentally validated, and the results are fed back into the model for further fine-tuning. This "closes the loop," dramatically improving prediction accuracy for subsequent rounds, as evidenced by a three-fold increase in positive predictive value [23].
  • Metric Analysis: Predictions are compared against experimental validation data (e.g., from Perturb-seq) using metrics like Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity [23].

ExperimentalFlow Data Large-scale scRNA-seq Data (e.g., CELLxGENE, PanglaoDB) PreTrain Self-Supervised Pretraining (Masked Gene Modeling) Data->PreTrain BaseModel Base Foundation Model (scFM) PreTrain->BaseModel Task1 Cell Type Annotation BaseModel->Task1 Task2 Cross-Species Generalization BaseModel->Task2 Task3 In Silico Perturbation BaseModel->Task3 Eval1 Evaluation: Accuracy, F1-score Task1->Eval1 Eval2 Evaluation: Zero-shot Accuracy Task2->Eval2 Eval3 Evaluation: PPV, NPV, AUROC Task3->Eval3

Figure 2: A core experimental workflow for developing and evaluating single-cell foundation models, showing key downstream tasks and their associated evaluation metrics.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key computational tools and data resources that are essential for research and experimentation in scFM tokenization.

Table 3: Essential Research Reagents and Resources for scFM Development

Resource Name Type Primary Function in Research Relevance to Tokenization
CZ CELLxGENE [1] [17] Data Platform Provides unified access to standardized, annotated single-cell datasets (>100M cells). Source of diverse, high-quality data for pretraining and benchmarking tokenization strategies.
PanglaoDB [1] [22] Curated Database A collection of annotated scRNA-seq data with marker genes. Used as a training corpus and for evaluating cell type annotation performance.
Scanpy [8] Computational Toolkit A Python library for pre-processing and analyzing single-cell data. Used for essential preprocessing steps (QC, normalization) before tokenization.
spaCy [24] NLP Library A library for advanced natural language processing in Python. Provides NER models (e.g., ennercraft_md) for extracting biological entities from text, aiding in automated marker gene curation.
scGPT / scBERT [8] [22] Foundation Models Open-source, pretrained scFMs for various downstream tasks. Serve as reference architectures and baselines for comparing novel tokenization methods.
Gene Vocabulary [24] Feature List A predefined list of human/mouse protein-coding genes (e.g., from Cell Ranger). Acts as the standard "dictionary" for gene tokenization, enabling consistent input representation across datasets.

Tokenization is the foundational step that enables single-cell foundation models to "read" the language of biology, drawing powerful inspiration from NLP but requiring significant innovation to address the unique challenges of genomic data. The parallels are strong in concept and overall architecture, but the critical differences—the non-sequential nature of gene expression, extreme sparsity, and high dimensionality—demand specialized solutions like expression-value-based ordering, innovative gene embedding algorithms, and the incorporation of biological metadata. The evaluation of these strategies through rigorous benchmarking on tasks like cell type annotation, cross-species generalization, and in silico perturbation is paramount. As the field progresses, the development of more biologically informed, efficient, and scalable tokenization methods will be a key driver in realizing the full potential of scFMs to power drug discovery and advance our understanding of cellular function and disease.

Implementing Tokenization Strategies: From Gene Ranking to Genomic Positioning

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, leveraging large-scale deep learning to interpret the vast datasets generated by single-cell genomics [17]. A core innovation enabling this progress is gene-centric tokenization, a process that converts raw gene expression data from individual cells into a structured format that deep learning models can process. In the architecture of scFMs, individual cells are treated analogously to sentences, while genes and their expression values become the words or tokens that form these cellular "sentences" [17]. This approach allows models to learn the fundamental principles of cellular biology by exposing them to millions of cells encompassing diverse tissues and conditions.

Tokenization serves a critical function in scFM development because it standardizes raw, often unstructured single-cell data into discrete units that transformer-based architectures can efficiently process [17] [25]. Unlike natural language, where words have inherent sequence, gene expression data lacks natural ordering, presenting a fundamental challenge for sequential models. Researchers have therefore developed specialized ranking and binning strategies to impose meaningful structure on gene tokens, enabling the application of powerful transformer architectures that have revolutionized natural language processing and computer vision [17]. These tokenization strategies form the foundational layer upon which scFMs build their understanding of cellular heterogeneity, gene regulatory networks, and biological mechanisms at single-cell resolution.

Core Methodologies for Expression-Based Tokenization

Expression-Based Ranking Strategies

A primary challenge in applying transformer architectures to single-cell data is the non-sequential nature of gene expression profiles. Unlike words in a sentence, genes in a cell have no inherent ordering [17]. To address this, researchers have developed deterministic ranking strategies that impose sequence structure based on expression values. The most common approach involves ranking genes within each cell by their expression levels and feeding the ordered list of top-expressed genes as the representative "sentence" for that cell [17]. This method transforms the unstructured gene expression profile into a deterministic sequence where gene position reflects its relative abundance in that specific cell.

Alternative ranking strategies have also emerged to capture different aspects of gene importance. Some models partition genes into bins based on their expression values and use these categorical rankings to determine positional relationships [17] [25]. The ranking step provides a non-parametric method for analyzing count data, effectively handling the high variance and sparsity characteristics of scRNA-seq data without requiring strong assumptions about data distribution [26]. Notably, some implementations report that complex ranking strategies offer no clear advantage over simpler normalized counts, suggesting that the fundamental value lies in applying a consistent, deterministic ordering rather than the specific algorithmic complexity [17]. This ranking approach aligns with broader efforts in single-cell analysis, such as the RankCorr method, which uses ranking as an intuitive, non-parametric approach for handling count data before performing marker selection [26].

Expression Binning and Quantization Strategies

Following expression-based ranking, quantization converts continuous normalized expression values into discrete tokens through a process of binning. This discretization is essential because current foundation models operate with finite vocabularies, requiring continuous expression values to be mapped to discrete tokens [25]. The quantization function typically defines a set of bins with centers and boundaries, mapping each normalized expression value to a specific bin index [25]. The corresponding dequantization function then maps discrete indices back to representative values during model training and inference.

Table 1: Comparison of Expression Quantization Strategies

Strategy Bin Placement Method Advantages Ideal Use Cases
Uniform Binning Evenly spaced bins across value range Robust to distributional shifts; treats all regions equally General-purpose applications; datasets with unknown distribution
Normal Binning Bin centers placed according to standard normal CDF Finer resolution near mean; coarser in tails Approximately Gaussian distributed expression data
Exponential-Decay Binning Bin spacing follows exponential CDF Denser bins near zero; emphasizes small fluctuations Heavy-tailed or skewed distributions; rare cell type detection

Research indicates that the specific configuration of scaling and quantization strategies significantly impacts model performance. Theoretical analyses demonstrate that the combination of mean scaling with normal binning or normal scaling with uniform binning often outperforms other combinations [25]. The width of the quantization range represents a critical trade-off parameter, balancing resolution around the distribution mean against error minimization in the distribution tails [25].

Integration of Biological Context

Beyond raw expression values, advanced tokenization approaches incorporate biological context to enrich the semantic meaning of gene tokens. Many models prepend special tokens representing cell identity metadata, enabling the model to learn cell-level context [17]. When analyzing multiple omics modalities, tokens indicating data source (e.g., scRNA-seq vs. scATAC-seq) can be included to provide modality context [17]. Some implementations further enhance tokens with gene metadata such as gene ontology terms or chromosomal location, providing additional biological context that helps the model learn regulatory relationships and functional associations [17].

The tokenization process culminates with the conversion of all tokens into embedding vectors processed by transformer layers. The forward pass through these layers typically generates two types of latent embeddings: individual embeddings for each gene token and a dedicated embedding representing the entire cell [17]. These embeddings form the foundation for subsequent pretraining tasks and downstream analytical applications, capturing both gene-level and cell-level biological patterns learned from the vast training corpora.

Theoretical Foundations and Performance Analysis

Scaling Methods for Normalization

Prior to quantization, scaling methods normalize expression values to address the wide variation in scale across genes and cells. Different scaling approaches transform raw expression values into numerically stable ranges suitable for subsequent quantization [25]:

  • Mean Normalization: Applies scaling factor a = 1/mean(|x|) with zero shift, preserving relative expression differences while controlling for overall abundance.
  • Min-Max Normalization: Uses a = 1/(x_max - x_min) and b = -a*x_min to map values to a standardized range, sensitive to extreme outliers.
  • Normal Normalization: Applies Z-score standardization with a = 1/σ_x and b = -μ_x/σ_x, ideal for approximately Gaussian distributions.

Table 2: Theoretical Performance of Tokenization Strategies by Vocabulary Size

Vocabulary Size Optimal Strategy Theoretical Error Bound Computational Efficiency
Small (50-200 tokens) Normal Scaling + Uniform Binning Low error across distribution High efficiency
Medium (200-1000 tokens) Mean Scaling + Normal Binning Minimal mean error Moderate efficiency
Large (>1000 tokens) Multiple strategies comparable Power law improvement Lower efficiency

Empirical studies demonstrate a clear power law relationship between vocabulary size and theoretical performance boundaries, with different tokenization strategies maintaining consistent relative advantages as vocabulary scales [25]. This relationship underscores the importance of selecting an optimal tokenization strategy early in model development, as performance differences persist across scaling regimes.

Comparative Performance of Tokenization Strategies

Research systematically evaluating tokenization components reveals that pretrained models consistently leverage well-designed tokenizers more effectively, particularly at smaller vocabulary sizes [25]. The interaction between tokenization strategy and model initialization significantly impacts final performance, with misaligned tokenization potentially diminishing or even reversing the benefits of pretraining [25]. This finding highlights the importance of coordinated design between data tokenization and model architecture.

Analysis of token space utilization shows that standard approaches often waste capacity through underutilized bins, while data clusters densely in narrow regions [25]. Alternative binning strategies that better match the data distribution can improve token space efficiency and final model performance. Theoretical work has established bounds for these tokenization methods, demonstrating that smoother data distributions closer to normal typically yield better model performance [25].

Practical Implementation and Research Applications

Experimental Workflow for Tokenization

The implementation of gene-centric tokenization follows a structured workflow that transforms raw single-cell data into model-ready tokens. The process begins with data selection and quality control from large-scale single-cell repositories such as CZ CELLxGENE, which provides standardized access to over 100 million unique cells [17]. Following data acquisition, the tokenization pipeline proceeds through sequential stages of preprocessing, gene selection, expression transformation, and finally token embedding.

G cluster_0 Tokenization Pipeline Raw_Data Raw_Data QC_Filtering QC_Filtering Raw_Data->QC_Filtering Count Matrix Normalization Normalization QC_Filtering->Normalization Filtered Cells Gene_Ranking Gene_Ranking Normalization->Gene_Ranking Normalized Values Expression_Binning Expression_Binning Gene_Ranking->Expression_Binning Ordered Genes Token_Embedding Token_Embedding Expression_Binning->Token_Embedding Discrete Tokens Model_Input Model_Input Token_Embedding->Model_Input Embedding Vectors

Figure 1: Tokenization Workflow for scRNA-seq Data

Research Reagent Solutions for Tokenization

Table 3: Essential Research Resources for Tokenization Implementation

Resource Category Specific Examples Primary Function Implementation Role
Data Resources CZ CELLxGENE [17]; PanglaoDB [17]; Human Cell Atlas [17] Provides standardized single-cell data Pretraining corpora for scFMs
Computational Frameworks scBERT [17]; scGPT [27]; Cell2Sentence [27] Implements tokenization pipelines Reference implementations for gene ranking and embedding
Analysis Ecosystems Seurat [28] [26]; Scanpy [28] [26] Data preprocessing and quality control Preparation of input data for tokenization

The rapid evolution of gene-centric tokenization continues to address significant challenges in single-cell foundation modeling. Current research focuses on developing more biologically-informed tokenization approaches that incorporate gene network information, spatial relationships, and multimodal context [17] [27]. The integration of large language models with single-cell analysis through frameworks like sciLaMA and Cell2Sentence represents a promising direction for enhancing the biological relevance of token representations [27].

As single-cell technologies advance to profile increasingly complex biological systems, tokenization strategies must evolve to handle multi-omic integration, temporal dynamics, and spatial relationships. Future work will likely focus on developing unified tokenization schemes that can represent diverse data types within a common embedding space, enabling more comprehensive foundation models of cellular biology [17] [27]. These advances will further establish scFMs as pivotal tools for unlocking deeper insights into cellular function, disease mechanisms, and therapeutic development.

The combination of expression-based ranking and intelligent binning strategies has proven essential for harnessing the power of transformer architectures in single-cell genomics. As these tokenization methods continue to mature, they will play an increasingly critical role in building more accurate, interpretable, and biologically-grounded foundation models that accelerate discoveries across biomedicine and therapeutic development.

Tokenization, the process of breaking complex data into smaller, manageable units for machine learning, has become fundamental to analyzing single-cell RNA sequencing (scRNA-seq) data in single-cell Foundation Models (scFMs) [29]. While traditional methods in Natural Language Processing (NLP) break text into words or subwords, biological data requires specialized strategies that preserve critical spatial and functional relationships. Patch-based cell tokenization addresses this need by decomposing raw biological data into discrete, often non-overlapping or adaptively sized "patches" that serve as the fundamental units, or tokens, for downstream machine learning models, typically transformer-based or graph neural networks [30].

This approach represents a significant shift from treating a cell's transcriptome as an unordered set of highly variable genes. Instead, it restructures the data to explicitly incorporate genomic context, enabling models to learn from the inherent spatial organization of the genome. For scRNA-seq data, this means moving beyond gene-level analysis to consider contiguous genomic regions, thereby capturing local dependencies and regulatory landscapes that are crucial for understanding cellular identity and function [30] [31]. This technical guide explores the principles, methodologies, and implementations of patch-based tokenization tailored for genomic positional information within the broader thesis of tokenization strategies for scRNA-seq data in scFM research.

Core Principles and Biological Rationale

From NLP to Genomics: The Adaptation of Tokenization

In NLP, tokenization transforms continuous text into discrete tokens (words, subwords, or characters), enabling models to process language. Similarly, genomic tokenization breaks nucleotide or gene sequences into defined units. However, genomics presents unique challenges: the "alphabet" is simple (A, T, C, G), but the functional units are complex and multi-scale [31]. Patch-based tokenization in genomics is designed to capture these functional units by grouping contiguous features, thereby providing a coarse-grained representation that focuses computational resources on biologically relevant substructures [30].

The Critical Role of Positional Information

Genomic function is deeply tied to physical location. Genes close to each other on a chromosome may be co-regulated, and elements like enhancers influence gene expression over specific genomic distances. Standard scRNA-seq analysis, which often uses highly variable genes selected without regard to genomic context, discards this positional information. Patch-based tokenization directly addresses this limitation by ensuring that the tokens themselves reflect the native linear architecture of the genome. This allows transformer models to more effectively learn the syntax and grammar of gene regulation [30].

Methodological Framework

Defining the Patch: Genomic Partitions as Tokens

In the context of scRNA-seq data for scFMs, a patch is typically defined as a contiguous stretch of the genome, representing a local pool of genomic features. Given a cell's raw expression profile vector (\mathbf{x} \in \mathbb{R}^L), where (L) is the total number of ordered features (e.g., genes or genomic bins), the profile is reshaped into (C) patches, each of size (P). Formally, this is represented as (\mathbf{x}_p \in \mathbb{R}^{C \times P}), where each patch serves as a token [30]. The embedding process for these tokens is then:

[ \mathbf{T} = [\mathbf{t}^{(1)}\mathbf{W}; \ldots; \mathbf{t}^{(C)}\mathbf{W}] + \mathbf{E}_{pos} ]

Where (\mathbf{W}) is a learnable projection matrix and (\mathbf{E}_{pos}) is the positional encoding. This approach ensures maximal retention of positional and contextual information, avoiding the information loss typical of highly variable gene selection [30].

K-mer Tokenization Strategies for Genomic Sequences

A common instantiation of patch-based tokenization in genomics is k-mer tokenization, where a sequence is broken into all possible overlapping or non-overlapping substrings of length (k) [31]. The strategy for generating these k-mers significantly impacts model performance and efficiency.

  • Fully Overlapping K-mers: A token of length (k) is extracted by sliding the window by one nucleotide. For a sequence "ATGCCT" with (k=3), this produces the tokens "ATG", "TGC", "GCC", "CCT". This method preserves the most local context but generates the largest number of tokens ((T_k = L - k + 1 + 2), where the +2 accounts for [CLS] and [SEP] tokens in transformer models) [31].
  • Non-Overlapping K-mers: Consecutive tokens do not share nucleotides. From the same "ATGCCT" sequence, this would yield only "ATG" and "CCT" ((T_k = \lceil L/k \rceil + 2)). This approach minimizes token redundancy and computational load but can lose fine-grained contextual information at token boundaries [31].
  • Adaptive Non-Overlapping (AgroNT Method): This method splits the genomic sequence into non-overlapping tokens of a fixed length (e.g., 6-mers). If tokens cannot be generated (e.g., at sequence ends or due to ambiguous 'N' bases), the sequence splits into single nucleotides. This balances consistency with handling of edge cases [31].

The vocabulary size for a k-mer tokenizer is determined by (V_k = 4^k + 5), accounting for the four nucleotides and five special tokens ([PAD], [MASK], [CLS], [SEP], [UNK]) [31].

Workflow Diagram: Patch-Based Tokenization for scRNA-seq Data

The following diagram illustrates the logical workflow for implementing patch-based tokenization from raw scRNA-seq data to tokenized model input.

G cluster_0 Input Data cluster_1 Tokenization Strategy cluster_2 Tokens & Embedding cluster_3 Model Input RawSeq Raw Genomic Sequence or Ordered Gene List Strat Fixed-Length Non-Overlapping Fixed-Length Overlapping Adaptive Semantic Patching RawSeq->Strat Tokens Discrete Token Sequence Strat:f0->Tokens Genomic Position Strat:f1->Tokens Genomic Position Strat:f2->Tokens Genomic Position Embed Patch Embedding with Positional Encoding Tokens->Embed Input Tokenized & Embedded Sequence Embed->Input

Experimental Protocols and Benchmarking

Pre-training and Fine-tuning Protocol for Genomic Language Models

A typical experimental protocol for evaluating tokenization strategies involves a two-stage process of pre-training and task-specific fine-tuning, as demonstrated in plant genomics research [31].

  • Pre-training Corpus Construction: Assemble a relevant corpus of reference genomes or genomic sequences. For scFM research, this could involve curated data from resources like the NCBI RefSeq database.
  • Lightweight Pre-training: Utilize a model architecture like BERT, implemented via frameworks such as Hugging Face Transformers.
    • Extract subsequences (e.g., 510 bp) from the reference data with a stride (e.g., 255 bp for 50% overlap).
    • Tokenize each subsequence using the k-mer strategy under investigation (e.g., k=3–8, fully overlapping).
    • Apply a masked language modeling objective with a standard masking rate of 15%, training the model to predict original tokens to learn contextual dependencies.
    • Train for a sufficient number of steps (e.g., 50,000–80,000) until loss convergence.
  • Task-Specific Fine-tuning:
    • For downstream tasks like splice site prediction or cell type annotation, fine-tune the pre-trained model.
    • During fine-tuning, evaluate different tokenization variants (e.g., overlapping vs. non-overlapping) derived from the same pre-trained checkpoint to isolate the effect of the tokenization scheme.

Quantitative Comparison of K-mer Tokenization Strategies

Systematic evaluation across tasks like splice site prediction and alternative polyadenylation site prediction reveals performance trade-offs.

Table 1: Performance Comparison of K-mer Tokenization Strategies

K-mer Size (k) Tokenization Scheme Prediction Accuracy (F1 Score) Computational Cost (Tokens/Sequence) Relative Efficiency
3 Fully Overlapping 0.89 ~510 Low
3 Non-Overlapping 0.85 ~171 High
4 Fully Overlapping 0.91 ~509 Low
4 Non-Overlapping 0.88 ~128 High
5 Fully Overlapping 0.93 ~508 Medium
5 Non-Overlapping 0.90 ~103 High
6 (AgroNT) Non-Overlapping 0.92 ~86 Very High
6 Fully Overlapping 0.94 ~507 Low
8 Fully Overlapping 0.95 ~505 Very Low
8 Non-Overlapping 0.93 ~64 Very High

Note: Performance metrics (F1 Score) are illustrative examples from plant genomic task benchmarks [31]. Computational cost is estimated for a sequence length L=510 bp.

Hierarchical and Graph-Based Extensions

Beyond linear sequences, patch-based tokenization can be extended to capture higher-order spatial relationships among genomic elements or cells. The C2P-GCN model exemplifies this by constructing a two-level graph [30]:

  • Cell-level Graph: Cells detected within a biological patch (e.g., via nuclei detection) form nodes in a patch-level spatial graph.
  • Patch-level Graph: Each patch, capturing local cellular organization, becomes a node in an image-level graph linked by feature similarity. This hierarchical approach is described by the graph convolution update: ( \mathbf{H}^{l+1} = \text{Dropout}(\text{ReLU}(\text{GCN}l(\mathbf{X}^l, \mathbf{A}'I; \mathbf{W}^l))) ), where (\mathbf{A}'_I) is the adjacency matrix, enabling efficient, structure-preserving analysis [30].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Key Research Reagent Solutions for scRNA-seq Tokenization Experiments

Item / Reagent Function / Application Example/Notes
Reference Genome Assemblies Provides the ordered sequence and structural context against which sequencing reads are aligned and features are ordered. NCBI RefSeq databases (e.g., for human, mouse, or specialized model organisms) [31].
Single-Cell RNA-Seq Datasets The primary input data for building scFMs, used for pre-training and/or fine-tuning models with patch-based tokenization. Public repositories like the Single-Cell Data Portal, CellXGene, or GEO, containing matrices of gene counts per cell.
K-mer Tokenizer Software component that implements the splitting of genomic sequences or ordered gene lists into discrete k-mer tokens. Custom scripts in Python; integrated into transformer libraries like Hugging Face Transformers [31].
Transformer Model Architecture The core machine learning model (e.g., BERT) that processes the tokenized sequences to learn representations. Implementations using PyTorch or TensorFlow, often leveraging the Hugging Face transformers library [31].
High-Performance Computing (HPC) Cluster Provides the necessary computational resources for pre-training large models on extensive genomic sequence corpora. GPU-equipped servers (e.g., with NVIDIA A100 or H100 GPUs) for efficient tensor operations.

Evaluation Metrics and Validation Framework

Rigorous evaluation is critical for validating the efficacy of any tokenization strategy. For patch-based tokenization in scFMs, a combination of quantitative metrics and qualitative biological insights should be employed.

  • Model Performance Metrics: Standard machine learning metrics including Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUROC), and Area Under the Precision-Recall Curve (AUPRC) are used for supervised tasks like cell type classification or splice site prediction [31].
  • Information Retention Metrics: To directly assess if positional information is captured, metrics like Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) can be used to evaluate the quality of clusters derived from the model's latent embeddings, with higher values indicating better preservation of biological groupings [30].
  • Computational Efficiency Metrics: Key practical metrics include inference time, memory footprint, and the total number of tokens generated per sequence, which directly impacts training and inference speed [30] [31].

Patch-based tokenization that incorporates genomic positional information represents a powerful strategy for advancing single-cell Foundation Models. By structuring scRNA-seq data into contiguous genomic patches, this approach allows transformer-based models to learn from the innate spatial organization of the genome, leading to more accurate and biologically interpretable representations of cellular state and function. Evidence suggests that thoughtful design of the tokenizer itself—considering factors like patch size, overlap, and adaptive partitioning—is a key determinant of success, often outweighing the impact of simply increasing model scale [31].

Future research directions will likely focus on developing more dynamic and adaptive patching strategies that can vary patch size based on local genomic complexity (e.g., gene density, chromatin state), deeper integration of multi-omic data (e.g., ATAC-seq, methylation) within the tokenization framework, and creating more computationally efficient models that make these advanced techniques accessible to a broader range of researchers. As the field progresses, patch-based tokenization is poised to remain a cornerstone technique for building scalable, context-aware, and powerful foundation models in biology.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling a unified framework for analyzing the rapidly expanding repositories of single-cell data. These large-scale deep learning models, pretrained on vast datasets, revolutionize data interpretation through self-supervised learning and possess remarkable capacity for various downstream tasks [1]. A critical technical challenge in developing these models lies in creating effective tokenization strategies—the process of converting raw molecular data into discrete, meaningful units that models can process. Tokenization serves as the foundational step that standardizes raw, often unstructured multi-omics data into structured representations that deep learning architectures can understand and process [1]. For single-cell multi-omics data, which encompasses transcriptomic (scRNA-seq), epigenomic (scATAC-seq), and proteomic information, this involves defining what constitutes a "token" from each modality and how these tokens collectively represent a single cell's complete molecular signature [1].

The tokenization problem in single-cell biology presents unique challenges compared to natural language processing. Gene expression data lacks natural sequential ordering, unlike words in a sentence, requiring innovative solutions to structure this information for transformer-based architectures that typically rely on sequence [1]. Furthermore, multi-omics integration introduces the additional complexity of harmonizing fundamentally different data types—continuous gene expression values, binary or continuous chromatin accessibility peaks, and protein abundance counts—into a cohesive token-based representation [32]. This technical guide provides an in-depth examination of current tokenization methodologies for RNA, ATAC, and protein data within single-cell foundation models, framed within the broader context of tokenization strategies for scRNA-seq data in scFMs research.

Foundations of Single-Cell Tokenization

Core Concepts and Definitions

In the context of single-cell foundation models, tokenization refers to the process of converting raw input data from single-cell assays into a sequence of discrete units called tokens [1]. This process is necessary because it standardizes heterogeneous molecular measurements into a structured format that deep learning models can process and learn from. The fundamental analogy treats individual cells as documents or sentences, with genes or other genomic features along with their quantitative values serving as words or tokens [1].

The tokenization pipeline typically involves several key stages: (1) raw data preprocessing and quality control, (2) feature selection and vocabulary definition, (3) value quantification and normalization, (4) sequence structuring and ordering, and (5) token embedding generation. Each stage presents unique technical considerations for different molecular modalities, requiring specialized approaches to effectively capture biological signal while minimizing technical noise and batch effects [1].

A critical consideration in tokenization strategy is determining the appropriate granularity of biological information to encode within each token. This spans a spectrum from fine-grained k-mer representations of DNA sequences [33] to gene-level or peak-level abstractions [32], each offering distinct trade-offs between biological resolution, computational efficiency, and model interpretability. The chosen tokenization approach fundamentally shapes what patterns a foundation model can learn, making this design decision paramount for model performance across diverse downstream tasks.

Architectural Context: Transformer-Based scFMs

Most single-cell foundation models are built on transformer architectures, which have revolutionized natural language processing and computer vision by capturing intricate long-range relationships in data [1]. Transformers are neural network architectures characterized by attention mechanisms that allow the model to learn and weight the relationships between any pair of input tokens [1]. In large language models, this enables the model to decide which words in a sentence to focus on when predicting the next word. By analogy, in scFMs, the attention mechanism can learn which molecular features in a cell are most informative of the cell's identity or state, how they covary across cells, and how they have regulatory or functional connections [1].

The gene expression profile of each cell is converted to a set of gene tokens, serving as inputs for the model, and its attention layers gradually build up a latent representation of each cell or gene [1]. Two predominant architectural configurations have emerged in scFMs: bidirectional encoder representations from transformers (BERT)-like encoder architectures with bidirectional attention mechanisms where the model learns from the context of all genes in a cell simultaneously, and Generative Pretrained Transformer (GPT)-inspired decoder architectures with unidirectional masked self-attention mechanisms that iteratively predict masked features conditioned on known features [1]. Hybrid designs are also being explored, though no single architecture has emerged as clearly superior for single-cell data [1].

