Single-Cell Foundation Models: The AI Revolution in Cell Biology and Drug Discovery

Eli Rivera Nov 29, 2025 176

Single-cell foundation models (scFMs) are large-scale AI systems, pre-trained on millions of single-cell transcriptomes, that are revolutionizing the analysis of cellular heterogeneity.

Single-Cell Foundation Models: The AI Revolution in Cell Biology and Drug Discovery

Abstract

Single-cell foundation models (scFMs) are large-scale AI systems, pre-trained on millions of single-cell transcriptomes, that are revolutionizing the analysis of cellular heterogeneity. This article provides a comprehensive guide for researchers and drug development professionals, explaining the core concepts of scFMs, their transformer-based architectures, and tokenization strategies that treat cells as sentences and genes as words. It delves into their transformative applications, from predicting drug responses and identifying therapeutic targets to creating 'virtual cells' for in-silico perturbation experiments. The content also addresses critical challenges, including data quality, computational demands, and model interpretability, while offering a comparative analysis of leading frameworks like scGPT, Geneformer, and scFoundation. Finally, it explores benchmarking efforts and future directions, positioning scFMs as pivotal tools for unlocking deeper insights into disease mechanisms and accelerating personalized medicine.

What Are Single-Cell Foundation Models? Demystifying the Core Concepts

Single-cell Foundation Models (scFMs) represent a paradigm shift in computational biology, leveraging large-scale deep learning to decipher the fundamental principles of cellular function. By treating cells as sentences and genes as words, these models learn a universal representation of biology from massive single-cell transcriptomics datasets. This whitepaper provides an in-depth technical examination of how scFMs master the 'language of cells,' detailing their underlying architecture, pretraining methodologies, and applications across diverse biological tasks. We present comprehensive benchmarking data, experimental protocols for model evaluation, and visualization of key computational workflows to guide researchers and drug development professionals in harnessing scFMs for biological discovery and therapeutic innovation.

The advent of high-throughput single-cell RNA sequencing (scRNA-seq) has generated vast amounts of transcriptomic data, providing unprecedented resolution for studying cellular heterogeneity [1] [2]. However, the high sparsity, dimensionality, and technical noise inherent to scRNA-seq data present significant analytical challenges [1]. Inspired by breakthroughs in natural language processing (NLP), researchers have developed single-cell Foundation Models (scFMs) that learn from extensive single-cell datasets and can be adapted for various biological analyses [2] [3]. These models treat individual cells as sentences and genes or genomic features along with their expression values as words or tokens, creating a framework for understanding the 'language' of cells [2] [3]. The premise is that by exposing a model to millions of cells across diverse tissues and conditions, it can learn fundamental biological principles generalizable to new datasets and downstream tasks [3].

Architectural Framework: How scFMs Process Cellular Language

Tokenization Strategies: From Gene Expression to Model Input

Tokenization converts raw gene expression data into discrete units called tokens that models can process and learn from [2] [3]. Unlike words in a sentence, genes in a cell have no inherent ordering, presenting a fundamental challenge for applying transformer architectures [2].

Table 1: Tokenization Strategies in Popular scFMs

Strategy Description Examples
Rank-based Genes are ranked by expression levels within each cell and the ordered list of top genes is treated as a 'sentence' scGPT, Geneformer
Bin-based Genes are partitioned into bins by their expression values, with rankings determining positions scBERT
Normalized counts Uses normalized counts without complex ranking strategies, reporting no clear advantages to ranking Some newer models

Each gene is typically represented as a token embedding combining a gene identifier and its expression value [2] [3]. Additional special tokens may be included, such as:

  • Cell identity tokens prepended to represent the cell's own identity and metadata [2]
  • Modality indicators for multi-omics data integration [3]
  • Gene metadata such as Gene Ontology terms or chromosome location [2]
  • Batch information tokens to address technical variations [2]

Model Architectures: Transformer-Based Frameworks

Most scFMs use transformer architectures characterized by attention mechanisms that learn and weight relationships between any pair of input tokens [2] [3]. The two primary architectural approaches are:

  • Encoder-based models: Adopt a BERT-like encoder architecture with bidirectional attention mechanisms where the model learns from the context of all genes in a cell simultaneously [3]. Suitable for classification and embedding tasks.

  • Decoder-based models: Use an architecture inspired by the GPT decoder, with unidirectional masked self-attention that iteratively predicts masked genes conditioned on known genes [3]. Effective for generation tasks.

architecture cluster_input Input Layer cluster_embedding Embedding Layer cluster_transformer Transformer Layers cluster_output Output Layer Genes Gene Expression Matrix Tokenization Tokenization Process Genes->Tokenization GeneEmbed Gene Embeddings Tokenization->GeneEmbed ValueEmbed Value Embeddings Tokenization->ValueEmbed PositionEmbed Position Embeddings Tokenization->PositionEmbed CombinedEmbed Combined Embeddings GeneEmbed->CombinedEmbed ValueEmbed->CombinedEmbed PositionEmbed->CombinedEmbed Attention Multi-Head Attention CombinedEmbed->Attention FFN Feed-Forward Network Attention->FFN LayerNorm Layer Normalization FFN->LayerNorm CellEmbedding Cell Embedding LayerNorm->CellEmbedding GeneEmbedding Gene Embedding LayerNorm->GeneEmbedding

Diagram 1: scFM Architecture Overview

Pretraining Strategies: Self-Supervised Learning on Cellular Corpora

Pretraining scFMs involves training on self-supervised tasks across unlabeled single-cell data, typically using masked language modeling objectives where random genes are masked and the model must predict them based on context [3]. Models are trained on massive datasets from public repositories like CZ CELLxGENE, which provides over 100 million unique cells standardized for analysis [2] [3]. The scale and diversity of pretraining data are crucial for developing robust representations that capture universal biological patterns [2].

Experimental Framework: Benchmarking scFM Performance

Evaluation Metrics and Benchmarking Protocols

Comprehensive benchmarking requires multiple evaluation perspectives. A recent benchmark study evaluated six scFMs against established baselines using 12 metrics spanning unsupervised, supervised, and knowledge-based approaches [1]. Key evaluation dimensions include:

  • Gene-level tasks: Assessing gene embeddings for predicting biological relationships including tissue specificity and Gene Ontology terms [1]
  • Cell-level tasks: Evaluating zero-shot scFM cell embeddings for dataset integration and cell type annotation [1]
  • Biologically-informed metrics: Novel metrics like scGraph-OntoRWR that measure consistency of cell type relationships with prior biological knowledge [1]

Table 2: Performance of scFMs Across Different Task Types

Model Gene-Level Tasks Cell-Type Annotation Batch Integration Perturbation Prediction
scGPT Strong Strong Strong Strong
Geneformer Strong Moderate Moderate Strong
scFoundation Strong Moderate Moderate Moderate
UCE Moderate Strong Strong Moderate
scBERT Weak Weak Weak Weak
scVI (Baseline) Moderate Moderate Strong Weak

Closed-Loop Framework for Enhanced Prediction

A significant advancement in scFM methodology is the "closed-loop" framework that incorporates experimental perturbation data during model fine-tuning [4]. This approach addresses the limitation of standard "open-loop" in silico perturbation (ISP) predictions by iteratively refining models with experimental feedback.

Experimental Protocol: Closed-Loop ISP Framework

  • Initial fine-tuning: Fine-tune scFM (e.g., Geneformer) to classify cell states using existing scRNA-seq data [4]
  • Open-loop ISP: Perform in silico perturbation across thousands of genes to simulate genetic interventions [4]
  • Experimental validation: Test predictions using orthogonal methods (e.g., Perturb-seq, flow cytometry) [4]
  • Incorporation of feedback: Fine-tune model with experimental perturbation data alongside original training data [4]
  • Closed-loop ISP: Perform updated ISP with refined model for improved predictions [4]

workflow Pretrained Pretrained scFM FineTune Fine-tuning on Target Data Pretrained->FineTune OpenLoop Open-Loop ISP Predictions FineTune->OpenLoop Experimental Experimental Validation OpenLoop->Experimental Incorporation Data Incorporation Experimental->Incorporation ClosedLoop Closed-Loop ISP Incorporation->ClosedLoop Refined Refined Predictions ClosedLoop->Refined Refined->Experimental Iterative Refinement

Diagram 2: Closed-Loop ISP Workflow

This closed-loop approach demonstrated significant improvements in prediction accuracy. In T-cell activation studies, it increased positive predictive value three-fold—from 3% to 9%—with concurrent improvements in negative predictive value (99%), sensitivity (76%), and specificity (81%) compared to open-loop ISP [4].

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

Resource Type Specific Examples Function/Application
Data Repositories CZ CELLxGENE, Human Cell Atlas, PanglaoDB Provide standardized single-cell datasets for model training and validation
Computational Frameworks BioLLM, scGPT, Geneformer Offer unified interfaces for model application and benchmarking
Benchmarking Platforms Custom evaluation pipelines with metrics like scGraph-OntoRWR Enable standardized performance assessment across multiple tasks
Perturbation Databases CRISPRi/a screens, Perturb-seq data Provide ground truth for validating in silico predictions
Ontology Resources Cell Ontology, Gene Ontology Offer biological knowledge for informed metric development

Applications and Performance in Biological Discovery

Drug Target Identification for Rare Diseases

scFMs have shown particular utility in rare disease research where patient samples are scarce. Application of the closed-loop framework to RUNX1-familial platelet disorder (RUNX1-FPD) identified several therapeutic targets, including mTOR and CD74-MIF signaling axis, and novel pathways such as protein kinase C and phosphoinositide 3-kinase [4]. The framework enabled prioritization of gene targets that could shift RUNX1-knockout hematopoietic stem cells toward a control-like state, demonstrating potential for accelerating rare disease drug discovery [4].

Limitations and Practical Considerations

Despite their promise, scFMs face several challenges:

  • Technical accessibility: Models are often difficult for biologists to use without computational expertise [5]
  • Interpretability: Understanding the biological relevance of latent embeddings remains challenging [2] [3]
  • Data quality: Inconsistencies across datasets due to batch effects and technical noise impact model performance [2]
  • Computational resources: Training and fine-tuning require significant computational intensity [2] [3]
  • Task-specific performance: No single scFM consistently outperforms others across all tasks [1]

Current benchmarking reveals that simpler machine learning models can be more adept at efficiently adapting to specific datasets, particularly under resource constraints [1]. The decision to use complex foundation models versus simpler alternatives should be guided by factors such as dataset size, task complexity, need for biological interpretability, and available computational resources [1].

The field of single-cell foundation models is rapidly evolving, with several promising directions for future development. These include creating more user-friendly interfaces to broaden accessibility [5], developing standardized benchmarking frameworks like BioLLM [6], enhancing model interpretability for biological insights, and expanding to multi-omic integration [2] [3]. The introduction of biology-driven evaluation metrics represents a crucial step toward ensuring these models capture meaningful biological patterns rather than merely optimizing computational performance [1].

As scFMs continue to mature, they hold immense potential to transform our understanding of cellular biology and accelerate therapeutic development. By truly learning the 'language of cells,' these models can serve as powerful tools for constructing comprehensive cell atlases, studying tumor microenvironments, guiding treatment decisions, and ultimately realizing the vision of predictive 'virtual cell' models for biomedical discovery.

Single-cell foundation models (scFMs) represent a revolutionary approach in computational biology, designed to interpret the vast and complex datasets generated by single-cell genomics technologies. These models are built upon three interdependent core components: Transformer-based architectures that process biological data, Self-Supervised Learning (SSL) strategies that leverage unlabeled data for pretraining, and Massive Datasets that provide the comprehensive biological context necessary for generalization [3] [1]. Together, these components enable the creation of models that can be adapted to a wide range of downstream biological tasks, from cell type annotation to drug response prediction, without requiring task-specific architectural redesign [5] [7]. The emergence of scFMs addresses an urgent need in single-cell genomics for unified frameworks capable of integrating and analyzing rapidly expanding data repositories, which now encompass hundreds of millions of cells across diverse tissues, species, and disease states [3] [2].

Transformers: The Architectural Backbone of scFMs

Core Architecture and Adaptation to Biological Data

Transformers form the fundamental architecture for most single-cell foundation models, providing the computational framework for processing complex gene expression patterns. Originally developed for natural language processing, transformers utilize attention mechanisms that allow the model to dynamically weight the importance of different input elements [3] [2]. In the context of scFMs, this means the model can learn which genes in a cell are most informative of cellular identity or state, and how they covary across different cellular contexts [3].

The adaptation of transformer architectures to single-cell data requires addressing fundamental differences between biological data and linguistic sequences. Unlike words in a sentence, genes in a cell have no inherent sequential ordering [3] [2]. To overcome this challenge, researchers have developed various strategies:

  • Gene ranking by expression levels: Genes are ordered from highest to lowest expression within each cell, creating a deterministic sequence for transformer processing [3] [2]
  • Expression binning: Genes are partitioned into bins based on their expression values, with these rankings determining their positional encoding [3]
  • Normalized count utilization: Some models forego complex ranking strategies and simply use normalized counts with appropriate positional encodings [3]

Architectural Variants and Their Applications

Different transformer architectures have been adapted for single-cell analysis, each with distinct strengths and applications:

Table 1: Transformer Architectures in Single-Cell Foundation Models

Architecture Type Key Characteristics Example Models Strengths
Encoder-based Bidirectional attention; processes all genes simultaneously scBERT [3] Effective for classification tasks and embedding generation
Decoder-based Unidirectional masked self-attention; predicts genes iteratively scGPT [3] Strong performance in generative tasks and zero-shot learning
Encoder-Decoder Combined architecture for complex input-output mappings Custom implementations [3] Flexible for multi-modal tasks and complex predictions

The attention mechanisms in these models gradually build latent representations at both the gene and cell levels, capturing hierarchical biological relationships that enable diverse downstream applications [3] [7]. Through this process, scFMs develop an understanding of cellular "grammar" and "syntax" analogous to how large language models understand linguistic structure.

Diagram: Transformer-Based Processing of Single-Cell Data

G cluster_input Single Cell Input cluster_processing Transformer Processing cluster_output Model Output Input Input Tokenization Tokenization Attention Attention Tokenization->Attention Transformer Transformer Output Output Cell Cell Cell->Tokenization Genes Genes Genes->Tokenization Embeddings Embeddings Attention->Embeddings MLP MLP Embeddings->MLP CellEmbedding CellEmbedding MLP->CellEmbedding GeneEmbedding GeneEmbedding MLP->GeneEmbedding Predictions Predictions MLP->Predictions

Self-Supervised Learning: Leveraging Unlabeled Data

Pretraining Strategies and Pretext Tasks

Self-supervised learning enables scFMs to learn meaningful biological representations without extensive manual labeling by creating pretext tasks that leverage the inherent structure of single-cell data [8] [9]. The SSL paradigm typically operates in two stages: (1) pretraining on large-scale unlabeled data using self-defined objectives, and (2) optional fine-tuning for specific downstream tasks [9]. This approach has proven particularly valuable in single-cell genomics where labeled data is scarce but unlabeled datasets are abundant.

The most common SSL strategies in scFMs include:

  • Masked autoencoding: Randomly masking portions of the input gene expression profile and training the model to reconstruct the masked values [3] [9]
  • Contrastive learning: Learning representations by contrasting positive pairs (similar cells) against negative pairs (dissimilar cells) [8] [9]
  • Gene program prediction: Predicting relationships between functionally related gene sets, incorporating biological prior knowledge [9]

Comparative Performance of SSL Approaches

Empirical studies have systematically evaluated different SSL approaches across multiple downstream tasks. Benchmarking analyses reveal that masked autoencoders generally outperform contrastive methods in single-cell genomics, diverging from trends observed in computer vision [9]. This superiority is particularly evident in gene-expression reconstruction and cross-modality prediction tasks.

Table 2: Performance Comparison of SSL Methods on Single-Cell Tasks

SSL Method Cell Type Prediction (Macro F1) Gene Expression Reconstruction Data Integration Cross-Modality Prediction
Masked Autoencoder 0.7466 ± 0.0057 0.892 ± 0.011 Strong Strong
Contrastive Learning 0.7013 ± 0.0077 0.845 ± 0.015 Moderate Moderate
Supervised Baseline 0.7124 ± 0.0062 0.801 ± 0.019 Weak Weak

The performance advantages of SSL are most pronounced in transfer learning scenarios, where models pretrained on large auxiliary datasets (such as the CELLxGENE census with over 20 million cells) are fine-tuned for specific applications [9]. This approach demonstrates significant improvements in classifying rare cell types and handling class imbalances, with macro F1 score improvements of up to 13% compared to supervised baselines [9].

Experimental Protocol: Implementing SSL for scFMs

Objective: Pretrain a transformer model using self-supervised learning on single-cell RNA-seq data Input: Large-scale unlabeled scRNA-seq dataset (e.g., CELLxGENE) Preprocessing:

  • Quality control filtering based on mitochondrial percentage and gene counts
  • Normalization and log-transformation of expression values
  • Selection of highly variable genes or all protein-coding genes

Pretraining Protocol:

  • Masking Strategy: Randomly mask 15-30% of input gene expression values
  • Model Architecture: Transformer encoder with gene embedding, value embedding, and positional encoding layers
  • Training Objective: Minimize reconstruction loss between predicted and actual expression values for masked genes
  • Optimization: AdamW optimizer with learning rate warmup and cosine decay

Evaluation:

  • Zero-shot assessment: Apply pretrained model to downstream tasks without fine-tuning
  • Fine-tuning: Adapt pretrained weights to specific tasks with limited labeled data
  • Benchmarking: Compare against supervised baselines and other SSL approaches

This protocol enables the model to learn fundamental biological principles of gene regulation and cellular function without manual annotation, creating a foundation that can be efficiently adapted to various downstream applications [3] [9].

Massive Datasets: The Fuel for Foundation Models

The development of effective scFMs requires massive, diverse datasets that capture the broad spectrum of cellular states across tissues, conditions, and individuals [3] [1]. Key data sources for pretraining scFMs include:

  • CZ CELLxGENE: Provides unified access to annotated single-cell datasets with over 100 million unique cells standardized for analysis [3] [2]
  • Human Cell Atlas: Offers broad coverage of cell types and states across human tissues [3]
  • Public repositories: NCBI GEO, SRA, and EMBL-EBI Expression Atlas host thousands of single-cell sequencing studies [3]
  • Curated compendia: PanglaoDB and Human Ensemble Cell Atlas collate data from multiple sources and studies [3]

The curation of high-quality pretraining datasets is as important as model architecture in building robust scFMs [3]. Effective pretraining requires careful selection of datasets, filtering of cells and genes, balancing dataset compositions, and rigorous quality control to address challenges such as batch effects, technical noise, and variations in processing steps [3] [2].

Impact of Dataset Scale and Diversity on Model Performance

The scale and diversity of pretraining data directly influence model performance across downstream tasks. Benchmarking studies demonstrate that models trained on larger and more diverse datasets show improved generalization and robustness [1] [9]. The relationship between pretraining data volume and downstream performance follows a logarithmic scaling law, with significant improvements observed as dataset size increases from thousands to millions of cells.

Table 3: Data Requirements and Specifications for scFM Pretraining

Dataset Characteristic Minimum Requirements Optimal Specifications Impact on Model Performance
Number of Cells 1-10 million 20+ million Directly correlates with generalization capability
Number of Cell Types 50+ 200+ Improves rare cell type recognition
Tissue Diversity 5+ major tissue types Comprehensive organ coverage Enhances cross-tissue inference
Technical Platforms 2+ sequencing technologies Multiple platforms and protocols Increases robustness to technical variance
Species Representation Single species Multiple species with orthology mapping Enables evolutionary insights

Pretraining on datasets encompassing diverse biological conditions enables scFMs to capture a wide spectrum of biological variation, forming a comprehensive understanding of cellular function that transfers effectively to new datasets and tasks [3] [1]. This comprehensive pretraining is essential for the emergent properties of scFMs, including zero-shot learning and few-shot adaptation capabilities [1].

Integration and Implementation: From Components to Functional Models

Tokenization: Bridging Biology and Computation

Tokenization transforms raw single-cell data into structured inputs that transformer models can process, serving as a critical bridge between biological measurements and computational analysis [3] [2]. In scFMs, tokenization involves defining discrete units (tokens) from single-cell data, typically representing individual genes or genomic features as tokens analogous to words in a sentence [3].

The tokenization process in scFMs includes several key considerations:

  • Gene representation: Each gene is represented as a token embedding that combines gene identity and expression value information [3]
  • Special tokens: Incorporation of special tokens for cell identity, modality indication, and batch information [3]
  • Positional encoding: Adaptation of positional encoding schemes to represent the relative ordering of genes within each cell [3]

Advanced tokenization strategies may incorporate biological prior knowledge through gene metadata such as gene ontology terms or chromosomal location, providing additional context for the model [3] [2]. After tokenization, all tokens are converted to embedding vectors that are processed by the transformer layers, resulting in latent embeddings for each gene token and often a dedicated embedding for the entire cell [3].

Implementing and researching single-cell foundation models requires specific computational resources and frameworks. The following toolkit outlines essential components for effective scFM development and application.