Modality-Specific Tokenization Strategies

RNA Sequencing Data Tokenization

Tokenization of single-cell RNA sequencing data presents the fundamental challenge that gene expression data is not naturally sequential. Unlike words in a sentence, genes in a cell have no inherent ordering, yet transformer architectures typically require input sequences with defined order [1]. To address this, several strategic approaches have been developed:

Expression-Based Ranking: A common strategy ranks genes within each cell by their expression levels and feeds the ordered list of top genes as the 'sentence' [1]. This provides a deterministic but arbitrary sequence based on expression magnitude. For example, models such as scBERT and others employ this approach, creating a consistent input structure while prioritizing highly expressed genes that typically carry more biological information [1].

Binning Strategies: Alternative approaches partition genes into bins by their expression values and use those rankings to determine their positions [1]. This method reduces sensitivity to exact expression values while maintaining the relative abundance relationships between genes. Some implementations combine gene identifiers with expression bin information in a single token representation [1].

Normalized Count Encoding: Several models report no clear advantages for complex ranking strategies and simply use normalized counts without sophisticated ordering [1]. In these approaches, the gene sequence order may be fixed based on a canonical ordering (e.g., chromosomal position or alphabetical gene symbol), with expression values incorporated through the token embedding rather than the sequence structure.

Table 1: Comparative Analysis of scRNA-seq Tokenization Methods

Method Sequence Ordering Expression Encoding Key Advantages Notable Implementations
Expression Ranking By expression value Direct in embedding Prioritizes informative genes scBERT, scGPT
Binning By expression bin Categorical bin value Robust to technical noise Various custom implementations
Normalized Counts Fixed canonical order Normalized value in embedding Simple implementation scFoundation, xTrimoGene
Hybrid Approaches Combination of strategies Multiple embedding components Flexible representation scSFUT, Cisformer

For most models, genes become input tokens, and the combinations of these tokens collectively represent a single cell [1]. Each gene is typically represented as a token embedding that might combine a gene identifier and its expression value in the given cell. With the various strategies above, positional encoding schemes are adapted to represent the relative order or rank of each gene in the cell [1].

ATAC Sequencing Data Tokenization

Tokenization of single-cell ATAC-seq data presents distinct challenges due to the sparsity and high dimensionality of chromatin accessibility data, which often encompasses hundreds of thousands to millions of potential peaks across the genome. The fundamental difference in data structure between RNA and ATAC requires specialized tokenization approaches:

Peak-Based Tokenization: Similar to gene-based tokenization in RNA, this approach treats individual chromatin accessibility peaks as distinct tokens [32]. The primary challenge is the extreme dimensionality, as the genome contains orders of magnitude more potential regulatory elements than protein-coding genes. Solutions include aggressive filtering based on accessibility thresholds or focusing only on peaks that show variability across cell populations [32].

Sequence-Based Tokenization: More granular approaches tokenize the actual DNA sequence underlying accessible chromatin regions. For example, Inter-Chrom employs dynamic tokenization using SentencePiece and Byte Pair Encoding (BPE) for processing DNA sequences [33]. This method treats DNA subsequences as tokens, allowing the model to learn sequence motifs directly rather than relying on pre-defined peak calls. The process involves constructing a fixed-size vocabulary based on the co-occurrence frequency of DNA words within the sequences [33].

Innovative Genome Index Encoding: Cisformer introduces a novel indexing method for processing millions of chromatin peaks that represents a significant innovation for handling ultra-long sequences from chromatin accessibility data [32]. This approach encodes genomic coordinates by processing each digit of the peak index individually and embedding the resulting representations subsequently combined, rather than directly embedding the index as a whole. This strategy proves more effective for representing genomic position information [32].

Feature Duplication and Selection: To address the sequence length challenge in ATAC data, Cisformer implements a feature duplication and selection strategy [32]. For RNA-to-ATAC generation, the model focuses on expressed genes and selects active cis-regulatory elements (CREs) after binarization, then balances the sequences by incorporating an equal number of inactive CREs. This process generates multiple pseudo-cells from a single original cell, serving as a form of data augmentation [32].

Table 2: ATAC-seq Tokenization Methods for Multi-omics Integration

Method Token Definition Scale/Resolution Dimensionality Management Representative Models
Peak-Based Accessibility peaks Peak-level Frequency filtering, variability selection BABEL, scButterfly
Sequence-Based DNA k-mers Base-level BPE vocabulary, sequence compression Inter-Chrom
Index Encoding Digit-based coordinates Peak-level Decomposed positional encoding Cisformer
Binary Activity Binarized CRE states CRE-level Active/inactive balancing Cisformer

Protein Data Tokenization

Tokenization of protein data from single-cell technologies such as CITE-seq presents different challenges again, as protein abundance measurements typically involve dozens to hundreds of features rather than thousands or millions. The lower dimensionality is offset by distinct data characteristics:

Surface Protein Tokenization: For cytometry-based protein measurements, each detected protein becomes a token, analogous to gene tokens in RNA sequencing [1]. The continuous antibody-derived tag (ADT) counts are typically normalized and potentially transformed before being incorporated into the token embedding. Since the number of proteins measured is typically small (dozens to hundreds), sequence length is less challenging than with ATAC data.

Integration Challenges: The primary challenge with protein data tokenization lies in its integration with other modalities rather than the tokenization itself. Proteins often serve as important cell surface markers that can help validate cell identities inferred from transcriptomic data, but they represent a functionally distinct layer of biological information [1].

Multi-modal Token Integration: When integrating protein data with RNA and ATAC modalities, models typically employ special modality tokens that indicate the data type for each token [1]. This allows the transformer architecture to learn modality-specific and cross-modality relationships through its attention mechanism. The positional encoding must be adapted to handle these multi-modal sequences, either through separate encoding schemes or learned positional embeddings.

Integrated Multi-Modal Tokenization Frameworks

Cross-Modality Integration Architectures

Effective integration of multiple modalities requires specialized architectural considerations that go beyond simply concatenating tokens from different data types. Cross-attention mechanisms have emerged as a powerful approach for modeling interactions between different molecular modalities:

Cisformer's Cross-Attention Framework: Cisformer employs a decoder-only architecture with a cross-attention mechanism specifically designed for cross-modality generation between gene expression and chromatin accessibility [32]. This architecture strikes a balance between model complexity and biological interpretability. The cross-attention layers allow the model to directly learn relationships between RNA and ATAC tokens, enabling it to capture how chromatin accessibility influences gene expression and vice versa [32].

Dual-Aligned Variational Autoencoders: Models like scButterfly employ a dual-aligned variational autoencoder for single-cell cross-modality prediction [32]. While not strictly transformer-based, these approaches learn aligned latent representations across modalities that can then be decoded into the alternative modality. The alignment process effectively creates a shared token-like representation space.

Multi-Head Attention Across Modalities: Traditional transformer architectures can be adapted for multi-omics integration by using multi-head attention across modality-specific token sequences. This allows different attention heads to specialize in different types of cross-modality relationships, such as promoter-enhancer connections or protein-RNA correlations.

Sequence Structuring Strategies

Structuring the token sequence when combining multiple modalities presents significant design challenges. Several strategies have emerged for ordering tokens from different data types:

Modality-Blind Interleaving: This approach interleaves tokens from different modalities based on biological principles rather than treating each modality as a separate block. For example, genes and their regulatory elements might be positioned nearby in the sequence based on genomic proximity or predicted interactions.

Modality-Specific Segments: A more straightforward approach segments the sequence by modality, with special tokens indicating modality transitions. This simplifies the model's task of learning within-modality relationships but may make cross-modality relationships more difficult to capture.

Hierarchical Tokenization: Some approaches employ a hierarchical structure where higher-level tokens represent cells or cellular processes and lower-level tokens represent molecular features. This mirrors biological organization but introduces complexity in the model architecture.

Table 3: Multi-omics Integration Performance Comparison

Model Architecture Modalities Integrated Key Integration Strategy Reported Performance Advantages
Cisformer Transformer with cross-attention RNA-ATAC Cross-attention with feature selection Superior accuracy in cross-tissue generalization [32]
scButterfly Dual-aligned VAE RNA-ATAC Latent space alignment Competitive intra-dataset performance [32]
BABEL Autoencoder Multiple modalities Joint embedding Early pioneering approach [32]
scGPT Transformer RNA, ATAC, Protein Modality tokens and embedding General-purpose multi-omics foundation model [1]

Experimental Protocols and Methodologies

Benchmarking Frameworks for Tokenization Strategies

Evaluating the effectiveness of tokenization strategies requires carefully designed benchmarking frameworks that assess performance across multiple dimensions:

Cross-Dataset Generalization: A critical test for any tokenization approach is its ability to generalize across datasets with different technical characteristics. Cisformer implemented a comprehensive benchmarking strategy including intra-dataset train-test splitting, cell-type-level splitting within a dataset, training on one tissue and testing on a similar tissue, and training on one tissue and testing on a distinct tissue [32]. This progressive generalization test effectively reveals how well the tokenization strategy captures biological signals versus technical artifacts.

Modality Translation Accuracy: For multi-omics models, the accuracy of cross-modality prediction serves as an important indicator of how effectively the tokenization represents biological relationships. Evaluation metrics include clustering concordance (AMI, NMI, ARI, HOM), peak-level overlap metrics (precision, recall, F1 score), and cell-type-level correlation coefficients [32].

Downstream Task Performance: Ultimately, tokenization strategies must be evaluated based on their performance on biologically meaningful downstream tasks such as cell type annotation, differential expression analysis, and trajectory inference. The scSFUT model demonstrates how tokenization approaches can be evaluated specifically for cell type annotation accuracy across species [8].

Implementation Protocol: Cross-Modality Tokenization

Based on successful implementations, the following protocol provides a methodological framework for implementing multi-omics tokenization:

Step 1: Data Preprocessing and Quality Control

  • For scRNA-seq: Filter cells based on gene counts and mitochondrial percentage; normalize counts using library size normalization with potential log transformation.
  • For scATAC-seq: Call peaks using standardized pipeline; create cell-by-peak matrix; binarize or normalize accessibility scores.
  • For protein data: Normalize ADT counts using centered log-ratio transformation or similar approaches.

Step 2: Feature Selection and Vocabulary Construction

  • For RNA: Select highly variable genes or use all expressed genes depending on model requirements.
  • For ATAC: Select variable peaks or use all peaks passing minimum accessibility thresholds.
  • Construct separate vocabularies for each modality, ensuring token ID spaces do not overlap.

Step 3: Token Sequence Construction

  • Implement modality-specific tokenization: expression-based ranking for RNA, genomic coordinate encoding for ATAC, direct mapping for proteins.
  • Determine sequence ordering strategy: modality-blocking versus interleaving based on biological priors.
  • Add special tokens for modality indicators, cell identifiers, and sequence boundaries.

Step 4: Token Embedding Generation

  • Create embedding layers for each modality, potentially with separate embedding dimensions based on information density.
  • Incorporate value embeddings for quantitative measurements alongside feature identity embeddings.
  • Implement positional encoding appropriate for the sequence structuring strategy.

Multi-omics Tokenization Workflow: This diagram illustrates the comprehensive pipeline for tokenizing RNA, ATAC, and protein data, highlighting modality-specific processing steps and integration strategies.

Successful implementation of multi-omics tokenization strategies requires both experimental reagents and computational resources. The following table details essential components of the multi-omics tokenization toolkit:

Table 4: Research Reagent Solutions for Multi-omics Tokenization

Category Specific Tool/Resource Function/Purpose Key Features Integration Considerations
Experimental Platforms 10x Genomics Chromium X Series Single-cell multi-ome profiling Simultaneous RNA+ATAC from same cell Provides naturally paired data for training [34]
BD Rhapsody HT System High-throughput scRNA-seq Whole transcriptome analysis Compatible with protein detection [34]
Mission Bio Tapestri Platform Single-cell DNA+protein multi-omics Targeted DNA sequencing with proteins Specialized for mutation profiling [34]
Computational Frameworks Scanpy scRNA-seq analysis in Python Scalable data structures and algorithms Interfaces with scvi-tools [35]
Seurat scRNA-seq analysis in R Multi-modal integration and visualization Anchoring method for cross-dataset alignment [35]
scvi-tools Deep generative modeling Probabilistic modeling with VAEs Extensible to custom tokenization [35]
Foundation Models scGPT General-purpose scFM Multi-omics support with transformer Modular tokenization implementation [1]
Cisformer Cross-modality generation RNA-ATAC translation with cross-attention Specialized for regulatory inference [32]
scBERT Cell type annotation BERT-like architecture for scRNA-seq Expression-based token ranking [1]

Future Directions and Emerging Challenges

The field of multi-omics tokenization is rapidly evolving, with several emerging challenges and research directions:

Scalability to Massive Vocabularies: As single-cell datasets grow to encompass millions of cells and multiple modalities, developing tokenization strategies that scale efficiently remains challenging. Approaches like dynamic tokenization and hierarchical representations show promise for managing computational complexity while preserving biological information [33].

Interpretable Token Representations: A significant limitation of current tokenization approaches is the difficulty in interpreting what biological concepts individual tokens or token combinations represent. Future work should focus on developing more biologically grounded tokenization schemes that maintain interpretability while enabling powerful deep learning [1].

Standardization and Interoperability: The lack of standardized tokenization approaches across different foundation models hampers comparability and reproducibility. The field would benefit from community-developed standards for multi-omics tokenization similar to tokenization standards in natural language processing.

Integration with Spatial Omics: The rapid advancement of spatial transcriptomics and proteomics technologies introduces new dimensionality to single-cell data. Developing tokenization strategies that incorporate spatial relationships represents an important frontier for multi-omics integration [35].

Dynamic and Temporal Modeling: Current tokenization approaches largely represent static snapshots of cellular states. Future methods must evolve to tokenize temporal dynamics and cellular trajectories, enabling foundation models to learn not just cellular states but state transitions during processes like differentiation and disease progression.

In conclusion, tokenization strategies for multi-omics data represent a critical foundational element in the development of single-cell foundation models. Effective tokenization requires careful consideration of modality-specific characteristics while enabling cross-modality integration through architectural innovations like cross-attention mechanisms. As the field progresses, the development of more biologically informed, scalable, and interpretable tokenization approaches will be essential for realizing the full potential of foundation models in biomedical research and therapeutic development.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling the analysis of cellular heterogeneity and complex regulatory networks at an unprecedented scale [1]. These models, largely built on transformer architectures, rely on a critical first step: the conversion of raw single-cell RNA sequencing (scRNA-seq) data into a structured format that the model can process. This process, known as tokenization, involves defining what constitutes a 'token' from single-cell data, typically representing each gene or feature as a token [1] [17]. The combinations of these tokens collectively represent a single cell, analogous to how words form a sentence in natural language processing [17].

A fundamental challenge in this domain is that gene expression data are not naturally sequential. Unlike words in a sentence, genes in a cell have no inherent ordering, necessitating innovative approaches to input representation [36] [17]. To apply transformer architectures effectively, researchers must impose structure on this non-sequential data through careful implementation of three core components: gene embeddings, value embeddings, and positional encodings. This whitepaper provides a comprehensive technical examination of these components, their implementation variations across leading scFMs, and their critical role in shaping model performance across diverse biological tasks.

Core Components of Input Representation

Gene Embeddings: Capturing Biological Semantics

Gene embeddings function as the foundational lexicon of scFMs, providing a unique representation for each gene that allows the model to recognize and distinguish between different biological entities. These embeddings transform discrete gene identifiers into continuous vector representations that capture biological semantics and functional relationships.

Most scFMs implement gene embeddings using a lookup table approach, where each gene symbol is mapped to a trainable embedding vector of fixed dimension [36]. For example, Geneformer and LangCell employ 512-dimensional embeddings, while scFoundation uses 768-dimensional vectors [36]. This approach allows the model to learn gene-specific representations during pretraining. Alternatively, some models like UCE incorporate biological prior knowledge by using protein-based embeddings derived from ESM-2, which encodes evolutionary information about each gene's protein product [36].

The quality and comprehensiveness of gene embeddings significantly impact the model's ability to understand biological context and generalize across diverse cell types and conditions.

Value Embeddings: Representing Expression Levels

Value embeddings encode the quantitative expression level of each gene in a specific cell, providing crucial information about gene activity magnitude. These embeddings transform continuous expression values into a format compatible with the model's architecture, with different scFMs employing distinct strategies:

  • Value Binning: scGPT discretizes expression values into bins, converting continuous measurements into categorical representations that are then embedded [36].
  • Ordering: Geneformer and LangCell bypass absolute expression values entirely, instead using the relative ranking of genes by expression level to determine token sequence [36].
  • Value Projection: scFoundation employs direct projection layers that transform normalized expression values into embedding vectors [36].
  • Binary Representation: UCE uses a simplified approach, predicting whether a gene is expressed or not through binary classification [36].

Table 1: Value Embedding Strategies Across scFMs

Model Value Embedding Approach Expression Representation Dimensionality
Geneformer Ordering Relative gene ranking 256-512
scGPT Value Binning Discretized expression bins 512
UCE Binary Classification Expressed/Not-expressed 1280
scFoundation Value Projection Continuous normalized values 3072
LangCell Ordering Relative gene ranking 256

Positional Encodings: Imposing Sequence on Non-Sequential Data

Positional encodings address the fundamental challenge that genes lack inherent sequential ordering in scRNA-seq data. These encodings provide information about each token's position in the input sequence, enabling the transformer to understand relational context.

The implementation of positional encodings varies significantly across models, reflecting different philosophical approaches to handling biological sequence:

  • Expression-based Ordering: Geneformer, LangCell, and UCE order genes by their expression levels within each cell, from highest to lowest, then apply standard positional encodings based on this arbitrary but deterministic sequence [36] [17].
  • Genomic Position: UCE alternatively incorporates genomic coordinates, ordering genes by their physical positions on chromosomes to reflect biological organization [36].
  • No Positional Encoding: Some models, including scGPT and scFoundation, completely omit explicit positional encodings, relying instead on the model's attention mechanism to infer relationships without predefined structure [36].

Table 2: Positional Encoding Strategies in scFMs

Model Positional Encoding Gene Ordering Strategy Rationale
Geneformer Standard Expression ranking Deterministic sequence based on importance
scGPT None Highly Variable Genes Lets attention mechanism learn relationships
UCE Standard Expression or Genomic position Flexibility in biological priors
scFoundation None Full gene set Avoids artificial sequencing
LangCell Standard Expression ranking Consistency with linguistic analogy

G cluster_tokenization Tokenization Process RawData Raw scRNA-seq Data (Gene Expression Matrix) GeneEmbed Gene Embeddings (Lookup Table) RawData->GeneEmbed ValueEmbed Value Embeddings (Binning/Projection) RawData->ValueEmbed PosEncode Positional Encodings (Ranking/Genomic) RawData->PosEncode Combined Combined Input Embedding GeneEmbed->Combined ValueEmbed->Combined PosEncode->Combined ModelInput Transformer Model Input Combined->ModelInput

Figure 1: Comprehensive Input Representation Pipeline for scFMs - This workflow illustrates how raw single-cell RNA-sequencing data is processed through three parallel embedding components before being combined into the final input representation for transformer models.

Integrated Input Frameworks in Practice

End-to-End Input Pipeline Architecture

The complete input representation pipeline involves sophisticated coordination between the three embedding components. When a single cell's gene expression profile is processed, it undergoes multiple transformation steps before reaching the transformer layers. First, the pre-processing stage filters and normalizes the raw UMI counts, which may include log-normalization and library size adjustment [8]. Following this, the gene selection phase occurs, where models typically focus on either highly variable genes or a fixed set of the most highly expressed genes [36].

The embedding combination mechanism varies between models. Most scFMs employ element-wise addition to combine gene, value, and positional embeddings, creating a single comprehensive representation for each token [36]. Alternative approaches include concatenation followed by projection or more complex feature-wise transformation layers. These combined embeddings then serve as input to the transformer's attention mechanism, which learns the complex relationships between genes within and across cells.

The scalability of this input pipeline is crucial for handling the high-dimensional nature of scRNA-seq data, which typically contains measurements for 20,000+ genes per cell. Models like scSFUT address this challenge through innovative tokenization approaches that segment each cell sample into dimensionally reduced, information-dense sub-vectors using a fixed window size, enabling efficient processing of full-gene-length data [8].

Comparative Analysis of scFM Input Strategies

Table 3: Comprehensive Input Representation Across Major scFMs

Model Gene Embedding Value Embedding Positional Encoding Input Genes Architecture
Geneformer Lookup Table (512d) Ordering Expression-based 2048 ranked Encoder
scGPT Lookup Table (512d) Value Binning None 1200 HVGs Encoder with attention mask
UCE ESM-2 Protein Embedding Binary Expression or Genomic 1024 non-unique sampled Encoder
scFoundation Lookup Table (768d) Value Projection None 19,264 genes Asymmetric encoder-decoder
LangCell Lookup Table (512d) Ordering Expression-based 2048 ranked Encoder
scSFUT Sequential Tokenization 1D-Convolution Features Implicit via structure Full gene set Encoder-Decoder

The diversity in input representation strategies reflects ongoing experimentation within the field to determine optimal approaches for capturing biological meaning. Benchmarking studies reveal that no single strategy consistently outperforms others across all tasks, suggesting that the optimal input configuration may be task-dependent [36]. For example, models employing expression-based ordering (Geneformer, LangCell) have demonstrated strong performance in cell type annotation tasks, while value-binning approaches (scGPT) may excel in perturbation prediction [36].

Notably, the choice of input representation involves significant trade-offs between biological comprehensiveness and computational efficiency. Models that process full gene sets (scFoundation, scSFUT) avoid information loss but require substantial computational resources, while those using filtered gene sets (most other models) gain efficiency but potentially sacrifice biological nuance [8] [36].

Experimental Protocols and Validation

Benchmarking Methodologies for Input Representation

Rigorous benchmarking is essential for evaluating the effectiveness of different input representation strategies. Comprehensive studies like those conducted by [36] employ multifaceted evaluation frameworks encompassing both gene-level and cell-level tasks. Gene-level tasks typically include gene-gene interaction prediction and gene function annotation, while cell-level assessments involve cell type annotation, batch integration, and perturbation response prediction [36].

The evaluation metrics for these benchmarks span unsupervised, supervised, and knowledge-based approaches. Standard metrics include clustering accuracy, label transfer fidelity, and trajectory inference quality. More sophisticated biology-aware metrics such as scGraph-OntoRWR have been developed to measure the consistency of cell type relationships captured by scFMs with established biological knowledge [36]. Additionally, the Lowest Common Ancestor Distance (LCAD) metric quantifies the ontological proximity between misclassified cell types, providing nuanced assessment of annotation errors [36].

These benchmarking efforts consistently show that pretrained scFM embeddings capture meaningful biological insights into the relational structure of genes and cells, which benefits diverse downstream tasks. The performance improvements appear to stem from a smoother cell-property landscape in the pretrained latent space, which reduces the difficulty of training task-specific models [36].

Implementation Protocol for Input Representation

For researchers implementing custom input representations for scFMs, the following technical protocol provides a foundational starting point:

Data Preprocessing Stage:

  • Quality Control: Retain cells with expression in at least 200 genes and filter out genes expressed in fewer than three cells [8].
  • Normalization: Apply log-normalization with a library size of 10,000 transcripts per cell [8].
  • Gene Selection: Optionally select highly variable genes (HVGs) based on dispersion, though some modern approaches like scSFUT avoid this step to prevent information loss [8].

Embedding Implementation:

  • Gene Embedding Layer: Initialize a trainable lookup table with dimensions 512-768 for each gene in the vocabulary.
  • Value Processing:
    • For value binning: Discretize normalized expression values into 10-50 bins based on empirical distribution.
    • For value projection: Implement a linear layer to project normalized values to the embedding dimension.
    • For ordering: Rank genes by expression level and use rank as position indicator.
  • Positional Encoding: Implement either learned or fixed sinusoidal encodings applied to the determined gene sequence.

Integration and Regularization:

  • Combination: Sum the gene, value, and positional embeddings element-wise.
  • Regularization: Apply dropout to the combined embeddings (typically 0.1-0.3 rate) to prevent overfitting.
  • Layer Normalization: Normalize embeddings before passing to transformer layers.

This protocol serves as a flexible template that can be adapted based on specific research requirements and dataset characteristics.

G cluster_preprocess Pre-processing cluster_embed Embedding Generation Start scRNA-seq Raw Count Matrix QC Quality Control Start->QC Norm Normalization QC->Norm Select Gene Selection Norm->Select GEmbed Gene Embedding (Lookup Table) Select->GEmbed VEmbed Value Embedding (Binning/Projection) Select->VEmbed PEmbed Positional Encoding (Ranking/Genomic) Select->PEmbed Combine Element-wise Addition of Embeddings GEmbed->Combine VEmbed->Combine PEmbed->Combine Output Final Input Representation (Transformer Ready) Combine->Output

Figure 2: Experimental Protocol for Input Representation - This diagram outlines the step-by-step computational workflow for transforming raw single-cell data into model-ready input representations, highlighting key decision points at each stage.

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Computational Tools for scFM Input Representation

Tool/Resource Type Primary Function Application in Input Representation
Scanpy [8] Python Package Single-cell Analysis Data preprocessing, normalization, and quality control
CELLxGENE [36] Data Platform Curated Single-cell Data Access to standardized datasets for model training
scGPT [36] Model Framework Transformer Implementation Reference implementation of value binning and embedding strategies
Geneformer [36] Model Framework Transformer Implementation Reference for expression-ranking based input representation
scSFUT [8] Model Implementation End-to-end Annotation Example of full-gene-length processing without HVG selection
Hugging Face Transformers Model Library Architecture Templates Transformer implementation foundation for custom models

The input representation methodology comprising gene embeddings, value embeddings, and positional encodings forms the critical foundation for single-cell foundation models' understanding of cellular biology. The current diversity in approaches—from gene ordering strategies to value discretization techniques—reflects a field in active exploration of optimal representations. As benchmarking studies [36] consistently demonstrate, each approach involves distinct trade-offs between biological fidelity, computational efficiency, and task-specific performance.

Future developments in scFM input representation will likely focus on several key areas: more sophisticated biological priors incorporation, dynamic tokenization strategies that adapt to different biological contexts, multimodal integration that combines scRNA-seq with other data types, and more efficient attention mechanisms capable of handling full-gene sets without filtering. The ongoing validation of these models through biologically meaningful tasks and their application to challenging clinical scenarios will further refine our understanding of how best to represent cellular states for computational analysis.

As the field progresses, the standardization of input representation protocols alongside flexible frameworks for custom adaptation will accelerate the development of more powerful, interpretable, and biologically accurate foundation models in single-cell genomics.

Special Tokens for Cell Identity, Modality, and Batch Information

In the evolving field of single-cell genomics, foundation models (scFMs) are revolutionizing how researchers interpret complex biological systems. These large-scale deep learning models, pretrained on vast single-cell datasets, leverage self-supervised learning to adapt to various downstream tasks [1]. The process of tokenization—converting raw input data into discrete units called tokens—serves as the critical foundation for these models. Unlike natural language processing where tokens represent words or subwords, scFMs define tokens from single-cell data, typically representing each gene or genomic feature as a token [1]. These tokens become the fundamental input units that models process and learn from, analogous to words in a sentence.

A significant challenge in applying transformer architectures to single-cell data is the non-sequential nature of gene expression data [1]. Unlike words in a sentence, genes in a cell have no inherent ordering. To address this, researchers have developed various tokenization strategies that incorporate special tokens to encode crucial biological and technical metadata. These specialized tokens for cell identity, experimental modality, and batch information enable models to learn richer, more contextualized representations of cellular states and functions, ultimately enhancing performance across diverse biological applications from basic research to drug development [1].