Table 4: Essential Research Reagents and Computational Resources for scFM Development

Resource Category Specific Tools/Frameworks Function/Purpose Implementation Considerations
Data Resources CELLxGENE, Human Cell Atlas, PanglaoDB Provide standardized, annotated single-cell data for model training Data quality control, batch effect management, and format standardization
Model Frameworks BioLLM, scGPT, Geneformer Offer standardized implementations and APIs for scFMs Architecture selection, hyperparameter tuning, and scalability optimization
Evaluation Metrics scGraph-OntoRWR, LCAD, ASW Assess biological relevance and technical performance of models Biological validation, benchmarking against baselines, and error analysis
Computational Infrastructure GPU clusters, High-memory servers Enable training and inference on large-scale models Resource allocation, distributed training strategies, and cost management

Frameworks like BioLLM have emerged to address challenges in scFM implementation by providing unified interfaces that standardize model access despite architectural differences [6] [7]. These frameworks support both zero-shot inference and fine-tuning approaches, enabling comprehensive benchmarking and practical application of scFMs to diverse biological questions [7].

Diagram: End-to-End Workflow for scFM Development

G cluster_resources Computational Resources DataCollection Data Collection & Curation Preprocessing Data Preprocessing & Tokenization DataCollection->Preprocessing Pretraining Self-Supervised Pretraining Preprocessing->Pretraining FineTuning Task-Specific Fine-Tuning Pretraining->FineTuning Deployment Model Deployment & Inference FineTuning->Deployment Data Massive Datasets (20M+ cells) Data->DataCollection Architecture Transformer Architecture Architecture->Pretraining SSL SSL Framework SSL->Pretraining

The power of single-cell foundation models emerges from the synergistic integration of transformers, self-supervised learning, and massive datasets—three components that form an interdependent ecosystem rather than functioning in isolation [3] [1] [9]. Transformer architectures provide the computational framework for modeling complex gene relationships; self-supervised learning enables effective pretraining on unlabeled data by defining biologically meaningful pretext tasks; and massive datasets furnish the comprehensive cellular context necessary for robust generalization [3] [9].

Benchmarking studies reveal that this integrated approach yields models capable of capturing deep biological principles, with scFMs demonstrating particular strength in transfer learning scenarios, handling rare cell types, and enabling zero-shot inference on novel datasets [1] [9]. However, the field continues to face challenges in standardization, interpretation, and computational efficiency [3] [7]. As research advances, the continued refinement of these core components—through more biologically informed architectures, more efficient SSL strategies, and more diverse datasets—will further enhance the capability of scFMs to unravel the complexity of cellular systems and accelerate biomedical discovery [1] [5].

The explosion of single-cell RNA sequencing (scRNA-seq) data has created both an unprecedented opportunity and a significant computational challenge in molecular biology. With archives like CZ CELLxGENE now containing over 100 million unique cells [3], researchers face the complex task of extracting meaningful biological insights from massive, high-dimensional datasets characterized by inherent sparsity and technical noise [1]. Inspired by the revolutionary success of transformer-based architectures in natural language processing (NLP), computational biologists have developed a powerful conceptual framework: treating cellular transcriptomes as linguistic constructs. This approach forms the foundation of single-cell foundation models (scFMs), which leverage the analogy that cells are sentences, genes are words, and expression patterns provide contextual meaning [3] [10].

This whitepaper explores the technical foundations, methodological implementations, and practical applications of this linguistic analogy in single-cell genomics. We examine how treating gene expression data as a "language of biology" enables the development of large-scale models that learn fundamental principles of cellular function and organization, ultimately advancing capabilities in drug discovery and therapeutic development.

Core Conceptual Framework

The Fundamental Analogy

The linguistic analogy in scFMs establishes a direct correspondence between elements of natural language and components of single-cell transcriptomic data:

  • Genes as Words: Individual genes represent the basic vocabulary of cellular language, with each gene identifier functioning as a unique token in the biological lexicon [3] [10].
  • Cells as Sentences: The complete set of genes expressed in a single cell forms a coherent "sentence" that describes the cell's identity, state, and function [3].
  • Expression as Context: The expression level of each gene provides contextual meaning, analogous to how word usage and semantics create meaning in natural language [3] [11].
  • Biological Processes as Grammar: The regulatory relationships and functional pathways that govern cellular behavior constitute the grammatical rules underlying the biological language [11].

From Analogy to Mathematical Representation

This conceptual framework is implemented mathematically through tokenization processes that convert raw gene expression data into structured sequences suitable for transformer architectures. The expression profile of each cell is transformed into a ordered sequence of gene tokens, typically ranked by expression magnitude [3] [10]. This transformation enables the application of self-supervised learning techniques similar to those used in large language models, such as masked gene prediction, where the model learns to reconstruct missing elements of the cellular "sentence" based on contextual clues [3].

Technical Implementation

Data Transformation and Tokenization Methods

The process of converting raw single-cell data into a format suitable for foundation models involves several critical steps:

Data Preprocessing Pipeline:

  • Quality Control: Filtering cells with fewer than 200 genes expressed and genes expressed in less than 200 cells [10]
  • Normalization: Row-normalization (summing to 10,000) followed by log-normalization using the formula: ( C'{i,j} = \log{10}(1 + 10^4 \times \frac{C{i,j}}{\sum{j=1}^k C_{i,k}}) ) [10]
  • Rank-Order Transformation: Genes are sorted by decreasing expression levels to create a deterministic sequence [10]

Tokenization Strategies:

  • Expression-Based Ranking: Genes are ordered by expression magnitude within each cell [3] [10]
  • Expression Binning: Genes are partitioned into bins based on expression values [3]
  • Value Embeddings: Numerical expression values are incorporated alongside gene identifiers [1]

Table 1: Tokenization Approaches in Major scFMs

Model Tokenization Strategy Expression Representation Positional Encoding
Geneformer [1] Expression-based ranking Expression bins Learned positional embeddings
scGPT [3] [1] Value embeddings Continuous normalized counts Gene rank-based encoding
scBERT [3] Expression binning Expression categories Standard transformer encoding
Cell2Sentence [10] Rank-order transformation Implicit in gene order Not applicable

Model Architectures and Training Approaches

Current scFMs predominantly leverage transformer architectures, with specific adaptations for biological data:

Architectural Variants:

  • Encoder Models (e.g., BERT-like): Employ bidirectional attention mechanisms that process all genes simultaneously, ideal for classification tasks like cell type annotation [3]
  • Decoder Models (e.g., GPT-like): Utilize unidirectional attention with masked self-attention mechanisms, better suited for generative tasks [3]
  • Hybrid Architectures: Combine encoder and decoder components for multifaceted analysis [3]

Pretraining Strategies:

  • Masked Gene Prediction: Randomly mask gene tokens and train the model to reconstruct them based on context [3]
  • Next Gene Prediction: In decoder models, predict subsequent genes in the sequence [3]
  • Multimodal Pretraining: Incorporate additional data modalities such as scATAC-seq, spatial transcriptomics, and proteomics [3]

Experimental Protocols and Methodologies

Cell2Sentence Implementation Protocol

The Cell2Sentence (C2S) methodology provides a standardized approach for transforming single-cell data into textual representations [10]:

Transformation Workflow:

  • Input: Preprocessed scRNA-seq count matrix ( C ) with dimensions ( m \times n ) (m cells, n genes)
  • Normalization: Apply the transformation ( C'{i,j} = \log{10}(1 + 10^4 \times \frac{C{i,j}}{\sum{j=1}^k C_{i,k}}) ) to each cell [10]
  • Rank-Ordering: For each cell i, sort genes by decreasing expression in ( C'i ) to generate sequence ( si )
  • Sequence Formatting: Convert the ordered gene list into a text sequence using gene identifiers as tokens

Reverse Transformation: To convert generated cell sentences back to expression values, C2S uses a linear model based on the inverse-rank relationship: ( ei = ad \times \log(ri) + bd ) [10] Where ( ei ) is the expression value for gene i, ( ri ) is its rank in the generated sentence, and ( ad ), ( bd ) are dataset-specific parameters learned during initial conversion.

Benchmarking Framework for scFM Evaluation

Compprehensive evaluation of scFMs requires multiple biological tasks and metrics [1]:

Gene-Level Tasks:

  • Functional Similarity Prediction: Assess whether embeddings of functionally related genes cluster together
  • Gene Ontology Term Prediction: Evaluate if gene embeddings can predict known biological annotations

Cell-Level Tasks:

  • Cell Type Annotation: Measure accuracy in classifying cell identities
  • Batch Integration: Evaluate ability to remove technical artifacts while preserving biological variation
  • Developmental Trajectory Inference: Assess reconstruction of cellular differentiation paths

Novel Evaluation Metrics:

  • scGraph-OntoRWR: Measures consistency of cell type relationships captured by scFMs with prior biological knowledge [1]
  • Lowest Common Ancestor Distance (LCAD): Quantifies ontological proximity between misclassified cell types [1]

Raw Count Matrix Raw Count Matrix Quality Control Quality Control Raw Count Matrix->Quality Control Normalization Normalization Quality Control->Normalization Rank-Order Transformation Rank-Order Transformation Normalization->Rank-Order Transformation Cell Sentences Cell Sentences Rank-Order Transformation->Cell Sentences LLM Fine-Tuning LLM Fine-Tuning Cell Sentences->LLM Fine-Tuning Cell Generation Cell Generation LLM Fine-Tuning->Cell Generation Task 1 Cell Type Annotation Cell Type Annotation LLM Fine-Tuning->Cell Type Annotation Task 2 Perturbation Response Perturbation Response LLM Fine-Tuning->Perturbation Response Task 3

Diagram 1: C2S Transformation and Task Workflow

Quantitative Performance Analysis

Benchmarking Results Across Biological Tasks

Rigorous evaluation of scFMs against traditional methods reveals context-dependent performance advantages [1]:

Table 2: Performance Comparison of scFMs vs. Traditional Methods

Task Category Best Performing scFM Traditional Baseline Performance Advantage Key Limitation
Novel Cell Type Annotation scGPT [1] ACTINN [10] +12.3% accuracy Requires fine-tuning on target dataset
Batch Integration Geneformer [1] Harmony [1] +8.7% batch removal score Higher computational cost
Drug Sensitivity Prediction scFoundation [1] Random Forest +5.2% AUC Limited to training drug classes
Perturbation Response scGPT [3] scGen [10] +15.1% prediction accuracy Performance varies by cell type
Cross-Tissue Generalization UCE [1] Seurat [1] +10.4% integration score Diminishes with high heterogeneity

Model Selection Guidelines

Based on comprehensive benchmarking, model selection should consider [1]:

  • Dataset Size: For small datasets (<10,000 cells), traditional methods often outperform scFMs
  • Task Complexity: scFMs excel in tasks requiring biological knowledge transfer
  • Computational Resources: scFMs require significant GPU memory and training time
  • Interpretability Needs: Some scFMs offer better biological interpretability than others

No single scFM consistently outperforms all others across every task and dataset, emphasizing the importance of context-specific model selection [1].

Advanced Applications and Extensions

Spatial Transcriptomics Integration

The linguistic analogy extends to spatial transcriptomics through models like Nicheformer, which integrates single-cell data with spatial context to reconstruct tissue organization [12]. This approach enables:

  • Spatial Context Transfer: Projecting spatial information onto dissociated single-cell data [12]
  • Cellular Neighborhood Analysis: Identifying functionally relevant cell communities within tissues
  • Tissue Organization Inference: Reconstructing spatial relationships from single-cell data alone

Multimodal Foundation Models

Next-generation scFMs incorporate multiple data modalities to create more comprehensive cellular representations:

  • Multiome Integration: Simultaneous processing of scRNA-seq and scATAC-seq data [3]
  • Proteomic Incorporation: Adding protein expression data from CITE-seq [3]
  • Cross-Modality Attention: Using specialized attention mechanisms to weight different data types [1]

Input Data Input Data Tokenization Tokenization Input Data->Tokenization Gene Embeddings Gene Embeddings Tokenization->Gene Embeddings Value Embeddings Value Embeddings Tokenization->Value Embeddings Positional Embeddings Positional Embeddings Tokenization->Positional Embeddings Transformer Encoder Transformer Encoder Gene Embeddings->Transformer Encoder Pathway Analysis Pathway Analysis Gene Embeddings->Pathway Analysis Perturbation Prediction Perturbation Prediction Gene Embeddings->Perturbation Prediction Value Embeddings->Transformer Encoder Positional Embeddings->Transformer Encoder Transformer Encoder->Gene Embeddings Cell Embedding Cell Embedding Transformer Encoder->Cell Embedding Cell Type Annotation Cell Type Annotation Cell Embedding->Cell Type Annotation Drug Response Drug Response Cell Embedding->Drug Response Spatial Mapping Spatial Mapping Cell Embedding->Spatial Mapping

Diagram 2: scFM Architecture and Output Tasks

Research Reagent Solutions

Table 3: Essential Research Tools for scFM Development

Resource Category Specific Tools Function Access
Data Repositories CZ CELLxGENE [3], GEO [3], Single-Cell Expression Atlas [3] Standardized single-cell datasets for pretraining Public
Processing Tools Scanpy [10], Seurat [1] Data preprocessing, normalization, and quality control Open source
Model Architectures scGPT [3], Geneformer [1], scBERT [3] Transformer-based model implementations Open source
Benchmarking Frameworks scGraph-OntoRWR [1], LCAD metric [1] Performance evaluation against biological ground truth Open source
Computational Resources GPU clusters, Hugging Face [10] Model training and deployment Variable

The linguistic analogy of "cells as sentences" and "genes as words" has established a productive framework for developing foundation models in single-cell biology. As the field advances, several key directions emerge:

  • Improved Biological Interpretability: Developing methods to extract mechanistic insights from model attention patterns [1]
  • Temporal Dynamics Modeling: Incorporating time-series data to model cellular transitions and differentiation pathways [11]
  • Multiscale Integration: Linking cellular-level models with tissue-level and organism-level phenotypes [12]
  • Clinical Translation: Adapting scFMs for personalized medicine applications, including patient-specific treatment response prediction [1]

For drug development professionals, scFMs offer promising capabilities in target identification, patient stratification, and drug response prediction. However, successful implementation requires careful consideration of model selection, data quality, and computational resources. As benchmark studies demonstrate, scFMs work best as powerful components within a broader analytical pipeline rather than universal solutions [1].

The rapid evolution of single-cell foundation models represents a paradigm shift in how we extract knowledge from biological systems. By leveraging the linguistic structure inherent in gene expression data, these models provide a unified framework for understanding cellular identity, function, and organization—ultimately accelerating therapeutic development and advancing precision medicine.

The advent of high-throughput single-cell RNA sequencing (scRNA-seq) has fundamentally transformed biological research, providing an unprecedented granular view of transcriptomics at cellular resolution and revolutionizing our understanding of developmental processes, tissue homeostasis, and disease mechanisms [13]. However, this technological revolution brought significant computational challenges: scRNA-seq data characteristically exhibits high sparsity, high dimensionality, and low signal-to-noise ratio, presenting substantial obstacles for traditional machine learning approaches attempting to extract meaningful biological insights [13]. Inspired by the remarkable success of foundation models in natural language processing (NLP) and computer vision—large-scale deep learning models pretrained on vast datasets using self-supervised learning—researchers recognized an opportunity to overcome these limitations [3]. The accumulation of tens of millions of single-cell omics datasets in public repositories created the fertile ground needed for training specialized foundation models for single-cell data, giving rise to single-cell foundation models (scFMs) around 2022 [3]. These models promised to learn universal biological principles from massive, diverse cellular datasets, enabling zero-shot learning and efficient adaptation to various downstream analytical tasks that were previously challenging with conventional methods [13].

The Early Foundations: Conceptual and Technical Beginnings (2022)

Initial Architectures and Pretraining Strategies

The first wave of scFMs, including pioneering models like scBERT, emerged around 2022, establishing the fundamental paradigm of treating individual cells as sentences and genes or genomic features as words or tokens [3]. These early models primarily focused on scRNA-seq data and leveraged transformer architectures, which had revolutionized NLP through their attention mechanisms that capture intricate long-range relationships in data [3]. The critical innovation was applying self-supervised pretraining objectives, often through predicting masked segments of gene expression data, enabling models to learn generalizable patterns of cellular biology without requiring labeled datasets [3]. During this formative period, researchers established the essential scaffolding for scFM development: compiling large and diverse training corpora from public archives like CZ CELLxGENE (containing over 100 million unique cells), developing tokenization strategies to convert non-sequential gene expression data into structured model inputs, and adapting transformer architectures to handle the unique characteristics of biological data [3].

Table: Pioneering Single-Cell Foundation Models (circa 2022)

Model Name Architecture Pretraining Data Key Innovation
scBERT Transformer-based encoder Millions of single-cell transcriptomes Early application of BERT-like architecture for cell type annotation
Geneformer Transformer encoder 30 million cells Gene ranking by expression level; mechanistic network learning
Early scGPT GPT-inspired decoder 33 million cells Multimodal capability; generative pretraining approach

Foundational Technical Challenges and Solutions

The development of early scFMs required solving unique computational challenges not present in traditional NLP applications. Unlike words in a sentence, genes in a cell have no inherent ordering, necessitating innovative tokenization approaches to structure the input data for transformer models [3]. Researchers experimented with various strategies, including ranking genes within each cell by their expression levels and feeding the ordered list of top genes as a "sentence," partitioning genes into bins by their expression values, or simply using normalized counts without complex ranking schemes [3]. Additionally, models incorporated specialized embeddings to represent gene identifiers, expression values, and positional information, with some approaches prepending tokens representing cellular identity and metadata to enable models to learn cell-level context [3]. These technical innovations established the foundational practices that would enable more sophisticated models in subsequent years.

Architectural Evolution: From Single-Modality to Multimodal Integration

Expansion Beyond Transcriptomics

As the field matured past its initial phase, scFMs evolved from primarily processing scRNA-seq data to incorporating multiple omics modalities, creating more comprehensive foundation models [3]. Advanced models developed capacities to integrate single-cell ATAC sequencing (scATAC-seq) for chromatin accessibility, multiome sequencing for simultaneous gene expression and chromatin profiling, spatial transcriptomics for tissue context preservation, and even single-cell proteomics data [3]. This multimodal integration represented a significant architectural advancement, enabling researchers to build more holistic representations of cellular states beyond what transcriptomics alone could reveal. Models began incorporating modality-specific tokens and developing specialized attention mechanisms to effectively weight information from different biological measurement types, moving toward a more unified understanding of cellular function [3].

Specialized Architectures for Biological Data

The architectural landscape of scFMs diversified significantly beyond the initial transformer implementations. While early models largely adopted either BERT-like encoder architectures with bidirectional attention mechanisms or GPT-inspired decoder architectures with unidirectional masked self-attention, subsequent research explored hybrid designs and custom modifications specifically optimized for biological data [3]. Researchers experimented with asymmetric encoder-decoder combinations and introduced domain-specific architectural innovations to better capture the complex dependencies and regulatory relationships in gene expression data [3]. Unlike words in natural language, genes interact dynamically without fixed sequential ordering, prompting architectural adjustments to more effectively model these non-sequential but highly structured biological relationships [3]. This period of architectural specialization and optimization significantly improved the biological plausibility of model representations and their utility for downstream tasks.

G cluster_early Initial Focus cluster_advanced Current State Early Early scFMs (2022) Multi Multimodal Integration Early->Multi Specialized Specialized Architectures Early->Specialized RNA scRNA-seq only Early->RNA BasicArch Basic Transformer Adaptation Early->BasicArch SelfSupervised Self-Supervised Pretraining Early->SelfSupervised Spatial Spatial Context Multi->Spatial MultiOmics Multi-Omics Integration Multi->MultiOmics ContextAware Spatial Context Awareness Spatial->ContextAware BioOptimized Biologically-Optimized Architectures Specialized->BioOptimized

The Current State-of-the-Art: Capabilities and Benchmarking

Leading Contemporary scFMs and Their Specializations

The current landscape of scFMs comprises several prominent models, each with distinct architectural features, pretraining strategies, and specialized capabilities. The field has matured to offer researchers a diverse toolkit of models optimized for different biological questions and data types. Contemporary models have scaled significantly in both architecture and training data, with parameter counts ranging from 40 million to 650 million and pretraining datasets encompassing up to 50 million cells [13]. This scaling has enabled more robust representations and improved performance across diverse downstream tasks. The table below summarizes the key characteristics of leading contemporary scFMs based on comprehensive benchmarking studies.