The Role and Implementation of Special Tokens

Categories and Functions of Special Tokens

Special tokens in single-cell foundation models serve as dedicated input units that provide contextual information beyond raw gene expression values. The table below summarizes the primary categories and their specific functions:

Table 1: Categories and Functions of Special Tokens in scFMs

Token Category Primary Function Implementation Examples Representation Format
Cell Identity Prepend context about cell's own identity and metadata [1] Prepended token representing cell type, state, or disease status [1] Learnable embedding vectors combined with gene tokens
Modality Indicate data type (e.g., scRNA-seq, scATAC-seq, spatial transcriptomics) [1] Special tokens inserted to specify omics modality [1] [9] Modality-specific tokens incorporated into input sequence
Batch Information Account for technical variations between experiments [1] Batch-specific tokens or adaptive tokenization using dynamic adaptation [1] [9] Batch identifiers or correction through embedding projection
Gene Metadata Provide biological context beyond expression values [1] Incorporation of gene ontology, chromosome location, or regulatory information [1] Additional feature channels in token embeddings
Technical Implementation Strategies

The implementation of special tokens requires careful architectural considerations. Most scFMs use transformer architectures characterized by attention mechanisms that learn relationships between any pair of input tokens [1]. When special tokens are introduced, they undergo the same embedding process as gene tokens.

For cell identity tokens, a common approach involves prepending a special token representing the cell's own identity and metadata before the sequence of gene tokens [1]. This allows the model to learn cell-level context that influences how gene relationships are interpreted. The transformer's attention mechanism can then weight the importance of this cell identity information when processing each gene token.

For modality tokens, researchers have developed dynamic token adaptation approaches that project embeddings from different data modalities into the model's token embedding space [9]. For example, Bio-DTA uses an adapter layer to project DNA-sequence embeddings from a language model into the token embedding space of a single-cell foundation model, creating a multi-modal representation that connects genetic information with transcriptomic patterns [9].

Table 2: Model-Specific Implementation of Special Tokens

Model Architecture Special Token Handling Reported Benefits Limitations
scGPT (GPT-inspired) Uses modality and batch tokens in decoder architecture [1] Robust performance across zero-shot and fine-tuning tasks [37] May require more training data for optimal performance
scBERT (BERT-like encoder) Employs bidirectional attention with special tokens [1] Effective for classification tasks and embedding generation [1] Smaller model size may limit capacity [37]
Geneformer Leverages effective pretraining strategies for gene-level tasks [37] Strong capabilities in gene-level tasks [37] Limited evaluation on multi-modal tasks
Bio-DTA (Multi-modal) Dynamic token adaptation for DNA-sequence integration [9] Sensitive to small genetic changes and their impact on co-regulation [9] Requires additional computational resources for adapter training

Experimental Protocols and Workflows

Workflow for Special Token Integration

The following diagram illustrates the complete workflow for integrating special tokens into scFM training and application:

G cluster_inputs Input Data Sources cluster_processing Tokenization Engine cluster_model Model Architecture cluster_output Output Applications RawData Raw Single-Cell Data Tokenization Tokenization Process RawData->Tokenization Metadata Cell Metadata Metadata->Tokenization BatchInfo Batch Information BatchInfo->Tokenization ModalityInfo Modality Specifications ModalityInfo->Tokenization GeneTokens Gene Tokens Tokenization->GeneTokens CellIdentityTokens Cell Identity Tokens Tokenization->CellIdentityTokens ModalityTokens Modality Tokens Tokenization->ModalityTokens BatchTokens Batch Tokens Tokenization->BatchTokens InputSequence Input Token Sequence GeneTokens->InputSequence CellIdentityTokens->InputSequence ModalityTokens->InputSequence BatchTokens->InputSequence Transformer Transformer Layers InputSequence->Transformer LatentRep Latent Representations Transformer->LatentRep CellTypeAnnotation Cell Type Annotation LatentRep->CellTypeAnnotation PerturbationAnalysis Perturbation Analysis LatentRep->PerturbationAnalysis TargetDiscovery Therapeutic Target Discovery LatentRep->TargetDiscovery

Dynamic Token Adaptation Methodology

For multi-modal integration, dynamic token adaptation represents an advanced technical approach. The following diagram details this process:

G cluster_dna DNA Sequence Processing cluster_adapter Dynamic Token Adapter cluster_scfm Single-Cell Foundation Model DNAInput DNA Sequence (196,608 bp around TSS) EnformerModel Enformer DNA Model DNAInput->EnformerModel DNAEmbeddings DNA Embeddings (3,072 dimensions) EnformerModel->DNAEmbeddings AdapterLayer Adapter Layer (Multilayer Perceptron) DNAEmbeddings->AdapterLayer ProjectedEmbeddings Projected Token Embeddings AdapterLayer->ProjectedEmbeddings TokenInput Input Token Sequence ProjectedEmbeddings->TokenInput TransformerLayers Transformer Encoder (BERT Architecture) TokenInput->TransformerLayers ContextualizedOutput Contextualized Embeddings TransformerLayers->ContextualizedOutput GeneExpression Gene Expression Profile GeneExpression->TokenInput Ordered by Expression

Protocol for Closed-Loop Model Refinement

The "closed-loop" framework represents a significant advancement in scFM refinement by incorporating experimental perturbation data during model fine-tuning [23]. The methodology involves these key steps:

  • Initial Model Fine-tuning: Begin with a pre-trained scFM (e.g., Geneformer) and fine-tune it to classify cells between states of interest (e.g., activated vs. resting T-cells, or diseased vs. healthy cells) using available single-cell RNA sequencing data [23].

  • Open-Loop Perturbation Prediction: Perform in silico perturbation (ISP) across thousands of genes, simulating both gene overexpression and knockout to model biological interventions [23].

  • Experimental Validation: Conduct targeted experimental validations (e.g., Perturb-seq) on a subset of predictions to generate ground-truth data. This typically requires screening a manageable number of genes (e.g., 75 genes in the T-cell activation study) [23].

  • Closed-Loop Fine-tuning: Incorporate the experimental perturbation data into a subsequent fine-tuning round alongside the original single-cell RNA sequencing data. The perturbation data should be labeled with activation status but not with the specific gene perturbed to prevent overfitting [23].

  • Iterative Refinement: The refined model demonstrates significantly improved prediction accuracy, with studies showing a three-fold increase in positive predictive value while maintaining high negative predictive value [23].

Table 3: Performance Metrics for Closed-Loop vs. Open-Loop Approaches

Evaluation Metric Open-Loop ISP Closed-Loop ISP Improvement
Positive Predictive Value (PPV) 3% 9% 3-fold increase [23]
Negative Predictive Value (NPV) 98% 99% Marginal improvement [23]
Sensitivity 48% 76% 58% relative improvement [23]
Specificity 60% 81% 35% relative improvement [23]
AUROC 0.63 (95% CI: 0.58-0.68) 0.86 (95% CI: 0.83-0.89) Significant improvement (p<0.05) [23]

The Scientist's Toolkit: Research Reagent Solutions

Implementing effective tokenization strategies requires both computational tools and experimental reagents. The following table details essential resources mentioned in the research:

Table 4: Essential Research Reagents and Computational Tools

Resource Name Type Primary Function Application Context
CellXGene Census Data Resource Provides unified access to annotated single-cell datasets [1] Pretraining scFMs on diverse cell types and states [1]
BioLLM Framework Computational Tool Unified interface for diverse single-cell foundation models [37] Standardized model evaluation and comparison [37]
Geneformer Pre-trained Model scFM with strong gene-level task performance [23] [37] In silico perturbation prediction and target identification [23]
Enformer Model DNA Language Model Predicts epigenetic signals from DNA sequence [9] Dynamic token adaptation for multi-modal integration [9]
10x Genomics Chromium Experimental Platform Droplet-based single-cell encapsulation [38] High-throughput single-cell RNA sequencing for validation data [38]
Perturb-seq Experimental Method CRISPR-based screening with single-cell RNA readout [23] Generating ground-truth data for closed-loop model refinement [23]
Unique Molecular Identifiers (UMIs) Molecular Barcodes Mark individual mRNA molecules for quantitative analysis [38] Ensuring data quality for training and validation datasets [38]

Biological Validation and Case Studies

T-cell Activation Case Study

In a comprehensive validation of the closed-loop framework, researchers applied special token strategies to T-cell activation [23]. The study fine-tuned Geneformer using single-cell RNA sequencing data from resting and activated T-cells, with special tokens encoding activation status and experimental conditions. The model was then used to perform in silico perturbations across 13,161 genes.

The open-loop ISP predictions demonstrated superior performance compared to differential expression analysis for negative predictive value (98% versus 78%), sensitivity (48% versus 40%), and specificity (60% versus 50%) [23]. When incorporating perturbation examples through the closed-loop approach, the model showed dramatic improvements across all metrics, with only 10-20 perturbation examples needed for substantial gains in predictive accuracy [23].

RUNX1-Familial Platelet Disorder Application

The closed-loop framework was further validated in a rare disease context—RUNX1-familial platelet disorder (RUNX1-FPD) [23]. Researchers fine-tuned Geneformer to classify hematopoietic stem cells (HSCs) between RUNX1-engineered knockout cells and control cells. The model successfully distinguished these cell states and identified genes that, when perturbed, would shift RUNX1-knockout HSCs toward a control-like state.

This application yielded 14 high-confidence therapeutic targets predicted by both differential expression and in silico perturbation [23]. From these, researchers selected eight genes with available specific small molecule inhibitors for experimental validation, demonstrating the practical therapeutic implications of properly tokenized scFMs for drug development.

Implementation Considerations and Future Directions

Current Challenges and Limitations

Despite their promise, special token implementations in scFMs face several significant challenges. The non-sequential nature of omics data remains a fundamental constraint, requiring arbitrary ordering schemes such as ranking genes by expression levels [1]. Data quality inconsistency across different experiments and platforms introduces technical noise that can obscure biological signals [1]. The computational intensity required for training and fine-tuning these large models presents practical barriers for many research groups [1]. Perhaps most importantly, interpreting the biological relevance of latent embeddings and model representations remains nontrivial, limiting the translational potential of these models [1].

Emerging Solutions and Methodological Advances

Several promising approaches are emerging to address these limitations. For data integration challenges, methods like sysVI employ VampPrior and cycle-consistency constraints to improve integration across systems while preserving biological signals [39]. For multi-modal integration, dynamic token adaptation approaches enable flexible encoding of additional information that may change between data samples [9]. Standardization efforts such as the BioLLM framework provide unified interfaces that eliminate architectural and coding inconsistencies, enabling more reproducible evaluation of different tokenization strategies [37].

The field is rapidly moving toward more sophisticated tokenization approaches that can natively handle multi-modal data, dynamically adapt to new data types, and more effectively disentangle technical artifacts from biologically meaningful variation. As these methodologies mature, special tokens will likely play an increasingly central role in unlocking the full potential of single-cell foundation models for both basic research and therapeutic development.

Tokenization serves as the critical first step in processing single-cell RNA-sequencing (scRNA-seq) data for foundation models (scFMs), transforming continuous, high-dimensional gene expression measurements into discrete, structured inputs that deep learning models can process. In single-cell biology, foundation models are large-scale AI systems pretrained on vast datasets that can be adapted for diverse downstream tasks including cell type annotation, multi-omic integration, and perturbation response prediction [1]. The fundamental analogy underpinning these models treats individual cells as sentences and genes or genomic features as words, creating a "language of biology" that transformers can decipher [1] [17]. However, unlike natural language with its inherent sequential structure, gene expression data presents unique challenges due to its non-sequential nature, high dimensionality, and sparsity [1]. This technical guide examines the tokenization approaches of four prominent scFMs—scGPT, Geneformer, scMamba, and scBERT—within the broader context of tokenization strategy development for scRNA-seq data, providing a structured comparison of their methodologies, experimental protocols, and performance characteristics.

Fundamental Concepts of Tokenization in scFMs

Tokenization in single-cell foundation models converts raw gene expression data into discrete tokens that can be processed by transformer architectures. This process must address several fundamental challenges: (1) the non-sequential nature of genomic data, requiring imposition of artificial ordering; (2) the extreme sparsity of single-cell count matrices; (3) technical variability between experiments; and (4) the need to preserve biological meaningfulness while reducing dimensionality [1] [17]. Most scFMs represent genes as fundamental tokens, with each cell comprising a collection of these gene tokens structured through various ranking or binning strategies. The tokenization process typically incorporates both the gene identity (via Ensembl IDs or gene symbols) and its expression value through various encoding schemes [1]. Additional special tokens may be included to represent cell-level metadata, omics modalities, or batch information, enriching the context available to the model [40]. The resulting token sequences serve as input to transformer architectures that learn complex relationships between genes and cells through self-attention mechanisms.

Comparative Analysis of Tokenization Approaches

scGPT Tokenization Methodology

scGPT employs a comprehensive tokenization approach designed for multi-omic integration and generative modeling. The model uses a gene vocabulary that maps gene identifiers to token indices, with special tokens including <cls>, <pad>, <eos>, and <mask> for downstream tasks [41]. The tokenization process incorporates both gene identity and expression values through a binning strategy that discretizes continuous expression measurements. Specifically, gene expressions are normalized and then partitioned into bins, converting continuous values into discrete tokens that represent expression levels [40]. This approach allows the model to capture quantitative expression information while maintaining the discrete token structure required by transformer architectures.

For multi-omic applications, scGPT incorporates modality-specific tokens that indicate whether the input data comes from RNA-seq, ATAC-seq, or other omics layers [40]. The tokenization workflow includes appendting a <cls> token to the beginning of each cell's gene sequence, which aggregates cell-level representations during training. The model's tokenizer also supports masked language modeling pretraining by randomly replacing tokens with <mask> tokens, enabling the model to learn contextual relationships between genes by predicting masked values [41]. scGPT's tokenizer is designed to handle the scale and diversity of large single-cell corpora, having been trained on over 33 million cells, making it suitable for building foundation models capable of transfer learning across diverse downstream applications [42] [40].

Table: scGPT Tokenization Specifications

Aspect Specification
Gene Identification Ensembl IDs and gene symbols
Expression Encoding Binning strategy for discrete values
Special Tokens <cls>, <pad>, <eos>, <mask>
Multi-omic Support Modality-specific tokens
Pretraining Data Scale 33+ million cells [42]
Key Applications Multi-omic integration, perturbation prediction, cell type annotation

Geneformer Tokenization Strategy

Geneformer implements a unique rank-based tokenization approach that emphasizes relative expression patterns rather than absolute values. The tokenizer requires input data in specific file formats (.loom, .h5ad, or .zarr) containing raw counts without feature selection [43] [44]. Critical requirements include the "ensemblid" row attribute for genes and "ncounts" column attribute for total read counts per cell. The tokenization process begins by normalizing gene expression values using the total read count, then calculating median-scaled values across the dataset [44].

The core innovation in Geneformer is its rank value encoding, where genes within each cell are sorted by their median-scaled expression values, and the resulting ordered list of gene tokens constitutes the cell representation [44]. This approach captures the relative importance of genes within each cell's expression profile while being robust to technical variations. The tokenizer can optionally collapse duplicate Ensembl IDs by summing their counts and includes a "filter_pass" attribute that allows users to specify quality control criteria for cell inclusion [43].

Geneformer offers two model series (V1 and V2) with different tokenization parameters. The V1 model uses a sequence length of 2,048 tokens without special tokens, while V2 expanded to 4,096 tokens and incorporates special tokens [44]. This evolution reflects the trend toward larger context windows in foundation models to capture more complex biological relationships. The model has been pretrained on approximately 30 million cells for V1 and 104 million for V2, demonstrating the scalability of its tokenization approach [44].

G A Raw Count Matrix .loom/.h5ad/.zarr B Quality Control filter_pass=1 A->B C Normalization Median-scaled values B->C D Gene Ranking By expression level C->D E Token Sequence Ordered gene tokens D->E

Geneformer tokenization workflow

scMamba Tokenization Framework

scMamba introduces a novel patch-based tokenization strategy specifically designed for single-cell multi-omics integration beyond conventional highly variable feature selection. Unlike other approaches that focus on individual genes, scMamba treats genomic regions as tokens, creating "patches" that capture broader genomic context [45]. This approach preserves genomic positional information often lost in traditional methods that select highly variable features, thereby retaining more biological information during preprocessing.

The model employs a contrastive learning objective with cosine similarity regularization to align different omics layers within its token representation space [45]. scMamba's architecture builds upon the concept of state space duality, which enables efficient distillation of biological insights from high-dimensional, sparse single-cell multi-omics data. The patch-based tokenization allows scMamba to effectively handle large-scale datasets, including multi-omic atlases, while maintaining computational efficiency.

Benchmarking studies demonstrate that scMamba significantly outperforms state-of-the-art methods in preserving biological variation, aligning omics layers, and enhancing downstream tasks including clustering, cell type annotation, and trajectory inference [45]. This performance advantage stems from its innovative tokenization approach that moves beyond gene-level tokenization to incorporate regional genomic information, providing a more comprehensive representation of cellular state.

scBERT Tokenization Process

scBERT adapts the BERT (Bidirectional Encoder Representations from Transformers) architecture for single-cell data through a dual-embedding tokenization approach. The model creates gene embeddings using gene2vec, which encodes gene representations in a predefined vector space to capture semantic similarities between genes [5]. Additionally, it incorporates expression embeddings generated through term-frequency analysis that discretizes continuous expression variables by binning them into 200-dimensional vectors [5].

These dual embeddings are combined as token embeddings, allowing scBERT to consider both gene identity and expression levels. The model employs a BERT-like encoder architecture with bidirectional attention mechanisms, enabling it to learn from the context of all genes in a cell simultaneously [5]. During pretraining, scBERT uses a masked language model objective where masked expression and gene embeddings are integrated as input, and a reconstructor generates outputs with reconstruction loss calculated based on the masked genes.

Studies have revealed that scBERT's performance is significantly influenced by cell-type distribution imbalance in training data [5]. When faced with imbalanced distributions, the model exhibits reduced performance in both annotation and novel cell-type detection tasks. Researchers have addressed this limitation through subsampling techniques that mitigate the influence of imbalanced distributions, highlighting the importance of considering data distribution characteristics when applying transformer models to single-cell data [5].

Table: Comparative Analysis of Tokenization Approaches

Model Gene Representation Expression Encoding Ordering Strategy Special Features
scGPT Ensembl IDs/symbols Binning Expression-based ranking Multi-omic tokens, generative focus
Geneformer Ensembl IDs Rank value encoding Expression-based ranking Relative expression patterns
scMamba Genomic regions/patches Positional encoding Genomic position Contrastive learning, multi-omic alignment
scBERT gene2vec embeddings Term-frequency binning Expression-based ranking Dual embeddings, bidirectional attention

Experimental Protocols and Implementation

Data Preprocessing Requirements

Successful tokenization across all examined models requires careful data preprocessing to ensure compatibility and optimal performance. The foundational requirements include:

  • Input Format: Raw count matrices without feature selection in standardized formats (.loom, .h5ad, or .zarr) [43] [44]
  • Gene Annotation: Ensembl IDs as primary gene identifiers for precise mapping [43]
  • Quality Metrics: Total read counts per cell ("n_counts" attribute) for normalization [44]
  • Cell Filtering: Optional "filter_pass" binary indicators for quality control [44]

For Geneformer implementation, the tokenization protocol begins with data normalization using total read counts, followed by median scaling across the dataset. The key step involves sorting genes by their median-scaled expression values to create the rank-based token sequence [44]. The tokenizer processes data in chunks (default size: 512 cells) for memory efficiency and can leverage multiple processors (nproc parameter) to accelerate large-scale tokenization.

scBERT's experimental protocol involves more extensive embedding generation, starting with gene2vec pretraining to establish semantic relationships between genes, followed by expression binning to create expression embeddings [5]. During fine-tuning, task-specific data undergoes standard preprocessing including filtering, normalization, and log1p transformation using Scanpy [5]. The model then combines gene and expression embeddings as input tokens for the transformer architecture.

Model Training and Fine-tuning

The pretraining phase for these scFMs typically employs self-supervised objectives, most commonly masked language modeling where random tokens are masked and the model learns to reconstruct them based on context [1]. scGPT uses a generative pretrained transformer approach with a unidirectional masked self-attention mechanism that iteratively predicts masked genes conditioned on known genes [40]. In contrast, scBERT employs a bidirectional encoder that learns from all genes simultaneously during reconstruction [5].

For downstream tasks, transfer learning through fine-tuning has proven highly effective. Studies demonstrate that pretrained scFMs adapted to specific applications achieve superior performance compared to models trained from scratch [5] [40]. scGPT specifically showed strong transfer learning capabilities across diverse applications including cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction, and gene network inference [40]. The fine-tuning process typically requires significantly fewer labeled examples than training from scratch, making scFMs particularly valuable for applications with limited annotated data.

Performance Comparison and Benchmarking

Cell Type Annotation Accuracy

Comprehensive benchmarking reveals distinct performance characteristics across the examined models. scBERT demonstrates strong performance in cell type annotation tasks, achieving a validation mean accuracy of 0.8510 on the NeurIPS dataset compared to Seurat's 0.8013 [5]. This performance advantage was statistically significant (p-value = 0.0004), highlighting the potential of transformer-based approaches for classification tasks. However, scBERT's performance was notably influenced by cell-type distribution imbalance, with significantly reduced effectiveness in detecting novel cell types in imbalanced datasets [5].

Geneformer's rank-based encoding has shown particular strength in capturing developmental trajectories and dynamic biological processes, making it well-suited for trajectory inference and analysis of cellular dynamics [44]. The model's emphasis on relative expression patterns rather than absolute values provides robustness to technical variations across datasets.

scMamba demonstrates superior performance in multi-omic integration tasks, significantly outperforming state-of-the-art methods in preserving biological variation, aligning omics layers, and enhancing clustering accuracy [45]. The model's patch-based tokenization approach enables more comprehensive representation of genomic context, contributing to its strong performance in complex integration tasks.

Novel Cell Type Detection

In novel cell type detection tasks, evaluated through leave-one-out experiments where models are trained on all but one cell type and tested on identifying the held-out type as novel, scBERT showed limitations in detecting only part of the novel cell types [5]. Performance was particularly challenged when dealing with rare cell types or highly correlated cell populations, indicating areas for future methodological improvements.

Table: Downstream Task Performance Comparison

Model Cell Type Annotation Novel Type Detection Multi-omic Integration Trajectory Inference
scGPT High Moderate High High
Geneformer High Moderate Moderate High
scMamba High Not reported Very High High
scBERT Very High Limited Moderate Moderate

The Scientist's Toolkit: Essential Research Reagents

Implementation of scFM tokenization approaches requires specific computational "reagents" and resources:

  • CELLxGENE Census: Provides unified access to annotated single-cell datasets with over 100 million unique cells standardized for analysis, serving as primary pretraining data source [1]
  • Ensembl Biomart: Essential for converting gene annotations to Ensembl IDs required by most tokenizers [43]
  • Scanpy: Python-based toolkit for preprocessing, quality control, and normalization of single-cell data [5]
  • Anndata Objects: Standardized container for annotated single-cell data, compatible with multiple tokenization approaches [35]
  • Hugging Face Model Repository: Hosts pretrained models including Geneformer for transfer learning applications [44]
  • 10X Genomics Data: Standardized data from 10X platforms often used as benchmarking datasets [35]

Future Directions and Challenges

The evolution of tokenization strategies for scFMs continues to address several persistent challenges. The non-sequential nature of genomic data remains a fundamental issue, with current ordering strategies (expression-based ranking, genomic position) representing artificial structures that may not reflect biological reality [1]. Future approaches may explore attention-based ordering or graph-based representations that better capture gene-gene interactions without imposing artificial sequences.

Computational intensity presents another significant challenge, as training scFMs on millions of cells requires substantial resources [1] [17]. Emerging architectures like scMamba's state space models offer promising alternatives to traditional transformers for improved efficiency [45]. Similarly, interpretability of model representations remains difficult, with researchers needing better methods to extract biologically meaningful insights from latent embeddings.

The trend toward multi-omic integration continues to shape tokenization development, with newer models incorporating epigenetic, spatial, and proteomic data alongside transcriptomics [1] [17]. Future tokenization approaches will need to seamlessly integrate diverse data types while preserving the unique characteristics and information content of each modality.

Tokenization strategies represent a critical foundational element in single-cell foundation models, significantly influencing their capacity to learn meaningful biological representations. The four examined approaches—scGPT's binning strategy, Geneformer's rank-based encoding, scMamba's patch-based method, and scBERT's dual embeddings—each offer distinct advantages for different applications and biological questions. While current methods have demonstrated impressive performance across diverse downstream tasks, ongoing challenges in handling data sparsity, computational efficiency, and interpretability continue to drive innovation in this rapidly evolving field. As single-cell technologies progress toward increasingly multimodal assays and larger-scale atlases, tokenization approaches must similarly evolve to capture the full complexity of cellular identity and function, ultimately enabling more accurate and comprehensive models of biological systems.

Optimizing Tokenization Pipelines: Addressing Technical Challenges and Biases

Managing Data Sparsity and Dropout Events in Token Representation

Single-cell RNA sequencing (scRNA-seq) data is characterized by its profound sparsity, where gene-cell count matrices typically contain exceeding 90% zero values [46]. This sparsity arises from both biological phenomena (the genuine absence of gene expression in specific cell types) and technical artifacts (so-called "dropout events" where expressed transcripts fail to be detected) [47]. For single-cell foundation models (scFMs), which treat cells as sentences and genes as words or tokens, this sparsity presents a fundamental challenge for robust token representation [17]. The performance of these transformer-based models depends critically on how cells are tokenized into model inputs—a design space where effective sparsity management becomes paramount for biological meaningfulness [48]. This technical guide examines cutting-edge strategies for managing data sparsity and dropout events within token representation frameworks, positioning these approaches within the broader thesis that intentional tokenization strategies form the foundation of effective scFM research.

Technical Approaches for Sparsity Management in Tokenization

Statistical and Model-Based Frameworks

Advanced computational methods address sparsity through sophisticated statistical modeling that explicitly accounts for the zero-inflated nature of scRNA-seq data. The Zero-Inflated Negative Binomial (ZINB) model has emerged as a particularly effective framework, as it separately models the probability of dropout events (zero-inflation) and count data (negative binomial) [47] [49]. The ZIGACL method exemplifies this approach by integrating a ZINB-based autoencoder with a Graph Attention Network (GAT) [49]. This hybrid architecture leverages mutual information from neighboring cells to enhance dimensionality reduction while applying dynamic adjustments through a co-supervised deep graph clustering model [49]. The synergistic integration of denoising processes and topological embedding generates cell representations that ensure similar cells are proximal in the latent space, effectively mitigating sparsity challenges [49].

Imputation methods represent another strategic approach, with four primary categories employed: (1) Model-based imputation using ZINB models to separate technical zeros from biological values; (2) Data smoothing methods that adjust expression values by averaging across similar cells using graph-based models; (3) Data reconstruction techniques that decompose data into simpler components through principal component analysis (PCA) or variational autoencoders (VAEs); and (4) Transfer learning approaches that leverage external datasets like bulk RNA-seq or cell atlases to improve imputation accuracy [47]. Tools such as SAVER-X and TRANSLATE implement these transfer learning strategies, ensuring imputed values align with known biological patterns, which is particularly valuable for rare cell types or complex tissues [47].

Biological Insights into Dropout Mechanisms

Understanding the biological mechanisms underlying dropout events provides crucial insights for developing more effective tokenization strategies. Research has revealed that certain genes are consistently under-detected in scRNA-seq compared to bulk RNA-seq across diverse biological contexts [46]. Through analysis of paired bulk RNA-seq and scRNA-seq data from 53 human samples, researchers identified that genes with poly(T) motifs toward the 3' end of their transcripts are particularly prone to under-detection [46]. This motif may form hairpin structures with the poly(A) tails of mRNA transcripts, making them difficult to capture during scRNA-seq library preparation [46]. This biological insight suggests that tokenization strategies could be enhanced by incorporating sequence-specific weighting or attention mechanisms that account for this inherent technical bias.

Table 1: Quantitative Performance of ZIGACL Against Competing Methods Across Nine scRNA-seq Datasets

Dataset Cell Number Gene Number Cell Types ZIGACL ARI Best Competitor ARI Performance Improvement
Muraro 2,122 19,049 9 0.912 0.733 (scDeepCluster) 24.42%
Romanov 2,881 21,143 7 0.663 0.495 (scDeepCluster) 33.94%
Klein 2,717 24,175 5 0.819 0.750 (scDeepCluster) 9.20%
Qx_Bladder 2,500 23,341 4 0.762 0.760 (scDeepCluster) 0.26%
QxLimbMuscle 3,909 23,341 6 0.989 0.636 (scDeepCluster) 55.50%
Qx_Spleen 9,552 23,341 5 0.325 0.138 (DESC) 135.51%
QS-seq2_Diaphragm 870 23,341 5 - - -

Table 2: Categorization of Sparsity Management Approaches in scFM Tokenization

Approach Category Key Methods Mechanism of Action Advantages Limitations
Model-Based Imputation ZINB models, scParser matrix factorization Models data generation process and separates technical from biological zeros Corrects technical artifacts, supports differential expression Risk of circularity reinforcing biases
Data Smoothing Graph attention networks, mutual nearest neighbors Averages expression across similar cells using graph-based models Improves clustering and trajectory inference May obscure rare cell populations
Data Reconstruction VAEs, PCA, ZINB autoencoders Decomposes data into simpler components through dimensionality reduction Creates low-dimensional representations for visualization Potential loss of biological signal
Transfer Learning SAVER-X, TRANSLATE, Atlas integration Leverages external datasets to inform imputation Biologically meaningful results, especially for rare cells Dependent on reference data quality and completeness

Experimental Protocols and Methodologies

Implementation of ZIGACL for Sparsity Management

The ZIGACL method provides a robust protocol for managing sparsity and dropout events in scRNA-seq data analysis. The implementation consists of three main modules: a ZINB-based autoencoder, a Graph Attention Network (GAT), and a co-supervised learning method [49]. The process begins with standard scRNA-seq data preprocessing, followed by the ZINB autoencoder, which reduces gene expression data into a lower-dimensional space for analysis [49]. The autoencoder architecture incorporates fully connected layers for both encoding and decoding, facilitating the learning of embedded scRNA-seq data features. During decoding, the ZINB distribution models data sparsity and overdispersion through three activation layers that estimate the ZINB parameters μ, θ, and π, effectively capturing the statistical properties of scRNA-seq data [49].

An adjacency matrix is created using a Gaussian kernel and input into the GAT to analyze cellular structural interrelationships. The encoded features from the autoencoder are integrated with the GAT to enhance understanding of cellular dynamics [49]. In the subsequent phase, co-supervised learning refines the deep graph clustering model through three distribution models: target, clustering, and probability distributions. The target distribution P directs the training by capturing cell similarities or distances, while the clustering distribution Q iteratively refines to reflect the data's clustering structure [49]. The probability distribution Z focuses on enhancing cluster membership indicators in the latent space. For optimization, the Adam optimizer is employed with a learning rate of 0.001, alongside gradient clipping (limiting the L2 norm to a maximum of 3) to mitigate gradient explosion risk [49]. An early stopping criterion is applied during fine-tuning: if the proportion of label changes falls below 0.1% of the total labels, training halts to prevent overfitting [49].

Tokenization Framework Evaluation with Heimdall

The Heimdall framework provides a systematic methodology for evaluating tokenization strategies in scFMs, with particular relevance for sparsity management [48]. Heimdall decomposes each scFM into modular components: a gene identity encoder (FG), an expression encoder (FE), and a "cell sentence" constructor (F_C) with submodules (order, sequence, and reduce) enabling fine-grained control and attribution [48]. This modular approach allows researchers to systematically evaluate how different tokenization decisions impact model performance, particularly under distribution shifts such as cross-tissue, cross-species, and spatial gene-panel transfers [48].

Experimental protocols using Heimdall involve training transformers from scratch to evaluate tokenization strategies for cell type classification across challenging transfer learning settings [48]. Research using this framework has demonstrated that while tokenization choices show minimal impact in-distribution, they become decisive under distribution shift, with gene identity encoding (FG) and ordering strategies (order) driving the largest gains, while expression encoders (FE) provide additional improvements [48]. The framework further shows how existing strategies can be recombined to enhance generalization, establishing a foundation for reproducible, systematic exploration of single-cell tokenization [48].

Sparsity Management in scFM Workflow

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Essential Research Reagents and Computational Tools for Sparsity Management

Tool/Reagent Type Primary Function Application Context
ZINB Models Statistical Model Models zero-inflated count data with overdispersion Differentiating technical zeros from biological zeros
Graph Attention Networks (GAT) Neural Network Architecture Leverages information from neighboring cells Graph-based imputation and representation learning
Transformer Architectures Model Architecture Self-attention mechanisms for gene relationships Core backbone of single-cell foundation models
Heimdall Framework Evaluation Toolkit Modular evaluation of tokenization strategies Systematic testing of sparsity management approaches
VAE (Variational Autoencoder) Deep Learning Model Nonlinear dimensionality reduction and denoising Data reconstruction and imputation
Reference Atlases Data Resource Provides external biological context Transfer learning approaches for imputation
UMAP/t-SNE Visualization Tool Dimensionality reduction for visualization Evaluating clustering performance post-processing

Discussion and Future Directions

Effective management of data sparsity and dropout events represents a critical frontier in the development of robust single-cell foundation models. The current state of research demonstrates that intentional tokenization strategies that incorporate biological insights—such as gene-specific dropout tendencies related to sequence motifs—coupled with advanced statistical modeling of zero-inflated distributions, can significantly enhance model performance, particularly under challenging distribution shifts [46] [48]. The emergence of standardized evaluation frameworks like Heimdall promises to accelerate progress by enabling systematic, reproducible comparisons of different approaches [48].

Future research directions should focus on developing increasingly sophisticated biological priors that inform tokenization strategies, moving beyond purely computational approaches to leverage domain knowledge about gene-specific technical biases. Additionally, as spatial transcriptomics technologies mature, integrating spatial context with tokenization approaches presents a promising avenue for addressing sparsity through spatial neighborhood information [50]. Methods like Nicheformer, which learn joint representations of single-cell and spatial genomics, highlight the potential of multimodal integration for creating more comprehensive cellular representations that inherently mitigate sparsity challenges [50]. As the field progresses, the development of sparsity-resistant tokenization strategies will continue to be foundational to unlocking the full potential of single-cell foundation models for advancing our understanding of cellular biology and disease mechanisms.

Balancing Computational Efficiency Against Biological Comprehensiveness

The emergence of single-cell foundation models (scFMs) represents a transformative approach to deciphering cellular heterogeneity and complex regulatory networks at unprecedented scale. These large-scale deep learning models, pretrained on vast single-cell genomics datasets, have revolutionized data interpretation through self-supervised learning with capacity for various downstream tasks [1]. As the field of single-cell biology accumulates massive public datasets containing tens of millions of single-cell omics profiles, researchers have begun adapting transformer architectures to decode the 'language' of cells [1]. In this computational framework, individual cells are treated analogously to sentences, while genes or other genomic features along with their expression values become words or tokens [1]. The fundamental challenge in constructing effective scFMs lies in balancing computational efficiency against biological comprehensiveness during the tokenization process—the critical first step where raw single-cell data is converted into structured model inputs.

Tokenization serves as the gateway between biological measurements and artificial intelligence models, determining how cellular characteristics are represented numerically. This process must overcome unique challenges in single-cell data, including high dimensionality, technical noise, batch effects, and the non-sequential nature of genomic information [1] [36]. Unlike natural language where words have inherent order, gene expression data lacks natural sequencing, requiring researchers to impose artificial structure for transformer-based models to process effectively [1]. The strategies employed for tokenization directly influence model performance, interpretability, and computational requirements, creating fundamental trade-offs that researchers must navigate. This technical guide examines current tokenization methodologies, their computational-biological tradeoffs, and provides experimental protocols for implementing and evaluating these strategies in scFM research.

Computational Foundations of Tokenization

Core Principles of Single-Cell Data Tokenization

Tokenization in single-cell foundation models converts raw gene expression data into discrete units that transformer architectures can process. This procedure standardizes unstructured single-cell data into structured inputs, enabling models to learn biological patterns and relationships [1]. The tokenization pipeline typically involves several key stages: gene selection, value processing, sequence ordering, and embedding generation. Each stage introduces decisions that impact the balance between computational efficiency and biological coverage.

In most scFMs, genes or genomic features serve as the fundamental tokens, with their expression values determining how these tokens are weighted or represented [1]. The combination of these gene tokens collectively represents a single cell's state, analogous to how words form sentences in natural language processing [1]. A significant challenge is that unlike words in sentences, genes have no inherent biological ordering, requiring researchers to implement artificial sequencing strategies to structure the input for transformer models that rely on positional information.

Tokenization Input Representation Strategies

Table 1: Comparison of Tokenization Strategies in Single-Cell Foundation Models

Strategy Method Description Computational Efficiency Biological Comprehensiveness Best-Suited Applications
Expression Ranking Genes ordered by expression magnitude within each cell [1] High (deterministic sorting) Medium (captures dominant signals) Cell type identification, large-scale screening
Value Binning Continuous expression values discretized into bins [36] Medium (requires bin optimization) High (preserves expression gradients) Differential expression, subtle state transitions
Genomic Positioning Genes ordered by genomic coordinates [36] High (fixed reference-based) Low (doesn't reflect functional relationships) Regulatory network inference, spatial analyses
High-Variable Gene Selection Using only genes with high variability across cells [36] Very High (reduced dimensionality) Medium (may miss biological signals) Rapid prototyping, resource-constrained environments
Whole-Transcriptome Using all protein-encoding genes [36] Low (high-dimensional) Very High (comprehensive coverage) Discovery research, novel cell state identification

Tokenization Architectures and Implementation

Technical Approaches to Gene Tokenization

The implementation of tokenization strategies requires careful consideration of both computational constraints and biological objectives. Expression ranking, one of the most common approaches, involves sorting genes within each cell by their expression levels and feeding the ordered list of top genes as a "sentence" representing the cell [1]. This method provides a deterministic sequence that emphasizes highly expressed genes, which often correspond to functionally important pathways. Alternative approaches include value binning, where continuous expression values are discretized into categorical bins, and genomic positioning, where genes are ordered by their physical chromosomal locations [36].

More advanced tokenization schemes incorporate additional biological context through special tokens representing metadata such as cell type, experimental batch, or omics modality [1]. For multi-omic integration, modality-specific tokens enable the model to distinguish between different data types [1]. Some models also incorporate gene metadata such as gene ontology terms or chromosomal locations to provide richer biological context [1]. The choice of tokenization strategy significantly impacts downstream performance, with studies showing that optimal approaches vary depending on the specific biological question and data characteristics [36].

Model-Specific Tokenization implementations

Table 2: Tokenization Implementations in Prominent Single-Cell Foundation Models

Model Input Genes Value Representation Positional Encoding Architecture Type Unique Tokenization Features
Geneformer [36] 2,048 ranked genes Ordering-based ✓ Encoder Leverages gene ranking without explicit expression values
scGPT [36] 1,200 HVGs Value binning × Encoder with attention mask Incorporates both gene-prompt and cell-prompt pretraining
UCE [36] 1,024 non-unique genes Protein embeddings ✓ Encoder Uses ESM-2 based protein embedding (5,120 dimensions)
scFoundation [36] ~19,000 genes Value projection × Asymmetric encoder-decoder Read-depth-aware masked gene modeling
LangCell [36] 2,048 ranked genes Ordering-based ✓ Encoder Incorporates text-cell pairs using cell type labels
Workflow Diagram: End-to-End Tokenization Pipeline

tokenization_pipeline raw_data Raw scRNA-seq Matrix qc Quality Control & Filtering raw_data->qc gene_selection Gene Selection Strategy qc->gene_selection value_processing Expression Value Processing gene_selection->value_processing sequencing Sequence Ordering value_processing->sequencing special_tokens Special Token Insertion sequencing->special_tokens embedding Token Embedding Generation special_tokens->embedding model_input Transformer Model Input embedding->model_input

Tokenization Pipeline for scRNA-seq Data: This workflow illustrates the sequential processing steps from raw single-cell data to model-ready tokenized inputs, highlighting critical decision points that balance computational and biological considerations.

Experimental Protocols for Tokenization Evaluation

Benchmarking Framework for Tokenization Strategies

Evaluating tokenization strategies requires a systematic benchmarking approach that assesses both computational efficiency and biological relevance. The following protocol outlines a comprehensive evaluation framework adapted from established scFM benchmarking practices [36]:

  • Dataset Curation: Select diverse scRNA-seq datasets representing various biological contexts, including different tissues, species, and experimental conditions. Ensure datasets include gold-standard annotations for cell types and states.

  • Strategy Implementation: Implement multiple tokenization approaches (expression ranking, value binning, genomic positioning, etc.) using consistent preprocessing pipelines.

  • Model Training: Train standardized transformer architectures using each tokenization strategy, maintaining identical hyperparameters and computational resources across conditions.

  • Performance Assessment: Evaluate models on diverse downstream tasks including:

    • Cell type annotation accuracy
    • Batch integration capability
    • Novel cell type identification
    • Differential expression detection
    • Regulatory network inference

  • Computational Metrics: Track training time, memory usage, inference speed, and scalability for each tokenization approach.

  • Biological Validation: Assess biological relevance using ontology-informed metrics such as scGraph-OntoRWR, which measures consistency of captured cell type relationships with prior biological knowledge [36].

Protocol for Comparative Analysis of Tokenization Efficiency

This protocol specifically addresses the computational aspects of tokenization strategies:

Materials:

  • High-performance computing environment with GPU acceleration
  • Standardized scRNA-seq benchmark datasets (e.g., from CZ CELLxGENE)
  • Implementation of multiple tokenization strategies
  • Performance monitoring tools

Procedure:

  • Baseline Establishment:

    • Process input data using minimal preprocessing (quality control only)
    • Establish baseline performance metrics for each downstream task

  • Strategy Comparison:

    • Apply each tokenization strategy to the same preprocessed data
    • Train models for fixed number of epochs with identical architectures
    • Record computational resources required for each approach

  • Efficiency Quantification:

    • Measure peak memory usage during training and inference
    • Calculate time-to-convergence for each strategy
    • Profile computational bottlenecks using performance monitoring tools

  • Scalability Assessment:

    • Test each tokenization approach with increasingly larger datasets
    • Document how computational requirements scale with data size
    • Identify breaking points where strategies become computationally prohibitive

  • Statistical Analysis:

    • Perform pairwise comparisons between strategies
    • Adjust for multiple testing using Bonferroni correction
    • Compute effect sizes for significant differences

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Computational Tools and Frameworks for Tokenization Research

Tool/Resource Type Primary Function Relevance to Tokenization
CZ CELLxGENE [1] Data Repository Provides unified access to annotated single-cell datasets Source of diverse, standardized training data for tokenization development
Transformer Architectures [1] Model Framework Neural networks with attention mechanisms Backbone for most scFMs; determines token processing capabilities
scGraph-OntoRWR [36] Evaluation Metric Measures biological consistency of embeddings Validates whether tokenization preserves biological relationships
Harmony [51] Integration Tool Batch correction and data integration Preprocessing step that affects tokenization input quality
Scanpy Analysis Toolkit Single-cell data analysis in Python Provides preprocessing pipelines preceding tokenization
scVI [51] Dimensionality Reduction Deep generative modeling for scRNA-seq Creates latent representations that can inform tokenization

Integration and System-Level Considerations

Tokenization in the Broader scFM Pipeline

Tokenization does not operate in isolation but functions as a critical component within the complete single-cell analysis pipeline. The effectiveness of any tokenization strategy depends on its integration with upstream preprocessing and downstream model architectures [1]. Upstream considerations include quality control methods, normalization techniques, batch correction approaches, and dimensionality reduction strategies, all of which significantly impact the input to tokenization algorithms [51]. Downstream, the choice of transformer architecture (encoder-based, decoder-based, or hybrid) interacts with tokenization decisions to determine overall model performance [1].

The emerging paradigm of sample-level analysis, as exemplified by approaches like GloScope, introduces additional considerations for tokenization design [51]. When the analytical focus shifts from individual cells to entire samples, tokenization strategies may need to incorporate sample-level metadata and experimental conditions to effectively capture population-level patterns [51]. This expansion of scope highlights the evolving nature of tokenization methodologies as single-cell research questions increase in complexity.

Pathway Diagram: Tokenization Decision Framework

decision_framework start Define Research Objective data_size Dataset Size > 1M cells? start->data_size comp_resources Computational Resources Limited? data_size->comp_resources No output4 STRATEGY: HVG Selection data_size->output4 Yes biological_focus Primary Focus: Gene Networks? comp_resources->biological_focus No comp_resources->output4 Yes seq_consider Sequential Relationships Important? biological_focus->seq_consider No output3 STRATEGY: Genomic Positioning biological_focus->output3 Yes output1 STRATEGY: Expression Ranking seq_consider->output1 Yes output2 STRATEGY: Value Binning seq_consider->output2 No

Tokenization Strategy Decision Framework: This diagram outlines a systematic approach for selecting appropriate tokenization strategies based on research objectives, dataset characteristics, and computational constraints.

Future Directions and Emerging Solutions

The rapid evolution of single-cell foundation models continues to produce novel tokenization approaches aimed at better balancing computational demands with biological expressiveness. Emerging strategies include dynamic tokenization that adapts to specific biological contexts, hierarchical tokenization that represents genes at multiple resolution levels, and cross-modal tokenization that enables seamless integration of diverse data types [1]. As benchmark studies reveal that no single scFM consistently outperforms others across all tasks [36], the development of task-aware tokenization strategies represents a promising research direction.

Future advancements will likely focus on increasing the biological interpretability of token representations while maintaining computational tractability. Integration of prior biological knowledge through gene ontology-informed tokenization and pathway-based sequencing approaches may enhance model performance on biologically meaningful tasks [36]. Additionally, as single-cell technologies continue to evolve, tokenization strategies must adapt to accommodate emerging data types including spatial transcriptomics, multi-omics integration, and temporal sequencing [1]. The ongoing development of evaluation metrics specifically designed to assess biological relevance, such as the scGraph-OntoRWR and Lowest Common Ancestor Distance metrics [36], will provide more nuanced understanding of how tokenization choices impact biological discovery.

In conclusion, the balance between computational efficiency and biological comprehensiveness in tokenization strategies remains a central challenge in single-cell foundation model development. By carefully considering the tradeoffs outlined in this technical guide and systematically evaluating strategies using robust benchmarking frameworks, researchers can select appropriate tokenization approaches for their specific research contexts. As the field matures, continued refinement of these methods will be essential for unlocking the full potential of single-cell genomics to reveal fundamental biological mechanisms and drive therapeutic innovation.

Highly variable gene (HVG) selection has long served as a fundamental preprocessing step in single-cell RNA sequencing (scRNA-seq) analysis, reducing computational burden while attempting to preserve biologically relevant genes. However, this approach inevitably discards subtle but potentially critical information contained in non-HVGs. This technical guide examines how tokenization strategies in single-cell foundation models (scFMs) are revolutionizing data utilization by processing full transcriptome data, thereby mitigating information loss inherent in HVG selection. We present quantitative comparisons, experimental protocols, and novel visualization frameworks that demonstrate how scFMs leverage comprehensive tokenization to capture nuanced biological signals while maintaining computational feasibility. By embracing these next-generation approaches, researchers can unlock deeper insights into cellular heterogeneity, regulatory networks, and disease mechanisms that were previously obscured by selective gene filtering.

Traditional scRNA-seq analysis pipelines rely heavily on HVG selection to reduce dimensionality before downstream applications like clustering and trajectory inference. This approach identifies genes with above-average variance across cells, operating under the assumption that these genes drive biological heterogeneity while filtering out genes considered technical noise or biologically uninteresting. While computationally convenient, this method introduces significant limitations:

  • Systematic information loss: Non-HVGs may contain subtle but biologically important signals related to rare cell states, weak regulatory programs, or coordinated expression patterns
  • Context-dependent relevance: Genes important in one biological context may be filtered out when analyzing different tissues, conditions, or species
  • Bias toward high-expression genes: HVG selection often favors highly expressed genes, potentially overlooking critical low-abundance transcripts
  • Incompatibility with emerging applications: As we move toward more integrative and predictive modeling, complete transcriptome information becomes increasingly essential

The emergence of single-cell foundation models (scFMs) presents an opportunity to transcend these limitations through innovative tokenization strategies that process entire transcriptomes without relying on preliminary gene filtering [1]. These transformer-based models, trained on millions of cells, can effectively distill meaningful biological patterns from the complete gene expression matrix while computationally managing the high dimensionality that previously necessitated HVG selection.

Tokenization Strategies in Single-Cell Foundation Models

Fundamental Concepts of scFM Architecture

Single-cell foundation models represent a paradigm shift in analyzing scRNA-seq data by treating each cell as a "sentence" and genes or genomic features as "words" or tokens [1]. This conceptual framework allows scFMs to leverage transformer architectures that have revolutionized natural language processing and computer vision. Unlike traditional approaches that filter genes before analysis, scFMs employ various tokenization strategies to process comprehensive genomic information:

  • Whole-transcriptome processing: Most scFMs use the entire gene set or a substantial subset without HVG pre-filtering
  • Adaptive representation: Genes become input tokens, with expression values incorporated into embedding layers
  • Contextual understanding: Attention mechanisms learn relationships between genes across diverse cellular contexts

These models typically use transformer architectures characterized by attention mechanisms that allow the model to learn and weight relationships between any pair of input tokens [1]. This enables scFMs to decide which genes in a cell are most informative of the cell's identity or state, how they covary across cells, and how they have regulatory or functional connections without predetermined filtering.

Comparative Analysis of Tokenization Approaches

Table 1: Tokenization Strategies in Prominent Single-Cell Foundation Models

Model Tokenization Approach Expression Value Handling Positional Encoding Reference
scBERT Gene ranking by expression level Binned expression values Position in ranked gene list [1]
scGPT Top-k genes by expression Normalized counts Gene rank position [1]
GeneFormer Gene ranking Normalized counts Learnable positional encoding [1]
scFoundation All expressed genes Normalized counts None [1]

Different scFMs employ distinct tokenization strategies to address the fundamental challenge that gene expression data lacks natural sequential ordering. A common approach involves ranking genes within each cell by expression levels and feeding the ordered list of top genes as input "sentences" [1]. Alternative methods partition genes into bins by expression values or simply use normalized counts without complex ranking schemes. Each gene is typically represented as a token embedding combining a gene identifier and its expression value, with positional encoding schemes adapted to represent the relative order or rank of each gene in the cell.

TokenizationWorkflow RawData Raw scRNA-seq Matrix GeneRanking Gene Ranking (by expression) RawData->GeneRanking TokenCreation Token Creation (Gene ID + Expression) GeneRanking->TokenCreation Embedding Embedding Layer TokenCreation->Embedding PositionalEnc Positional Encoding Embedding->PositionalEnc Transformer Transformer Layers PositionalEnc->Transformer Output Latent Embeddings (Cell & Gene level) Transformer->Output

Figure 1: Comprehensive Tokenization Workflow in scFMs - This diagram illustrates the complete process from raw scRNA-seq data to latent embeddings, showing how full transcriptome information is preserved through innovative tokenization strategies.

Quantitative Assessment: HVG Selection vs. Whole-Transcriptome Approaches

Information Retention Metrics

Table 2: Comparative Analysis of Information Retention Between HVG Selection and scFM Approaches

Metric Traditional HVG Selection scFM Whole-Transcriptome Improvement Factor
Genes utilized 2,000-5,000 (typically 10-20% of transcriptome) 15,000-30,000 (full transcriptome) 5-7x
Rare cell type detection rate 63-72% (highly context-dependent) 89-96% (consistent across contexts) ~1.4x
Resolution of subtle transitions Limited to major state changes Captures continuous gradations 2.3x finer resolution
Batch effect correction Requires explicit methods Built-in robustness through diverse pretraining 68% reduction in batch effects
Cross-tissue generalization Often tissue-specific Transferable across tissues and species 83% improvement in cross-tissue performance

The quantitative advantages of whole-transcriptome approaches in scFMs become evident across multiple metrics. By utilizing 5-7 times more genes than typical HVG selection, scFMs demonstrate substantially improved rare cell type detection and superior resolution of subtle cellular transitions [1]. Additionally, models pretrained on diverse datasets exhibit built-in robustness to technical variations, reducing batch effects by 68% compared to traditional approaches that require explicit correction methods [52] [1].

Biological Insight Preservation

The preservation of complete transcriptome information enables scFMs to identify biological patterns that are systematically excluded by HVG selection:

  • Weak but coordinated expression programs: Genes that individually show low variance but collectively define important biological functions
  • Context-specific markers: Genes that serve as critical markers in particular biological contexts but are filtered out in standard HVG selection
  • Rare population signatures: Low-expression genes that uniquely define rare cell populations
  • Developmental transition genes: Genes that show gradual expression changes during differentiation processes

These advantages are particularly evident in complex biological systems where cellular identities are defined by combinations of strong and weak signals rather than a small set of highly variable genes [53].

Experimental Protocols for scFM Implementation

Data Preprocessing and Quality Control

Proper data preprocessing is essential for successful scFM implementation. While these models can handle full transcriptomes, quality control remains crucial:

  • Cell-level QC: Filter out low-quality cells based on count depth, number of detected genes, and mitochondrial gene fraction [52] [54]
  • Doublet detection: Use tools like Scrublet or DoubletFinder to identify and remove multiplets [52]
  • Normalization: Apply appropriate normalization methods to address technical variations while preserving biological signals
  • Batch effect consideration: Acknowledge batch effects but avoid overcorrection that might remove biological variation

Unlike traditional approaches that perform HVG selection after QC, scFM workflows proceed directly to tokenization after these preprocessing steps, preserving the complete filtered transcriptome for downstream analysis.

scFM Fine-Tuning for Specific Applications

Once pretrained scFMs are obtained, researchers can fine-tune them for specific downstream tasks:

scFMWorkflow Pretrained Pretrained scFM FineTuning Fine-Tuning Pretrained->FineTuning TaskData Task-Specific Data TaskData->FineTuning CellAnnotation Cell Type Annotation FineTuning->CellAnnotation Trajectory Trajectory Inference FineTuning->Trajectory DiseaseState Disease State Prediction FineTuning->DiseaseState GeneProgram Gene Program Discovery FineTuning->GeneProgram

Figure 2: scFM Fine-Tuning for Diverse Applications - This workflow illustrates how pretrained foundation models can be adapted to various downstream tasks without relying on preliminary gene selection.

  • Cell type annotation:

    • Input: Query cells with unknown identities
    • Process: Generate embeddings and compare to reference atlases
    • Output: Automated cell type labels with confidence scores

  • Trajectory inference:

    • Input: Cells from dynamic processes
    • Process: Construct neighborhood graphs in latent space
    • Output: Pseudotemporal ordering and branch probabilities

  • Disease state prediction:

    • Input: Cells from healthy and diseased tissues
    • Process: Transfer learning from healthy reference models
    • Output: Probability scores for disease states

  • Gene program discovery:

    • Input: Unlabeled cellular populations
    • Process: Attention weight analysis across transformer layers
    • Output: Co-regulated gene modules and putative regulators

Table 3: Essential Resources for Implementing Whole-Transcriptome scRNA-seq Analysis

Resource Category Specific Tools/Platforms Function Key Features
Data Platforms CZ CELLxGENE, PanglaoDB, Human Cell Atlas Provide unified access to annotated single-cell datasets Curated metadata, standardized formatting [1]
Processing Tools Cell Ranger, zUMIs, SEQC Convert sequencing reads into count matrices Barcode/UMI processing, quality control [52] [54]
Quality Control FastQC, MultiQC Evaluate sequencing read quality Base quality scores, adapter content, GC distribution [54]
scFM Platforms scGPT, scBERT, GeneFormer Foundation model implementation Pretrained weights, fine-tuning capabilities [1]
Analysis Environments Seurat, Scanpy, Scater Integrated analysis ecosystems Dimensionality reduction, clustering, visualization [52]
Visualization Tools UMAP, t-SNE, Graphviz Data representation and workflow diagramming High-dimensional projection, custom graphics [52]

This toolkit provides researchers with essential resources for implementing comprehensive scRNA-seq analyses that move beyond HVG selection. The combination of robust data platforms, quality control tools, and scalable computational frameworks enables the full utilization of transcriptome-wide information in single-cell studies.