Table: Contemporary Single-Cell Foundation Models (2024-2025)

Model Parameters Pretraining Data Modalities Architecture Specialization
Geneformer 40M 30M cells scRNA-seq Encoder Gene network learning; mechanistic insights
scGPT 50M 33M cells scRNA-seq, scATAC-seq, CITE-seq, spatial Encoder with attention mask Multimodal integration; generative tasks
UCE 650M 36M cells scRNA-seq Encoder Protein-language model integration
scFoundation 100M 50M cells scRNA-seq Asymmetric encoder-decoder Large-scale pretraining; broad applicability
LangCell 40M 27.5M scRNA-text pairs scRNA-seq Encoder Text integration; cell type descriptions
Nicheformer Not specified 110M cells scRNA-seq, spatial Transformer Spatial context integration; tissue organization

Performance Benchmarking Across Biological Tasks

Comprehensive benchmarking studies have evaluated scFMs against traditional methods across diverse biological tasks, providing crucial insights into their current capabilities and limitations. Evaluations span both gene-level tasks (such as gene function prediction and gene-gene interaction inference) and cell-level tasks (including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction) [13]. The results reveal a nuanced landscape: while scFMs demonstrate robustness and versatility across diverse applications, simpler machine learning models can sometimes outperform them for specific datasets, particularly under resource constraints [13]. Notably, no single scFM consistently outperforms all others across every task, emphasizing the importance of task-specific model selection based on dataset size, task complexity, required biological interpretability, and available computational resources [13].

Performance evaluations using novel ontology-informed metrics like scGraph-OntoRWR (which measures consistency of captured cell type relationships with prior biological knowledge) demonstrate that pretrained zero-shot scFM embeddings indeed capture meaningful biological insights into the relational structure of genes and cells [13]. However, benchmarking studies specifically focused on perturbation effect prediction have revealed limitations, with scFM embeddings failing to provide consistent improvements over simpler baseline models, especially under distribution shift conditions [14]. All models struggle with predicting strong or atypical perturbation effects, highlighting the need for specialized architectures and higher-quality datasets capturing broader cellular states [14].

Methodological Deep Dive: Experimental Protocols and Workflows

Standardized Pretraining Methodology

The development of state-of-the-art scFMs follows rigorous experimental protocols beginning with large-scale data compilation from public repositories such as CZ CELLxGENE, Human Cell Atlas, and various GEO and SRA datasets [3]. The standard pretraining protocol involves several critical steps: (1) careful dataset selection and quality control to manage batch effects and technical noise; (2) gene filtering and normalization to handle the high dimensionality and sparsity of single-cell data; (3) tokenization strategy implementation, which may involve gene ranking by expression, value binning, or genomic position ordering; and (4) self-supervised pretraining using masked gene modeling objectives where random subsets of genes are masked and the model must predict their expression values based on context [3] [13]. Most contemporary models use variants of transformer architectures trained with cross-entropy or mean squared error loss functions, with training typically conducted distributed across multiple GPUs over several days or weeks due to the computational intensity [3].

Downstream Task Evaluation Framework

The evaluation of scFMs employs standardized benchmarking frameworks that assess model performance across multiple categories of biological tasks. The established evaluation protocol includes: (1) zero-shot embedding extraction without additional fine-tuning to assess inherent representation quality; (2) application to diverse downstream tasks including batch integration, cell type annotation, cancer cell identification, and drug response prediction; (3) performance quantification using both standard metrics (clustering accuracy, silhouette scores) and novel biology-aware metrics (scGraph-OntoRWR, Lowest Common Ancestor Distance); and (4) comparative analysis against traditional baseline methods including highly variable gene selection, anchor-based integration (Seurat), clustering-based harmonization (Harmony), and generative models (scVI) [13]. This comprehensive evaluation framework ensures rigorous assessment of whether large-scale pretraining provides tangible benefits over specialized, task-specific models.

G cluster_data Data Preparation Phase cluster_tasks Evaluation Tasks Data Data Collection & Curation Preprocess Data Preprocessing Data->Preprocess Source Public Repositories: CELLxGENE, GEO, SRA Data->Source Tokenize Tokenization Preprocess->Tokenize QC Quality Control & Batch Effect Management Preprocess->QC Filter Gene Filtering & Normalization Preprocess->Filter Pretrain Self-Supervised Pretraining Tokenize->Pretrain Embed Embedding Extraction Pretrain->Embed Evaluate Downstream Evaluation Embed->Evaluate Batch Batch Integration Evaluate->Batch Annotation Cell Type Annotation Evaluate->Annotation Cancer Cancer Cell ID Evaluate->Cancer Drug Drug Response Evaluate->Drug

The development and application of scFMs requires specialized computational resources and infrastructure. The substantial scale of these models, coupled with the enormous datasets required for effective pretraining, demands significant computational power typically available only through high-performance computing clusters or cloud computing platforms. Key infrastructure components include multiple high-end GPUs with substantial VRAM (often NVIDIA A100 or H100 series), fast storage systems capable of handling terabyte-scale datasets, and distributed training frameworks to parallelize computation across multiple nodes [3]. The computational intensity of training these models necessitates careful resource management, with training times ranging from days to weeks depending on model size and dataset scope. For applied researchers seeking to leverage pretrained scFMs without undertaking full model development, optimized inference frameworks and fine-tuning protocols have been developed to enable efficient adaptation to specific downstream tasks with more modest computational requirements.

The scFM research ecosystem relies on carefully curated data resources and standardized benchmarking tools. Essential research reagents for this field include large-scale curated single-cell datasets like SpatialCorpus-110M (containing over 110 million cells with spatial context) and organized collections from the Human Cell Atlas, CZ CELLxGENE, and other multiorgan atlases that provide broad coverage of cell types and states [3] [12]. Critical benchmarking frameworks such as PertEval-scFM provide standardized evaluation protocols for assessing model performance on specific tasks like perturbation effect prediction, while more comprehensive benchmarks evaluate models across multiple biological tasks including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [13] [14]. These resources function as essential research reagents, enabling reproducible development and fair comparison of different architectural approaches and training methodologies.

Table: Essential Research Reagents for scFM Development

Resource Category Specific Examples Key Function Access/Availability
Data Repositories CZ CELLxGENE, Human Cell Atlas, GEO, SRA Provide standardized, annotated single-cell datasets for pretraining and evaluation Publicly available with standardized APIs
Benchmarking Frameworks PertEval-scFM, scGraph-OntoRWR Standardized evaluation of model performance on biological tasks Open-source implementations available
Pretrained Models Geneformer, scGPT, UCE, Nicheformer Enable transfer learning and fine-tuning without costly pretraining Some models publicly available with restrictions
Processing Libraries Scanpy, Seurat, SCANPY Standardized preprocessing and analysis of single-cell data Open-source Python/R packages

Future Directions: The Path Toward Virtual Cell Models

The evolution of scFMs is progressing toward increasingly comprehensive and biologically realistic models of cellular behavior within their native tissue contexts. The development of Nicheformer, which integrates single-cell analysis with spatial transcriptomics to reconstruct how cells are organized and interact in tissues, represents a significant step toward this future [12]. This model demonstrates the feasibility of "transferring" spatial context back onto dissociated single-cell data, essentially reconstructing how individual cells fit into the broader tissue architecture—a capability crucial for understanding complex biological systems like tumor microenvironments [12]. This research connects to the emerging concept of a "Virtual Cell," a computational representation of how cells behave and interact within their native environments that could ultimately transform how we study health and disease and guide the development of new therapies [12].

Future architectural innovations will likely focus on better integration of multimodal data, improved handling of temporal dynamics in cellular processes, and more effective incorporation of prior biological knowledge through specialized attention mechanisms or hybrid symbolic-neural architectures. As noted in benchmarking studies, future progress will also require addressing current limitations in perturbation effect prediction and improving model performance under distribution shift conditions [14]. The development of tissue foundation models that learn physical relationships between cells represents an important next frontier, with potential applications in analyzing complex disease processes and predicting therapeutic responses with greater accuracy and biological relevance [12].

Why Now? The Confluence of Massive Single-Cell Data and AI Breakthroughs

The field of single-cell biology is undergoing a revolutionary transformation, driven by the convergence of two powerful forces: the exponential growth of single-cell genomic data and breakthroughs in artificial intelligence (AI). This confluence has given rise to single-cell foundation models (scFMs), large-scale deep learning models pretrained on vast datasets that can be adapted for a wide range of downstream biological tasks [3]. The emergence of this technology represents a paradigm shift in how researchers analyze cellular heterogeneity, interpret complex biological systems, and approach drug discovery.

The timing of this development is not accidental. The past decade has witnessed an unprecedented accumulation of single-cell RNA sequencing (scRNA-seq) data in public repositories, providing the critical mass of information needed to train sophisticated AI models [3]. Concurrently, transformer architectures that have revolutionized natural language processing have been successfully adapted to biological data, creating models that can decipher the "language of cells" [15] [3]. This whitepaper examines the technical foundations, current capabilities, and future directions of scFMs, with particular emphasis on their applications in pharmaceutical research and development.

The Data Revolution: Building the Biological Corpus

The Explosion of Single-Cell Data

The development of scFMs has been fueled by the creation of massive, curated single-cell data repositories. These resources have organized millions of cells from diverse tissues, species, and biological conditions into unified, accessible formats ideal for training foundation models.

Table 1: Major Data Sources for Single-Cell Foundation Model Pretraining

Data Resource Scale Description Applications in scFMs
CZ CELLxGENE [1] [3] >100 million unique cells [3] Unified access to annotated single-cell datasets Primary pretraining corpus for multiple scFMs
Human Cell Atlas [3] Multiorgan coverage Comprehensive reference map of all human cells Capturing broad spectrum of biological variation
PanglaoDB [3] Curated compendium Collated data from multiple sources and studies Training data diversity enhancement
NCBI GEO & SRA [3] Thousands of studies Public repositories for sequencing data Supplementary training materials

The scale of available data is staggering. Platforms like CZ CELLxGENE now provide unified access to over 100 million unique cells standardized for analysis, representing a sufficiently large "biological corpus" to train sophisticated models [3]. This massive data accumulation addresses a fundamental requirement for foundation models: extremely large and diverse datasets that capture universal patterns to be utilized for various general tasks [3].

Technical Challenges in Single-Cell Data

Single-cell technologies, particularly scRNA-seq, present unique computational challenges that have necessitated advanced analytical approaches. scRNA-seq data characteristics include:

  • High dimensionality: Each cell can be represented by thousands of gene expression measurements [15]
  • High sparsity: Technical limitations result in many genes having zero counts despite being expressed [1]
  • Low signal-to-noise ratio: Biological signal is often obscured by technical variability [1]
  • Batch effects: Data integration is complicated by technical artifacts across different experiments [1] [3]

Traditional machine learning approaches struggled to effectively harness knowledge from this complex data to build general-purpose models [1]. The unique characteristics of single-cell data have driven the development of specialized AI architectures tailored to biological contexts.

AI Breakthroughs: Architectural Innovations for Biological Data

Adapting Transformer Architectures for Single-Cell Biology

The core innovation enabling scFMs is the adaptation of transformer architectures, originally developed for natural language processing, to biological data. This requires reimagining fundamental concepts from language modeling in biological terms:

Table 2: Comparison of Natural Language and Single-Cell Foundation Model Components

Component Natural Language Processing Single-Cell Biology
Token Words or subwords Genes or genomic features [3]
Sentence Sequence of words Single cell represented by its genes [3] [5]
Vocabulary All possible words All possible genes in the compendium [5]
Positional Encoding Word order in sentence Gene rank by expression level [3] [5]
Value Embedding N/A Gene expression level [1]

Two predominant architectural approaches have emerged in scFMs:

  • Encoder-based models (e.g., BERT-like): Utilize bidirectional attention mechanisms where the model learns from the context of all genes in a cell simultaneously [3] [16]. These are particularly effective for classification tasks and embedding generation.
  • Decoder-based models (e.g., GPT-like): Employ unidirectional masked self-attention that iteratively predicts masked genes conditioned on known genes [3] [17]. These excel in generative tasks.

Despite these architectural differences, no single design has emerged as clearly superior for single-cell data—both approaches have demonstrated significant success in various applications [3].

Tokenization Strategies for Non-Sequential Biological Data

A fundamental challenge in applying transformers to single-cell data is that gene expression data are not naturally sequential. Unlike words in a sentence, genes in a cell have no inherent ordering [3]. scFMs have developed several innovative strategies to address this limitation:

  • Expression-based ranking: Genes are ranked within each cell by expression levels, and the ordered list of top genes is treated as the "sentence" [3] [1] [3]
  • Expression binning: Genes are partitioned into bins based on expression values [3] [17] [16]
  • Normalized counts: Some models report no clear advantages for complex ranking strategies and simply use normalized counts [3] [4]

The tokenization process typically combines multiple elements: gene embeddings (analogous to word embeddings), value embeddings (representing expression levels), and positional embeddings (indicating rank or order) [1]. Additional special tokens may include cell identity metadata, modality indicators for multi-omics models, and batch information [3] [17].

Figure 1: Single-Cell Data Tokenization Workflow

The successful implementation and application of single-cell foundation models requires a comprehensive suite of computational tools, data resources, and experimental platforms. The table below details key resources mentioned in the literature.

Table 3: Essential Research Reagent Solutions for Single-Cell Foundation Model Work

Resource Category Specific Tools/Platforms Function Key Features
scFM Models Geneformer [1] [4], scGPT [1] [3], scBERT [3], UCE [1], scFoundation [1] Pretrained foundation models for single-cell analysis Various architectures; pretrained on millions of cells
Data Platforms Parse Biosciences Evercode v3 [18], 10X Genomics [16] Scalable single-cell RNA sequencing Combinatorial barcoding for millions of cells; high-throughput
Computational Frameworks CellLENS [19], CytoTRACE 2 [17], Perturb-seq computational tools [16] Specialized analysis of cell states, potency, and perturbations Multi-omic data integration; AI-powered pattern recognition
Public Data Repositories CZ CELLxGENE [1] [3], Human Cell Atlas [3], GEO/SRA [3] Curated single-cell datasets for training and validation Standardized formats; community annotations

Experimental Protocols: Methodologies for scFM Development and Validation

scFM Pretraining Workflow

The development of a single-cell foundation model follows a rigorous multi-stage process:

  • Data Curation and Quality Control

    • Collect diverse single-cell datasets from public repositories and internal sources [3]
    • Apply stringent quality control metrics: filter cells by mitochondrial percentage, number of detected genes, and doublet detection [16]
    • Normalize counts using methods like SCTransform or log-normalization [16]

  • Gene Selection and Vocabulary Definition

    • Select highly variable genes (HVGs) that capture biological heterogeneity [1] [16]
    • Define model vocabulary based on conserved gene features across datasets [3]
    • For cross-species models, establish orthology mappings [3]

  • Self-Supervised Pretraining Objectives

    • Masked gene modeling: Randomly mask a portion of input genes (typically 15-30%) and train the model to reconstruct them from context [3] [17]
    • Next gene prediction: In decoder models, predict subsequent genes in the expression-ranked sequence [3]
    • Contrastive learning: Encourage similar representations for biologically similar cells [3]

Figure 2: scFM Pretraining Workflow

Closed-Loop Fine-Tuning for Therapeutic Discovery

Recent advances have introduced "closed-loop" frameworks that iteratively incorporate experimental data to improve model predictions. The protocol below, demonstrated in a study on RUNX1-familial platelet disorder and T-cell activation, illustrates this approach [4]:

  • Base Model Selection and Initial Fine-Tuning

    • Select a pretrained scFM (e.g., Geneformer-30M-12L) [4]
    • Fine-tune on target biological context (e.g., RUNX1-engineered HSCs vs control HSCs) using annotated scRNA-seq data
    • Validate model performance on hold-out test sets (achieving >99% accuracy in published studies) [4]

  • In Silico Perturbation (ISP) Screening

    • Perform systematic in silico perturbations across the genome (13,161 genes in the reference study) [4]
    • Simulate both gene knockout and overexpression scenarios
    • Compare predictions with differential expression analysis as a baseline

  • Experimental Validation and Model Refinement

    • Validate top predictions using CRISPRi/CRISPRa screens with functional readouts (e.g., IL-2 and IFN-γ production for T-cells) [4]
    • Incorporate perturbation examples (as few as 10-20) into model fine-tuning [4]
    • Iterate predictions with the refined "closed-loop" model

This protocol demonstrated a three-fold improvement in positive predictive value (from 3% to 9%) while maintaining high negative predictive value (99%) when applied to T-cell activation [4].

Benchmarking scFM Performance: Quantitative Assessment Across Tasks

Comprehensive benchmarking studies have evaluated scFMs against traditional methods across diverse biological tasks. The table below summarizes performance metrics from a recent benchmark evaluating six scFMs against established baselines [1].

Table 4: Performance Comparison of Single-Cell Foundation Models Across Tasks

Task Category Best Performing Models Key Metrics Performance vs. Baselines
Batch Integration scGPT, Harmony [1] Local structure preservation, batch mixing scFMs robust across diverse datasets; traditional methods competitive in specific scenarios
Cell Type Annotation scBERT, scGPT [1] [3] Accuracy, Lowest Common Ancestor Distance (LCAD) scFMs show advantages for novel cell type identification
Perturbation Prediction Geneformer, scGPT [1] [4] Positive Predictive Value (PPV), Specificity Open-loop: 3% PPV; Closed-loop: 9% PPV [4]
Drug Sensitivity Prediction Multiple scFMs [1] AUC, Precision-Recall Performance varies by cancer type and drug; no single model dominates all tasks

Critical insights from benchmarking studies include:

  • No single scFM consistently outperforms others across all tasks [1]
  • Simpler machine learning models can be more efficient for specific datasets with limited resources [1]
  • scFMs demonstrate particular strength in capturing biological relationships aligned with prior knowledge [1]
  • The roughness index (ROGI) can serve as a proxy for model selection in a dataset-dependent manner [1]

Signaling Pathways Elucidated by scFM Analysis

Single-cell foundation models have enabled the discovery and validation of novel signaling pathways involved in disease processes and therapeutic responses. The diagram below illustrates key pathways identified through scFM analysis.

Figure 3: Signaling Pathways Identified via scFM Analysis

Key pathway discoveries enabled by scFM approaches include:

  • mTOR and CD74-MIF signaling identified as therapeutic targets for RUNX1-familial platelet disorder through closed-loop ISP [4]
  • Protein kinase C and phosphoinositide 3-kinase pathways revealed as novel mechanisms in the same disorder [4]
  • Cholesterol metabolism and fatty acid synthesis surprisingly associated with multipotency states across diverse cell types, discovered through CytoTRACE 2 analysis [17]
  • T-cell activation networks including IL2RA, VAV1, ZAP70, CD3D, CD3G, and LCP2 validated through combined DE and ISP approaches [4]

Future Directions and Challenges

Despite rapid advancement, several challenges remain in the widespread implementation of scFMs in biological research and drug discovery:

Technical Limitations
  • Computational intensity: Training and fine-tuning scFMs requires substantial computational resources [3]
  • Data quality inconsistency: Batch effects and technical noise across datasets complicate integration [1] [3]
  • Interpretability gaps: Understanding the biological relevance of latent embeddings remains challenging [3]
Practical Barriers
  • Accessibility: Most scFMs lack user-friendly interfaces, limiting adoption by biologists [5]
  • Validation lag: Few novel scFM predictions have been experimentally validated in disease contexts [5]
  • Domain expertise requirement: Effective application often requires collaboration between computational and biological experts [5]
Promising Research Directions
  • Multi-omic integration: Expanding beyond transcriptomics to incorporate epigenomic, proteomic, and spatial data [3] [19]
  • Temporal modeling: Predicting dynamic cellular responses across time [4]
  • Rare disease applications: Leveraging in silico perturbation for conditions with limited samples [4]
  • Therapeutic optimization: Accelerating drug discovery through improved target identification and validation [18] [20]

The convergence of massive single-cell data and AI breakthroughs represents a pivotal moment in biological research. As these technologies mature and overcome current limitations, they hold extraordinary potential to transform our understanding of cellular biology and accelerate the development of novel therapeutics for human disease.

How scFMs Work and Transform Research: From Architecture to Real-World Applications

Single-cell foundation models (scFMs) represent a paradigm shift in computational biology, enabling researchers to decipher cellular heterogeneity, developmental trajectories, and disease mechanisms at unprecedented resolution. These models adapt transformer architectures—originally developed for sequential natural language processing—to single-cell omics data, which is inherently non-sequential and lacks inherent ordering in its feature space [3]. A single-cell can be viewed as a "sentence" where genes constitute the "words," but unlike natural language, the order of genes carries no semantic meaning [3] [1]. This fundamental difference presents unique architectural challenges that have driven innovative adaptations in tokenization, attention mechanisms, and positional encoding strategies. The development of scFMs marks a critical evolution from traditional single-task analytical pipelines toward generalizable frameworks capable of unifying diverse biological contexts [21]. This technical guide examines the core architectural innovations enabling transformers to effectively process non-sequential omics data, providing researchers and drug development professionals with a comprehensive understanding of both theoretical foundations and practical implementations.

Architectural Adaptations for Non-Sequential Data

Tokenization Strategies for Gene Expression Data

Tokenization converts raw single-cell data into discrete units processable by transformer models. For non-sequential omics data, this requires specialized approaches that differ significantly from natural language processing pipelines.

  • Gene-Based Tokenization: The most prevalent approach treats individual genes as tokens, where each gene's expression level becomes an input feature [3]. Unlike words in a sentence, genes have no natural order, requiring models to impose artificial sequencing for transformer processing.
  • Expression-Based Ordering: To address the non-sequential nature of gene expression, many scFMs employ expression-value ordering, ranking genes within each cell by their expression levels [3]. This creates a deterministic sequence where highly expressed genes receive earlier positions, though some models report limited benefits from complex ranking strategies compared to using normalized counts [3].
  • Metadata-Enhanced Tokenization: Advanced tokenization incorporates biological context through special tokens representing cell-type metadata, experimental conditions, or batch information [3]. For multimodal integration, modality-specific tokens distinguish between transcriptomic, epigenomic, and proteomic features [3] [22].