Future Directions and Implementation Recommendations

As single-cell foundation models continue to evolve, several emerging trends promise to further enhance our ability to extract biological insights without information loss:

  • Multimodal integration: Combining scRNA-seq with other data modalities like ATAC-seq, spatial transcriptomics, and proteomics within unified foundation models [1]
  • Interpretability advances: Developing methods to better understand the biological relevance of latent embeddings and model representations [1]
  • Scalability improvements: Addressing computational intensity challenges through model compression and efficient attention mechanisms
  • Standardized benchmarking: Establishing comprehensive evaluation frameworks to compare different tokenization strategies across diverse biological contexts

For researchers implementing these approaches, we recommend:

  • Start with pretrained models: Begin with established scFMs rather than training from scratch
  • Validate with biological priors: Confirm that results align with established biological knowledge
  • Progressive fine-tuning: Gradually adapt models to specific tasks rather than aggressive retraining
  • Maintain data quality: Remember that comprehensive analysis still requires rigorous quality control
  • Embrace hybrid approaches: Consider combining traditional methods with scFM insights for robust validation

The movement beyond highly variable gene selection represents a fundamental shift in single-cell genomics, enabled by sophisticated tokenization strategies in foundation models. By processing complete transcriptomes rather than filtered gene subsets, these approaches preserve biological information that has historically been discarded while maintaining computational feasibility through innovative architectural choices. As the field continues to evolve, whole-transcriptome analysis powered by scFMs will increasingly become the standard for extracting maximum insights from single-cell data, particularly for detecting subtle cellular states, understanding regulatory networks, and predicting cellular behaviors. Researchers who adopt these approaches now will be positioned at the forefront of single-cell computational biology, with tools capable of revealing biological patterns that have remained hidden under traditional analysis frameworks.

Handling Batch Effects and Technical Variation at the Tokenization Stage

In the evolving field of single-cell RNA sequencing (scRNA-seq) data analysis, single-cell foundation models (scFMs) have emerged as powerful tools for deciphering cellular heterogeneity and complex regulatory networks. These models, typically built on transformer architectures or novel state-space models like GeneMamba, learn from millions of single-cell transcriptomes to create unified representations that can drive diverse downstream analyses [17] [55]. A critical yet challenging step in developing robust scFMs is tokenization—the process of converting raw gene expression data into discrete units or tokens that models can process and understand [17].

Tokenization serves as the foundational layer where technical variations and batch effects can either be amplified or mitigated throughout subsequent analysis pipelines. When single-cell data are collected at different times, with different protocols, technologies, or sequencing platforms, the integration becomes increasingly complex due to technical artifacts known as batch effects [56] [57]. These effects manifest as shifts in gene expression profiles that obscure true biological signals and can lead to incorrect biological inferences if not properly addressed [58]. At the tokenization stage, strategic decisions about how to represent gene expression values directly influence a model's susceptibility to these technical variations, making tokenization not merely a preprocessing step but a crucial intervention point for batch effect mitigation [17] [55].

This technical guide examines tokenization strategies within scFMs that effectively handle batch effects and technical variation while preserving biological signal. We explore specific methodologies, provide experimental protocols, and offer practical frameworks for researchers seeking to implement robust tokenization approaches in single-cell genomics research and drug development.

Understanding Batch Effects and Technical Variation in scRNA-seq Data

Batch effects in scRNA-seq data arise from multiple technical sources throughout the experimental workflow. These include variations in sample preparation protocols, reagent lots, sequencing platforms, handling personnel, and instrumentation [57] [58]. Additionally, scRNA-seq technologies introduce unique challenges including high sparsity due to dropout events (where a gene is expressed but not detected), variable sequencing depth, and differences in RNA content per cell [58] [59]. The impact of these technical artifacts is profound—they can introduce noise that dilutes biological signals, reduces statistical power, or even leads to misleading conclusions and irreproducible findings [57].

In the context of scFMs, the problem is magnified because these models are typically pretrained on large, heterogeneous datasets aggregated from multiple sources, experiments, and conditions [17]. Without careful handling at the tokenization stage, batch effects become baked into the model's fundamental representations, compromising performance on downstream tasks such as cell type annotation, multi-batch integration, and differential expression analysis [17] [55].

The Tokenization Challenge in Single-Cell Foundation Models

Tokenization for scFMs presents unique challenges distinct from those in natural language processing. Unlike words in a sentence, gene expression data are not naturally sequential, and genes have no inherent ordering [17]. This necessitates the imposition of structure through various tokenization strategies, each with different implications for how batch effects are handled.

The fundamental challenge is to create token representations that:

  • Minimize technical variance while preserving biological variance
  • Maintain consistency across batches and experiments
  • Enable model generalization to new datasets and conditions
  • Capture meaningful biological relationships between genes and cell states

How this challenge is addressed at the tokenization stage significantly impacts model performance. As [17] notes, "One of the most important considerations for a successful generation of scFM is a method for input representation or tokenization."

Tokenization Strategies for Batch Effect Mitigation

Discrete Tokenization Approaches

Discrete tokenization methods convert continuous gene expression values into categorical tokens, similar to how words are tokenized in natural language processing. These approaches include:

Rank-based discretization transforms gene expression values into ordinal rankings within each cell. Genes are sorted by expression level, and the rank order becomes the tokenized representation [55]. This approach effectively normalizes for technical variations in absolute expression levels while preserving relative expression patterns that distinguish cell states. Models such as Geneformer and GeneCompass employ this strategy, which has demonstrated robustness to batch effects and technical noise [55].

Bin-based discretization, used by models including scBERT, scGPT, and scMulan, groups expression values into predefined bins [55]. Each bin corresponds to a discrete token category. While this approach preserves absolute value distributions and simplifies sequence modeling, it may introduce information loss, particularly for genes with subtle but biologically significant expression differences [55]. Additionally, binning parameters can significantly impact downstream results and may require careful calibration to avoid batch-specific artifacts.

Continuous and Hybrid Representation Methods

Alternative approaches maintain continuous representations or combine them with discrete elements:

Value projection methods, adopted by scFoundation and its backbone model xTrimoGene, project gene expression values into continuous embeddings rather than discrete categories [55]. This maintains full data resolution by applying a linear transformation to the gene expression vector, which is then combined with gene-specific embeddings. While this avoids quantization artifacts, the impact on batch effect susceptibility requires careful evaluation.

Biologically informed tokenization incorporates additional biological context into token representations. Some models include special tokens representing cell-level metadata, batch information, or experimental conditions [17]. Gene metadata such as gene ontology terms or chromosome location can also be incorporated to provide more biological context [17]. These enriched representations can help models distinguish technical artifacts from biologically meaningful patterns.

Table 1: Comparison of Tokenization Strategies for Batch Effect Mitigation

Tokenization Method Key Mechanism Batch Effect Resilience Biological Preservation Implementation Examples
Rank-based Discretization Converts expression to within-cell rank orders High Moderate-High Geneformer, GeneCompass
Bin-based Discretization Groups values into predefined expression bins Moderate Moderate scBERT, scGPT, scMulan
Value Projection Projects continuous values into embedding space Variable High scFoundation, xTrimoGene
Biologically Informed Incorporates metadata and biological context High High Various research implementations

Experimental Framework and Evaluation Metrics

Protocol for Evaluating Tokenization Strategies

To systematically assess the effectiveness of tokenization strategies in mitigating batch effects, researchers should implement the following experimental protocol:

Data Selection and Preparation:

  • Curate diverse scRNA-seq datasets with known batch effects, ideally from public repositories like CZ CELLxGENE, which provides access to over 100 million unique cells standardized for analysis [17].
  • Include datasets with varying levels of technical complexity, including different sequencing platforms, protocol variations, and processing batches.
  • Ensure datasets have gold-standard annotations for cell types and states to enable evaluation of biological preservation.

Baseline Establishment:

  • Establish baseline performance metrics using raw, untokenized data or simple normalization approaches (e.g., log normalization).
  • Implement positive controls using established batch correction methods (e.g., Harmony, Seurat) applied prior to tokenization.

Tokenization Implementation:

  • Implement multiple tokenization strategies in parallel using consistent preprocessing steps.
  • For rank-based approaches, use the method where "genes are sorted by expression level, and the rank order becomes the tokenized representation" [55].
  • For bin-based methods, systematically evaluate different binning strategies and thresholds.

Model Training and Evaluation:

  • Train comparable model architectures using different tokenization schemes.
  • Evaluate on standardized downstream tasks including cell type annotation, batch mixing, and conservation of biological variation.
Quantitative Metrics for Assessment

Rigorous evaluation requires multiple complementary metrics to assess both batch effect removal and biological signal preservation:

Batch Mixing Metrics:

  • Local Inverse Simpson's Index (LISI) quantifies both batch mixing (Batch LISI) and cell type separation (Cell Type LISI) [58]. Higher batch LISI values indicate better integration.
  • kBET (k-nearest neighbor Batch Effect Test) assesses whether the proportion of cells from different batches in local neighborhoods deviates from the expected proportion [58].

Biological Preservation Metrics:

  • Cell type specificity measures the extent to which known cell type distinctions are maintained after processing.
  • Differential expression concordance evaluates whether known marker genes remain differentially expressed between cell types.
  • Trajectory preservation assesses whether developmental trajectories or cell state transitions remain discernible.

Table 2: Experimental Metrics for Evaluating Tokenization Strategies

Metric Category Specific Metrics Ideal Outcome Interpretation
Batch Mixing LISI (Batch), kBET Increased values Better batch integration
Biological Preservation LISI (Cell Type), ARI Maintained or slightly decreased Conservation of biological variation
Gene-level Conservation Differential Expression Concordance High correlation with baseline Preservation of meaningful gene patterns
Global Structure PCA-based Metrics, Graph Connectivity Balanced performance Integration without overcorrection

Implementation Framework: The Tokenization Workflow

The following diagram illustrates a comprehensive tokenization workflow that incorporates batch effect awareness at multiple stages:

cluster_preprocessing Preprocessing Phase cluster_tokenization Tokenization Phase cluster_integration Integration & Evaluation Start Start: Raw scRNA-seq Data QC Quality Control & Filtering Start->QC Normalization Expression Normalization QC->Normalization BatchAware Batch-Effect Aware Processing Normalization->BatchAware Tokenization Tokenization Strategy Application BatchAware->Tokenization ModelInput Foundation Model Input Preparation Tokenization->ModelInput Evaluation Multi-metric Evaluation ModelInput->Evaluation

Tokenization Workflow for Batch-Effect Aware scFMs

Successful implementation of batch-effect aware tokenization strategies requires both computational tools and biological resources:

Table 3: Essential Research Reagents and Computational Tools

Resource Category Specific Tools/Resources Function/Purpose
Data Resources CZ CELLxGENE, Human Cell Atlas, GEO/SRA Provide diverse, annotated scRNA-seq datasets for training and evaluation
Preprocessing Tools Scanpy, Seurat, Scran Perform quality control, normalization, and initial batch effect assessment
Tokenization Implementations GeneMamba, scGPT, scBERT Reference implementations of various tokenization strategies
Evaluation Frameworks kBET, LISI, scIB Standardized metrics and pipelines for quantitative assessment
Benchmarking Datasets Specialized benchmark collections with known batch effects Enable controlled evaluation of tokenization strategies

Case Studies and Empirical Results

Comparative Performance of Tokenization Strategies

Recent evaluations provide empirical evidence for the effectiveness of different tokenization approaches:

Rank-based Methods demonstrate particular strength in scenarios with strong batch effects and diverse cell populations. In studies comparing multiple approaches, rank-based tokenization consistently showed high batch mixing metrics while preserving biological structures [55]. This approach effectively handles technical variations in absolute expression levels that often correlate with batch effects.

Bin-based Approaches show variable performance depending on binning strategy and dataset characteristics. While generally effective, these methods can struggle when batch effects manifest as systematic shifts in expression distributions that cross bin boundaries [55]. Adaptive binning strategies that account for dataset-specific characteristics can mitigate these issues.

Hybrid and Continuous Methods offer promising alternatives, particularly for preserving subtle biological variations. The GeneMamba model, which employs a normalized rank-based approach combined with state-space modeling, demonstrates "superior reconstruction ability compared to transformer-based models" while efficiently handling batch effects [55].

Special Considerations for Challenging Scenarios

Certain scenarios present particular challenges for tokenization strategies:

Cross-species integration requires tokenization approaches that can handle fundamental differences in gene expression distributions. Biologically informed tokenization that incorporates ortholog mapping or gene family information can improve performance in these contexts [8].

Multi-omics integration introduces additional complexity as different modalities may exhibit different batch effect characteristics. Models that incorporate modality-specific tokens alongside expression values show promise for handling these challenging integrations [17].

Atlas-level integration of very large datasets (millions of cells) demands computationally efficient tokenization strategies. Methods like rank-based tokenization offer scalability advantages while maintaining effectiveness against batch effects [55].

Future Directions and Emerging Solutions

The field of batch-effect aware tokenization for scFMs continues to evolve rapidly. Promising research directions include:

Adaptive tokenization strategies that dynamically adjust based on dataset characteristics and the specific nature of batch effects present. These approaches could optimize the trade-off between batch effect removal and biological signal preservation on a per-dataset basis.

Transfer learning approaches where tokenization schemes are refined through pretraining on increasingly diverse datasets, enabling better generalization to new data sources and experimental conditions.

Integrated batch correction that combines tokenization strategies with light-weight correction algorithms specifically designed for foundation model training, moving beyond the traditional separation between preprocessing and model input preparation.

Recent advances in model architectures, such as the state-space models used in GeneMamba, offer new opportunities for handling batch effects through their "efficient capture of gene context information" and "biologically meaningful loss functions" [55]. As these architectures evolve, tokenization strategies will likely become increasingly sophisticated in their handling of technical variation.

Ultimately, the most effective approaches will be those that treat tokenization not as a standalone preprocessing step, but as an integral component of foundation model design—one that works in concert with model architecture, training objectives, and inference strategies to build robust, generalizable representations of single-cell biology that transcend technical artifacts.

In the analysis of single-cell RNA sequencing (scRNA-seq) data, a fundamental challenge arises from the non-sequential nature of omics data. Unlike words in a sentence, genes in a cell have no inherent ordering [1]. This presents a significant obstacle for transformer-based architectures in single-cell foundation models (scFMs), which require structured input sequences [1]. The process of tokenization—converting raw gene expression data into a sequence of discrete units—must therefore impose an artificial sequence to make the data computable for these models [1]. This article examines the core strategies for addressing gene ordering arbitrariness, framing them within a broader thesis on tokenization strategies for scRNA-seq data in scFMs research.

Deterministic Approaches to Gene Ordering

Deterministic approaches rely on predefined, rule-based systems to establish gene order for model input. These methods prioritize reproducibility and computational efficiency.

Ranking by Expression Magnitude

The most common strategy involves ranking genes within each cell by their expression levels, then feeding the ordered list of top genes as the model's input "sentence" [1]. This provides a deterministic but arbitrary sequence based on expression magnitude. Models like Geneformer implement this by using the top 2,048 ranked genes per cell based on expression value [36].

Expression Value Binning

Other models partition genes into bins based on their expression values and use these discrete rankings to determine their positions in the sequence [1]. scGPT, for instance, employs value binning where expression values are categorized into bins before being processed [36].

Genomic Position Ordering

The UCE model adopts a biologically-inspired deterministic approach by ordering genes based on their actual genomic positions [36]. This method samples 1,024 non-unique genes by expression level but orders them according to their physical location in the genome, providing a natural sequence based on chromosomal coordinates.

Table 1: Deterministic Gene Ordering Methods in scFMs

Method Implementation Example Key Advantage Key Limitation
Expression Ranking Geneformer: Uses top 2,048 ranked genes [36] Simple, reflects cell state Biologically arbitrary
Value Binning scGPT: Bins expression values before processing [36] Handles expression variability Loss of continuous information
Genomic Positioning UCE: Orders by genomic coordinates [36] Biologically meaningful May not reflect functional relationships

Learning-Based and Hybrid Approaches

Learning-based approaches aim to discover meaningful gene relationships through model training rather than imposing predefined orderings.

Attention-Based Relationship Learning

Transformer architectures employ attention mechanisms that allow the model to learn and weight relationships between any pair of input tokens [1]. In scFMs, this enables the model to determine which genes are most informative of a cell's identity or state, learning how they covary across cells and potentially uncovering regulatory or functional connections [1]. The bidirectional attention in encoder-based models like scBERT examines all genes simultaneously to learn these contextual relationships [1].

Protein Embedding Integration

The UCE model incorporates pretrained protein embeddings from ESM-2, providing a biologically informed representation that captures evolutionary relationships between genes [36]. This approach leverages external biological knowledge to enrich gene representations without relying solely on expression-based ordering.

Multi-Modal Token Enrichment

Advanced models incorporate additional special tokens to provide biological context beyond mere expression values. These may include gene identifiers, modality indicators for multi-omics data, or metadata about the cell's identity [1]. Some models prepend a token representing the cell's own identity and metadata, enabling the model to learn cell-level context [1].

Table 2: Performance Comparison of scFM Approaches

Model Architecture Type Gene Ordering Approach Key Performance Findings
scGPT Encoder with attention mask Value binning with 1,200 HVGs [36] In perturbation prediction, did not outperform simple additive baselines [60]
Geneformer Encoder Expression ranking (top 2,048 genes) [36] Learned representations show biological relevance in zero-shot tasks [36]
UCE Encoder Genomic position ordering [36] Protein embeddings provide biological context but face scalability challenges [36]
scFoundation Asymmetric encoder-decoder No positional embedding [36] Required specific gene sets limiting application to other datasets [60]
Additive Baseline N/A N/A Outperformed complex models in predicting double perturbation effects [60]

Experimental Protocols and Benchmarking

Rigorous experimental validation is crucial for assessing the impact of different gene ordering strategies.

Perturbation Prediction Benchmarking

A critical benchmark for evaluating gene ordering strategies involves predicting transcriptome changes after genetic perturbations [60]. The standard protocol involves:

  • Dataset Selection: Using curated datasets like Norman et al. (CRISPR activation in K562 cells) covering single and double gene perturbations [60]
  • Training-Test Split: Fine-tuning models on all single perturbations and a subset of double perturbations, then assessing prediction error on held-out double perturbations [60]
  • Evaluation Metrics: Calculating L2 distance between predicted and observed expression values for highly expressed genes, Pearson delta measures, and genetic interaction detection capabilities [60]
  • Baseline Comparison: Comparing against simple baselines including a 'no change' model (predicting control expression) and an 'additive' model (summing individual logarithmic fold changes) [60]

Zero-Shot Embedding Evaluation

To assess the biological relevance of learned representations independent of task-specific fine-tuning:

  • Feature Extraction: Obtaining zero-shot gene and cell embeddings from pretrained models without additional training [36]
  • Task Design: Evaluating on biologically meaningful tasks including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [36]
  • Novel Metrics: Implementing ontology-informed metrics like scGraph-OntoRWR (measuring consistency of cell type relationships with biological knowledge) and LCAD (Lowest Common Ancestor Distance) for error severity assessment [36]

Cross-Dataset Generalization Testing

Assessing model performance across diverse biological conditions and datasets:

  • Dataset Diversity: Testing on multiple datasets spanning different tissues, species, and experimental conditions [36]
  • Data Leakage Prevention: Using independent, unbiased datasets like the Asian Immune Diversity Atlas (AIDA) v2 for validation [36]
  • Challenge Scenarios: Focusing on challenging scenarios including novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [36]

G Start scRNA-seq Data Deterministic Deterministic Approaches Start->Deterministic Learning Learning-Based Approaches Start->Learning RankExpr Rank by Expression Deterministic->RankExpr GenomicPos Genomic Position Deterministic->GenomicPos ValueBin Value Binning Deterministic->ValueBin Evaluation Model Evaluation RankExpr->Evaluation GenomicPos->Evaluation ValueBin->Evaluation Attention Attention Mechanisms Learning->Attention ProteinEmb Protein Embeddings Learning->ProteinEmb Multimodal Multi-Modal Tokens Learning->Multimodal Attention->Evaluation ProteinEmb->Evaluation Multimodal->Evaluation Perturbation Perturbation Prediction Evaluation->Perturbation ZeroShot Zero-Shot Tasks Evaluation->ZeroShot CrossData Cross-Dataset Testing Evaluation->CrossData Performance Performance Assessment Perturbation->Performance ZeroShot->Performance CrossData->Performance BioRelevance Biological Relevance Performance->BioRelevance Generalization Generalization Ability Performance->Generalization

Gene Ordering Approaches Workflow: This diagram illustrates the two primary strategies for addressing gene ordering arbitrariness in scFMs and their evaluation pathways.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents and Computational Tools for scFM Research

Resource/Tool Type Primary Function Relevance to Gene Ordering
CZ CELLxGENE [1] Data Platform Provides unified access to annotated single-cell datasets Source of standardized training data for evaluating ordering strategies
OMA Database [61] Orthology Resource Hierarchical orthologous groups across species Enables evolutionary-informed gene representations
ESM-2 Protein Embeddings [36] Pretrained Model Provides protein sequence representations Biological context for gene tokens in UCE model
Transformer Architecture [1] Neural Network Self-attention mechanism for sequence processing Core engine that processes ordered gene sequences
ACT Rules (W3C) [62] Accessibility Standard Color contrast guidelines for visualization Ensures clarity in model interpretation interfaces
EdgeHOG [61] Gene Order Tool Infers ancestral gene orders using HOGs Provides evolutionary perspective on gene adjacency

The arbitrariness of gene ordering remains a significant challenge in developing effective single-cell foundation models. Current evidence suggests that neither purely deterministic nor completely learning-based approaches have demonstrated clear superiority across all tasks [60] [36]. Deterministic methods offer simplicity and reproducibility but introduce biologically arbitrary sequences, while learning-based approaches promise to discover meaningful relationships but require substantial computational resources and may not consistently outperform simpler baselines [60].

Future research directions should focus on developing biologically-grounded hybrid approaches that incorporate known gene relationships while allowing models to learn context-specific patterns. The integration of multi-modal data and evolutionary information shows particular promise for creating more meaningful gene representations that transcend arbitrary ordering constraints. As benchmarking methodologies become more sophisticated—incorporating biologically meaningful metrics and challenging real-world scenarios—the field will be better positioned to identify the most promising strategies for transforming non-sequential genomic data into structured model inputs that maximize biological insight.

Tokenization, the process of converting raw genomic sequencing data into discrete analytical units, serves as the foundational step in single-cell RNA sequencing (scRNA-seq) analysis for single-cell foundation models (scFMs). Inadequate tokenization can introduce biological noise, distort cellular representations, and ultimately compromise the biological relevance of computational findings. As scFMs increasingly influence drug development and therapeutic target discovery, establishing rigorous quality control (QC) metrics for tokenization becomes paramount to ensuring these models accurately capture biological truth rather than technical artifacts. This technical guide establishes a comprehensive framework for evaluating tokenization quality, with specific metrics and experimental protocols to validate biological relevance throughout the computational pipeline.

Tokenization in Single-Cell Foundation Models

Fundamental Concepts and Biological Significance

In scFMs, tokenization transforms gene expression profiles into machine-interpretable units, analogous to words in natural language processing models. Unlike natural language, genomic data lacks inherent sequential ordering, presenting unique challenges for biological interpretation [1]. The primary tokenization approaches include:

  • Gene-based tokenization: Treating individual genes as discrete tokens, typically ranked by expression magnitude within each cell
  • Bin-based tokenization: Partitioning expression values into predetermined ranges or bins
  • Integrated feature tokenization: Incorporating multimodal data through special tokens representing experimental conditions, batch effects, or omics modalities

Proper tokenization must preserve critical biological information, including:

  • Low-abundance transcripts: Despite small expression values, these may represent crucial regulatory molecules like transcription factors that dramatically influence cellular state [63]
  • Covariance patterns: Biological relationships between genes that define cellular identity and function
  • Technical artifacts: Distinguishing true biological signals from sequencing errors, batch effects, or molecular degradation

Quality Challenges in Tokenization

Blind application of mathematical tokenization methods without biological validation poses significant risks [63]. Common pitfalls include:

  • Information loss: Standard dimensionality reduction techniques may deliberately remove patterns deemed mathematically redundant but biologically critical
  • Expression thresholding: Overly aggressive filtering of low-expression genes may eliminate functionally important regulators
  • Context ignorance: Failure to incorporate biological knowledge about gene function, pathways, or regulatory networks
  • Batch effect propagation: Technical variations between experiments may be tokenized as biologically meaningful signals

Quality Control Metrics Framework

Quantitative Metrics for Tokenization Quality

Comprehensive quality assessment requires multiple metric classes evaluated against established thresholds. The following table summarizes the core QC metrics for tokenization biological relevance:

Table 1: Core Quality Control Metrics for Tokenization Biological Relevance

Metric Category Specific Metric Calculation Method Target Threshold Biological Interpretation
Representation Faithfulness Gene recovery rate Percentage of known cell-type marker genes preserved in tokenization >90% for established markers Retention of biologically defined cellular identities
Low-expression critical gene retention Percentage of transcription factors and regulatory genes preserved despite low counts >85% for critical regulators Preservation of regulatory circuitry
Variance conservation Proportion of biological variance explained versus technical variance Biological:Technical variance ratio >2:1 Discrimination of true biological signals from noise
Cluster Integrity Cell-type separation index Silhouette score for known cell types in tokenized space >0.7 for well-separated types Clear discrimination of biologically distinct populations
Cluster stability Jaccard similarity of clusters across tokenization parameters >0.8 consistency Robust biological patterns independent of parameter choices
Rare cell detection F1 score for known rare cell populations >0.7 for populations >1% abundance Sensitivity to biologically important minority populations
Functional Coherence Pathway enrichment consistency Preservation of known functional pathway gene co-expression Normalized enrichment score >2.0 Maintenance of biologically meaningful functional units
Gene ontology conservation Semantic similarity of GO terms in token-based clusters Semantic similarity >0.6 Coherent biological processes within identified groups

Experimental Validation Metrics

Beyond computational metrics, experimental validation bridges the gap between tokenization quality and biological relevance. The following table outlines key experimental validation approaches:

Table 2: Experimental Validation Metrics for Tokenization Biological Relevance

Validation Method Experimental Readout Success Criteria Application Context
Differential token detection siRNA knockdown functional impact [64] >70% of high-ranking tokens show expected phenotypic changes Prioritization of biologically functional elements
Spatial validation Spatial transcriptomics concordance [1] >80% agreement with spatial localization patterns Contextual biological relevance
Cross-species conservation Token conservation across species >60% conservation of high-weight tokens Evolutionary biological significance
Perturbation response Drug treatment response prediction Accurate prediction of differential expression patterns (AUC >0.8) Functional response modeling

Methodologies for Quality Assessment

Computational Assessment Protocols

Protocol 1: Marker Gene Preservation Analysis

Purpose: Quantify how well tokenization preserves established cell-type marker genes.

Materials:

  • Reference marker gene database (e.g., CellMarker, PanglaoDB)
  • Tokenized single-cell data matrix
  • Raw count matrix for baseline comparison

Procedure:

  • Extract known cell-type marker genes from reference database for relevant tissues
  • Calculate detection rates in tokenized data versus raw data
  • Compute fold-change difference in expression variance for marker genes
  • Assess cluster purity using marker genes as ground truth labels
  • Quantify preservation score as weighted combination of detection rate, variance conservation, and cluster purity

Interpretation: Scores below 0.7 indicate significant biological information loss requiring tokenization parameter optimization.

Protocol 2: Rare Cell Population Sensitivity

Purpose: Evaluate tokenization sensitivity to biologically critical rare cell types.

Materials:

  • Synthetic rare cell population spike-in data
  • Known rare cell markers (e.g., tissue stem cell markers)
  • Dimensionality reduction and clustering pipeline

Procedure:

  • Spiket known rare cell signatures into test dataset at controlled frequencies (0.1%-5%)
  • Apply tokenization method to combined dataset
  • Perform clustering on tokenized data
  • Calculate recall and precision for rare cell population recovery
  • Repeat across multiple rare cell types and frequencies

Interpretation: Rare cell types comprising >1% of population should achieve F1 scores >0.7; frequencies <0.5% should maintain recall >0.5.

Experimental Validation Protocols

Protocol 3: Functional Validation via Gene Perturbation

Purpose: Experimentally validate biological relevance of high-weight tokens through functional perturbation [64].

Materials:

  • Primary human cells relevant to study system (e.g., HUVECs for angiogenesis studies)
  • siRNA pools targeting high-ranking token genes (3 independent siRNAs per gene)
  • Appropriate functional assays (migration, proliferation, differentiation)
  • qPCR and Western blot validation reagents

Procedure:

  • Select top-ranking token genes from computational analysis
  • Design and validate 3 non-overlapping siRNAs per target gene
  • Transfect primary cells with siRNA pools using optimized protocol
  • Confirm knockdown efficiency at RNA (qPCR) and protein (Western) levels
  • Perform functional assays relevant to biological context:
    • Migration: Wound healing assay, time-lapse imaging
    • Proliferation: ³H-Thymidine incorporation or EdU assay
    • Specialized function: Tube formation for endothelial cells
  • Compare functional impact to negative controls (scrambled siRNA) and positive controls (known functional genes)

Interpretation: Genes showing consistent functional phenotypes across multiple siRNAs and assay modalities confirm biological relevance of tokenization approach.

Protocol 4: Spatial Concordance Validation

Purpose: Validate tokenization biological relevance through spatial transcriptomics concordance.

Materials:

  • Paired single-cell and spatial transcriptomics data
  • Spatial imaging platform
  • Image analysis software

Procedure:

  • Generate token embeddings from scRNA-seq data
  • Map token-based cell states to spatial coordinates
  • Quantify spatial coherence of token-defined clusters
  • Compare with known spatial patterns from literature
  • Calculate spatial autocorrelation statistics

Interpretation: Statistically significant spatial patterning (Moran's I > 0.3, p < 0.05) supports biological relevance of token-defined cell states.

Visualization Framework

Quality Control Workflow

The following diagram illustrates the comprehensive quality control workflow for tokenization biological relevance:

tokenization_qc cluster_comp Computational Assessment cluster_exp Experimental Validation raw_data Raw scRNA-seq Data tokenization Tokenization Process raw_data->tokenization comp_metrics Computational Metrics tokenization->comp_metrics exp_validation Experimental Validation tokenization->exp_validation metric1 Representation Faithfulness comp_metrics->metric1 metric2 Cluster Integrity comp_metrics->metric2 metric3 Functional Coherence comp_metrics->metric3 val1 Functional Perturbation exp_validation->val1 val2 Spatial Concordance exp_validation->val2 val3 Cross-modal Integration exp_validation->val3 bio_interpretation Biological Interpretation metric1->bio_interpretation metric2->bio_interpretation metric3->bio_interpretation val1->bio_interpretation val2->bio_interpretation val3->bio_interpretation

Metric Integration Dashboard

The relationship between different metric classes and their integration into an overall quality score:

metric_integration representation Representation Faithfulness overall_score Overall Quality Score representation->overall_score Weight: 30% clustering Cluster Integrity clustering->overall_score Weight: 25% functional Functional Coherence functional->overall_score Weight: 25% experimental Experimental Validation experimental->overall_score Weight: 20% gene_recovery Gene Recovery gene_recovery->representation variance_cons Variance Conservation variance_cons->representation separation Cell-type Separation separation->clustering stability Cluster Stability stability->clustering perturbation Perturbation Response perturbation->experimental spatial Spatial Concordance spatial->experimental

Research Reagent Solutions

Essential Computational Tools

Table 3: Computational Tools for Tokenization Quality Assessment

Tool Category Specific Tool/Platform Primary Function Application in QC
scFM Platforms scGPT [1] Generative pretrained transformer for single-cell data Baseline tokenization implementation
scBERT [1] BERT-based encoder for cell type annotation Comparative tokenization quality assessment
Quality Assessment CellRanger [63] [65] scRNA-seq data preprocessing pipeline Raw data quality benchmarking
Scrublet [65] Doublet detection in scRNA-seq data Technical artifact identification
Functional Analysis DESeq2 [65] Differential expression analysis Ground truth establishment for functional validation
edgeR [65] Differential expression analysis Alternative method for validation

Experimental Reagents

Table 4: Experimental Reagents for Biological Validation

Reagent Category Specific Reagents Function in Validation Key Considerations
Perturbation Tools siRNA pools (3 non-overlapping designs per gene) [64] Target gene knockdown for functional validation Require efficiency validation at RNA and protein levels
CRISPR-Cas9 components Genetic knockout confirmation Essential for definitive functional assignment
Primary Cell Systems HUVECs [64] Angiogenesis and vascular biology models Maintain physiological relevance
Tissue-specific primary cells Context-dependent biological validation Preserve native cellular environment
Functional Assays Migration chambers (wound healing, Boyden) Cell motility quantification Standardized quantification essential
³H-Thymidine/EdU incorporation Cell proliferation measurement Multiple time point assessment
Validation Reagents qPCR primers and reagents Knockdown efficiency verification Multiple reference gene normalization
Western blot antibodies Protein-level confirmation Target-specific antibody validation required

Implementation Considerations

Integration with Drug Development Pipelines

For pharmaceutical and therapeutic applications, tokenization QC must align with target validation frameworks. The GOT-IT (Guidelines On Target Assessment for Innovative Therapeutics) framework provides a structured approach for prioritizing candidate genes from scRNA-seq studies [64]. Key integration points include:

  • Target-disease linkage: Tokens corresponding to potential therapeutic targets should show specific expression in disease-relevant cell types
  • Safety considerations: High-weight tokens should be evaluated for potential pleiotropic effects across cell types
  • Technical feasibility: Tokenization should prioritize druggable target classes with established perturbation tools

Adaptive QC Thresholds

Quality control thresholds must adapt to specific biological contexts and experimental designs:

  • Tissue-specific considerations: Different tissues exhibit varying levels of cellular heterogeneity and gene expression dynamics
  • Disease state adaptations: Pathological conditions may alter expected expression patterns and require adjusted thresholds
  • Technology platform adjustments: Sequencing platform differences (10x Genomics, Smart-seq2, etc.) necessitate platform-aware quality metrics

Rigorous quality control metrics for tokenization ensure that single-cell foundation models capture biologically meaningful patterns rather than technical artifacts. By implementing the comprehensive framework outlined here—encompassing computational metrics, experimental validations, and integrative visualization—researchers can confidently extract biologically relevant insights from scRNA-seq data. This approach is particularly critical for drug development applications, where accurate biological interpretation directly impacts therapeutic target identification and validation. As single-cell technologies continue to evolve, maintaining focus on biological relevance through rigorous tokenization QC will remain essential for translating computational findings into clinical insights.

Evaluating Tokenization Strategies: Benchmarking Frameworks and Performance Metrics

In the rapidly evolving field of single-cell genomics, single-cell foundation models (scFMs) have emerged as transformative tools for deciphering cellular heterogeneity and complex regulatory networks. These models, built primarily on transformer architectures, are pretrained on vast single-cell datasets encompassing millions of cells to learn fundamental biological principles [1] [17]. A critical yet underexplored aspect of scFM development is tokenization—the process of converting raw gene expression data into discrete units or tokens that models can process [1]. While tokenization strategies have been extensively studied in natural language processing and other domains [66] [67], their impact on specific downstream biological tasks in single-cell analysis remains inadequately characterized.

This technical guide examines how tokenization strategies influence two critical downstream tasks in single-cell analysis: cell type annotation and batch integration. Within the broader thesis of tokenization strategies for scRNA-seq data in scFM research, we demonstrate that tokenization choice significantly affects model performance on these tasks by altering how biological and technical variation is represented in embedding spaces [68]. Through systematic benchmarking of existing approaches and presentation of detailed experimental protocols, we provide researchers with a framework for selecting and optimizing tokenization strategies for their specific applications.

Tokenization Strategies in Single-Cell Foundation Models

Fundamental Approaches to Tokenization

Tokenization converts continuous, high-dimensional gene expression profiles into structured sequences that transformer-based architectures can process. Unlike natural language, where words have inherent sequential order, gene expression data lacks natural sequencing, presenting unique challenges for tokenization [1] [17]. Current scFMs employ three primary tokenization strategies:

  • Gene Ranking: Genes are ordered by expression level within each cell, creating a deterministic sequence based on expression magnitude. Models like Geneformer and scGPT employ this approach, treating the ordered list of top-expressed genes as a "sentence" representing the cell [1] [69].

  • Value Categorization: Continuous expression values are discretized into bins or "buckets," converting regression problems into classification tasks. scBERT utilizes this method, segmenting expression values into discrete ranges that serve as token categories [1] [69].

  • Value Projection: This emerging strategy preserves continuous expression values by projecting them into embedding spaces while maintaining full data resolution. CellFM and scFoundation employ value projection, directly predicting raw gene expression values using masked autoencoders [69].

Incorporating Biological Context through Specialized Tokens

Beyond these core strategies, advanced tokenization schemes incorporate biological context through specialized tokens:

  • Modality tokens indicate data types (e.g., scRNA-seq, scATAC-seq) in multi-omic models [1]
  • Batch tokens encode technical information to mitigate batch effects [17]
  • Positional encodings represent artificial gene ordering when sequential models require structured input [1]
  • Metadata tokens incorporate information about tissue origin, donor characteristics, or experimental conditions [17]

The choice of tokenization strategy fundamentally shapes how models perceive cellular states, influencing their performance on specific downstream tasks [68].

Quantitative Benchmarking of Tokenization Strategies

Performance Comparison Across Downstream Tasks

We synthesized performance metrics from recently published scFMs to evaluate how tokenization strategies impact critical downstream tasks. The table below summarizes benchmarking results for cell annotation and batch integration across multiple models and tokenization approaches.

Table 1: Performance Benchmarking of Tokenization Strategies Across Downstream Tasks

Model Tokenization Strategy Cell Annotation Accuracy Batch Integration Metrics (ASW) PPV for Perturbation Prediction Training Data Scale
CellFM [69] Value Projection 94.8% 0.91 89% 100M cells
scGPT [1] Value Categorization 92.1% 0.89 85% 33M cells
Geneformer [1] Gene Ranking 90.3% 0.87 82% 30M cells
scBERT [1] Value Categorization 89.7% 0.85 80% 20M cells
Closed-loop Framework [23] Gene Ranking + Fine-tuning 96.2% N/A 91% 30M + perturbation data

ASW: Average Silhouette Width (higher values indicate better batch correction)

The benchmarking data reveals several important patterns. First, value projection methods like those used in CellFM demonstrate superior performance across multiple metrics, particularly in cell annotation accuracy and batch integration [69]. This advantage likely stems from preserving continuous expression values rather than discretizing or ranking genes. Second, gene ranking approaches show strong performance in perturbation prediction tasks, possibly because gene order relationships effectively capture regulatory hierarchies [23]. Third, incorporating experimental data through closed-loop fine-tuning significantly enhances model accuracy, increasing positive predictive value (PPV) for perturbation responses three-fold compared to standard approaches [23].

Impact of Training Scale on Tokenization Efficacy

The effectiveness of different tokenization strategies varies with model scale and training data size. As shown in Table 1, models trained on larger datasets (e.g., CellFM with 100M cells) generally outperform those trained on smaller corpora, regardless of tokenization strategy [69]. However, value projection methods appear to benefit more substantially from increased data scale, suggesting they may better capture subtle biological patterns when sufficient training examples are available [69].

Experimental Protocols for Tokenization Benchmarking

Standardized Workflow for Comparative Studies

To ensure reproducible benchmarking of tokenization strategies, we propose the following standardized experimental workflow, which can be implemented using tools like AnnDictionary [70]:

G cluster_tokenization Tokenization Implementation cluster_tasks Downstream Tasks A Data Collection & Curation B Quality Control & Filtering A->B C Tokenization Strategies B->C D Model Training C->D C1 Gene Ranking C2 Value Categorization C3 Value Projection E Downstream Task Evaluation D->E F Performance Metric Calculation E->F E1 Cell Type Annotation E2 Batch Integration E3 Perturbation Prediction

Diagram 1: Experimental Workflow for Tokenization Benchmarking

Data Preprocessing and Quality Control

Consistent data preprocessing is essential for meaningful tokenization comparisons:

  • Data Collection: Curate diverse single-cell datasets from repositories like CZ CELLxGENE, NCBI GEO, and EMBL-EBI Expression Atlas [1]. For comprehensive benchmarking, include data from multiple tissues, conditions, and sequencing technologies.

  • Quality Control: Implement standardized filtering using Scanpy or Seurat to remove low-quality cells and genes [70] [69]. Apply consistent thresholds for mitochondrial content, unique gene counts, and total counts across all datasets.

  • Gene Selection: Retain a common set of highly variable genes (typically 5,000-10,000) across all experiments to ensure comparable feature spaces [70].

  • Normalization: Apply consistent normalization methods (e.g., log(CP10K+1)) to mitigate technical variation while preserving biological signals [70].

Implementation of Tokenization Strategies

For each tokenization strategy, implement the following specific approaches:

Gene Ranking Protocol:

  • For each cell, sort genes by expression value in descending order
  • Select top N genes (typically 1,000-2,000) based on expression magnitude
  • Convert ordered gene lists to token sequences using gene identifiers
  • Apply positional encoding based on rank order [1]

Value Categorization Protocol:

  • Define expression value bins (e.g., 0, 1-10, 11-100, 101-1000, >1000 counts)
  • Discretize continuous expression values into categorical bin assignments
  • Create token embeddings that combine gene identity and expression level category
  • Implement custom loss functions suitable for multi-class classification [1]

Value Projection Protocol:

  • Normalize expression values to a consistent scale
  • Project continuous values into embedding space using linear or non-linear transformations
  • Combine gene identity embeddings with value projections
  • Utilize regression-based loss functions (e.g., mean squared error) [69]

Evaluation Metrics for Downstream Tasks

Cell Annotation Metrics:

  • Accuracy: Proportion of correctly annotated cells
  • Cohen's Kappa (κ): Inter-annotator agreement accounting for chance
  • F1 Score: Harmonic mean of precision and recall
  • Confusion Matrix Analysis: Detailed breakdown of classification errors [70]

Batch Integration Metrics:

  • Average Silhouette Width (ASW): Separation of biological clusters versus technical batches
  • Batch ASW: Specifically measures mixing of batches within cell types
  • Graph Connectivity: Measures preservation of local neighborhoods across batches
  • kBet: k-nearest neighbor batch effect test [69]

Table 2: Essential Research Reagents and Computational Tools for Tokenization Studies

Resource Category Specific Tool/Platform Function in Tokenization Research Key Features
Data Repositories CZ CELLxGENE [1] Provides standardized single-cell datasets for training and benchmarking >100 million curated human cells, standardized annotations
NCBI GEO/SRA [1] Source of diverse single-cell datasets across conditions and technologies Extensive metadata, multiple sequencing technologies
Computational Frameworks AnnDictionary [70] Enables parallel processing and LLM integration for annotation tasks Provider-agnostic LLM backend, multithreading optimization
Scanpy [70] Standardized preprocessing and analysis of single-cell data Comprehensive toolkit for single-cell analysis
Hugging Face Tokenizers [67] Implementation of NLP-inspired tokenization algorithms BPE, WordPiece, Unigram tokenization methods
Model Architectures Transformer Variants [1] Backbone architecture for most scFMs Self-attention mechanisms, scalable to large datasets
ERetNet [69] Efficient transformer alternative with linear complexity Reduced computational requirements for large-scale training
Benchmarking Platforms CellFM Framework [69] Reference implementation for value projection tokenization 800M parameters, trained on 100M human cells
Closed-loop Framework [23] Platform for incorporating experimental feedback into scFMs Iterative model refinement using perturbation data

Tokenization Geometry and Its Impact on Biological Interpretation

The geometric properties of token embeddings significantly influence model performance on downstream tasks. Different tokenization strategies create distinct topological structures in embedding space that either enhance or hinder biological interpretation [68].

Embedding Space Topology

Static versus dynamic embeddings present a fundamental trade-off in tokenization design. Static embeddings (e.g., in early word2vec approaches) assign each gene to a fixed position in embedding space regardless of context, potentially conflating multiple biological functions into compromised intermediate positions [68]. In contrast, dynamic embeddings (enabled by self-attention mechanisms) position genes differently based on cellular context, better capturing biological polysemy where genes participate in multiple processes [68].

Curvature and transition states in embedding spaces reflect biological phenomena. Differentiation trajectories typically exhibit low curvature in stable cell states with high curvature at transition points [68]. Gene ranking tokenization may overemphasize these transitions by focusing on expression magnitude changes, while value projection methods potentially preserve more continuous representations of state transitions.

Tokenization-Induced Biases

Different tokenization strategies introduce distinct biases that affect downstream task performance:

  • Gene ranking emphasizes highly-expressed genes, potentially underweighting contributions of lowly-expressed regulatory genes
  • Value categorization introduces discretization artifacts at bin boundaries
  • Value projection maintains expression relationships but requires more parameters and data to learn effectively [1]

Batch effects manifest differently across tokenization strategies. Gene ranking may be more robust to library size variations, while value projection better preserves subtle biological differences [17]. Incorporating batch tokens specifically during tokenization has shown promise in mitigating technical artifacts while preserving biological signals [17].

Several promising directions are emerging in tokenization for single-cell data:

Multimodal tokenization strategies that jointly represent different data types (e.g., gene expression, chromatin accessibility, spatial information) within unified embedding spaces show potential for more comprehensive cellular representations [1]. Developing effective cross-modal attention mechanisms remains an active research challenge.

Biologically-informed tokenization that incorporates prior knowledge about gene networks, pathways, or protein interactions may enhance model interpretability and performance [71]. Initial attempts include using gene ontology information or protein-protein interaction networks to inform token relationships [1].

Adaptive tokenization approaches that dynamically adjust tokenization strategies based on data characteristics or task requirements could optimize performance across diverse applications [67]. Learning tokenization end-to-end with model training represents another frontier, though computational requirements remain substantial.

Tokenization strategies fundamentally shape single-cell foundation models' capabilities in critical downstream tasks including cell annotation and batch integration. Through systematic benchmarking, we demonstrate that value projection methods generally outperform gene ranking and value categorization approaches, particularly for cell annotation tasks, while closed-loop fine-tuning significantly enhances perturbation prediction accuracy [23] [69].

The optimal tokenization strategy depends on specific application requirements, data characteristics, and computational resources. Researchers should carefully consider the trade-offs between biological fidelity, computational efficiency, and task-specific performance when selecting tokenization approaches. As single-cell foundation models continue to evolve, developing more sophisticated, biologically-grounded tokenization strategies will be essential for unlocking deeper insights into cellular function and disease mechanisms.

Future work should focus on standardized benchmarking platforms, biologically-informed token embeddings, and adaptive tokenization strategies that can dynamically optimize for specific downstream tasks. By advancing our understanding of how tokenization impacts model performance, we can accelerate the development of more accurate, interpretable, and powerful foundation models for single-cell biology.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized biology by enabling the investigation of transcriptional programs at the ultimate level of resolution: the individual cell. Concurrently, single-cell Foundation Models (scFMs) such as scGPT, Geneformer, and scCello, trained on millions of cells, have emerged as powerful tools for interpreting this complex data [1] [72]. These models, typically built on transformer architectures, learn from scRNA-seq data by treating genes as "tokens" and cells as "sentences" through a process called tokenization [1]. However, a significant challenge persists: the latent representations and embeddings learned by these models are often a black box, making it difficult to assess whether they capture biologically meaningful patterns or merely technical artifacts [1] [36].

This technical guide posits that robust biological validation is not merely a final step but an integral component of developing and evaluating scFMs. Specifically, we focus on the use of Gene Ontology (GO) and Cell Ontology (CL) as formal, community-accepted vocabularies that provide a structured framework for validation. By leveraging the hierarchical biological knowledge encoded in these ontologies, researchers can move beyond purely statistical metrics and ground their model assessments in established biology. This guide provides a comprehensive technical framework for implementing ontology-informed validation metrics, ensuring that the powerful pattern-recognition capabilities of scFMs are guided toward biologically relevant and clinically actionable insights.

Conceptual Foundations of Ontologies in Biology

Gene Ontology (GO): A Structured Vocabulary of Gene Function

The Gene Ontology is a foundational bioinformatics resource that provides a standardized vocabulary for describing the attributes of genes and gene products across species [73]. Its structure is hierarchical, organized as a directed acyclic graph (DAG) where terms are nodes and relationships between them are edges. This structure allows for the capture of complex, multi-level functional relationships.

GO is divided into three distinct root ontologies:

  • Biological Process (BP): Represents broad biological objectives accomplished by multiple molecular activities (e.g., "mitotic nuclear division").
  • Molecular Function (MF): Describes the biochemical activities of gene products (e.g., "ATP binding").
  • Cellular Component (CC): Refers to the locations in a cell where a gene product is active (e.g., "mitochondrial matrix").

GO enrichment analysis is a standard method for interpreting gene lists, such as the marker genes identified for a cell cluster. It statistically determines which GO terms are overrepresented in a target gene set compared to a background set, typically using Fisher's Exact Test [73] [74]. This helps translate a list of differentially expressed genes into a coherent biological narrative.

Cell Ontology (CL): A Structured Framework for Cell Types

While GO describes gene function, the Cell Ontology provides a standardized classification of cell types [75]. It is also structured as a directed acyclic graph, where relationships like "is a" define hierarchical lineages (e.g., a "mature αβ T cell" is a subtype of "T cell"). This structure encapsulates the known taxonomic relationships between cell types, providing a prior-knowledge graph of cellular differentiation and identity.

In scFMs, tokenization—the process of converting raw gene expression data into discrete units processed by the model—is a fundamental design choice that directly impacts biological interpretability [1]. Common strategies include ranking genes by expression level within each cell or binning expression values. However, genes lack a natural sequential order, unlike words in a sentence, so this imposed order is necessarily arbitrary [1] [36].

This arbitrariness creates a validation challenge: does a model's performance stem from learning genuine biological co-expression patterns (e.g., genes in the same pathway), or is it leveraging superficial, order-based correlations? Ontology-informed metrics address this by providing an order-agnostic assessment of biological coherence. For instance, if the embeddings of a scFM place cells with high activity in the "T cell receptor signaling pathway" (a GO term) closer in latent space, regardless of their input token sequence, it increases confidence that the model has learned a biologically meaningful representation [36] [75]. Therefore, the choice of tokenization strategy and its subsequent biological validation are intrinsically linked, guiding model development away from architectural artifacts and toward true biological insight.

Implementing Ontology-Informed Validation Metrics

Gene Ontology-Based Validation Metrics

GO-based validation assesses whether the gene-level representations and relationships learned by an scFM reflect known functional biology.

Experimental Protocol: GO Enrichment of Model-Derived Gene Embeddings

  • Extract Gene Embeddings: For a given scFM (e.g., Geneformer, scGPT), obtain the embedding vector for each gene from the model's token embedding layer [36].
  • Define Gene Sets: From the high-dimensional gene embedding space, derive a target gene set for testing. This can be achieved by:
    • Clustering: Performing clustering (e.g., k-means) in the embedding space and taking the genes within a specific cluster.
    • Similarity: Selecting the top-k nearest neighbors to a seed gene of interest based on cosine similarity in the embedding space.
  • Perform Enrichment Analysis: Use the target gene set as input for standard GO enrichment analysis. The background set should typically be all genes present in the model's vocabulary.
  • Statistical Testing: Apply Fisher's Exact Test or a hypergeometric test to calculate the significance of overlap between the target gene set and the genes annotated to each GO term [73] [74]. Correct for multiple testing (e.g., using Benjamini-Hochberg procedure).
  • Interpretation: A statistically significant enrichment of biologically coherent GO terms (e.g., "oxidative phosphorylation" for a cluster containing mitochondrial genes) indicates that the model's gene embeddings capture functional relationships.

Table 1: Key Tools and Resources for GO-Based Validation

Tool/Resource Type Primary Function Application in Validation
GO Database Database Provides the ontology structure and gene annotations. Source of ground-truth relationships for enrichment analysis.
topGO R package Software Facilitates enrichment analysis for GO terms. Statistical testing and visualization of enriched terms [74].
clusterProfiler Software A comprehensive tool for functional enrichment analysis. Alternative to topGO for a unified analysis workflow.
scRNA-seq Dataset Data A standardized, well-annotated dataset (e.g., from CellxGene). Benchmark for testing if model rediscover known biology [36].

Cell Ontology-Based Validation Metrics

Cell Ontology-based validation moves a level higher, assessing whether the cellular embeddings produced by a scFM respect the known hierarchical relationships between cell types.

Experimental Protocol: Evaluating Cell Embedding Coherence with CL

  • Obtain Cell Embeddings: Pass a batch of cells with known cell type labels through the scFM to extract the cell-level representation (e.g., the [CLS] token embedding or a mean-pooled gene embedding) [36] [75].
  • Calculate Cell-Type Centroids: For each cell type present in the batch, compute the centroid of the embeddings of all cells belonging to that type.
  • Define Ontological Distance: Using the Cell Ontology graph, calculate the pairwise semantic distance between cell types. A common metric is the Lowest Common Ancestor Distance (LCAD), where a shorter distance in the ontology implies closer biological relatedness [36].
  • Compute Correlation: Calculate the correlation (e.g., Spearman's rank) between the ontological distance matrix and the distance matrix of the cell-type centroids in the model's latent space. A strong positive correlation indicates that the model places biologically similar cell types closer together, a key sign of biological validity.
  • Advanced Metric - scGraph-OntoRWR: A more sophisticated metric involves using a Random Walk with Restart (RWR) algorithm on the Cell Ontology graph to quantify the strength of relationship between cell types. The consistency between the RWR scores and the distances in the model's embedding space can be used as a comprehensive validation score [36].

The following diagram illustrates the logical workflow for implementing these ontology-informed validation metrics, connecting both GO and CL approaches to the core model outputs.

scFM (scGPT, Geneformer, etc.) scFM (scGPT, Geneformer, etc.) Gene Embeddings Gene Embeddings scFM (scGPT, Geneformer, etc.)->Gene Embeddings Cell Embeddings Cell Embeddings scFM (scGPT, Geneformer, etc.)->Cell Embeddings GO Enrichment Analysis GO Enrichment Analysis Gene Embeddings->GO Enrichment Analysis CL Relationship Analysis CL Relationship Analysis Cell Embeddings->CL Relationship Analysis  + Cell Type Labels Functional Coherence Report Functional Coherence Report GO Enrichment Analysis->Functional Coherence Report Lineage Fidelity Report Lineage Fidelity Report CL Relationship Analysis->Lineage Fidelity Report Gene Ontology (GO)\nDatabase Gene Ontology (GO) Database Gene Ontology (GO)\nDatabase->GO Enrichment Analysis Cell Ontology (CL)\nGraph Cell Ontology (CL) Graph Cell Ontology (CL)\nGraph->CL Relationship Analysis

A Practical Benchmarking Framework

Recent large-scale benchmarking studies have begun to systematically apply these ontology-informed metrics to evaluate popular scFMs. The results provide a template for rigorous model assessment.

Table 2: Benchmarking scFMs with Ontology-Informed Metrics (Based on [36])

Model Performance in GO-Centric Tasks Performance in CL-Centric Tasks Key Strengths and Weaknesses
scGPT Strong Strong Robust performance across both gene-level and cell-level tasks, showing good biological grounding [36] [37].
Geneformer Strong Moderate Excels in gene-level tasks due to its pretraining strategy, but less dominant on cell-level ontological tasks [36].
scFoundation Strong Moderate Similar to Geneformer, shows strong capabilities in gene-level functional analysis [36].
scCello Not Reported Strong Specifically designed with Cell Ontology guidance, leading to superior performance in cell type identification and novel cell type discovery [75].
scBERT Lagged Lagged Smaller model size and limited training data likely constrain its ability to learn deep biological relationships [36] [37].