The following table summarizes key tokenization approaches used in prominent single-cell foundation models:

Table 1: Tokenization Strategies in Single-Cell Foundation Models

Method Token Unit Ordering Strategy Special Tokens Representative Models
Expression Ranking Gene IDs Rank by expression value Cell-type, Modality scGPT, Geneformer
Value Bin Partitioning Gene IDs Partition into expression bins Batch information scBERT
Normalized Counts Gene IDs Arbitrary or no ordering Limited metadata scFoundation
K-mer Tokenization DNA subsequences Natural sequence order Sequence elements DNABERT, Nucleotide Transformer

Positional Encoding Adaptations

In natural language processing, positional encodings provide crucial information about word order. For non-sequential omics data, standard positional encodings are biologically meaningless and potentially misleading. scFMs have developed several innovative solutions to this challenge:

  • Gene-Specific Positional Encoding: Some models implement learned positional embeddings where each gene has a fixed position in the sequence regardless of the cell being processed [1]. This approach provides consistent gene localization but cannot represent expression-level dynamics across cells.
  • Expression-Adaptive Ordering: Models using expression-based ranking inherently incorporate expression-level information into the sequence structure, creating dynamic positional encodings that vary by cell [3]. While computationally efficient, this approach may introduce artifacts from the arbitrary ordering.
  • Relative Position Awareness: Advanced implementations employ attention mechanisms that focus on gene-gene interactions without relying on absolute position, emphasizing functional relationships over sequential proximity [1].

G cluster_a Non-Sequential Input Data cluster_b Tokenization & Ordering cluster_c Positional Encoding Strategies cluster_d Transformer Architecture RawData Single-Cell Expression Matrix Tokenization Gene Tokenization (Genes as Tokens) RawData->Tokenization Ordering Expression-Based Ordering Tokenization->Ordering Metadata Metadata Integration (Cell Type, Batch) Ordering->Metadata FixedPos Gene-Specific Fixed Encoding Metadata->FixedPos AdaptivePos Expression-Adaptive Ordering Metadata->AdaptivePos RelativePos Relative Position Attention Metadata->RelativePos Attention Multi-Head Attention (Gene-Gene Interactions) FixedPos->Attention AdaptivePos->Attention RelativePos->Attention Output Latent Representations (Cell & Gene Embeddings) Attention->Output

Attention Mechanism Innovations

The self-attention mechanism forms the core of transformer architectures, enabling the model to weigh relationships between all elements in a sequence. For single-cell omics, attention mechanisms learn which genes interact most informatively to define cellular identity and state:

  • Bidirectional Attention: Encoder-based models like scBERT employ bidirectional attention, simultaneously considering all genes in a cell to build comprehensive representations of cellular state [3].
  • Masked Autoregressive Attention: Decoder-based models like scGPT use unidirectional masked attention, iteratively predicting masked genes based on known expression patterns in a self-supervised manner [3].
  • Efficient Attention Variants: To handle the high dimensionality of genomic data, models implement optimized attention mechanisms. scmFormer introduces scm-attention, specifically designed for multimodal single-cell data, while OmniReg-GPT employs hybrid local-global attention to capture both nucleotide-level patterns and long-range genomic interactions [22] [23].

Implementation Frameworks and Model Architectures

Prominent Single-Cell Foundation Models

Several scFMs have demonstrated the effectiveness of transformer architectures for non-sequential omics data, each with distinctive architectural features:

  • scGPT: Utilizing a decoder-style GPT architecture, scGPT has been pretrained on over 33 million cells and demonstrates exceptional performance in zero-shot cell type annotation, perturbation response prediction, and gene regulatory network inference [21] [24]. The model employs masked gene modeling as its primary pretraining objective, learning to reconstruct portions of the gene expression profile.
  • Geneformer: This model employs a encoder-based architecture pretrained on millions of cells, demonstrating strong capabilities in gene-level tasks by leveraging context-aware representations [1] [24]. Geneformer uses a rank-based preprocessing approach that orders genes by expression level before processing.
  • scBERT: Based on the BERT architecture, scBERT was among the early transformer models adapted for single-cell transcriptomics, focusing primarily on cell-type annotation tasks [3]. While demonstrating feasibility, benchmarking studies indicate it may lag behind larger models due to constrained parameter size and training data [24].
  • scmFormer: Specifically designed for multimodal integration, scmFormer excels at combining transcriptomic, epigenomic, and proteomic data, achieving a 54.5% higher average F1 score compared to secondary methods in transferring cell-type labels from transcriptomics to proteomics data [22].

Table 2: Architectural Comparison of Single-Cell Foundation Models

Model Architecture Type Pretraining Scale Multimodal Capability Key Strengths
scGPT Decoder (GPT-based) 33+ million cells Limited Zero-shot annotation, perturbation modeling
Geneformer Encoder-based Millions of cells Limited Gene-level tasks, representation learning
scBERT Encoder (BERT-based) Smaller scale Limited Cell-type annotation feasibility
scmFormer Custom Transformer 1.48+ million cells Strong (RNA+protein) Multimodal integration, label transfer
scFoundation Not specified Large scale Limited General-purpose representations
OmniReg-GPT Hybrid Local-Global Genome-wide Genomic focus Long-sequence modeling, regulatory elements

Benchmarking Performance Across Tasks

Comprehensive evaluations reveal that no single scFM consistently outperforms all others across diverse applications, highlighting the importance of task-specific model selection [1]. Benchmarking studies employing multiple metrics provide insights into relative performance:

  • Cell-Type Annotation: scGPT demonstrates robust performance in zero-shot cell type identification, leveraging its extensive pretraining to generalize across diverse cellular contexts [21] [24].
  • Multimodal Integration: scmFormer excels at integrating transcriptomic and proteomic data, significantly outperforming traditional methods like Seurat, Harmony, and scVI in cell-type label transfer from RNA to protein data [22].
  • Batch Effect Correction: In integrating datasets with technical variations, scGPT and sysVI show particular effectiveness at removing batch artifacts while preserving biological signal [21].
  • Perturbation Modeling: Foundation models pretrained on diverse cellular states demonstrate enhanced capability in predicting cellular responses to genetic and chemical perturbations [21].

G cluster_a Input Data Types cluster_b Model Selection Guide cluster_c Performance Evaluation RNA scRNA-seq Data CellType Cell-Type Annotation: scGPT, scBERT RNA->CellType BatchCorrect Batch Correction: scGPT, sysVI RNA->BatchCorrect Perturbation Perturbation Modeling: Geneformer, scGPT RNA->Perturbation ATAC scATAC-seq Data Multimodal Multimodal Integration: scmFormer, scGPT ATAC->Multimodal Protein Proteomics Data Protein->Multimodal Spatial Satial Data Spatial->Multimodal Metrics Evaluation Metrics: - Clustering Accuracy - F1 Score - Biological Relevance - Batch Correction CellType->Metrics Multimodal->Metrics BatchCorrect->Metrics Perturbation->Metrics

Experimental Protocols and Methodologies

Standardized Benchmarking Frameworks

The evaluation of scFMs requires carefully designed experimental protocols to assess performance across diverse biological contexts. Recent initiatives have established standardized benchmarking frameworks:

  • BioLLM: Provides a unified interface for integrating and evaluating multiple scFMs, eliminating architectural and coding inconsistencies to enable fair comparison [24]. This framework supports both zero-shot and fine-tuning evaluation paradigms across multiple tasks.
  • Task-Specific Benchmarks: Comprehensive evaluations assess model performance on gene-level tasks (gene function prediction, regulatory inference) and cell-level tasks (cell-type annotation, batch integration, perturbation response) [1].
  • Biological Relevance Metrics: Beyond technical metrics, novel evaluation approaches measure biological insight capture, including scGraph-OntoRWR (assessing consistency with known biological relationships) and LCAD (evaluating ontological proximity of misclassifications) [1].

Multimodal Integration Protocol

For integrating single-cell proteomics with transcriptomics data—a particularly challenging task due to feature dimension disparity and technical bias—scmFormer employs a systematic protocol:

  • Input Processing: Gene expression and protein abundance data are normalized separately using UMI normalization followed by z-score transformation [22].
  • Modality Alignment: The model processes each modality through separate embedding layers while learning cross-modal attention patterns [22].
  • Multi-Task Learning: Simultaneous optimization of data recovery (reconstructing input features) and data fusion (aligning modalities in shared latent space) objectives [22].
  • Label Transfer Validation: Evaluation using F1 score and accuracy metrics to assess cell-type annotation transfer between modalities [22].

Data Augmentation and Synthesis

Addressing data scarcity for rare cell types represents another critical application of transformer architectures. The scGFT (Generative Fourier Transformer) framework provides an innovative approach:

  • Frequency Domain Transformation: Individual cell expression profiles are mapped to complex space using Discrete Fourier Transform (DFT) [25].
  • Controlled Perturbation: Selective modification of complex components introduces biologically plausible variation while preserving fundamental cellular identity [25].
  • Profile Reconstruction: Inverse Fourier Transform converts modified frequency representations back to synthetic gene expression profiles [25].
  • Fidelity Validation: Synthetic cells are evaluated using clustering coherence (percentage aligning with original cells) and maximum mean discrepancy scores to ensure biological realism [25].

Research Reagent Solutions

The experimental workflows leveraging scFMs depend on both computational resources and biological datasets. The following table outlines key components of the single-cell foundation model research ecosystem:

Table 3: Essential Research Resources for Single-Cell Foundation Model Development

Resource Category Specific Examples Function/Role Key Characteristics
Data Repositories CZ CELLxGENE [3], DISCO [21], Human Cell Atlas [21] Provide standardized, annotated single-cell datasets for model training and validation Curated collections with quality controls, some containing 100M+ cells
Benchmarking Platforms BioLLM [24], NT-Bench [26] Standardized evaluation of model performance across diverse tasks Unified APIs, consistent metrics, reproducible protocols
Computational Frameworks scGPT [21], scmFormer [22], Geneformer [1] Pretrained models and architectures for specific analytical tasks Varying scales (1M-33M+ cells), multimodal capabilities, task specializations
Proteomics Integration Tools scTEL [27], totalVI [27], sciPENN [27] Mapping between transcriptomic and proteomic data modalities Address cost barriers of CITE-seq, predict protein from RNA data

Implementation Considerations

Successful application of transformer architectures to non-sequential omics data requires attention to several practical considerations:

  • Computational Requirements: Training large-scale scFMs demands significant GPU memory and processing power, though inference can often be performed on personal computers for models like scmFormer [22].
  • Data Quality and Preprocessing: Appropriate normalization and batch effect correction are essential, as technical artifacts can propagate through model training and confound biological interpretations [21] [1].
  • Model Selection Strategy: Choice of scFM should be guided by specific research questions, with consideration of dataset size, task complexity, and required biological interpretability [1].

The application of transformer architectures to non-sequential omics data continues to evolve rapidly, with several promising research directions emerging. Multimodal integration represents a frontier, with approaches like tensor-based fusion and pathology-aligned embeddings combining transcriptomic, epigenomic, proteomic, and spatial imaging data [21]. Improved interpretability methods are needed to extract biologically meaningful insights from model attention patterns and latent representations [21] [1]. Federated learning approaches will enable collaborative model development while addressing data privacy concerns [21]. Finally, enhanced generative capabilities may enable in silico simulation of cellular responses to perturbations, potentially accelerating drug discovery and therapeutic development [23] [25].

Transformer architectures have fundamentally transformed the analysis of single-cell omics data, despite the inherent challenge of adapting sequential processing frameworks to non-sequential biological data. Through innovative tokenization strategies, positional encoding adaptations, and specialized attention mechanisms, scFMs now enable comprehensive exploration of cellular heterogeneity and function. As these models continue to evolve in scale, multimodal capacity, and biological interpretability, they hold increasing promise for uncovering fundamental mechanisms of health and disease, ultimately bridging the gap between cellular omics and actionable biological understanding.

In the burgeoning field of single-cell biology, single-cell foundation models (scFMs) are revolutionizing our ability to extract insights from the complex, high-dimensional data generated by single-cell RNA sequencing (scRNA-seq) [1]. These models, inspired by advancements in natural language processing (NLP), require a critical first step: the conversion of raw gene expression data into a structured format that computational models can understand. This process, known as tokenization, presents unique challenges in the biological domain. Unlike natural language, where words have established semantic boundaries, gene expression data lacks clear "words" or a definitive grammar, requiring sophisticated strategies to transform continuous, sparse, and noisy biological measurements into meaningful model inputs [1] [28]. The choice of tokenization strategy directly impacts a model's ability to capture underlying biological relationships, such as gene function and cellular identity, and ultimately determines its performance on downstream tasks like cell type annotation, perturbation prediction, and clinical outcome forecasting [1].

At its core, tokenization for scFMs involves representing each cell's transcriptome—the complete set of RNA molecules—as a sequence of discrete tokens. scRNA-seq data is characterized by its high dimensionality (tens of thousands of genes), high sparsity (many zero counts representing undetected genes), and low signal-to-noise ratio [1]. These characteristics pose significant challenges for traditional machine learning approaches. Foundation models address this by leveraging large-scale, diverse datasets during pre-training, learning universal biological knowledge that can be efficiently adapted to various downstream tasks [1]. The tokenization layer is the foundational bridge between the raw biological data and the powerful deep learning architectures that constitute these models.

Core Tokenization Strategies for Gene Expression Data

Gene-Level Tokenization

The most direct approach treats each gene as a unique token, analogous to words in a vocabulary [5]. In this framework, a cell's expression profile is represented as a sequence of gene tokens. However, since expression levels are continuous measurements rather than simple presences or absences, models must also incorporate expression value embeddings. This is often achieved through rank-based encoding, where genes are ordered from highest to lowest expressed within a cell, providing a normalized, comparative view of expression that is consistent across cells [5]. The sequence typically begins with a special start token, followed by the ranked list of gene tokens.

A significant challenge with this approach is the vocabulary size. With over 20,000 protein-coding genes in the human genome, the token vocabulary becomes extremely large, leading to computational inefficiency. To mitigate this, practitioners often filter genes to a subset of highly variable genes (HVGs)—those showing the highest variation across cells, which are most likely to represent biologically meaningful signals [1]. This pre-processing step dramatically reduces the sequence length and computational burden while preserving the most informative features of the data.

Value and Positional Encoding

Beyond identifying which genes are expressed, scFMs must capture the magnitude of expression and the contextual relationships between genes. This is achieved through two critical components:

  • Value Embeddings: These represent the quantitative expression level of each gene. Instead of using raw counts, which are highly variable between cells and experiments, models typically use normalized expression values (e.g., log-transformed counts per million) [5]. Some models incorporate specialized encoding schemes for these values, creating a continuous representation that complements the discrete gene token.

  • Positional Embeddings: In NLP, positional encodings inform the model about word order in a sentence. For gene expression, where there's no natural sequential order, models employ rank-value encoding [5]. Genes are positioned in the sequence based on their expression rank within each cell (from highest to lowest), allowing the model to learn from the relative importance of genes rather than their genomic coordinates.

Table 1: Core Components of Tokenization in scFMs

Component Description Biological Analogy Example Implementation
Gene Embedding Represents the identity of each gene Dictionary definition of a word Unique identifier for each gene in the genome
Value Embedding Encodes the expression level of a gene Emphasis or tone of a spoken word Normalized, log-transformed expression value
Positional Embedding Indicates the rank-order of expression Word position in a sentence Gene's position when sorted by expression level
Special Tokens Task-specific control tokens Punctuation marks [CLS], [MASK], [PAD] for model operations

K-mer and Subword Tokenization Approaches

While gene-level tokenization is prevalent, other strategies inspired by genomic sequence analysis offer alternative approaches. K-mer tokenization, widely used in DNA sequence models, involves breaking sequences into overlapping subsequences of length k [28]. For example, a DNA sequence "ATGGCT" could be tokenized into 3-mers as ["ATG," "TGG," "GGC," "GCT"]. When applied to gene expression, this approach could represent patterns of co-expressed genes or pathway activations rather than individual genes.

Another powerful approach is Byte-Pair Encoding (BPE), a data compression algorithm that iteratively merges the most frequent pairs of tokens to create a vocabulary of common "subwords" [28] [29]. In genomics, BPE has been shown to create more balanced token distributions and capture meaningful biological motifs better than fixed k-mers. A hybrid approach combining 6-mer tokenization with BPE-600 (BPE with 600 merge operations) has demonstrated improved performance in DNA language models, better preserving both local sequence structure and global contextual information [29]. While these methods are more established for DNA and protein sequences, their principles could inform future tokenization strategies for gene expression data.

Quantitative Comparison of Tokenization Performance

Evaluating tokenization strategies requires robust benchmarking across diverse biological tasks. Recent comprehensive studies have assessed scFMs using metrics spanning unsupervised, supervised, and knowledge-based approaches [1]. Performance varies significantly based on the task, dataset characteristics, and evaluation metrics.

Table 2: Performance Comparison of Tokenization and Modeling Approaches

Model/Strategy Tokenization Approach Key Strengths Limitations Notable Performance Results
scGPT Gene-level with rank-value encoding Robust performance across all tasks; strong in zero-shot and fine-tuning [6] Computational intensity for large datasets Excels in cell type annotation and batch integration [1]
Geneformer Gene-level with expression filtering Strong gene-level task performance; effective pretraining [6] Limited context window Superior in capturing gene-gene relationships and tissue specificity [1]
scFoundation Gene-level with value encoding Competitive on gene-level tasks [6] Less effective on cell-level tasks Effective pretraining strategy transferable to multiple applications [1]
DNABERT2 Byte-Pair Encoding (BPE) Balanced token distribution; captures global context [28] Primarily for DNA sequences, not expression Reduced memory and computational demands versus k-mer approaches [28]
Hybrid Tokenization (6-mer+BPE-600) Combines k-mer and BPE Preserves local structure and global context [29] Complexity of implementation 10.78% accuracy for 3-mer prediction, outperforming NT, DNABERT2 [29]

The effectiveness of tokenization strategies can be measured through both intrinsic and extrinsic evaluations. Intrinsic evaluation assesses how well the token embeddings capture known biological relationships, such as grouping functionally similar genes together [1]. Extrinsic evaluation measures performance on practical tasks like cell type annotation, batch integration, and drug sensitivity prediction [1]. Novel metrics like scGraph-OntoRWR have been developed specifically to measure the consistency of cell type relationships captured by scFMs with prior biological knowledge from cell ontologies [1].

Experimental Protocols for Tokenization Evaluation

Benchmarking Framework for scFM Tokenization

To rigorously evaluate tokenization strategies, researchers should implement a standardized benchmarking pipeline. The following protocol, adapted from comprehensive scFM evaluations [1], ensures fair comparison across methods:

  • Dataset Curation: Select diverse scRNA-seq datasets with high-quality manual annotations that vary in size, complexity, and sources of batch effects (inter-patient, inter-platform, inter-tissue). These datasets should encompass both pre-clinical (e.g., cell atlas construction) and clinically relevant tasks (e.g., cancer cell identification, drug sensitivity prediction).

  • Task Formulation: Design both gene-level and cell-level evaluation tasks:

    • Gene-level tasks: Predict tissue specificity and Gene Ontology (GO) term associations to evaluate how well token embeddings capture functional gene relationships.
    • Cell-level tasks: Assess performance on dataset integration, cell type annotation, and novel cell type identification using metrics that preserve biological variation while removing technical batch effects.

  • Metric Selection: Employ a comprehensive set of evaluation metrics (typically 12+ metrics) spanning:

    • Unsupervised metrics: Assess clustering quality and batch correction.
    • Supervised metrics: Measure classification accuracy for cell type annotation.
    • Knowledge-based metrics: Utilize cell ontology-informed metrics like Lowest Common Ancestor Distance (LCAD) to measure ontological proximity between misclassified cell types.

  • Model Training and Evaluation: Implement a zero-shot evaluation protocol where pre-trained models generate embeddings without task-specific fine-tuning. This directly tests the biological knowledge encoded during pre-training rather than the model's ability to adapt to specific tasks.

Implementing a Hybrid Tokenization Strategy

For implementing advanced tokenization methods like the hybrid k-mer+BPE approach, the following detailed methodology has shown success in genomic applications [29]:

  • Vocabulary Generation:

    • Apply 6-mer tokenization to the entire training corpus, extracting all unique 6-mer tokens.
    • Independently apply Byte-Pair Encoding with 600 merge operations (BPE-600) to the same corpus.
    • Merge the unique tokens from both methods, removing duplicates to create a unified, balanced vocabulary that captures both short patterns (via k-mers) and frequent longer motifs (via BPE).

  • Model Architecture Configuration:

    • Implement a transformer-based architecture with standard parameters (e.g., 6 layers, 512 hidden dimensions, 8 attention heads).
    • Use next-k-mer prediction as the pre-training objective, where the model learns to predict subsequent k-mers in a sequence.

  • Training Protocol:

    • Pre-train the model on large-scale genomic data using the masked token prediction objective.
    • Fine-tune the pre-trained model on specific downstream tasks (e.g., promoter identification, protein-DNA binding prediction).
    • Validate model performance intrinsically using next-k-mer prediction accuracy across different k-values (3-mer, 4-mer, 5-mer).

HybridTokenization cluster_input Input DNA Sequence cluster_process Parallel Tokenization cluster_output Vocabulary Merging Input Raw DNA Sequence Kmer 6-mer Tokenization Input->Kmer BPE BPE-600 Tokenization Input->BPE KmerVocab 6-mer Vocabulary Kmer->KmerVocab BPEVocab BPE Vocabulary BPE->BPEVocab Merge Merge & Deduplicate KmerVocab->Merge BPEVocab->Merge HybridVocab Hybrid Vocabulary Merge->HybridVocab

Diagram 1: Hybrid tokenization workflow combining k-mer and BPE strategies.

The Scientist's Toolkit: Essential Research Reagents

Implementing and evaluating tokenization strategies for scFMs requires both computational tools and biological resources. The following table details key components of the research pipeline:

Table 3: Essential Research Reagents and Computational Tools

Resource Category Specific Tool/Resource Function in Research Implementation Notes
Benchmarking Datasets AIDA v2 (Asian Immune Diversity Atlas) [1] Provides independent, unbiased validation data to mitigate data leakage risks Accessed through CellxGene platform [1]
Evaluation Frameworks BioLLM [6] Unified interface for integrating and benchmarking diverse scFMs with standardized APIs Supports both zero-shot and fine-tuning evaluation protocols [6]
Single-Cell Analysis Seurat [1], Harmony [1], scVI [1] Established baselines for comparing scFM performance against traditional methods Provide reference performance metrics for data integration and cell type annotation
Tokenization Algorithms Byte-Pair Encoding (BPE) [28] [29], WordPiece [28], Unigram [28] Data-driven tokenization methods that create optimal vocabularies from biological sequences BPE-600 (600 merge operations) has shown particular effectiveness for genomic data [29]
Biological Knowledge Bases Gene Ontology (GO) [1], Cell Ontologies [1] Provide ground truth for evaluating biological relevance of learned representations Enable metrics like scGraph-OntoRWR that measure consistency with prior knowledge [1]

Tokenization represents a fundamental challenge and opportunity in the development of single-cell foundation models. Current strategies, primarily based on gene-level tokenization with value and positional encoding, have enabled significant advances in biological discovery. However, no single approach consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on dataset size, task complexity, and computational resources [1].

The future of tokenization in scFMs will likely involve more biologically informed strategies that move beyond direct NLP analogies to develop methods specifically designed for genomic and transcriptomic data. Hybrid approaches, like combining k-mer and BPE tokenization, show promise in capturing both local sequence structure and global contextual information [29]. As noted in benchmark studies, "scFMs are robust and versatile tools for diverse applications while simpler machine learning models are more adept at efficiently adapting to specific datasets, particularly under resource constraints" [1].

For researchers and drug development professionals, understanding these tokenization strategies is crucial for selecting appropriate models, interpreting results, and advancing the field. Continued development of standardized evaluation frameworks like BioLLM will facilitate fair comparisons and accelerate progress [6]. As tokenization methods mature, they will unlock deeper biological insights from single-cell data, ultimately enhancing our understanding of cellular mechanisms and accelerating therapeutic discovery.

A fundamental challenge in modern oncology is the variability in how individual patients or specific cancer cell populations respond to treatment. Accurately predicting Cancer Drug Response (CDR) is therefore critical for developing personalized therapies that maximize effectiveness and minimize adverse effects [30]. The half-maximal inhibitory concentration (IC50) serves as a crucial quantitative measure in this process, indicating the potency of a drug by measuring the concentration required to inhibit a biological process by 50% in vitro [31]. Traditional CDR prediction methods often rely on bulk genomic data, which can mask critical cellular heterogeneity within tumors. The emergence of single-cell technologies and sophisticated deep learning models now enables researchers to decipher this complexity at unprecedented resolution. This whitepaper explores the integration of single-cell foundation models (scFMs) and other advanced computational approaches to enhance the accuracy and interpretability of CDR and IC50 value prediction, thereby powering the next generation of drug discovery.

Understanding IC50 in Cancer Drug Response

The IC50 value is a central metric in pharmacological research for evaluating drug potency [31]. It is a quantitative measure that indicates how much of a particular inhibitory substance is needed to inhibit a given biological process or component by half. In cancer research, this typically refers to the concentration of a drug required to reduce cancer cell line growth or viability by 50% in a controlled in vitro setting [32].

  • Definition and Significance: IC50 values provide a standardized way to compare the potency of different anticancer compounds. A lower IC50 value indicates a more potent drug, as less of the substance is required to achieve the desired inhibitory effect. This measurement is foundational for screening drug candidates and prioritizing them for further development [31] [33].

  • Key Considerations:

    • Relative vs. Absolute IC50: The most common and pharmacologically relevant definition is the "relative IC50," which is the concentration that reduces the response to a point halfway between the top and bottom plateaus of the dose-response curve. The "absolute IC50," which defines 50% inhibition relative to predefined maximum and minimum control values, is less common and considered less useful for quantifying drug potency [32].
    • Experimental Context: IC50 values are highly dependent on experimental conditions. Factors such as cell line characteristics, assay duration, and substrate concentration (for competitive inhibitors) can significantly influence the results. This variability necessitates careful standardization when comparing IC50 values across different studies [31] [32].

Table 1: Key Aspects of IC50 Measurement

Aspect Description Considerations in CDR
Definition Concentration for 50% inhibition Standardized measure of drug potency [31]
Measurement Determined from dose-response curves Requires defined 0% and 100% response levels [32]
Interpretation Lower IC50 = higher potency Must be contextualized with efficacy (max effect) [32]
Variability Influenced by assay conditions Critical for cross-study comparisons [31]

Single-Cell Technologies and Data for CDR Prediction

Single-cell technologies have revolutionized the study of cancer heterogeneity by enabling the profiling of genomic, transcriptomic, epigenomic, and proteomic landscapes at the resolution of individual cells [20] [34]. This granular view is crucial for understanding the complex cellular subpopulations within tumors that contribute to drug resistance and treatment failure.

  • Technology Spectrum: Key single-cell omics technologies include:

    • Single-cell RNA sequencing (scRNA-seq): Reveals the transcriptomic heterogeneity of cancer cell populations, identifying distinct cell states and their gene expression signatures [20].
    • Single-cell DNA sequencing (scDNA-seq): Uncovers genomic variations, such as mutations and copy number alterations, at the single-cell level [34].
    • Single-cell ATAC sequencing (scATAC-seq): Profiles the epigenomic state by identifying accessible chromatin regions, providing insights into gene regulatory mechanisms [3].

  • Advantages for CDR: These technologies help identify key regulators of therapeutic resistance and sensitive cellular subpopulations that are often obscured in bulk sequencing data [34]. The resulting high-resolution data provides the foundational corpus for training sophisticated deep learning models, including single-cell foundation models (scFMs), to predict drug sensitivity and resistance mechanisms [20] [1].

Single-Cell Foundation Models (scFMs): Core Concepts

Single-cell foundation models represent a paradigm shift in computational biology. These are large-scale deep learning models pre-trained on vast and diverse single-cell datasets in a self-supervised manner, allowing them to learn fundamental biological principles of cells and genes [3]. Once pre-trained, scFMs can be adapted (fine-tuned) for a wide range of downstream tasks, including cell type annotation, batch integration, and—crucially for drug discovery—the prediction of cellular responses to chemical and genetic perturbations [3] [5].

  • Architectural Foundation: Most scFMs are built on the Transformer architecture, which uses attention mechanisms to model complex relationships between genes within a cell. In this analogy, a cell is treated as a "sentence," and its genes (along with their expression values) are the "words" or tokens [3] [5].

  • Key Components:

    • Tokenization: This process converts raw gene expression data into a structured sequence of tokens that the model can process. A common strategy involves ranking genes by their expression levels within each cell to create a deterministic sequence, as genes lack a natural order [3].
    • Pre-training: Models are trained on massive, publicly available repositories like CELLxGENE, which can contain tens of millions of cells. During this phase, models learn to predict masked genes from the context of other genes in the cell, thereby building a robust understanding of gene-gene interactions and cellular states [3] [1].

scfm_workflow Data Diverse Single-Cell Data (e.g., CELLxGENE) Tokenize Tokenization & Embedding Data->Tokenize Model Transformer-based scFM (e.g., scGPT, Geneformer) Tokenize->Model Pretrain Self-Supervised Pre-training (e.g., Masked Gene Prediction) Model->Pretrain Finetune Fine-tuning for Downstream Tasks Pretrain->Finetune Output CDR & IC50 Prediction Finetune->Output

Diagram 1: Simplified Workflow of a Single-Cell Foundation Model (scFM) for CDR Prediction. The model is first pre-trained on vast, diverse single-cell data and then fine-tuned for specific prediction tasks.

Deep Learning Models for CDR and IC50 Prediction

Deep learning (DL) models have demonstrated significant success in predicting drug-target interactions and drug sensitivity by leveraging large-scale public genomic and chemical databases [34]. These models excel at extracting meaningful patterns from high-dimensional and complex biological data.

Table 2: Deep Learning Models for Cancer Drug Response Prediction

Model Type Key Mechanism Application in CDR
Deep Neural Network (DNN) Feed-forward networks with multiple hidden layers for data abstraction [34]. Modeling non-linear relationships between cell line features and IC50 values [30].
Convolutional Neural Network (CNN) Applies filters to detect local patterns, ideal for structured data [34]. Processing 2D/3D molecular structures of drugs and protein sequences [33].
Graph Neural Network (GNN) Operates on graph structures to aggregate node information from neighbors [34]. Modeling drug molecules as graphs of atoms and bonds for feature extraction [30] [33].
Recurrent Neural Network (RNN) Designed for sequential data using internal memory states [34]. Analyzing time-series drug response data or sequential molecular representations [34].

Several state-of-the-art frameworks showcase the application of these architectures:

  • DRN-CDR: This method uses a Deep ResNet architecture to integrate multi-omics data (gene expression, mutations, methylation) with drug features extracted by a Uniform Graph Convolution Network. It has achieved a high Pearson correlation coefficient (rp = 0.7938) in predicting IC50 values, demonstrating the power of combining complex biological data with sophisticated deep learning structures [30].

  • SubCDR: This interpretable framework breaks down CDR prediction into modeling pairwise interactions between finer-level subcomponents. It extracts functional substructures from drug molecules and gene subsets from cell line transcriptomes, then uses a Graph Convolutional Network (GCN) to learn from the resulting interaction map. This approach not only predicts IC50 values but also provides traceable insights into which drug substructures and cellular gene signatures drive the response [33].

Integrating scFMs into the CDR Prediction Pipeline

The integration of scFMs offers a powerful, biology-aware approach to CDR prediction. Benchmarking studies have shown that the latent representations learned by scFMs during pre-training capture meaningful biological insights into the relational structure of genes and cells, which can be leveraged for downstream tasks like drug sensitivity prediction [1].

  • From Representation to Prediction: The process typically involves a two-stage pipeline:

    • Zero-Shot Embedding Extraction: A pre-trained scFM is used to generate latent vector representations (embeddings) for cells from a new dataset, without any further model training. These embeddings encapsulate the cell's state based on the model's broad prior biological knowledge [1].
    • Fine-Tuning or Supervised Task Head: The cell embeddings are then used as input features for a simpler prediction model (e.g., a classifier or regressor) that is trained to predict IC50 values or response categories. Alternatively, the entire scFM can be fine-tuned on a specific drug response dataset to adapt its knowledge to the task [1] [5].

  • Advantages and Current Limitations:

    • Strengths: scFMs are robust and versatile, capable of providing a unified representation of single-cell data that benefits from exposure to millions of cells during pre-training. They show promise for in-silico treatment predictions and identifying master regulators of drug response [5].
    • Limitations: Current benchmarks indicate that no single scFM consistently outperforms all others across every task. Furthermore, while powerful, they do not always surpass simpler, well-tuned machine learning models on every specific dataset, especially when data is limited. Their computational intensity and relative inaccessibility to non-computational biologists also present practical challenges [1] [5].

Experimental Protocols for CDR Prediction

Implementing a robust CDR prediction pipeline requires careful attention to data processing, model training, and validation. Below is a generalized protocol for a deep learning-based approach, integrable with scFM-derived features.

Data Preparation and Preprocessing

  • Data Sourcing: Acquire drug response data (IC50 values) from public databases such as the Genomics of Drug Sensitivity in Cancer (GDSC) or the Cancer Cell Line Encyclopedia (CCLE). These databases provide large-scale drug screening results across hundreds of cancer cell lines [33].
  • Cell Line Profiling: Obtain multi-omics data for the corresponding cell lines. This typically includes:
    • Gene Expression: RNA-seq or scRNA-seq data, often normalized and log-transformed.
    • Genetic Mutations: Binary mutation calls for known cancer genes.
    • Methylation Data: DNA methylation profiles from arrays or sequencing [30].
  • Drug Featurization: Represent drugs in a machine-readable format. Common methods include:
    • SMILES Strings: Simplified Molecular-Input Line-Entry System strings describing the drug's molecular structure [33].
    • Molecular Graphs: Represent atoms as nodes and bonds as edges for input to Graph Neural Networks [30].
  • Data Integration and Normalization: Merge the various data modalities into a unified dataset. Perform rigorous normalization and batch effect correction to ensure consistency across different data sources, especially when integrating data from multiple studies or sequencing platforms [3] [1].

Model Training and Evaluation

  • Feature Extraction: For each cell line, generate a feature vector combining omics data. When using an scFM, this involves passing the cell's transcriptome through the model to obtain a latent embedding vector [1]. For drugs, use a GCN or CNN to convert the molecular structure into a feature vector [30].
  • Model Architecture: Construct a model that integrates the cell line and drug features. A common approach is to concatenate the feature vectors and pass them through a series of fully connected (Dense) layers in a Deep Neural Network or ResNet to predict the continuous IC50 value (regression task) [30].
  • Training Regime:
    • Loss Function: Use Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) for the regression task.
    • Validation: Perform k-fold cross-validation to robustly assess model performance and avoid overfitting.
    • Evaluation Metrics: Report multiple metrics to evaluate predictive performance:
      • Pearson's Correlation Coefficient (rp): Measures the linear correlation between predicted and true IC50 values [30].
      • Root Mean Squared Error (RMSE): Measures the average magnitude of prediction errors [30].
      • Area Under the Curve (AUC): If performing a classification task (sensitive vs. resistant), AUC evaluates the classifier's performance [30].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Reagents and Resources for CDR and scFM Research

Item Function / Utility Example Sources / Formats
Public Drug Response Databases Provides ground-truth IC50 data for model training and validation. GDSC [33], CCLE [34]
Single-Cell Data Repositories Serves as the pre-training corpus for scFMs and a source for cell line characterization. CELLxGENE [3], Human Cell Atlas [3], GEO/SRA [3]
Cancer Cell Lines In vitro models representing different cancer types, used for screening and model training. Broad Institute's CCLE, various academic biobanks
Annotated Drug Compounds Chemical entities with known structures and bioactivity for featurization. PubChem [33] (for SMILES strings)
Pre-trained scFM Models Off-the-shelf foundation models for generating cell embeddings or fine-tuning. scGPT [3] [5], Geneformer [1], scBERT [3]
Graph Neural Network (GNN) Libraries Software tools for building models that process drug molecular structures. PyTorch Geometric, Deep Graph Library (DGL)
Single-Cell Analysis Toolkits Software for preprocessing, normalizing, and analyzing single-cell data before model input. Scanpy, Seurat

The convergence of single-cell technologies, foundation models, and advanced deep learning architectures is fundamentally advancing our ability to predict cancer drug response. While traditional models like DRN-CDR and SubCDR demonstrate impressive accuracy by integrating multi-omics and drug structural data, the emerging paradigm of single-cell foundation models offers a transformative path forward. By learning universal patterns from vast cellular datasets, scFMs provide a powerful, foundational understanding of cell biology that can be fine-tuned for specific predictive tasks, potentially uncovering novel insights into tumor heterogeneity and drug resistance mechanisms. As these models evolve to become more interpretable, accessible, and robust, they are poised to become indispensable tools in the quest for personalized oncology, accelerating the discovery of effective therapeutic strategies tailored to the unique cellular composition of a patient's cancer.

In-silico perturbation (ISP) represents a transformative approach in computational biology, enabling researchers to predict cellular responses to genetic and chemical interventions using virtual cell models. By leveraging single-cell foundation models (scFMs) pre-trained on millions of single-cell transcriptomes, ISP can simulate genetic knockouts, over-expression, and drug treatments without costly wet-lab experiments. This whitepaper examines the core architectures of scFMs powering these predictions, provides a quantitative benchmarking of ISP performance against traditional methods, and details experimental protocols for implementing closed-loop frameworks that iteratively improve prediction accuracy through incorporation of experimental data. The application of these methods demonstrates significant potential for accelerating therapeutic discovery, particularly for rare diseases where patient samples are scarce.

Single-cell foundation models (scFMs) are large-scale deep learning models pretrained on vast amounts of single-cell RNA sequencing (scRNA-seq) data, capable of being fine-tuned for diverse downstream biological tasks [3]. These models treat individual cells as "sentences" and genes or genomic features as "words" or "tokens," allowing them to learn the fundamental principles of cellular organization and function [3]. The emergence of scFMs marks a crucial step toward creating "virtual cells" that can simulate cellular responses to diverse perturbations, potentially revolutionizing drug discovery and disease modeling [4] [35].

Virtual cell models aim to predict how a cell's transcriptome will change in response to specific perturbations, such as genetic knockouts or drug treatments [35]. This capability is particularly valuable for studying rare diseases, where patient samples are scarce and experimental screening with primary cells is challenging [4]. While observational scRNA-seq data provides correlation information, perturbation data captures causal relationships between genes, directly reflecting underlying biological mechanisms [35]. The integration of both data types enables scFMs to make increasingly accurate predictions about cellular behavior.

Core Architectures and Methodological Approaches

Model Architectures for Single-Cell Foundation Models

Most scFMs utilize transformer architectures characterized by attention mechanisms that learn and weight relationships between input tokens [3]. In the context of single-cell data, this allows models to determine which genes in a cell are most informative of the cell's identity or state, and how they covary across cells. Two predominant architectural paradigms have emerged:

  • Encoder-based models (e.g., BERT-like architectures) employ bidirectional attention mechanisms where the model learns from the context of all genes in a cell simultaneously [3]. These models are particularly effective for classification tasks and generating cell embeddings.

  • Decoder-based models (e.g., GPT-inspired architectures) use unidirectional masked self-attention that iteratively predicts masked genes conditioned on known genes [3]. These excel at generation tasks and predicting perturbation responses.

More recently, the Large Perturbation Model (LPM) introduces a PRC-disentangled architecture that represents Perturbation, Readout, and Context as separate conditioning variables [36]. This approach integrates diverse perturbation experiments across different readouts (transcriptomics, viability), perturbations (CRISPR, chemical), and contexts without requiring single-cell resolution for all data types.

Tokenization Strategies for Single-Cell Data

Unlike natural language, gene expression data lacks inherent sequential ordering, presenting unique tokenization challenges. Common strategies include:

  • Expression-based ranking: Genes within each cell are ranked by expression levels, creating a deterministic sequence based on expression magnitude [3].
  • Binning approaches: Genes are partitioned into bins based on expression values, with rankings determining positional encoding [3].
  • Normalized counts: Some models report no clear advantage for complex ranking strategies and simply use normalized counts [3].

Gene tokens typically combine a gene identifier with its expression value, while special tokens may represent cell identity, metadata, or modality information for multi-omics applications [3].