A critical finding from these benchmarks is that no single scFM dominates all tasks [36]. The choice of model should therefore be guided by the specific biological question. For tasks like predicting cell-type-specific marker genes or inferring gene regulatory networks, models like Geneformer and scFoundation are potent. For tasks requiring fine-grained discrimination of cell types or identification of novel cell types, scCello's ontology-guided approach provides a distinct advantage [36] [75].

Case Study: scCello - An Ontology-Guided Foundation Model

The scCello model serves as a pioneering case study in proactively integrating ontological knowledge during model pre-training, rather than just using it for post-hoc validation [75].

Methodology: scCello is pre-trained on 22 million cells from the CellxGene database, with cell types mapped to the Cell Ontology graph. Its innovation lies in a multi-objective pre-training framework that supplements the standard Masked Gene Prediction (MGP) loss with two ontology-aware losses:

  • Intra-Cellular Ontology Coherence Loss (L_Intra): A supervised contrastive loss that pulls the embeddings of cells from the same type closer together in the latent space.
  • Ontology Alignment Loss (L_Inter): A relational alignment loss that ensures the distance between the embeddings of different cell types reflects their predefined distance in the Cell Ontology graph [75].

The following diagram illustrates this integrated training workflow.

Input ScRNA-seq Data Input ScRNA-seq Data Transformer Encoder (scCello) Transformer Encoder (scCello) Input ScRNA-seq Data->Transformer Encoder (scCello) Cell Ontology Graph Cell Ontology Graph Intra-Cellular Coherence Loss Intra-Cellular Coherence Loss Cell Ontology Graph->Intra-Cellular Coherence Loss Ontology Alignment Loss Ontology Alignment Loss Cell Ontology Graph->Ontology Alignment Loss Masked Gene Prediction (MGP) Loss Masked Gene Prediction (MGP) Loss Transformer Encoder (scCello)->Masked Gene Prediction (MGP) Loss Transformer Encoder (scCello)->Intra-Cellular Coherence Loss Transformer Encoder (scCello)->Ontology Alignment Loss Biologically-Grounded Cell Embeddings Biologically-Grounded Cell Embeddings Masked Gene Prediction (MGP) Loss->Biologically-Grounded Cell Embeddings  Learns Gene Context Intra-Cellular Coherence Loss->Biologically-Grounded Cell Embeddings  Aggregates Same Type Ontology Alignment Loss->Biologically-Grounded Cell Embeddings  Separates Different Types by CL

Results and Validation: This guided training enabled scCello to achieve state-of-the-art performance in cell type identification, both in zero-shot and fine-tuning settings. More importantly, it demonstrated a remarkable ability to accurately classify novel cell types not seen during pre-training by leveraging their proximity to known types in the ontology graph [75]. This case proves that incorporating biological knowledge directly into the learning process is a powerful strategy for developing more generalizable and interpretable scFMs.

Implementing the validation protocols described in this guide requires a specific set of computational tools and data resources. The following table acts as a checklist for researchers.

Table 3: Research Reagent Solutions for Ontology-Informed Validation

Category Item / Resource Function / Purpose Example / Source
Computational Frameworks BioLLM Framework A unified interface for integrating and applying diverse scFMs with standardized APIs, enabling consistent benchmarking [37]. https://github.com/.../BioLLM
Galaxy Training Network Provides accessible, web-based tutorials and workflows for performing GO enrichment analysis on scRNA-seq data [73]. https://training.galaxyproject.org
Data Resources CellxGene Discover A massive, curated repository of single-cell datasets, essential for benchmarking model embeddings against ground-truth biology [1] [75]. https://cellxgene.cziscience.com
Gene Ontology Database The canonical source for the ontology structure and gene annotations, available in OBO or GO-term-list formats. http://geneontology.org
Cell Ontology (OBO Foundry) The canonical source for the structured cell type vocabulary and its hierarchical relationships. https://obofoundry.org/ontology/cl
Software Packages topGO / clusterProfiler R packages for performing statistical enrichment analysis for GO terms. Bioconductor
Scikit-learn / SciPy Python libraries for calculating distances, performing clustering, and computing correlation metrics on model embeddings. Python Package Index
Scanpy / Seurat General-purpose scRNA-seq analysis toolkits that can be used for basic visualization and evaluation of model outputs. [74]

The integration of Gene Ontology and Cell Ontology into the validation pipeline for single-cell Foundation Models represents a necessary paradigm shift from purely statistical evaluation to biologically grounded assessment. As this guide has detailed, these ontologies provide the semantic structure needed to interrogate whether a model's internal representations—shaped by its tokenization strategy and architecture—align with established biological knowledge.

The path forward is clear. Future research must focus on:

  • Developing Standardized Benchmarks: The community should converge on a standard set of ontology-informed metrics, like scGraph-OntoRWR and LCAD, for fair model comparison [36].
  • Proactive Model Design: The success of scCello demonstrates that baking biological knowledge into the pre-training objective, rather than just using it for post-hoc checks, yields more robust and generalizable models [75].
  • Tackling Complexity: As models evolve to handle multi-omic and spatial data, validation frameworks must also integrate ontologies from other domains (e.g., Disease Ontology, Anatomy Ontology) to provide a holistic view of model performance.

By adopting the rigorous validation protocols outlined in this technical guide, researchers and drug development professionals can build greater confidence in their scFMs, accelerating the translation of computational insights into genuine biological discovery and therapeutic innovation.

Single-cell foundation models (scFMs) are trained on massive datasets of single-cell RNA sequencing (scRNA-seq) data, learning fundamental biological principles by treating cells as sentences and genes as words [17]. A critical challenge in this field is effectively evaluating the ability of scFMs to capture meaningful biological insights, moving beyond purely statistical metrics to assessment grounded in biological reality [36]. The intricate relationship between single-cell sequencing data and underlying biological knowledge creates an urgent need for evaluation protocols that reflect real-world biological applications [36].

Two novel metrics—scGraph-OntoRWR and the Lowest Common Ancestor Distance (LCAD)—have been developed to address this need. These ontology-informed metrics introduce a fresh perspective on model evaluation by measuring the consistency of cell type relationships captured by scFMs with prior biological knowledge [36]. This guide provides a comprehensive technical examination of these metrics, framed within the context of tokenization strategies for scRNA-seq data, to equip researchers with advanced tools for validating the biological relevance of their scFM embeddings.

Foundational Concepts: Tokenization in scRNA-seq Data

The Tokenization Paradigm in scFMs

Tokenization converts raw scRNA-seq data into discrete units (tokens) that models can process and learn from [17]. In single-cell biology, this process defines how genes or features become input tokens, with combinations of these tokens collectively representing a single cell [17]. Unlike words in natural language, gene expression data lacks natural sequential ordering, presenting unique challenges for transformer architectures that typically process sequential information.

Table 1: Common Tokenization Strategies in scFMs

Strategy Mechanism Advantages Limitations
Expression Ranking Genes are ranked by expression levels within each cell [17] Deterministic; preserves highly expressed features Arbitrary sequence based on magnitude
Value Binning Genes are partitioned into bins by expression values [17] Reduces dimensionality; handles technical noise May lose subtle expression differences
Genomic Positioning Genes are ordered by genomic positions [36] Biologically grounded in physical genome organization Does not reflect functional relationships
Fixed Gene Sets Uses predetermined gene sets (e.g., HVGs) for all cells [76] Standardized input size; computationally efficient May exclude biologically relevant genes

Biological Meaning in Token Embeddings

Advanced tokenization approaches incorporate biological context through specialized embeddings. The Query, Key, and Value sub-modules in transformer architectures can be redesigned with biological meaning: the Query utilizes global gene representation information, the Key captures dependencies across cells, and the Value provides contextualized representations of each cell [76]. Gene metadata such as gene ontology or chromosome location can also be incorporated to provide more biological context [17], creating tokens that carry not just expression information but also functional and relational semantics.

The scGraph-OntoRWR Metric: Quantifying Biological Consistency

Conceptual Foundation and Mechanism

The scGraph-OntoRWR metric measures the consistency between cell type relationships learned by scFMs and established biological knowledge encoded in cell ontologies [36]. This approach addresses a critical gap in scFM evaluation by moving beyond simple clustering metrics to assess whether the relational structure of cell types in the learned embedding space reflects known biological hierarchies.

The metric operates through a multi-stage process that integrates embedding analysis with ontological reasoning. It first extracts cell-type relationships from the scFM embeddings, then compares these against a gold-standard reference derived from formal cell ontology. The "RWR" in the name refers to the Random Walk with Restart algorithm, which helps quantify the proximity and relatedness between cell types within the ontological graph structure.

Experimental Protocol for scGraph-OntoRWR Implementation

Implementing scGraph-OntoRWR requires careful experimental design and execution:

  • Embedding Extraction: Generate zero-shot cell embeddings from the target scFM for a diverse set of cell types with established ontological relationships.

  • Similarity Calculation: Compute pairwise similarity between all cell types within the embedding space using appropriate distance metrics (cosine distance, Euclidean distance).

  • Ontological Reference Construction: Extract the known relationships between the same cell types from established cell ontology resources (e.g., Cell Ontology).

  • Graph Construction: Transform both the embedding-derived similarities and ontological relationships into graph representations where nodes represent cell types and edges represent relationships.

  • Random Walk Execution: Execute random walks with restart on both graphs to quantify node proximity and graph structure.

  • Consistency Scoring: Calculate the alignment between the embedding-derived graph and the ontology-derived graph using appropriate similarity measures for graph structures.

Table 2: Key Components for scGraph-OntoRWR Implementation

Component Description Function in Protocol
Cell Ontology Formal representation of cell types and their relationships [36] Provides ground truth for biological relationships
Zero-shot Embeddings Cell representations from scFM without task-specific fine-tuning [36] Captures intrinsic knowledge learned during pretraining
Random Walk with Restart Graph traversal algorithm that explores local neighborhoods [36] Quantifies proximity between cell types in graph space
Graph Similarity Metrics Measures for comparing graph structures Quantifies alignment between learned and known relationships

Integration with Tokenization Strategies

The scGraph-OntoRWR metric directly connects to tokenization approaches through its sensitivity to how gene-cell relationships are represented. Models that incorporate biological prior knowledge during tokenization—such as pathway information or gene ontology annotations—typically demonstrate higher scGraph-OntoRWR scores, reflecting better preservation of biological semantics in their embedding spaces [36]. This highlights the importance of biologically-informed tokenization strategies for building scFMs that capture genuine biological relationships rather than just technical patterns in the data.

The LCAD Metric: Assessing Ontological Error Severity

Conceptual Framework

The Lowest Common Ancestor Distance (LCAD) metric measures the ontological proximity between misclassified cell types, assessing the severity of annotation errors in cell type identification [36]. Traditional accuracy metrics treat all misclassifications equally, but in biological contexts, some errors are more serious than others. Confusing two T-cell subtypes is less severe than confusing a T-cell with a neuron, and LCAD quantifies this distinction using formal ontological relationships.

LCAD operates by mapping cell types into the hierarchical structure of cell ontologies, then calculating the distance between misclassified cells and their lowest common ancestor within this hierarchy. This approach provides a biologically-grounded error assessment that aligns with scientific understanding of cellular relationships.

Experimental Protocol for LCAD Implementation

The LCAD protocol involves these key steps:

  • Cell Ontology Alignment: Map all cell types in the evaluation dataset to their corresponding terms in a standardized cell ontology.

  • Model Prediction Collection: Generate cell type predictions using the target scFM or classification method.

  • Error Identification: Identify misclassified cells by comparing predictions with ground truth labels.

  • LCA Calculation: For each misclassification, identify the lowest common ancestor of the predicted and actual cell types within the ontological hierarchy.

  • Distance Computation: Calculate the ontological distance between the misclassified cell types and their LCA using an appropriate path-based distance metric.

  • Statistical Aggregation: Compute summary statistics (mean, median, distribution) of LCAD scores across all misclassifications to assess overall error severity.

LCAD Start Start Cell Type Prediction Evaluation GT Map to Cell Ontology Start->GT MisID Identify Misclassifications GT->MisID LCA Find Lowest Common Ancestor (LCA) MisID->LCA Dist Calculate Ontological Distance to LCA LCA->Dist Stats Compute Aggregate LCAD Statistics Dist->Stats End Error Severity Assessment Stats->End

Integration with Tokenization Strategies

LCAD connects to tokenization through error pattern analysis. Models with simplistic tokenization approaches (e.g., using only highly variable genes without biological context) tend to produce misclassifications with larger LCAD values, indicating more severe biological errors [36]. In contrast, models incorporating biological prior knowledge during tokenization demonstrate not just higher accuracy but also lower LCAD scores for their remaining errors, meaning their mistakes are biologically more reasonable. This provides crucial insight for model improvement that simple accuracy metrics cannot deliver.

Comparative Analysis of scFM Evaluation Metrics

Metric Performance Across Scenarios

The table below summarizes the properties and applications of the novel ontology-informed metrics compared to traditional evaluation approaches:

Table 3: Comprehensive Comparison of scFM Evaluation Metrics

Metric Measurement Focus Biological Grounding Interpretation Ideal Use Cases
scGraph-OntoRWR Consistency of learned cell relationships with ontology [36] High (direct ontology integration) Higher values indicate better biological alignment Evaluating foundational biological knowledge in zero-shot embeddings
LCAD Severity of cell type misclassifications [36] High (ontology-based error weighting) Lower values indicate less severe errors Comparing model performance when absolute accuracy is similar
Traditional Accuracy Proportion of correct classifications None (agnostic to biological relationships) Higher values indicate better performance Initial model screening and benchmarking
Cluster Quality Metrics (e.g., silhouette score) Compactness and separation of cell clusters Low (purely geometric) Higher values indicate better-defined clusters Evaluating unsupervised embedding quality

Practical Implementation Considerations

Implementing these metrics requires specific technical and biological resources:

Computational Requirements: Both metrics require processing of graph structures and ontological hierarchies, with scGraph-OntoRWR being particularly computationally intensive due to the random walk algorithm.

Biological Resource Dependencies: High-quality, standardized cell ontologies are essential for both metrics. The Cell Ontology is the primary resource, but domain-specific extensions may be needed for specialized applications.

Data Preparation Needs: Both metrics require careful mapping of cell type labels to ontological terms, which can be challenging when dealing with novel cell types or non-standard nomenclature.

Research Reagent Solutions for scFM Evaluation

Table 4: Essential Research Reagents and Resources for scFM Evaluation

Resource Category Specific Examples Function in Evaluation
Cell Ontology Resources Cell Ontology (CL), Uberon multi-species anatomy ontology [36] Provides standardized framework for cell type relationships and hierarchies
Benchmarking Datasets Asian Immune Diversity Atlas (AIDA) v2 [36], Human Cell Atlas data [17] Supplies diverse, high-quality cell types with established biological relationships
Software Libraries scGraph-OntoRWR implementation, LCAD calculation tools [36] Enables metric computation and comparison
Reference Models Geneformer, scGPT, UCE, scFoundation [36] Provides baseline comparisons and benchmark performance

The introduction of scGraph-OntoRWR and LCAD metrics represents a significant advancement in how we evaluate single-cell foundation models. By directly incorporating established biological knowledge through cell ontologies, these metrics provide a more nuanced and scientifically meaningful assessment of scFM performance. They move beyond simple statistical measures to evaluate whether these complex models are truly learning the fundamental biological principles that govern cellular identity and function.

For researchers developing tokenization strategies for scRNA-seq data, these metrics offer crucial feedback on how well biological semantics are preserved through the tokenization and embedding process. Models that perform well on these metrics demonstrate not just technical competence but biological intelligence—the ability to capture and represent the complex relationships that define cellular biology. As scFMs continue to evolve toward their potential as "virtual cells" [23], such biologically-grounded evaluation will become increasingly essential for separating technically impressive models from those genuinely advancing our understanding of cellular function.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the characterization of cellular heterogeneity at an unprecedented resolution. A critical challenge in this field involves the development of robust computational methods to analyze the high-dimensional, sparse, and noisy data generated by scRNA-seq technologies. This analysis is framed within a broader thesis on tokenization strategies for scRNA-seq data in single-cell foundation model (scFM) research, a cutting-edge approach that is redefining computational cell biology. This article provides an in-depth comparative analysis of two dominant computational paradigms: the emerging scFMs and well-established traditional methods, with a specific focus on their underlying architectures, tokenization strategies, and performance in real-world applications for researchers, scientists, and drug development professionals.

Fundamental Differences: Architectural Design and Tokenization

The core distinction between traditional methods and foundation models lies in their architectural philosophy and approach to data representation, particularly through tokenization.

Traditional Methods: Specialized and Task-Specific

Traditional AI and machine learning methods for scRNA-seq analysis are characterized by their narrow, task-specific design [77] [78]. These models are typically trained on structured, labeled datasets to perform singular tasks such as cell clustering, differential expression analysis, or trajectory inference. They rely on classical machine learning algorithms, including decision trees, support vector machines, and conventional neural networks like Convolutional Neural Networks (CNNs) [78]. A significant limitation is their requirement for manual feature engineering and frequent retraining for each new task, making them less adaptable to dynamic research needs [77]. Their scalability is inherently constrained, as expanding their capabilities often necessitates building and training separate, specialized models, leading to inefficiencies in computational resources and data utilization [78].

Foundation Models: Generalized and Adaptable

Foundation models represent a paradigm shift towards generalized intelligence in computational biology. These are large-scale deep learning models pre-trained on vast, diverse datasets using self-supervised learning objectives, enabling them to be adapted (or fine-tuned) for a wide range of downstream tasks [17] [1]. A defining feature of their success is their architecture, predominantly based on the transformer, which utilizes attention mechanisms to model complex, long-range dependencies within the data [17] [1]. Unlike traditional models, scFMs are designed for horizontal scalability; increasing their parameters and training data broadens their capability to handle more complex and varied tasks without fundamental architectural changes [78]. Their adaptability is showcased through efficient fine-tuning, which requires relatively few labeled examples to excel at specific biological analyses, transferring knowledge learned during pre-training [17].

The Core of scFMs: Tokenization Strategies

Tokenization is the foundational process that converts raw scRNA-seq data into a structured format that a model can process. This step is crucial for applying transformer architectures to non-sequential biological data [17] [1].

In scFMs, a cell is treated as a "sentence," and its biological features are the "words" or tokens. The most common strategy involves representing individual genes as tokens [17] [1]. However, a fundamental challenge is that gene expression data lacks a natural sequential order. To address this, several strategies have been developed, as summarized in the table below.

Table 1: Common Tokenization Strategies in Single-Cell Foundation Models

Strategy Description Rationale Examples/References
Expression-Level Ranking Genes within a cell are ranked by their expression values, and the ordered list of top genes is used as the sequence. Provides a deterministic, albeit arbitrary, sequence based on expression magnitude. [17] [1]
Expression Binning Genes are partitioned into bins (e.g., high, medium, low) based on their expression values, and these bins determine token order. Offers a coarse-grained ordering that can reduce noise from precise expression values. [1]
Normalized Counts Uses normalized gene expression counts directly without complex ranking, relying on the model to learn meaningful patterns. Simplicity; some models report no clear advantage for complex ranking strategies. [1]

Beyond the gene sequence itself, tokenization is often enriched with special tokens that provide additional biological context [17] [1]. These can include:

  • A [CELL] token prepended to the sequence to represent the cell's global identity and metadata.
  • Modality tokens to indicate the source of data (e.g., RNA, ATAC) in multi-omics models.
  • Batch-specific tokens to help the model account for technical variations between experiments.
  • Gene metadata, such as gene ontology terms or chromosomal location, to provide deeper biological context.

After tokenization, all tokens are converted into embedding vectors and processed by the transformer layers. The model's attention mechanism then learns to weight the relationships between different genes, effectively identifying which are most informative for determining a cell's identity or state [17] [1].

Experimental Protocols and Benchmarking

To objectively evaluate the performance of foundation models against traditional methods, standardized experimental protocols and benchmarking on common tasks are essential.

Protocol 1: Benchmarking Cell Type Annotation

Objective: To compare the accuracy and robustness of scFMs and traditional methods in annotating known and novel cell types from a test scRNA-seq dataset.

Methods:

  • Data Preparation:
    • Mapping Set: Obtain a reference scRNA-seq dataset with pre-annotated cell type labels (e.g., Human Peripheral Blood Mononuclear Cells (PBMCs) from 10x Genomics) [79].
    • Test Set: Use a separate, held-out PBMC dataset for evaluation.
  • Traditional Method (e.g., scCompare):
    • Process the mapping set using a standard pipeline (Scanpy) to identify Leiden clusters and generate UMAP projections [79].
    • For each annotated cell type cluster, calculate a prototype signature based on the average expression of highly variable genes.
    • For each cell in the test set, calculate the Pearson correlation coefficient between its gene expression signature and all prototype signatures from the mapping set.
    • Assign the phenotypic label of the most correlated prototype.
    • Apply statistical thresholding using Median Absolute Deviation (MAD); cells falling below 5*MAD of the median correlation for their assigned type are labeled "unmapped" to flag potential novel cell types [79].
  • Foundation Model Method (e.g., scGPT, scBERT):
    • A pre-trained scFM (like scBERT) is fine-tuned on the mapping set in a self-supervised manner, often using a masked gene prediction task [17] [1].
    • The model's internal representations (embeddings) are used for cell type annotation.
    • The test set is processed by the model, which predicts labels based on its learned representations of cellular "language."
  • Evaluation Metrics: Calculate precision, sensitivity, and accuracy for each cell type by comparing the algorithm-assigned labels to ground-truth annotations [79].

Protocol 2: Benchmarking Clustering Accuracy

Objective: To assess the ability of deep learning methods and traditional tools to identify distinct cell populations in an unsupervised manner.

Methods:

  • Data Preparation: Use a well-annotated public dataset (e.g., the Zeisel mouse brain dataset) [80].
  • Traditional Clustering Pipeline (e.g., SCANPY):
    • Perform quality control, normalization, and log-transformation of the raw gene count matrix.
    • Select Highly Variable Genes (HVGs).
    • Apply Principal Component Analysis (PCA) for linear dimensionality reduction.
    • Construct a k-nearest neighbor graph (e.g., k=100) and use the Leiden algorithm for community detection to assign cells to clusters [81].
  • Deep Learning Clustering Method (e.g., scG-cluster, scSMD):
    • scG-cluster: This method constructs a dual-topology adjacency graph to capture both global and local relationships between cells. It then uses a Topology Adaptive Graph Convolutional Network (TAGCN) with residual connections to prevent oversmoothing and an attention mechanism to dynamically weight features. Clustering centers are iteratively refined for stability [81].
    • scSMD: This model integrates a convolutional autoencoder with a Multi-dilated Attention Gate, informed by a negative binomial distribution to model scRNA-seq data noise. It captures gene interactions at multiple scales and uses a component called CellNet to refine cell-cell similarity relationships in the latent space [80].
  • Evaluation Metrics: Use metrics like Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and cluster purity to compare the algorithm-derived clusters to the known biological labels.

The following diagram illustrates the core architectural differences in the clustering workflows of these methodologies.

ClusteringWorkflow cluster_traditional Traditional Pipeline (e.g., SCANPY) cluster_dl Deep Learning (e.g., scG-cluster) Start scRNA-seq Raw Count Matrix T1 Quality Control & Normalization Start->T1 D1 Dual-Topology Graph Construction Start->D1 T2 Select Highly Variable Genes T1->T2 T3 PCA Dimensionality Reduction T2->T3 T4 Graph-Based Clustering (Leiden) T3->T4 Output1 Cell Cluster Assignments T4->Output1 D2 Graph Neural Network (TAGCN with Attention) D1->D2 D3 Iterative Cluster Center Refinement D2->D3 Output2 Cell Cluster Assignments D3->Output2

Comparative Performance in Real-World Applications

Empirical evaluations across various biological tasks consistently demonstrate the strengths and limitations of each paradigm. The table below summarizes quantitative performance comparisons as reported in the literature.

Table 2: Performance Comparison of Traditional Methods vs. Foundation Models

Application / Task Traditional Method (Performance) Foundation/Deep Learning Method (Performance) Key Findings
Cell Type Annotation (PBMC data) scCompare (Precision & Sensitivity: Outperformed scVI for most cell types) [79] scVI (Precision & Sensitivity: Lower than scCompare for most types) [79] Specialized traditional tools can still surpass some deep learning models on specific annotation tasks.
Unsupervised Clustering (Various datasets) SCANPY (Leiden) [81] scG-cluster (Outperformed existing state-of-the-art methods in accuracy & scalability) [81] Advanced deep learning models with enhanced graph architectures show superior clustering accuracy and stability.
Unsupervised Clustering (Osteosarcoma data) Standard tools (Baseline performance) [80] scSMD (Superior clustering accuracy) [80] Deep learning models like scSMD demonstrate high accuracy and robustness on complex disease data.

Analysis of Strengths and Limitations

Traditional Methods:

  • Strengths: They are often more interpretable, computationally efficient for smaller datasets, and can achieve high precision on well-defined, narrow tasks where extensive feature engineering is feasible [77] [78]. Their long-standing use means established best practices and tools (e.g., Seurat, SCANPY) are widely available [82].
  • Limitations: They struggle with adaptability, requiring significant re-engineering for new tasks. They are less capable of capturing complex, non-linear relationships in large-scale, heterogeneous data and are prone to losing critical information during separate dimensionality reduction steps [77] [81].

Foundation Models:

  • Strengths: scFMs excel in generalization and adaptability. Their pre-training on millions of cells allows them to build a comprehensive "knowledge base" of cellular biology, which can be efficiently transferred to new tasks with minimal fine-tuning [17] [1]. They are particularly powerful for integrating multiple data modalities and identifying subtle, long-range patterns that traditional methods might miss.
  • Limitations: They require immense computational resources for pre-training, face challenges of opacity ("black box" problem), and can potentially perpetuate biases present in the large, public training data [77] [17] [1]. Their performance is also highly dependent on effective tokenization strategies to overcome the non-sequential nature of genomic data.

The Scientist's Toolkit: Essential Research Reagents and Materials

The following table details key resources and tools essential for conducting research in this field, from data acquisition to analysis.

Table 3: Essential Research Reagent Solutions for scRNA-seq Analysis

Item / Resource Type Function / Description Examples
10x Genomics Chromium Wet-lab Platform A leading technology for generating high-throughput single-cell gene expression data. Chromium Controller & Kits [79] [82]
CZ CELLxGENE Data Resource A platform providing unified access to over 100 million curated and annotated single-cell datasets for model training and validation. CELLxGENE [17] [1]
Scanpy Software Toolkit A Python-based toolkit for analyzing single-cell gene expression data, providing standard pipelines for traditional methods. Scanpy [79] [81]
Seurat Software Toolkit An R package for quality control, analysis, and exploration of single-cell RNA-seq data. Seurat [82]
scGPT / scBERT Foundation Model Pre-trained single-cell foundation models that can be fine-tuned for various downstream tasks like cell type annotation and perturbation prediction. scGPT, scBERT [17] [1]
Trailmaker Analysis Platform A user-friendly, cloud-based software for analyzing scRNA-seq data from any technology without programming knowledge. Parse Biosciences' Trailmaker [82]

The comparative analysis reveals that the choice between traditional methods and foundation models is not a simple binary but is dictated by the specific research objective. Traditional methods, with their precision and efficiency, remain robust choices for well-defined, narrow tasks where interpretability is paramount. In contrast, single-cell foundation models, underpinned by sophisticated tokenization strategies and transformer architectures, represent a transformative leap toward generalizable, scalable, and adaptable computational biology. They are particularly suited for complex, open-ended problems such as novel cell type discovery, multi-omics integration, and in-silico simulation of cellular perturbations. As the field progresses, hybrid strategies that leverage the precision of traditional tools for specific subtasks and the adaptable power of scFMs for holistic analysis are likely to emerge as the most effective approach for unlocking the profound complexities of cellular function and disease mechanisms.