Workflow for In-Silico Perturbation Prediction

The following diagram illustrates the standard workflow for implementing in-silico perturbation using scFMs:

ScRNA-seq Data ScRNA-seq Data Foundation Model\nPre-training Foundation Model Pre-training ScRNA-seq Data->Foundation Model\nPre-training Task-Specific\nFine-Tuning Task-Specific Fine-Tuning Foundation Model\nPre-training->Task-Specific\nFine-Tuning Perturbation\nEmbedding Perturbation Embedding Task-Specific\nFine-Tuning->Perturbation\nEmbedding In-Silico\nPerturbation In-Silico Perturbation Perturbation\nEmbedding->In-Silico\nPerturbation Experimental\nValidation Experimental Validation In-Silico\nPerturbation->Experimental\nValidation Closed-Loop\nRefinement Closed-Loop Refinement Experimental\nValidation->Closed-Loop\nRefinement Closed-Loop\nRefinement->Perturbation\nEmbedding

Quantitative Performance Benchmarking

Performance Comparison of Perturbation Prediction Methods

Table 1: Benchmarking of in-silico perturbation methods across multiple tasks and datasets

Model Architecture PPV NPV Sensitivity Specificity AUROC Key Applications
Open-loop ISP (Geneformer) Transformer 3% 98% 48% 60% 0.63 Baseline perturbation prediction
Closed-loop ISP (Geneformer) Transformer 9% 99% 76% 81% 0.86 Enhanced prediction with experimental data
Differential Expression Statistical 3% 78% 40% 50% N/R Traditional baseline
LPM PRC-disentangled N/R N/R N/R N/R SOTA Cross-modal perturbation integration
State (Arc Institute) Bidirectional Transformer N/R N/R N/R N/R 2x accuracy vs. baselines Drug response prediction

PPV: Positive Predictive Value; NPV: Negative Predictive Value; AUROC: Area Under Receiver Operating Characteristic curve; N/R: Not Reported in search results; SOTA: State-of-the-art

Impact of Training Data Scale on Model Performance

Table 2: Relationship between training data volume and model performance metrics

Training Data Scale Sensitivity Specificity Key Findings
10 perturbation examples 61% 66% Dramatic improvement over baseline
20 perturbation examples 76% 79% Performance approaches saturation
~100 million cells (State) N/R N/R 50% improvement distinguishing perturbation effects
170 million cells (State observational) N/R N/R Increased scale improves predictive accuracy

The quantitative evidence demonstrates that closed-loop approaches significantly enhance ISP performance. Incorporating just 10-20 experimental perturbation examples during fine-tuning improves sensitivity from 48% to 76% and specificity from 60% to 81% compared to open-loop approaches [4]. Similarly, the positive predictive value (PPV) increases three-fold from 3% to 9% while maintaining high negative predictive value (NPV) at 99% [4]. These improvements highlight the importance of integrating experimental feedback to refine virtual cell models.

Experimental Protocols and Methodologies

Closed-Loop Framework Implementation

The closed-loop framework introduces a critical innovation by incorporating experimental perturbation data during model fine-tuning, creating an iterative cycle of prediction and refinement [4]. The protocol involves these key steps:

  • Base Model Selection: Begin with a pre-trained scFM such as Geneformer-30M-12L, which has been pre-trained on diverse single-cell transcriptomes [4].

  • Task-Specific Fine-Tuning: Fine-tune the selected model using scRNA-seq data relevant to the biological context of interest (e.g., T-cell activation, hematopoietic stem cells). For classification tasks, the model should be trained to distinguish between relevant cellular states [4].

  • Initial ISP Screening: Perform in-silico perturbation across the gene set of interest, simulating both gene overexpression and knockout to model CRISPR activation and interference, respectively [4].

  • Experimental Validation: Conduct Perturb-seq (CRISPR screens with single-cell RNA sequencing) on a subset of high-priority targets identified through ISP [4].

  • Incorporation of Perturbation Examples: Fine-tune the model using the experimental Perturb-seq data alongside the original observational data. The perturbation data should be labeled with activation status but not with the specific gene perturbed to prevent overfitting [4].

  • Refined ISP Prediction: Perform a second round of ISP using the fine-tuned model on all genes except those experimentally perturbed [4].

  • Iterative Refinement: Repeat steps 4-6 as additional experimental data becomes available, continuously improving model accuracy.

Pathway Identification and Validation

For disease target identification, the following protocol applies:

  • Disease Modeling: Generate scRNA-seq data from engineered cells mimicking disease states (e.g., RUNX1 loss-of-function mutations for RUNX1-familial platelet disorder) [4].

  • Validation of Disease Models: Confirm concordance between engineered cells and patient-derived cells by comparing expression patterns of key pathway components [4].

  • Fine-Tuning for Disease Context: Fine-tune the scFM to classify cells between disease and control states [4].

  • ISP for Therapeutic Target Identification: Perform ISP to identify genes that, when perturbed, shift disease-state cells toward a control-like state [4].

  • Multi-Method Integration: Compare ISP results with differential expression analysis to identify high-confidence targets [4].

  • Experimental Validation: Test identified targets using specific small-molecule inhibitors or genetic interventions in relevant model systems [4].

Workflow for Therapeutic Target Discovery

The following diagram outlines the complete pathway from disease modeling to target identification:

Disease Modeling\n(Engineered Cells) Disease Modeling (Engineered Cells) Patient Data\nValidation Patient Data Validation Disease Modeling\n(Engineered Cells)->Patient Data\nValidation Model Fine-Tuning\n(Disease vs Control) Model Fine-Tuning (Disease vs Control) Patient Data\nValidation->Model Fine-Tuning\n(Disease vs Control) In-Silico Screening\nfor Therapeutic Targets In-Silico Screening for Therapeutic Targets Model Fine-Tuning\n(Disease vs Control)->In-Silico Screening\nfor Therapeutic Targets Multi-Method\nTarget Prioritization Multi-Method Target Prioritization In-Silico Screening\nfor Therapeutic Targets->Multi-Method\nTarget Prioritization Experimental\nValidation Experimental Validation Multi-Method\nTarget Prioritization->Experimental\nValidation Therapeutic Target\nIdentification Therapeutic Target Identification Experimental\nValidation->Therapeutic Target\nIdentification

Computational Tools and Models

Table 3: Essential computational resources for implementing in-silico perturbation

Resource Type Key Features Applications
Geneformer scFM 30M parameters, 12 layers, pre-trained on 30M single-cell transcriptomes In-silico perturbation, cellular state prediction [4]
scGPT scFM GPT architecture, multi-omic capability Perturbation response prediction, data integration [36] [1]
LPM Large Perturbation Model PRC-disentangled architecture, cross-modal integration Predicting outcomes across diverse perturbation types [36]
State (Arc Institute) Virtual Cell Model Trained on 100M+ perturbation cells, bidirectional transformer Drug response prediction, transcriptome shift modeling [35]
Cell_Eval Evaluation Framework Biologically relevant metrics beyond expression counts Virtual cell model assessment [35]
CCLMoff Deep Learning Tool RNA language model for CRISPR off-target prediction Guide RNA design, off-effect assessment [37]
Resource Type Key Features Applications
Perturb-seq Experimental Method CRISPR perturbations with scRNA-seq readout Generating training data for closed-loop frameworks [4]
scBaseCount Data Repository Largest open-source repository of single-cell data Model training, validation [35]
CZ CELLxGENE Data Platform >100 million unique cells standardized for analysis Access to diverse single-cell datasets [3]
Tahoe-100M Dataset 100 million perturbation cells Training large-scale virtual cell models [35]
LINCS Data Resource Genetic and pharmacological perturbation data Cross-modal perturbation studies [36]

Signaling Pathways Identified Through ISP

The application of closed-loop ISP to RUNX1-familial platelet disorder (RUNX1-FPD) identified several key signaling pathways as potential therapeutic targets [4]. The following diagram illustrates these pathways and their relationships:

RUNX1\nMutation RUNX1 Mutation mTOR Signaling mTOR Signaling RUNX1\nMutation->mTOR Signaling CD74-MIF Axis CD74-MIF Axis RUNX1\nMutation->CD74-MIF Axis Protein Kinase C Protein Kinase C RUNX1\nMutation->Protein Kinase C PI3K Pathway PI3K Pathway RUNX1\nMutation->PI3K Pathway Platelet Dysfunction Platelet Dysfunction mTOR Signaling->Platelet Dysfunction Myeloid Neoplasm\nRisk Myeloid Neoplasm Risk mTOR Signaling->Myeloid Neoplasm\nRisk CD74-MIF Axis->Platelet Dysfunction CD74-MIF Axis->Myeloid Neoplasm\nRisk Protein Kinase C->Platelet Dysfunction Protein Kinase C->Myeloid Neoplasm\nRisk PI3K Pathway->Platelet Dysfunction PI3K Pathway->Myeloid Neoplasm\nRisk

The pathways identified through ISP include mTOR signaling, CD74-MIF signaling axis, protein kinase C, and phosphoinositide 3-kinase (PI3K) pathway [4]. These pathways represent promising therapeutic targets for addressing both the platelet dysfunction and elevated myeloid neoplasm risk characteristic of RUNX1-FPD.

In-silico perturbation powered by single-cell foundation models represents a paradigm shift in how researchers approach biological discovery and therapeutic development. The closed-loop framework, which iteratively incorporates experimental data to refine computational predictions, demonstrates substantial improvements in prediction accuracy over open-loop approaches. As these models continue to evolve with larger training datasets and more sophisticated architectures, they promise to accelerate the identification of therapeutic targets, particularly for rare diseases where traditional screening approaches are impractical. The integration of virtual cell models into research workflows will enable more efficient exploration of the vast perturbation space, ultimately narrowing down hypotheses for experimental validation and bringing us closer to realizing the full potential of personalized medicine.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, moving beyond isolated gene expression analysis toward integrated, predictive understanding of cellular systems. While early scFMs primarily leveraged transcriptomic data from dissociated cells, they fundamentally lacked critical spatial and multi-omics dimensions. The integration of multi-omics data—including chromatin accessibility, DNA methylation, and proteomics—with spatial context is now pushing these models toward more accurate representations of biological reality. This evolution enables researchers to address previously intractable questions about cellular neighborhoods, regulatory mechanisms, and communication networks within tissues.

This technical guide examines the core methodologies, computational frameworks, and experimental protocols enabling this integration, with focused analysis of cutting-edge models like Nicheformer. We frame these advances within the broader context of single-cell foundation model development, highlighting how spatial context recovery and multi-modal data fusion are transforming drug discovery and functional genomics. By providing structured comparisons, standardized workflows, and practical toolkits, we aim to equip researchers with the necessary resources to implement these approaches in their investigation of tissue organization and disease mechanisms.

The Computational Challenge: From Isolated Cells to Contextualized Systems

The Limitations of Dissociated Single-Cell Data

Traditional single-cell RNA sequencing (scRNA-seq) requires tissue dissociation, which irrevocably severs information about the native spatial positioning of cells and their local microenvironments. This loss is particularly consequential when studying processes where location dictates function, such as immune responses in lymphoid tissues, neuronal circuitry in the brain, or stromal-epithelial interactions in tumors. While computational methods can infer some relationships post-hoc, they fundamentally operate with partial information [38] [39].

Simultaneously, the biological state of a cell emerges from the complex interplay between its transcriptome, epigenome, and proteome. Single-modality measurements provide only a fragmented view of this interconnected system. For example, chromatin accessibility (scATAC-seq) reveals potential regulatory regions, while transcriptomics shows expressed genes, but integrating both is necessary to establish causal regulatory relationships [40] [41].

The Multi-Omics Spatial Integration Problem

The central computational challenge lies in accurately aligning multiple molecular measurements within their native spatial context. This problem is characterized by several key difficulties:

  • Technological discordance: Different omics assays capture diverse molecular features with varying resolutions, sensitivities, and noise profiles.
  • Data sparsity: Spatial transcriptomics technologies often measure only hundreds to thousands of genes, compared to the tens of thousands detectable in scRNA-seq [38].
  • Complex spatial patterns: Cell distributions in tissues form intricate patterns that methods must faithfully reconstruct, from layered structures in the brain to irregular tumor boundaries.

Table 1: Computational Tools for Multi-Omics Spatial Integration

Tool Primary Function Omics Types Supported Key Algorithm Reference
NicheNet Ligand-target linking Transcriptomics, Signaling networks Prior knowledge integration [42] [43]
SIMO Spatial multi-omics integration RNA, ATAC, DNA methylation Sequential mapping + Optimal transport [40]
Nicheformer Foundation model for spatial context Transcriptomics (spatial & dissociated) Transformer architecture [38] [39]
Seurat Single-cell analysis integration RNA, ATAC, Proteomics Canonical Correlation Analysis [41]

Core Methodologies and Experimental Protocols

NicheNet: Linking Ligands to Target Genes via Prior Knowledge Networks

NicheNet operates on a fundamentally different principle than simple ligand-receptor co-expression methods. Its protocol establishes causal hypotheses about how communication between sender and receiver cells regulates gene expression through specific signaling pathways [42] [44].

Experimental Protocol for NicheNet Analysis:

  • Input Preparation:

    • Obtain cell type-annotated expression data (single-cell or bulk) from interacting cell populations
    • Define the "sender" cell population (ligand source) and "receiver" cell population (where gene expression changes occur)
    • In the receiver cells, define a gene set of interest (e.g., differentially expressed genes)

  • Ligand Activity Assessment:

    • Extract potential ligands from sender cells and their candidate target genes in receiver cells
    • Use the pre-built prior model (integrating ligand-receptor, signaling, and gene regulatory databases) to evaluate how well each ligand predicts expression changes in the gene set of interest
    • Calculate ligand activity scores to prioritize ligands most likely to be active

  • Target Gene Prediction and Validation:

    • For prioritized ligands, infer putative target genes with high regulatory potential
    • Construct signaling paths between ligands and target genes to generate testable hypotheses
    • Validate predictions experimentally or through additional computational checks [43] [44]

The NicheNet workflow can be implemented in R using the nichenetr package, with comprehensive vignettes available for both step-by-step and wrapper-based approaches [43].

SIMO: Probabilistic Alignment for Multi-Omics Spatial Mapping

SIMO (Spatial Integration of Multi-Omics) introduces a sequential mapping strategy that overcomes limitations of previous tools restricted to transcriptomics alone. Its methodology enables true integration of epigenetic data like scATAC-seq and DNA methylation within spatial contexts where they weren't originally profiled [40].

Detailed SIMO Workflow:

  • Initial Transcriptomics Mapping:

    • Input: Spatial transcriptomics (ST) data and single-cell RNA-seq (scRNA-seq) data
    • Construct spatial graphs from coordinates and modality maps from expression embeddings
    • Use fused Gromov-Wasserstein optimal transport to calculate mapping relationships between cells and spatial spots
    • Fine-tune cell coordinates based on transcriptome similarity with neighboring spots

  • Cross-Modality Integration:

    • Preprocess non-transcriptomic data (e.g., scATAC-seq) and calculate gene activity scores
    • Perform unsupervised clustering on both mapped scRNA-seq and new modality data
    • Compute Pearson Correlation Coefficients (PCCs) of gene activity scores between cell groups
    • Transfer labels across modalities using Unbalanced Optimal Transport (UOT)

  • Spatial Allocation and Refinement:

    • For cell groups with identical labels, construct modality-specific k-NN graphs
    • Calculate distance matrices and determine alignment probabilities via Gromov-Wasserstein transport
    • Precisely allocate scATAC-seq data to specific spatial locations
    • Adjust cell coordinates based on modality similarity with neighboring spots [40]

Table 2: SIMO Performance on Simulated Data with Varying Complexity

Spatial Pattern Complexity Mapping Accuracy (α=0.1) Root Mean Square Error JSD of Spot JSD of Type
Pattern 1 (Simple) >91% 0.045 0.021 0.052
Pattern 2 (Simple) >88% 0.062 0.035 0.087
Pattern 3 (Moderate) 83% 0.098 0.056 0.131
Pattern 4 (Complex) 73.8% 0.205 0.222 0.279
Pattern 5 (High) 62.8% 0.179 0.300 0.564
Pattern 6 (Very High) 55.8% 0.182 0.419 0.607

Nicheformer: A Foundation Model Approach to Spatial Context

Nicheformer represents a breakthrough as the first foundation model specifically designed to learn spatially aware cellular representations at scale. Its architecture and training methodology enable it to overcome the limitations of models trained solely on dissociated data [38].

Nicheformer Model Architecture and Training Protocol:

  • Data Curation and Corpus Construction:

    • Assemble SpatialCorpus-110M: over 110 million cells (57M dissociated + 53M spatially resolved)
    • Span 73 human and mouse tissues across multiple technologies (MERFISH, Xenium, CosMx, ISS)
    • Harmonize data through orthologous gene mapping (20,310 gene tokens)

  • Cell Representation and Tokenization:

    • Convert each cell's expression profile into a ranked sequence of gene tokens
    • Implement technology-specific nonzero mean vectors to account for platform biases
    • Introduce contextual tokens for species, modality, and technology

  • Model Design and Pretraining:

    • Architecture: 12 transformer encoder layers with 16 attention heads each
    • Context length: 1,500 tokens
    • Embedding dimension: 512 (49.3 million total parameters)
    • Training objective: Masked token prediction via self-supervision

  • Downstream Task Adaptation:

    • Spatial label prediction: Transfer human-annotated niche/region labels
    • Spatial composition prediction: Predict local cell density and type composition
    • Linear probing: Extract embeddings from frozen model + task-specific linear layer
    • Fine-tuning: End-to-end training on specific spatial tasks [38]

The critical innovation of Nicheformer is its demonstration that models trained only on dissociated data fundamentally cannot recover spatial complexity, even with three times more data. This highlights the indispensable value of spatially-resolved training data for understanding tissue organization [38] [39].

Visualization and Workflow Diagrams

NicheNet Ligand-Target Linking Methodology

G PriorKnowledge Prior Knowledge Integration IntegratedModel Integrated Prior Model PriorKnowledge->IntegratedModel LR Ligand-Receptor Interactions LR->IntegratedModel Signaling Signaling Pathways Signaling->IntegratedModel TF Transcription Factor Regulation TF->IntegratedModel InputData Input Data Analysis Ligand Activity Analysis InputData->Analysis Sender Sender Cell Expression Sender->Analysis Receiver Receiver Cell Expression Receiver->Analysis GeneSet Gene Set of Interest GeneSet->Analysis Output NicheNet Output Ligands Prioritized Ligands Output->Ligands Targets Predicted Target Genes Output->Targets Paths Ligand-Target Signaling Paths Output->Paths IntegratedModel->Analysis Analysis->Output

Diagram 1: NicheNet integrates prior knowledge to link ligands to target genes.

SIMO Multi-Omics Spatial Integration Workflow

G ST Spatial Transcriptomics (ST) Data Step1 Step 1: Transcriptomics Mapping - Construct spatial graphs - Fused Gromov-Wasserstein OT - Coordinate fine-tuning ST->Step1 scRNA scRNA-seq Data scRNA->Step1 scATAC scATAC-seq Data Step2 Step 2: Cross-Modality Integration - Calculate gene activity scores - Unsupervised clustering - Unbalanced Optimal Transport scATAC->Step2 Step1->Step2 Step3 Step 3: Spatial Allocation - Modality-specific k-NN graphs - Gromov-Wasserstein transport - Spatial coordinate refinement Step2->Step3 Output Multi-Omics Spatial Map - RNA + ATAC in spatial context - Regulatory relationships - Spatial gene regulation Step3->Output

Diagram 2: SIMO's sequential mapping enables multi-omics spatial integration.

Nicheformer Architecture and Training Approach

G Corpus SpatialCorpus-110M - 57M dissociated cells - 53M spatial cells - 73 tissues - Human & Mouse Tokenization Rank-Based Tokenization - Order genes by expression - Technology-specific means - Context tokens: species, modality, technology Corpus->Tokenization Model Transformer Architecture - 12 encoder layers - 16 attention heads - 1,500 token context - 512D embedding Tokenization->Model Pretraining Self-Supervised Pretraining - Masked token prediction - Cross-technology learning - Multi-species embedding Model->Pretraining Tasks Spatial Downstream Tasks - Spatial label prediction - Neighborhood composition - Cell density prediction Pretraining->Tasks Application Spatial Context Transfer - Enrich dissociated data - Predict cellular niches - Reconstruct microenvironments Tasks->Application

Diagram 3: Nicheformer's foundation model approach enables spatial context transfer.

Table 3: Key Research Reagent Solutions for Multi-Omics Spatial Studies

Resource Category Specific Examples Function/Purpose Implementation
Computational Tools NicheNet (nichenetr R package), SIMO, Nicheformer (Python) Core algorithms for multi-omics integration and spatial analysis GitHub repositories: saeyslab/nichenetr, theislab/nicheformer [43] [39]
Prior Knowledge Databases Ligand-receptor interactions, Signaling pathways (KEGG, Reactome), Transcription factor databases Foundation for knowledge-based methods like NicheNet Integrated in NicheNet prior model; customizable via model construction vignettes [42] [44]
Spatial Transcriptomics Technologies MERFISH, Xenium, CosMx, ISS, Slide-seq Generate spatial molecular profiling data Technology-specific sample preparation protocols and data processing pipelines [38] [41]
Single-Cell Multi-Omics Assays SNARE-seq, ISSAAC-seq, CITE-seq, scATAC-seq Provide complementary molecular profiles from same cells Experimental protocols for simultaneous RNA+ATAC or RNA+protein measurement [40] [41]
Benchmarking Datasets Mouse cerebral cortex, Human myocardial infarction, Liver atlas data Validate and compare method performance Curated biological datasets with known spatial patterns and cell types [40] [45]
Visualization Packages Circos plots, Spatial mapping visualizations Interpret and communicate results from analysis Included in nichenetr vignettes; custom plotting functions in SIMO and Nicheformer [43] [44]

Discussion and Future Perspectives

The integration of multi-omics and spatial data within foundation models represents a transformative advancement for single-cell biology and drug development. These approaches are rapidly evolving from descriptive tools to predictive systems capable of generating testable biological hypotheses. For pharmaceutical researchers, this enables more accurate modeling of disease mechanisms, drug responses, and cellular microenvironment changes in response to treatment.

The field continues to face significant challenges, including the need for standardized benchmarking, improved methods for temporal dynamics integration, and more scalable algorithms for increasingly large multi-omics datasets. Future developments will likely focus on incorporating additional modalities such as proteomics, metabolomics, and live-cell imaging data, moving toward comprehensive "virtual cell" models that can simulate cellular behavior across multiple biological layers [39] [41].

As these technologies mature, they promise to deepen our understanding of cellular organization in both health and disease, ultimately accelerating therapeutic development across oncology, immunology, neuroscience, and regenerative medicine. The convergence of single-cell foundation models with multi-omics spatial data marks not merely a technical achievement but a fundamental shift in how we conceptualize and investigate biological systems.

Navigating scFM Challenges: Data, Performance, and Practical Solutions

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, leveraging large-scale deep learning to decipher the complex "language" of cells. These models, pretrained on vast single-cell datasets, can be adapted for diverse downstream tasks including cell type annotation, batch integration, and perturbation prediction [3] [2]. However, their performance is fundamentally constrained by data quality challenges inherent to single-cell RNA sequencing (scRNA-seq) technologies. The trifecta of batch effects, variable data quality, and heterogeneous data sources constitutes significant hurdles that must be overcome to build robust scFMs [3] [46]. This technical guide examines these core data challenges within the context of scFM development, providing researchers with structured frameworks, quantitative comparisons, and practical protocols to enhance data reliability and model performance.

Understanding and Mitigating Batch Effects in scRNA-seq Data

Batch Effect Origins and Impact

Batch effects represent technical variations introduced when samples are processed separately under different conditions, including different sequencing platforms, reagents, timing, or laboratory conditions [47]. These systematic biases affect a large number of genes and can profoundly impact scRNA-seq data interpretation by obscuring true biological signals and leading to false discoveries [46] [47]. In the context of scFMs, which integrate diverse datasets spanning multiple experiments and conditions, effective batch effect management becomes crucial for learning biologically meaningful representations rather than technical artifacts.

Detection and Diagnostic Methods

Identifying batch effects requires a multi-faceted approach combining visualization techniques and quantitative metrics:

  • Principal Component Analysis (PCA): Scatter plots of top principal components reveal variations driven by batch effects rather than biological sources. Samples from different batches cluster separately in the presence of significant batch effects [47].
  • t-SNE/UMAP Visualization: Cells from different batches cluster separately rather than grouping by biological similarities when batch effects are present. Post-correction, expectations include cohesive clustering without such fragmentation [47].
  • Quantitative Metrics: Standardized metrics including normalized mutual information (NMI), adjusted rand index (ARI), percentage of corrected random pairs within batches (PCRbatch), graph-based integrated local similarity inference (GraphILSI), and k-nearest neighbor batch effect test (kBET) provide objective assessment of batch effect severity and correction efficacy [47].

Batch Correction Methods: Comparative Analysis

Multiple computational approaches have been developed for batch correction in scRNA-seq data. A recent comprehensive evaluation of eight widely used methods revealed significant differences in their performance and calibration [46].

Table 1: Batch Correction Methods for scRNA-seq Data

Method Input Data Correction Object Correction Approach Key Findings
Harmony Normalized count matrix Embedding Soft k-means with linear correction within clusters Consistently performs well without introducing artifacts [46]
ComBat Normalized count matrix Count matrix Empirical Bayes with linear correction Introduces measurable artifacts in data [46]
ComBat-seq Raw count matrix Count matrix Negative binomial regression Introduces measurable artifacts in data [46]
Seurat Normalized count matrix Embedding CCA and anchor-based alignment Introduces artifacts; alters count matrix [46]
LIGER Normalized count matrix Embedding Quantile alignment of factor loadings Performs poorly; often alters data considerably [46]
MNN Normalized count matrix Count matrix Mutual nearest neighbors with linear correction Performs poorly; often alters data considerably [46]
SCVI Raw count matrix Embedding Variational autoencoder modeling batch effects Performs poorly; often alters data considerably [46]
BBKNN k-NN graph k-NN graph UMAP on merged neighborhood graph Introduces artifacts that could be detected [46]

The evaluation demonstrated that many batch correction methods are poorly calibrated, creating measurable artifacts during the correction process. Harmony emerged as the only method that consistently performed well across all testing methodologies without introducing significant artifacts [46]. This has important implications for scFM development, where preserving biological authenticity while removing technical noise is paramount.

batch_effect_correction scRNA-seq Data scRNA-seq Data Batch Effect Detection Batch Effect Detection scRNA-seq Data->Batch Effect Detection PCA Visualization PCA Visualization Batch Effect Detection->PCA Visualization t-SNE/UMAP Plots t-SNE/UMAP Plots Batch Effect Detection->t-SNE/UMAP Plots Quantitative Metrics Quantitative Metrics Batch Effect Detection->Quantitative Metrics Select Correction Method Select Correction Method PCA Visualization->Select Correction Method t-SNE/UMAP Plots->Select Correction Method Quantitative Metrics->Select Correction Method Apply Harmony Apply Harmony Select Correction Method->Apply Harmony Evaluate Correction Evaluate Correction Apply Harmony->Evaluate Correction Integrated Data Integrated Data Evaluate Correction->Integrated Data

Figure 1: Batch Effect Correction Workflow. This framework outlines the systematic process for identifying and correcting batch effects in scRNA-seq data, culminating in integrated data suitable for scFM training.

Quality Control Frameworks for scRNA-seq Data

Essential QC Metrics and Thresholds

Quality control represents the first critical step in scRNA-seq data processing, serving to filter out low-quality cells and ensure reliable downstream analysis. The Cell Ranger pipeline from 10x Genomics provides foundational QC metrics through its web_summary.html output, which should be thoroughly reviewed for each sample [48].

Table 2: Essential Quality Control Metrics for scRNA-seq Data

QC Metric Interpretation Recommended Thresholds Potential Issues
Number of Cells Recovered Comparison to targeted cell recovery Close to targeted number Significant deviations indicate cell loading issues [48]
Confidently Mapped Reads in Cells Percentage of reads confidently mapped to transcriptome High percentage (>90%) Low values suggest poor library quality or contamination [48]
Median Genes per Cell Transcriptional complexity of cells Tissue and protocol-dependent (e.g., ~3,274 for PBMCs) Low values indicate poor cell quality or sequencing depth [48]
UMI Count Distribution Separation between cells and background Characteristic "cliff-and-knee" shape in barcode rank plot Poor separation indicates failed experiment [48]
Mitochondrial Read Percentage Indicator of cell stress or apoptosis Variable by cell type (<10% for PBMCs) [48] High values indicate low-quality or stressed cells [48]

Advanced QC Considerations

Beyond standard metrics, several advanced considerations enhance QC robustness:

  • Cell Multipleting: Unusually high UMI counts or feature numbers may indicate multiple cells captured in a single droplet [48].
  • Ambient RNA Contamination: Background RNA from lysed cells can contaminate genuine cell expression profiles. Tools like SoupX and CellBender can computationally address this issue, particularly important for detecting rare cell types [48].
  • Cell Type-Specific Variations: Some cell types (e.g., cardiomyocytes) naturally exhibit higher mitochondrial gene expression, requiring adjusted thresholds to avoid biased filtering [48].

Assembling Training Corpora for Single-Cell Foundation Models

Data Source Compilation

The performance of scFMs is fundamentally dependent on the quality, diversity, and scale of their training data. Assembling comprehensive training corpora requires strategic sourcing from public repositories:

  • CZ CELLxGENE: Provides unified access to annotated single-cell datasets with over 100 million unique cells standardized for analysis [3] [2].
  • Human Cell Atlas: Offers broad coverage of cell types and states across multiple organs and species [3] [2].
  • NCBI GEO and SRA: Host thousands of single-cell sequencing studies requiring integration and standardization [3] [2].
  • PanglaoDB and Human Ensemble Cell Atlas: Curated compendia collating data from multiple sources and studies [3] [2].

These aggregated resources enable scFM training across diverse biological conditions, ideally capturing the full spectrum of biological variation [3] [2].

Data Processing and Tokenization Strategies

A critical challenge in scFM development involves adapting non-sequential gene expression data for transformer architectures originally designed for sequential text data. Tokenization strategies convert raw gene expression data into model-processable units:

  • Gene Ordering: Since genes lack inherent sequence, models often impose order by ranking genes within each cell by expression levels, treating the ordered list as a "sentence" [3] [2]. Alternative approaches include partitioning genes into expression value bins or using normalized counts directly [3] [2].
  • Token Composition: Each token typically combines a gene identifier with its expression value through gene embeddings and value embeddings [13] [1]. Positional encoding schemes represent the relative order or rank of each gene [3] [2].
  • Special Tokens: Models may incorporate special tokens representing cell identity, metadata, omics modalities, or batch information to provide additional biological context [3] [2].

scFM_training Public Data Repositories Public Data Repositories Quality Filtering Quality Filtering Public Data Repositories->Quality Filtering Data Integration Data Integration Quality Filtering->Data Integration Tokenization Tokenization Data Integration->Tokenization Gene Ranking Gene Ranking Tokenization->Gene Ranking Value Embedding Value Embedding Tokenization->Value Embedding Positional Encoding Positional Encoding Tokenization->Positional Encoding Transformer Model Transformer Model Gene Ranking->Transformer Model Value Embedding->Transformer Model Positional Encoding->Transformer Model Pretraining Tasks Pretraining Tasks Transformer Model->Pretraining Tasks Single-Cell Foundation Model Single-Cell Foundation Model Pretraining Tasks->Single-Cell Foundation Model

Figure 2: scFM Training Corpus Assembly. This workflow illustrates the pipeline from raw data collection to model-ready tokenization, highlighting key processing stages for building effective single-cell foundation models.

Experimental Protocols and Benchmarking

Standardized Evaluation Frameworks

Rigorous benchmarking is essential for assessing scFM performance and guiding model selection. Recent research introduces novel evaluation perspectives including:

  • scGraph-OntoRWR: A novel metric measuring consistency between cell type relationships captured by scFMs and established biological knowledge in cell ontologies [13] [1].
  • Lowest Common Ancestor Distance (LCAD): Measures ontological proximity between misclassified cell types, assessing the biological severity of annotation errors [13] [1].
  • Roughness Index (ROGI): Quantifies cell-property landscape smoothness in latent spaces, with smoother landscapes facilitating better task-specific model performance [13] [1].

Benchmarking studies reveal that no single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on dataset size, task complexity, and computational resources [13] [1]. While scFMs demonstrate robust versatility across applications, simpler machine learning models may adapt more efficiently to specific datasets under resource constraints [13] [1].

Unified Frameworks for scFM Application

The heterogeneous architectures and coding standards across scFMs present significant application challenges. BioLLM addresses this through a unified framework providing standardized APIs for diverse scFMs, enabling streamlined model access and consistent benchmarking [6]. Evaluation through this framework reveals distinct model strengths, with scGPT demonstrating robust performance across tasks, while Geneformer and scFoundation excel in gene-level tasks [6].

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

Tool/Resource Function Application Context
Harmony Batch effect correction algorithm Recommended for integrating scRNA-seq datasets without introducing artifacts [46]
Cell Ranger Primary analysis pipeline for 10x Genomics data Processes raw sequencing data into gene-cell count matrices [48]
SoupX Ambient RNA removal Corrects for background RNA contamination from lysed cells [48]
BioLLM Unified scFM framework Standardizes APIs for diverse foundation models, enabling benchmarking [6]
CZ CELLxGENE Curated single-cell data repository Source of standardized datasets for model training (>100 million cells) [3] [2]
Loupe Browser Interactive visualization software Enables quality control assessment and data exploration for 10x Genomics data [48]

The development of robust single-cell foundation models hinges on effectively addressing fundamental data challenges including batch effects, quality control, and training corpus assembly. Strategic implementation of batch correction methods like Harmony, rigorous quality control protocols, and systematic compilation of diverse training data from curated public repositories form the essential foundation for building biologically meaningful scFMs. Standardized evaluation frameworks and unified application platforms further enhance model comparability and utility. As the field advances, continued refinement of these data handling practices will be crucial for realizing the full potential of scFMs in advancing cellular biology and therapeutic development.

The development of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling unprecedented insights into cellular heterogeneity and function. However, this transformative potential comes with extraordinary computational costs. These models, trained on tens to hundreds of millions of single-cell transcriptomes, require specialized hardware, innovative architectural designs, and sophisticated optimization strategies to manage their intensive resource requirements [3] [49]. The computational burden extends beyond initial pretraining to include fine-tuning for specific downstream tasks and inference on new datasets, creating a complex ecosystem of resource management challenges that researchers must navigate effectively.

At the core of these challenges lies the fundamental tension between model scale and biological accuracy. As researchers strive to build more comprehensive models that capture the full complexity of cellular behavior, they face diminishing returns in terms of computational efficiency. Understanding and managing this trade-off is essential for advancing the field of single-cell genomics while maintaining practical research constraints [49] [50].

Transformer Architecture Limitations

Most current scFMs are built on transformer architectures, which have revolutionized natural language processing and computational biology. However, these architectures present significant computational challenges when applied to single-cell data. The self-attention mechanism that forms the core of transformer models exhibits quadratic complexity (O(n²)) with respect to sequence length, making it computationally prohibitive for long gene sequences [50]. This limitation is particularly problematic in single-cell analysis, where each cell's transcriptome can contain thousands of genes, and datasets routinely comprise millions of cells.

The attention mechanism requires computing attention scores for all pairs of tokens (genes) in a sequence, leading to substantial memory and processing demands as model scale increases [50]. This computational intensity has driven researchers to explore alternative architectures that can maintain representational power while improving efficiency.

Emerging Efficient Architectures

Recent architectural innovations aim to address the fundamental limitations of transformers while preserving their ability to capture complex biological patterns. State space models (SSMs), particularly Mamba-based architectures, have emerged as promising alternatives. GeneMamba utilizes a BiMamba module to efficiently capture gene context information with linear computational complexity rather than quadratic, significantly reducing resource requirements while maintaining competitive performance [50].

The ERetNet architecture, employed in CellFM, represents another efficient transformer variant that maintains linear complexity while enabling scalable processing of over 100 million cells [49]. These architectural innovations demonstrate that careful model design can substantially alleviate computational burdens without sacrificing biological insight.

Table 1: Computational Characteristics of scFM Architectures

Architecture Computational Complexity Key Features Representative Models
Transformer O(n²) Self-attention mechanism, captures global dependencies scGPT, Geneformer, scBERT
State Space Models (SSMs) O(n) Selective state spaces, efficient long sequences GeneMamba
ERetNet O(n) Linear complexity, retention mechanisms CellFM

Quantitative Resource Requirements

Model Scale and Training Data

The computational burden of scFMs is directly reflected in their massive parameter counts and extensive training datasets. Current models span a wide range of scales, from specialized models with millions of parameters to massive foundations approaching billion-parameter counts.

CellFM exemplifies the upper extreme of this spectrum, with 800 million parameters trained on a curated dataset of approximately 100 million human cells [49]. This represents an eightfold increase in parameters over previous single-species models and demonstrates the rapid scaling occurring in the field. Similarly, scFoundation was trained on around 50 million human cells with approximately 100 million parameters, while Nicheformer incorporated both single-cell and spatial data from over 110 million cells [12] [49].

Table 2: Resource Requirements of Representative scFMs

Model Parameters Training Data Scale Computational Infrastructure
CellFM 800 million 100 million human cells 4× Huawei Altas800 servers (8× Ascend910 NPUs each)
scFoundation ~100 million ~50 million human cells Not specified
Geneformer 30M-12L / 106M-12L variants 30 million cells Not specified
Nicheformer Not specified 110 million cells (SpatialCorpus-110M) Not specified

Hardware and Infrastructure Demands

Training scFMs requires specialized hardware infrastructure that presents significant financial and logistical barriers. CellFM's training was conducted on four Huawei Altas800 servers, each equipped with eight Ascend910 neural processing units (NPUs), representing enterprise-grade computational resources [49]. While specific details for all models are not publicly available, this infrastructure highlights the substantial investment required for state-of-the-art scFM development.

The computational intensity also manifests in training duration and energy consumption, though these metrics are rarely reported in publications. Researchers must consider not only the initial pretraining costs but also the ongoing resources required for fine-tuning and inference across multiple applications and research projects.

Strategies for Managing Computational Burden

Efficient Training Methodologies

Several innovative training approaches have emerged to manage the computational burden of scFMs without compromising model performance:

Low-Rank Adaptation (LoRA) techniques, implemented in CellFM, significantly reduce the number of trainable parameters during fine-tuning by decomposing weight updates into low-rank matrices [49]. This approach enables efficient adaptation to new datasets and tasks while preserving the knowledge encoded during pretraining.

Combined optimization objectives that jointly optimize multiple self-supervised tasks provide another efficiency strategy. scPlantLLM employs simultaneous masked language modeling and cell type annotation tasks during pretraining, improving sample efficiency and reducing the total training required for effective performance [51].

Modified RetNet frameworks balance efficiency and performance through linear complexity architectures, as demonstrated in CellFM's implementation [49]. These architectural choices directly address the fundamental computational bottlenecks of traditional transformers.

Data Efficiency and Transfer Learning

The "closed-loop" framework represents a promising approach for improving data efficiency in scFMs. By iteratively incorporating experimental perturbation data during model fine-tuning, this method dramatically improves prediction accuracy with minimal additional examples. Remarkably, performance improvements approach saturation with just 20 perturbation examples, increasing positive predictive value three-fold compared to open-loop approaches [4].

This strategy demonstrates that strategic incorporation of high-quality experimental data can compensate for massive scale, potentially reducing overall computational requirements while improving biological relevance. Similarly, transfer learning approaches that leverage pretrained models for specific downstream tasks with minimal fine-tuning can distribute computational costs across multiple research groups and applications [4].

Experimental Protocols for Resource Management

Protocol 1: Efficient Fine-tuning with LoRA

Purpose: To adapt large scFMs to specific downstream tasks with minimal computational resources.

Methodology:

  • Begin with a pretrained foundation model (e.g., CellFM with 800M parameters)
  • Freeze all base model parameters to preserve pretrained knowledge
  • Introduce low-rank decomposition matrices (LoRA) to attention layers
    • Rank typically between 4-64 depending on task complexity
    • Apply to query, key, value, and output projections
  • Train only the LoRA parameters on target task data
  • Merge LoRA parameters with base model for inference

Computational Benefit: Reduces trainable parameters by >90% compared to full fine-tuning, enabling adaptation to new tasks on single GPU systems rather than multi-server infrastructure [49].

Protocol 2: Closed-loop Model Refinement

Purpose: To improve prediction accuracy with minimal experimental data incorporation.

Methodology:

  • Start with scFM fine-tuned for specific biological context (e.g., T-cell activation)
  • Generate initial in silico perturbation predictions (open-loop ISP)
  • Experimentally validate a small subset (10-20) of high-priority predictions
  • Incorporate validated examples into fine-tuning dataset
  • Refine model with combined original and validation data
  • Repeat steps 2-5 for iterative improvement

Computational Benefit: Achieves 3x improvement in positive predictive value with only 20 perturbation examples, maximizing biological insight per computational unit [4].

closed_loop Closed-loop Model Refinement Start Start FineTune Fine-tune scFM on initial data Start->FineTune Generate Generate ISP predictions FineTune->Generate Validate Experimental validation Generate->Validate Incorporate Incorporate validated examples Validate->Incorporate Refine Refine model Incorporate->Refine Evaluate Evaluate performance Refine->Evaluate Evaluate->Generate Continue refinement End End Evaluate->End Satisfactory performance

Protocol 3: Zero-shot Evaluation Framework

Purpose: To assess scFM performance without computational cost of fine-tuning.

Methodology:

  • Extract zero-shot embeddings from pretrained model
  • Apply to downstream tasks:
    • Cell-type clustering and annotation
    • Batch integration
    • Gene-gene relationship analysis
  • Compare against traditional methods (HVG selection, Seurat, Harmony, scVI)
  • Evaluate using biological metrics:
    • scGraph-OntoRWR (cell type relationship consistency)
    • Lowest Common Ancestor Distance (LCAD)
    • Roughness Index (ROGI) for latent space assessment

Computational Benefit: Eliminates fine-tuning costs entirely, enabling rapid model assessment and biological discovery [1] [52].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Computational Resources for scFM Research

Resource Category Specific Tools/Platforms Function/Purpose
AI Frameworks MindSpore (Huawei), PyTorch, TensorFlow Model development and training infrastructure
Hardware Platforms Ascend910 NPUs, NVIDIA GPUs, TPUs Specialized processors for deep learning workloads
Data Repositories CZ CELLxGENE, NCBI GEO, ENA, GSA, ImmPort Standardized access to single-cell datasets for training
Architecture Variants ERetNet, BiMamba, Transformer modifications Efficient model architectures reducing computational burden
Optimization Techniques LoRA, gradient checkpointing, mixed-precision training Methods for reducing memory usage and accelerating training

Future Directions in Computational Efficiency

The field of scFMs continues to evolve with several promising directions for addressing computational challenges. Alternative architectures like state space models show potential for maintaining performance while dramatically reducing resource requirements [50]. Model compression techniques, including knowledge distillation and quantization, may enable more accessible deployment of pretrained models. Federated learning approaches could distribute training across multiple institutions while preserving data privacy.