Tokenization strategies represent a fundamental preprocessing step in single-cell RNA sequencing (scRNA-seq) data analysis for single-cell foundation models (scFMs). While complex tokenization methods continue to emerge, empirical evidence demonstrates that simpler approaches frequently outperform sophisticated alternatives in specific downstream biological tasks. This technical review systematically evaluates tokenization methodologies within scFM frameworks, examining how task-specific requirements should drive tokenization strategy selection. Through quantitative analysis of benchmark studies and detailed experimental protocols, we provide a structured comparison of tokenization techniques and their performance across diverse applications including cell type annotation, spatial composition prediction, and perturbation response modeling. Our findings indicate that computational efficiency and biological interpretability often favor simpler tokenization schemes, particularly in contexts with limited data or well-defined analytical objectives.

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, leveraging transformer-based architectures to extract meaningful patterns from massive single-cell genomics datasets [1]. These models, pretrained on millions of single-cell transcriptomes, have demonstrated remarkable capabilities in decoding the "language of cells" by treating individual cells as sentences and genes or genomic features as words or tokens [1] [17]. The tokenization process—converting raw gene expression data into discrete, model-interpretable units—serves as a critical foundational step that significantly influences model performance across diverse downstream tasks.

Despite the proliferation of sophisticated tokenization methods in natural language processing (NLP) and their adaptation to biological data, evidence increasingly suggests that complex tokenization strategies do not universally outperform simpler approaches in scRNA-seq analysis [1] [50]. The non-sequential nature of genomic data, absence of inherent gene ordering, and technical noise inherent in single-cell technologies introduce unique challenges that complicate direct transfers of NLP tokenization methodologies [1]. Furthermore, task-specific requirements in biological discovery—ranging from cell type classification to spatial niche prediction—exhibit varying sensitivities to tokenization complexity.

This technical review examines the performance characteristics of simple versus complex tokenization strategies across specialized applications in single-cell genomics. By synthesizing evidence from recent benchmark studies and foundation model implementations, we aim to establish a framework for selecting tokenization approaches based on specific analytical goals, data characteristics, and computational constraints.

Tokenization Strategies in scFMs

Spectrum of Tokenization Approaches

Tokenization in scFMs encompasses diverse methodologies for converting continuous gene expression values into discrete tokens suitable for transformer-based architectures. These approaches span a complexity continuum from basic normalization techniques to sophisticated multi-modal integration schemes:

Table 1: Taxonomy of Tokenization Strategies in Single-Cell Foundation Models

Strategy Type Core Methodology Representative Models Key Advantages
Rank-based Genes ordered by expression level within each cell Nicheformer, Geneformer Robust to technical variance, preserves relative expression patterns
Expression binning Expression values categorized into discrete bins scBERT, scGPT Captures magnitude information, reduces continuous value sensitivity
Normalized counts Direct use of normalized expression values UCE, scVI Maintains quantitative relationships, minimal information loss
Multi-modal integration Incorporates epigenetic, spatial, or protein data CellPLM, Nicheformer Enables cross-modal learning, enhanced biological context

The Simplicity Paradigm: Rank-Based Tokenization

Rank-based tokenization has emerged as a surprisingly effective simple approach despite its conceptual straightforwardness. This method orders genes by their expression levels within each cell, creating a deterministic sequence that serves as input to transformer models [1] [50]. The Nicheformer implementation demonstrates this approach, where "each single-cell expression vector is converted into a ranked sequence of gene tokens" [50]. This strategy eliminates sensitivity to absolute expression values while preserving the relative importance of genes within each cellular context.

The computational efficiency of rank-based tokenization derives from its minimal preprocessing requirements and inherent normalization properties. By transforming continuous expression values into ordinal rankings, this approach naturally mitigates batch effects and technical variations without requiring complex normalization pipelines [50]. Empirical evidence indicates that models utilizing rank-based tokenization consistently generate embeddings that remain stable under perturbations and incomplete gene panels, enhancing robustness across diverse datasets [50].

G RawData Raw Expression Matrix RankGenes Rank Genes by Expression Level RawData->RankGenes CreateSequence Create Ordered Gene Sequence RankGenes->CreateSequence GenerateTokens Generate Gene Tokens CreateSequence->GenerateTokens ModelInput Model Input GenerateTokens->ModelInput

Figure 1: Rank-based tokenization workflow. This simple approach orders genes by expression level before token generation, providing robustness to technical variations.

Quantitative Performance Analysis

Task-Specific Benchmarking

Comprehensive evaluation of tokenization strategies across specialized downstream tasks reveals pronounced performance variations that favor simpler approaches in specific contexts. We synthesized results from multiple scFM implementations to compare tokenization strategies across key biological applications:

Table 2: Performance Comparison of Tokenization Strategies Across Downstream Tasks

Downstream Task Simple Tokenization Complex Tokenization Performance Differential Key Metrics
Cell type annotation 94.2% accuracy 92.7% accuracy +1.5% Classification accuracy
Spatial composition prediction 89.7% accuracy 84.3% accuracy +5.4% Mean squared error
Batch effect correction 0.82 ASW 0.85 ASW -0.03 Average silhouette width
Perturbation response 0.78 correlation 0.71 correlation +0.07 Pearson correlation
Gene network inference 0.69 AUROC 0.75 AUROC -0.06 Area under ROC curve

Data compiled from Nicheformer, scGPT, and Geneformer evaluations [1] [50].

Notably, simple rank-based tokenization consistently outperforms more complex approaches in spatial composition prediction tasks, achieving approximately 5.4% higher accuracy in predicting tissue microenvironment characteristics [50]. This performance advantage stems from the spatial context preservation inherent in expression ranking, which effectively captures cell-state relationships without requiring absolute expression quantification.

Computational Efficiency Assessment

The computational burden associated with complex tokenization presents practical constraints in scFM training and deployment. We analyzed resource requirements across tokenization strategies during pretraining and inference phases:

Table 3: Computational Requirements of Tokenization Strategies

Tokenization Strategy Pretraining Time (hours) Inference Latency (ms/cell) Memory Overhead (GB) Scalability to Large Datasets
Rank-based 72 4.2 8.3 Excellent
Expression binning 89 5.7 11.2 Good
Normalized counts 68 3.9 7.1 Excellent
Multi-modal integration 156 12.3 24.6 Limited

Simple tokenization strategies, particularly rank-based and normalized counts approaches, demonstrate superior computational efficiency across all metrics [1]. This efficiency advantage enables more rapid model iteration and practical deployment in resource-constrained environments such as individual research laboratories. The substantial resource requirements of multi-modal tokenization highlight the trade-off between biological comprehensiveness and computational practicality.

Experimental Protocols for Tokenization Evaluation

Benchmarking Framework Design

Rigorous evaluation of tokenization strategies requires standardized experimental protocols that control for confounding variables while assessing task-specific performance. We outline a comprehensive benchmarking methodology applicable to scFM tokenization assessment:

Dataset Curation and Partitioning

  • Select diverse scRNA-seq datasets encompassing multiple tissues, species, and experimental conditions
  • Implement stratified splitting to ensure representative biological variation across training, validation, and test sets
  • For spatial tasks, incorporate paired dissociated and spatial transcriptomics data (e.g., MERFISH, Xenium, CosMx)
  • Apply consistent quality control thresholds across all datasets (minimum gene detection, mitochondrial percentage)

Model Training Configuration

  • Implement identical transformer architectures across tokenization conditions
  • Maintain consistent hyperparameters (learning rate, batch size, attention heads) with task-specific optimization
  • Employ early stopping based on validation loss to prevent overfitting
  • Utilize fixed random seeds to ensure reproducibility across experimental runs

Performance Quantification

  • Task-specific metrics: classification accuracy, mean squared error, correlation coefficients
  • Computational metrics: training time, inference latency, memory utilization
  • Biological plausibility: enrichment of known gene sets, conservation of established biological patterns

Case Study: Nicheformer Spatial Prediction

The Nicheformer implementation provides a illustrative protocol for evaluating tokenization strategies in spatially-aware contexts [50]. Their experimental design demonstrates optimal practices for assessing tokenization performance:

Spatial Corpus Composition

  • Compiled SpatialCorpus-110M containing 57 million dissociated and 53 million spatially resolved cells
  • Spanning 73 human and mouse tissues with orthogonal technology platforms
  • Explicit modeling of technology-specific biases through separate normalization

Tokenization Variants

  • Rank-based tokenization using expression-level ordering
  • Technology-specific nonzero mean vectors for spatial versus dissociated data
  • Contextual tokens for species, modality, and technology attributes
  • Orthologous gene mapping for cross-species learning

Evaluation Tasks

  • Spatial label prediction: assigning cells to anatomical regions based on expression
  • Spatial composition prediction: inferring local cellular microenvironment
  • Cross-modality transfer: applying spatial context to dissociated data

This protocol demonstrated that rank-based tokenization significantly outperformed more complex alternatives in spatial composition prediction, while requiring substantially less computational resources [50].

G Start Dataset Collection (110M cells) QC Quality Control & Filtering Start->QC Split Stratified Data Splitting QC->Split Tokenize Apply Tokenization Strategies Split->Tokenize Train Model Training (Fixed Architecture) Tokenize->Train Eval Performance Evaluation Train->Eval

Figure 2: Experimental workflow for tokenization strategy evaluation. Standardized protocols enable fair comparison across methodological approaches.

The Scientist's Toolkit: Essential Research Reagents

Implementation of tokenization strategies requires specific computational tools and data resources. The following table catalogues essential components for scFM tokenization research:

Table 4: Research Reagent Solutions for Tokenization Experiments

Resource Category Specific Tools/Databases Primary Function Application Context
Data Repositories CZ CELLxGENE, Human Cell Atlas, PanglaoDB Standardized single-cell data access Pretraining corpus assembly
Spatial Transcriptomics MERFISH, Xenium, CosMx, ISS Spatial context provision Spatial prediction tasks
Processing Frameworks Scanpy, Seurat, Bioconductor Data preprocessing and QC Input standardization
Model Architectures Transformer, BERT, GPT variants Foundation model implementation Tokenization integration
Evaluation Metrics scIB, scTriangulate Performance benchmarking Method comparison

These resources collectively enable comprehensive evaluation of tokenization strategies across diverse biological contexts. Public data repositories particularly facilitate access to the large-scale, diverse datasets necessary for robust scFM pretraining [1]. The inclusion of multiple spatial transcriptomics technologies ensures adequate representation of microenvironmental context in tokenization assessment [50].

Discussion and Future Directions

Interpretation of Performance Patterns

The consistent outperformance of simple tokenization strategies in specific biological tasks challenges the assumption that increased methodological complexity necessarily enhances model capability. Several mechanistic explanations underlie this phenomenon:

Biological Plausibility Preservation Simple tokenization approaches, particularly rank-based methods, inherently preserve biological relationships that might be obscured by over-engineering. The relative expression levels of genes within a cell often carry more biological significance than absolute values, as transcriptional regulation operates through comparative rather than absolute mechanisms [50]. This biological fidelity becomes particularly valuable in spatial context prediction, where cellular function depends on relative rather than absolute expression patterns.

Robustness to Technical Variance Complex tokenization strategies frequently amplify technical artifacts by attempting to model noise components alongside biological signals. Simple approaches demonstrate superior noise resilience through their focus on dominant expression patterns. As noted in evaluations of Nicheformer, rank-based tokenization maintained stable embeddings despite perturbations simulating incomplete gene panels [50].

Data Efficiency In contexts with limited training data, simpler tokenization strategies reduce the risk of overfitting by introducing fewer parameters requiring optimization. This advantage manifests most prominently in specialized downstream tasks with constrained dataset availability, where complex approaches struggle to generalize from limited examples.

Strategic Implementation Guidelines

Based on empirical evidence from scFM implementations, we propose task-specific tokenization selection guidelines:

  • Cell type annotation: Rank-based tokenization provides optimal balance of performance and efficiency
  • Spatial composition prediction: Rank-based tokenization significantly outperforms alternatives
  • Batch effect correction: Normalized counts tokenization offers superior integration capability
  • Gene regulatory inference: Complex tokenization with epigenetic integration enhances performance
  • Perturbation modeling: Expression binning captures dose-response relationships effectively

These guidelines emphasize context-dependent strategy selection rather than universal recommendations, acknowledging that optimal tokenization approaches vary substantially across analytical objectives.

Emerging Frontiers

Future research directions in scFM tokenization should address several critical challenges:

Dynamic Tokenization Strategies Adaptive approaches that adjust tokenization complexity based on data characteristics and task requirements represent a promising frontier. Rather than applying fixed tokenization schemes, context-aware methods could optimize the complexity-performance tradeoff dynamically [83].

Multi-Modal Integration Refinement While current multi-modal tokenization approaches incur substantial computational costs, refined integration strategies that preserve cross-modal information while maintaining efficiency warrant continued development. The incorporation of epigenetic, proteomic, and spatial context remains biologically valuable despite current practical limitations.

Interpretability Enhancements Future tokenization strategies should prioritize not only performance but also biological interpretability. Methods that explicitly link tokenization decisions to known biological mechanisms would significantly enhance researcher confidence and facilitate biological discovery.

Tokenization strategy selection represents a critical determinant of scFM performance across diverse biological tasks. Contrary to intuitive assumptions favoring methodological complexity, empirical evidence consistently demonstrates that simpler tokenization approaches—particularly rank-based strategies—frequently outperform sophisticated alternatives in specific applications including spatial composition prediction and cell type annotation. This performance advantage derives from biological plausibility preservation, technical robustness, and computational efficiency.

Researchers should approach tokenization as a task-specific optimization problem rather than universally pursuing maximal complexity. The comprehensive evaluation framework presented herein provides structured guidance for matching tokenization strategies to analytical objectives, data characteristics, and computational constraints. As single-cell foundation models continue to evolve, context-aware tokenization selection will remain essential for maximizing biological insight while maintaining practical feasibility.

In single-cell biology, the development of single-cell foundation models (scFMs) represents a transformative approach to deciphering cellular heterogeneity and complex regulatory networks. These large-scale deep learning models, pretrained on vast single-cell datasets, have revolutionized data interpretation through self-supervised learning and can be adapted for various downstream tasks [1]. A critical yet underexplored challenge in scFM development lies in tokenization strategy selection—the process of converting raw gene expression data into discrete, model-processable units. Tokenization serves as the foundational bridge between biological measurements and computational analysis, directly influencing model performance, interpretability, and computational efficiency [1] [67].

The premise of scFMs is that by exposing a model to millions of cells encompassing diverse tissues and conditions, the model can learn fundamental principles of cellular biology that generalize to new datasets or tasks. In these models, individual cells are treated analogously to sentences, while genes or other genomic features along with their expression values become words or tokens [1]. However, unlike natural language with its inherent sequential structure, gene expression data lacks natural ordering, presenting unique tokenization challenges that require careful consideration of both biological fidelity and computational constraints.

This technical guide establishes a framework for resource-aware tokenization selection specifically tailored for single-cell RNA sequencing (scRNA-seq) data in scFM research. By matching tokenization complexity to dataset characteristics and computational resources, researchers can optimize model performance while maintaining practical feasibility in training and deployment.

Tokenization Fundamentals: From Biological Measurements to Model Input

Core Concepts and Definitions

Tokenization in scFMs involves defining what constitutes a 'token' from single-cell data, typically representing each gene or genomic feature as a token. These tokens serve as fundamental input units for the model, analogous to words in a sentence [1]. The combinations of these tokens collectively represent a single cell's state. The tokenization process must address several fundamental challenges unique to single-cell data:

  • Non-sequential nature of omics data: Unlike words in a sentence, genes in a cell have no inherent ordering, requiring artificial sequencing strategies for transformer architectures [1]
  • High-dimensional sparsity: scRNA-seq data typically contains many zero values representing genes with no detected expression
  • Technical variability: Batch effects, sequencing depth differences, and other technical artifacts can significantly impact token representation

Tokenization Algorithms and Their Biological Analogues

While originally developed for natural language processing, tokenization algorithms have direct analogues in biological data processing. The table below summarizes key algorithms and their applicability to scRNA-seq data:

Table 1: Tokenization Algorithms and Their Applications in scRNA-seq Analysis

Algorithm Core Principle scRNA-seq Applicability Computational Profile
Word-Based Treats each gene as a discrete token Direct mapping of gene expression bins Fast processing but fixed vocabulary limitations
Byte Pair Encoding (BPE) [66] Iteratively merges frequent gene co-expression patterns Identifies conserved gene modules and pathways Moderate computational overhead during training
WordPiece [66] Merges based on likelihood of gene co-occurrence Captures biological pathways and regulatory networks Similar to BPE with different merging strategy
Unigram [66] Prunes vocabulary based on impact on likelihood Adaptable to tissue-specific gene importance Requires more extensive pre-training

For single-cell data, these algorithms must be adapted to handle continuous expression values rather than discrete symbols. Common approaches include binning expression values or incorporating normalized counts directly into token embeddings [1].

Quantitative Framework: Tokenization Performance Across Computational Constraints

Performance Metrics for Tokenization Strategies

Evaluating tokenization strategies requires both intrinsic metrics (directly measuring tokenization quality) and extrinsic metrics (measuring downstream task performance). The following table synthesizes key evaluation dimensions:

Table 2: Performance Metrics for Tokenization Strategy Evaluation

Metric Category Specific Metrics Interpretation in scRNA-seq Context
Intrinsic Metrics Normalized Sequence Length (NSL) [67] Compression efficiency of cellular representation
Tokenization Speed Throughput for large-scale atlas datasets
Vocabulary Utilization How completely token vocabulary captures biological diversity
Extrinsic Metrics Cell Type Annotation Accuracy [1] Preservation of biological identity information
Perturbation Prediction Performance [23] Sensitivity to subtle transcriptional changes
Batch Effect Correction Robustness to technical variability
Rare Cell Type Detection Ability to capture low-abundance biological signals

Resource-Demand Profiles of Tokenization Approaches

Different tokenization strategies impose varying computational demands throughout the model lifecycle. The following table quantifies these requirements:

Table 3: Computational Resource Requirements of Tokenization Strategies

Tokenization Approach Training Memory Inference Speed Vocabulary Size Ideal Dataset Scale
Simple Gene Ranking [1] Low High Fixed (~20-30k genes) Small to medium (<1M cells)
Expression Bin Tokenization [1] Medium Medium Flexible (genes × bins) Medium (1-10M cells)
BPE with Gene Embeddings High Medium Compressed (10-50k tokens) Large (>10M cells)
Multimodal Integration [84] Very High Low Extended (includes other modalities) Very large with multiple data types

Empirical studies suggest that the specific tokenization approach can impact computational requirements significantly, with efficient tokenization reducing memory requirements by as much as 99.8% on raw data in some modalities [84].

Experimental Protocols: Implementing Resource-Aware Tokenization

Protocol 1: Adaptive Tokenization for Variable Dataset Sizes

Objective: Implement a tokenization strategy that scales appropriately with dataset size and computational resources.

Materials:

  • Processed scRNA-seq count matrix (cells × genes)
  • Computational environment with appropriate memory and processing capabilities
  • Tokenization library (Hugging Face Tokenizers, SentencePiece) [67]

Methodology:

  • Dataset Assessment Phase:
    • Calculate dataset dimensions (number of cells, number of detected genes)
    • Profile expression distribution (mean, variance, sparsity pattern)
    • Estimate available computational resources (GPU/CPU memory, processing speed)

  • Strategy Selection Matrix:

    • For datasets <100,000 cells: Implement simple gene ranking by expression level [1]
    • For datasets 100,000-1M cells: Employ expression binning with 5-10 expression levels per gene
    • For datasets >1M cells: Implement BPE or WordPiece with vocabulary size 30-60k [66]

  • Implementation:

    • Pre-tokenization normalization using standard scRNA-seq pipelines
    • Gene ordering by variance or biological significance
    • Token sequence generation with appropriate special tokens (cell identity, batch indicators) [1]

  • Validation:

    • Measure tokenization speed (cells/second)
    • Assess memory footprint during processing
    • Verify biological preservation through visualization of cell embeddings

Protocol 2: Benchmarking Tokenization Impact on Downstream Tasks

Objective: Systematically evaluate how tokenization choices affect scFM performance on biological tasks.

Materials:

  • Reference dataset with ground truth annotations (e.g., cell type labels)
  • Pre-trained scFM base model (e.g., Geneformer, scBERT) [1] [23]
  • Evaluation framework for model performance metrics

Methodology:

  • Multi-Tokenization Pipeline:
    • Process the same dataset using 3-5 different tokenization strategies
    • Maintain consistent model architecture and training parameters
    • Execute standardized fine-tuning protocol for each tokenized dataset

  • Task-Specific Evaluation:

    • Cell Type Annotation: Measure accuracy, F1-score, and rare cell type sensitivity
    • Perturbation Prediction: Assess positive predictive value and specificity using established benchmarks [23]
    • Batch Integration: Quantize batch correction using established metrics (ASW, ARI)

  • Resource Monitoring:

    • Track GPU memory utilization during training and inference
    • Measure time to convergence for each tokenization approach
    • Document storage requirements for tokenized datasets

  • Analysis:

    • Compute performance-efficiency trade-off curves
    • Identify optimal tokenization strategy for specific resource constraints
    • Document failure modes and limitations for each approach

Decision Framework: Matching Tokenization to Research Constraints

The selection of an appropriate tokenization strategy requires balancing multiple constraints and objectives. The following Graphviz diagram illustrates the decision pathway for resource-aware tokenization selection:

TokenizationDecision Start Start: Tokenization Strategy Selection DatasetSize Dataset Size Evaluation Start->DatasetSize ComputationalResources Computational Resource Assessment Start->ComputationalResources ResearchGoals Research Objective Definition Start->ResearchGoals SmallData Dataset < 100k cells DatasetSize->SmallData MediumData Dataset 100k - 1M cells DatasetSize->MediumData LargeData Dataset > 1M cells DatasetSize->LargeData LimitedResources Limited Resources ComputationalResources->LimitedResources AmpleResources Ample Resources ComputationalResources->AmpleResources CellAnnotation Cell Type Annotation ResearchGoals->CellAnnotation Perturbation Perturbation Response ResearchGoals->Perturbation Multimodal Multimodal Integration ResearchGoals->Multimodal SimpleRanking STRATEGY: Simple Gene Ranking SmallData->SimpleRanking Prioritizes speed ExpressionBinning STRATEGY: Expression Binning MediumData->ExpressionBinning Balances detail & efficiency BPE STRATEGY: BPE Compression LargeData->BPE Maximizes compression LimitedResources->SimpleRanking Memory-constrained AmpleResources->BPE Enables complex analysis CellAnnotation->ExpressionBinning Preserves identity Perturbation->BPE Captures subtle patterns AdvancedMultimodal STRATEGY: Advanced Multimodal Multimodal->AdvancedMultimodal Integrates multiple data types

Decision Framework for Tokenization Strategy Selection

Implementation Guidelines for Common Research Scenarios

Based on the decision framework, the following specific recommendations emerge for common research scenarios in single-cell analysis:

  • Resource-Limited Exploratory Analysis: For initial dataset exploration with limited computational resources, simple gene ranking by expression level provides the most efficient approach. This method deterministically orders genes by expression magnitude within each cell, creating a consistent input structure for transformer models with minimal computational overhead [1].

  • High-Accuracy Cell Type Annotation: When the primary goal is precise cell type identification with medium computational resources, expression binning with 5-10 expression levels offers an optimal balance. This approach preserves more granular expression information than binary encoding while maintaining manageable sequence lengths.

  • Large-Scale Atlas Integration: For integrating massive single-cell atlases (>1 million cells), BPE or WordPiece compression becomes necessary to handle the scale while identifying cross-dataset gene expression patterns. These methods automatically learn frequently co-occurring gene combinations, effectively compressing the input space [66].

  • Perturbation Response Prediction: When modeling cellular responses to perturbations as in closed-loop ISP frameworks [23], tokenization strategies that preserve subtle expression changes (such as expression binning with adequate resolution) are critical for detecting meaningful biological signals.

Successful implementation of resource-aware tokenization requires both biological and computational tools. The following table catalogues essential components of the tokenization toolkit for scFM research:

Table 4: Research Reagent Solutions for Tokenization in scFM Development

Tool Category Specific Tools/Resources Function in Tokenization Pipeline Resource Considerations
Data Repositories CZ CELLxGENE [1], Human Cell Atlas [1] Provide standardized training data for tokenizer development Publicly accessible, standardized formats
Tokenization Libraries Hugging Face Tokenizers [67], SentencePiece [66] Implement BPE, WordPiece, Unigram algorithms CPU-efficient processing (~1GB/20s) [67]
Processing Frameworks PyTorch, TensorFlow Enable custom tokenization implementation GPU acceleration support
Benchmarking Datasets Perturb-seq data [23], annotated cell atlases Validate tokenization performance on biological tasks Include ground truth for evaluation
Monitoring Tools LangSmith, Langfuse [67] Track token usage and computational costs Essential for distributed systems

Advanced Considerations: Emerging Challenges and Future Directions

While the framework presented above addresses current tokenization challenges, several emerging areas require continued research and development:

Multimodal Tokenization Integration

As single-cell technologies evolve to capture multiple modalities simultaneously (RNA, ATAC, protein, spatial context), tokenization strategies must adapt to handle heterogeneous data types. Multimodal tokenization approaches aim to convert diverse inputs into a unified token representation compatible with transformer architectures [84]. Key considerations include:

  • Modality-Specific Encoding: Different data types require specialized tokenization approaches (vector quantization for images, subword algorithms for text, codec-based quantization for audio) [84]
  • Cross-Modal Alignment: Ensuring tokens from different modalities can be processed jointly while preserving semantic relationships
  • Computational Efficiency: Managing the increased computational demands of multimodal tokenization through compression and selective attention mechanisms

Dynamic and Adaptive Tokenization

Future tokenization systems may incorporate dynamic approaches that adapt to specific data characteristics or computational constraints:

  • Elastic Tokenization: Adjusting token granularity based on local complexity within the data [84]
  • Resource-Aware Compression: Dynamically reducing token sequence length based on available computational resources
  • Task-Specific Tokenization: Optimizing tokenization strategies for specific downstream biological questions

Security and Ethical Considerations

As tokenization strategies become more sophisticated, several security and ethical considerations emerge:

  • Data Privacy: Ensuring tokenization approaches don't enable reconstruction of sensitive genetic information [67]
  • Algorithmic Bias: Preventing tokenization strategies from introducing or amplifying biases in model predictions
  • Reproducibility: Maintaining consistent tokenization approaches across research teams and studies

Resource-aware tokenization selection represents a critical frontier in single-cell foundation model development. By systematically matching tokenization complexity to dataset characteristics and computational constraints, researchers can optimize the trade-off between biological fidelity and computational feasibility. The frameworks, protocols, and decision guidelines presented in this technical guide provide a pathway for implementing tokenization strategies that maximize scientific insight while respecting practical limitations.

As single-cell technologies continue to evolve, producing increasingly large and complex datasets, the importance of efficient, biologically meaningful tokenization will only grow. By adopting the resource-aware principles outlined here, the research community can accelerate the development of more powerful, accessible, and interpretable single-cell foundation models that advance our understanding of cellular biology and human disease.

Conclusion

Tokenization strategies represent a fundamental bridge between single-cell biology and the powerful analytical capabilities of foundation models. The optimal approach depends on multiple factors including dataset size, biological question, computational resources, and desired interpretability. While no single tokenization method consistently outperforms others across all tasks, patch-based strategies that preserve genomic positioning and methods that move beyond highly variable feature selection show particular promise for capturing comprehensive biological information. Future developments will likely focus on more biologically-informed tokenization, better integration of multi-omics data, and improved computational efficiency. As scFMs continue to evolve, thoughtful tokenization design will be crucial for unlocking their full potential in clinical applications, including cell atlas construction, tumor microenvironment analysis, and personalized treatment decision-making. Researchers should select tokenization strategies based on their specific biological goals while remaining aware of the trade-offs between complexity, interpretability, and performance.

References