Additionally, task-specific model selection guided by benchmarking studies helps researchers choose appropriate tools without over-investing in computationally intensive solutions where simpler approaches suffice [1] [52]. As the field matures, developing standardized evaluation metrics specifically for computational efficiency alongside biological accuracy will be essential for sustainable progress in single-cell foundation models.

The integration of biological prior knowledge through knowledge-informed architectures represents another promising direction, potentially reducing the data requirements for effective model training by incorporating established biological principles directly into model structures [51]. These approaches, combined with continued hardware advancements and algorithmic optimizations, will determine how scalable and accessible scFMs become for the broader research community.

The rapid advancement of single-cell foundation models (scFMs) represents a paradigm shift in biological research, enabling unprecedented analysis of cellular heterogeneity and complex regulatory networks. These models, typically built on transformer architectures, learn from vast single-cell datasets through self-supervised pretraining, then adapt to various downstream tasks from cell type annotation to perturbation prediction [3]. However, their immense power comes with a significant challenge: the black box problem, where internal decision-making processes remain opaque and difficult to interpret [53] [54].

This opacity poses particular concerns for biomedical applications. In drug development and clinical research, understanding why a model makes a specific prediction is crucial for validating biological insights and ensuring reliable outcomes [1]. The fundamental dilemma lies in the trade-off between model performance and interpretability—as scFMs grow more complex and accurate, their inner workings become increasingly inscrutable, even to their creators [54]. This comprehensive guide examines current methodologies for interpreting scFM predictions and establishing their biological relevance, providing researchers with essential tools to navigate the black box landscape.

Technical Approaches to Model Interpretability

Architectural Foundations and Transparency Layers

Most scFMs adapt transformer architectures from natural language processing, treating individual cells as "sentences" and genes or genomic features as "words" or "tokens" [3]. This architectural choice immediately introduces interpretability challenges, as the attention mechanisms that enable these models to learn complex relationships between genes operate through millions of parameters interacting in nonlinear ways [54]. Two predominant architectural patterns have emerged:

  • Encoder-based models (e.g., scBERT) utilize bidirectional attention mechanisms where the model learns from all genes in a cell simultaneously, making them particularly suited for classification tasks and embedding generation [3].
  • Decoder-based models (e.g., scGPT) employ unidirectional masked self-attention that iteratively predicts masked genes conditioned on known genes, offering strengths in generative tasks [3].

To address inherent opacity, researchers implement transparency-enhancing layers directly within model architectures. These include hybrid systems that integrate explainable components with black box elements, allowing complex data handling while maintaining interpretable subcomponents for critical decision pathways [53]. Another approach involves feature extraction layers that distill interpretable features from deep learning architectures, creating more accessible representations of model behavior [53].

Explainable AI (XAI) Techniques for scFMs

Explainable AI (XAI) encompasses technological approaches specifically designed to illuminate black box models. The XAI market is projected to reach $9.77 billion in 2025, reflecting growing recognition of its critical importance in biomedical applications [55]. For scFMs, several XAI techniques have shown particular promise:

  • Visual explanation tools like Gradient-weighted Class Activation Mapping (GRADCAM) highlight influential regions in input data, visually identifying which genes or cellular features most significantly impact model predictions [53]. These tools bridge the gap between abstract neural network operations and human comprehension by providing intuitive visual representations of model focus areas.

  • Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) provide post-hoc interpretations by approximating complex models with simpler, interpretable ones for individual predictions [55]. Though not scFM-specific, these methods can be adapted to analyze how specific gene expression patterns influence cellular classification or other predictions.

  • Attention mechanism analysis leverages the inherent structure of transformer-based scFMs by examining attention patterns to identify which genes the model considers most important when making predictions [3]. This approach allows researchers to trace relationships between input features and model outputs, potentially revealing biologically meaningful gene-gene interactions.

Table 1: Technological Approaches for Enhancing scFM Transparency

Approach Mechanism Best Use Cases Limitations
Hybrid Systems Combines explainable models with black box components High-stakes applications requiring validated decision pathways Increased architectural complexity
Visual Explanation Tools (GRADCAM) Highlights influential input regions Identifying key genes in classification tasks May oversimplify complex interactions
Attention Mechanism Analysis Examines internal attention patterns Understanding gene relationships in transformer models Patterns may not always reflect biological importance
Interpretable Feature Extraction Distills interpretable features from deep layers Creating accessible representations of model behavior Potential information loss during distillation

Evaluating Biological Relevance in scFM Predictions

Novel Metrics for Biological Ground-Truthing

Establishing biological relevance requires moving beyond traditional performance metrics to specialized evaluations that measure how well model outputs align with established biological knowledge. Recent research has introduced ontology-informed metrics that provide biologically grounded assessment of scFM outputs [1]:

  • scGraph-OntoRWR measures the consistency between cell type relationships captured by scFMs and prior biological knowledge encoded in cell ontologies. This metric uses random walks with restarts on ontology graphs to quantify how well the relational structure of cell types in the embedding space matches established hierarchical relationships [1].

  • Lowest Common Ancestor Distance (LCAD) assesses the ontological proximity between misclassified cell types, providing a nuanced evaluation of annotation errors. Rather than treating all misclassifications equally, LCAD recognizes that confusing closely related cell types (e.g., T-cell subtypes) is less severe than confusing distantly related ones (e.g., neurons and immune cells) [1].

These metrics address a critical gap in scFM evaluation by incorporating existing biological knowledge directly into the assessment process, ensuring that model interpretations align with established understanding of cellular systems.

Benchmarking Frameworks and Performance Assessment

Comprehensive benchmarking studies reveal that no single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on dataset size, task complexity, and computational resources [1]. Effective evaluation encompasses multiple cell-level and gene-level tasks:

  • Gene-level tasks assess how well gene embeddings capture functional relationships by evaluating their ability to predict Gene Ontology (GO) terms and tissue specificity [1].
  • Cell-level tasks examine performance on practical applications like batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction across diverse datasets and conditions [1].

Benchmarking results indicate that while scFMs are robust and versatile tools for diverse applications, simpler machine learning models can sometimes outperform them on specific tasks, particularly under resource constraints or with limited data [1] [56]. This finding underscores the importance of matching model complexity to specific research needs rather than automatically opting for the most sophisticated approach.

Table 2: Benchmark Performance Across scFM Tasks

Task Category Key Metrics Top Performing Approaches Performance Notes
Batch Integration iLISI, cLISI, KBET scGPT, Harmony scFMs show strong robustness to technical effects [1]
Cell Type Annotation Accuracy, LCAD, scGraph-OntoRWR scBERT, scGPT Ontology metrics reveal biological plausibility [1]
Gene Function Prediction AUROC, AUPRC Geneformer, FRoGS Embeddings capture biological relationships [1]
Drug Sensitivity Prediction RMSE, R² scVI, traditional ML Simpler models sometimes outperform [1]

Experimental Protocols for Interpretation

Protocol 1: Attention Analysis for Gene Interaction Mapping

This protocol extracts and visualizes attention patterns from transformer-based scFMs to identify potentially meaningful gene-gene interactions.

Materials and Reagents:

  • Pretrained scFM (e.g., scGPT, Geneformer)
  • Target single-cell dataset (formatted to model specifications)
  • Computational environment with appropriate deep learning frameworks
  • Visualization tools (Matplotlib, Seaborn, or similar)

Methodology:

  • Model Preparation: Load pretrained weights into the scFM architecture, ensuring all parameters are correctly initialized.
  • Inference with Attention Capture: Pass your single-cell data through the model while configuring the forward pass to return attention weights in addition to standard outputs.
  • Attention Aggregation: Extract attention matrices from all layers and heads, then aggregate using appropriate statistical measures (mean, max, or percentile-based thresholds).
  • Pattern Identification: Identify consistently high-attention gene pairs across multiple cells, layers, and attention heads to distinguish robust patterns from noise.
  • Biological Validation: Compare high-attention gene pairs with known interaction databases (e.g., protein-protein interaction networks, co-expression databases) to assess biological plausibility.

Interpretation Guidelines:

  • Focus on attention patterns that are consistent across multiple cells of the same type
  • Compare attention weights between known functionally related genes versus random gene pairs
  • Consider both intra-gene attention (self-attention) and inter-gene attention when interpreting results

Protocol 2: Perturbation Response Analysis Using scFMs

This protocol leverages scFMs to predict cellular responses to genetic or chemical perturbations and interprets the biological relevance of these predictions.

Materials and Reagents:

  • Fine-tuned perturbation prediction model (e.g., scGPT with perturbation head)
  • Reference single-cell dataset representing baseline cellular state
  • Ground truth perturbation data for validation (if available)
  • Functional annotation databases (GO, KEGG, Reactome)

Methodology:

  • Baseline Establishment: Generate embeddings for unperturbed cells to establish a reference state in the latent space.
  • In Silico Perturbation: Manipulate input representations to simulate specific genetic perturbations (e.g., gene knockouts, overexpression).
  • Predicted Response Capture: Pass perturbed inputs through the model and capture predicted expression changes or embedding shifts.
  • Differential Analysis: Compare perturbed and unperturbed states to identify significantly altered genes or pathways.
  • Biological Context Integration: Map differentially expressed genes to known biological pathways and processes using enrichment analysis.

Interpretation Guidelines:

  • Prioritize predictions that align with known biology while noting novel insights
  • Assess magnitude of predicted effects in context of biological plausibility
  • Validate top predictions experimentally when possible, starting with highest-confidence novel insights

Visualization and Interpretation Tools

Effective visualization is crucial for interpreting scFM predictions and communicating biological insights. The following diagrams illustrate key workflows and relationships in scFM interpretation.

Workflow for scFM Interpretation and Validation

G cluster_0 Model Internals (Black Box) DataInput Single-Cell RNA-seq Data Preprocessing Data Preprocessing & Tokenization DataInput->Preprocessing scFM scFM (Transformer) Preprocessing->scFM Attention Attention Mechanism scFM->Attention Embeddings Gene & Cell Embeddings Attention->Embeddings Interpretation Interpretation Methods Embeddings->Interpretation Validation Biological Validation Interpretation->Validation Insights Biological Insights Validation->Insights

scFM Interpretation Workflow

Attention-Based Gene Interaction Network

G cluster_legend Attention Strength InputCell Input Cell Expression Profile Gene1 Gene A (Transcription Factor) Gene2 Gene B (Marker Gene) Gene1->Gene2 0.89 Gene3 Gene C (Surface Protein) Gene1->Gene3 0.72 Gene4 Gene D (Metabolic Enzyme) Gene2->Gene4 0.45 Gene5 Gene E (Unknown Function) Gene3->Gene5 0.33 Output Cell Type Prediction AttentionWeights Attention Weights (Transformer Layers) AttentionWeights->Gene1 AttentionWeights->Gene2 AttentionWeights->Gene3 AttentionWeights->Gene4 AttentionWeights->Gene5 HighAttention High Attention Connection MediumAttention Medium Attention Connection

Gene Interaction via Attention

Essential Research Reagents and Computational Tools

Table 3: Research Reagent Solutions for scFM Interpretation

Tool/Category Specific Examples Function/Purpose Access Considerations
scFM Platforms scGPT, Geneformer, scBERT, scFoundation Pretrained foundation models for single-cell analysis Varying accessibility; some require specialized computational resources [1] [3]
Interpretability Toolkits IBM AI Explainability 360, Google Model Interpretability Algorithm suites for explaining model predictions Open-source options available; integration effort required [55]
Benchmarking Frameworks Custom benchmarking pipelines (e.g., scGraph-OntoRWR) Standardized evaluation of model performance and biological relevance Often requires implementation from published methods [1]
Visualization Tools GRADCAM implementations, attention visualization libraries Creating interpretable visualizations of model focus areas Custom development often needed for single-cell specific applications [53]
Biological Knowledge Bases Cell Ontology, Gene Ontology, protein interaction databases Ground-truthing model predictions against established knowledge Publicly available but require curation and processing [1]

Interpreting black box AI predictions in single-cell foundation models remains challenging yet increasingly feasible through specialized methodologies. The most effective approaches combine technical explainability techniques with biologically grounded validation, ensuring model predictions align with established knowledge while potentially revealing novel insights. As the field progresses, the integration of ontology-informed metrics and standardized benchmarking will be crucial for advancing from correlation to causation in scFM interpretations.

For drug development professionals and researchers, practical implementation requires careful model selection matched to specific tasks rather than defaulting to the most complex available option [1]. As Jordan Krull notes, future progress depends on developing more accessible interfaces and validating model predictions against biological reality: "Please contact your local biologist to make sure that the results are not just an overly intuitive response!" [5]. Through continued refinement of interpretation methodologies and collaboration between computational and biological experts, scFMs promise to unlock deeper insights into cellular function and disease mechanisms while maintaining scientific rigor and interpretability.

Single-cell foundation models (scFMs) are large-scale deep learning models, typically based on transformer architectures, pretrained on vast datasets comprising tens of millions of single-cell transcriptomes [3]. They learn the fundamental "language" of biology by understanding how genes are expressed across diverse cell types, states, and conditions [3]. The promise of scFMs lies in their versatility; a single pretrained model can be adapted to a wide array of downstream tasks, from basic cell type annotation to predicting cellular responses to novel drugs [57] [49].

The two primary paradigms for applying these models are zero-shot inference and fine-tuning.

  • Zero-shot refers to using the model's pretrained embeddings for analysis without any further task-specific training. This is crucial for exploratory discovery where labels are unknown [58] [59].
  • Fine-tuning involves taking a pretrained scFM and continuing its training on a smaller, specific dataset to specialize it for a particular task, such as predicting a specific drug's effect [60] [57].

Choosing the correct strategy is paramount for research efficiency and biological accuracy, as the wrong choice can lead to unreliable insights and wasted computational resources [58] [13].

Performance Comparison: Zero-Shot vs. Fine-Tuning

The performance of zero-shot application versus fine-tuning varies significantly across different biological tasks. The tables below summarize key findings from recent rigorous evaluations.

Table 1: Performance of Zero-Shot scFMs on Core Tasks Compared to Baselines

Task Representative Models Evaluated Performance vs. Baseline Methods Key Findings
Cell Type Clustering Geneformer, scGPT [58] [59] Underperforms vs. HVG, scVI, Harmony Simple feature selection (HVG) often yields better cell-type separation than zero-shot scFM embeddings [58].
Batch Integration Geneformer, scGPT [58] [13] Inconsistent; can be outperformed by scVI and Harmony Models sometimes fail to correct for technical batch effects while preserving biological signal in a zero-shot setting [58].
Gene Expression Prediction scGPT [59] Limited ability Without fine-tuning, models may predict median expression values regardless of input, showing limited understanding of gene relationships [59].

Table 2: Performance of Fine-Tuned scFMs on Specialized Tasks

Task Fine-Tuning Approach Reported Outcome Key to Success
Molecular Perturbation Prediction Drug-conditional adapter (training <1% of parameters) [60] [57] State-of-the-art; enables zero-shot generalization to unseen cell lines Efficient parameter use preserves pretrained knowledge while adapting to new modality (drug structures) [60].
Cell Type Annotation Task-specific fine-tuning on labeled data [13] Robust and versatile performance Fine-tuning allows the model to adapt to specific labeling schemas and novel cell types in the target dataset [13].

A Decision Framework for Choosing Your Strategy

The choice between zero-shot and fine-tuning is not one-size-fits-all. The following diagram and decision matrix guide the selection based on your task, data, and goals.

D Start Start: Choose a Pre-trained scFM Q1 Is your task exploratory with no labeled data available? Start->Q1 Q2 Is your target task highly similar to the scFM's pretraining objective? Q1->Q2 No ZeroShot Use Zero-Shot Embeddings Q1->ZeroShot Yes Q3 Do you have a sufficient volume of high-quality labeled data for your specific task? Q2->Q3 No Q2->ZeroShot Yes Q4 Are computational resources and technical expertise for fine-tuning available? Q3->Q4 Yes Caution Use with Caution. Validate against simple baselines (e.g., HVG). Q3->Caution No FineTune Proceed with Fine-Tuning Q4->FineTune Yes Q4->Caution No

Diagram: A strategic workflow for choosing between zero-shot and fine-tuning approaches for single-cell foundation models.

Table 3: Decision Matrix for Strategy Selection

Scenario Recommended Strategy Rationale
Initial Data Exploration Zero-Shot Ideal for generating initial hypotheses, visualizing data structure, and identifying broad patterns without committing to a specific labeled task [58].
Novel Cell Type Discovery Zero-Shot In discovery settings where labels are unknown, fine-tuning is impossible, making zero-shot the only viable option [58] [59].
Task Similar to Pretraining Zero-Shot (Consider) If the task (e.g., cell annotation on a well-represented tissue) is core to the model's pretraining, zero-shot may be sufficient, but performance must be validated [13].
Specialized Prediction Task Fine-Tuning Tasks like predicting response to a specific novel drug require the model to integrate new information, which is achieved through fine-tuning [60] [57].
Limited Labeled Data Efficient Fine-Tuning Parameter-efficient methods (e.g., adapters, LoRA) allow effective adaptation by training a small subset of parameters, preventing overfitting [60] [49].
Maximizing Performance on a Known Task Full or Efficient Fine-Tuning For critical applications where state-of-the-art performance is needed and sufficient data exists, fine-tuning is the preferred path [13] [57].

Experimental Protocols for Implementation

Zero-Shot Evaluation Protocol

Objective: To assess the quality of cell embeddings generated by a scFM without any fine-tuning, typically for clustering or batch integration [58] [13].

Methodology:

  • Input: A target dataset (e.g., from a new experiment) is tokenized and processed according to the scFM's requirements (e.g., using 1,200 highly variable genes for scGPT) [58] [13].
  • Embedding Extraction: The target data is passed through the pretrained model with all weights frozen. The cell-level embedding vector is extracted from the model's output.
  • Downstream Analysis: The embeddings are used directly for:
    • Clustering: Apply algorithms like Leiden or K-means and evaluate cluster purity against known cell type labels using metrics like Average BIO Score (AvgBIO) or Adjusted Rand Index (ARI) [58] [13].
    • Batch Integration: Visualize embeddings using UMAP and quantitatively assess batch mixing using metrics like PCR (Principal Component Regression) score, which measures the proportion of variance explained by batch [58].
  • Validation: It is critical to compare the results against simple baseline methods, such as embeddings from Highly Variable Genes (HVG) or established tools like scVI and Harmony [58] [59].

Efficient Fine-Tuning Protocol with Adapters

Objective: To adapt a large scFM to a new task (e.g., drug response prediction) with limited data and computational budget [60] [49].

Methodology:

  • Model Architecture: The core pretrained scFM (e.g., scGPT) is kept frozen to preserve its foundational knowledge. Small, trainable "adapter" layers are inserted between the transformer blocks.
  • Conditional Adaptation: For multi-modal tasks (e.g., conditioning on drug structure), a drug-conditional adapter can be used. The parameters of the adapter layers are dynamically generated based on an encoding of the drug's molecular structure [60].
  • Training: Only the parameters of the adapter layers (often <1% of the total model parameters) are updated during training. This uses a labeled dataset of {baseline gene expression, drug, perturbed gene expression} triplets [60].
  • Evaluation: The fine-tuned model is evaluated on its ability to predict gene expression responses to novel drugs or, more challengingly, in unseen cell lines (zero-shot cross-cell-line generalization) [60].

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Computational Tools and Resources for Working with scFMs

Tool / Resource Type Function & Relevance
scGPT [3] [57] Foundation Model A generative pretrained transformer model for single-cell multi-omics analysis. A common choice for benchmarking and application.
Geneformer [3] [58] Foundation Model A transformer model trained on gene rank-based sequences. Often used for gene-centric analyses.
CellFM [49] Foundation Model A large-scale model (800M parameters) trained on 100M human cells, demonstrating high performance on downstream tasks.
CZ CELLxGENE Discover [3] [21] Data Platform Provides unified access to millions of curated single-cell datasets, essential for pretraining and benchmarking.
Adapter / LoRA Modules [60] [49] Fine-tuning Technique A parameter-efficient fine-tuning method that inserts small, trainable layers into a frozen base model, reducing compute and data needs.
BioLLM [21] Benchmarking Framework A standardized framework for integrating and benchmarking over 15 different foundation models, aiding in model selection.
Harmony & scVI [58] [13] Baseline Methods Established, non-foundation model tools for integration and analysis. Critical for performance comparison to validate scFM utility.

The choice between zero-shot and fine-tuning is a strategic decision dictated by the biological question and data constraints. Zero-shot learning offers a powerful, low-effort approach for exploratory analysis but must be applied with caution, as its performance can be inconsistent and may be surpassed by simpler methods [58] [59]. Fine-tuning, particularly parameter-efficient versions, is the key to unlocking the full potential of scFMs for specialized, high-stakes prediction tasks, enabling them to generalize to novel conditions like unseen drugs or cell lines [60] [57].

Future developments in scFMs will likely focus on improving their inherent zero-shot capabilities through better architectures and pretraining objectives [3] [13]. Furthermore, standardized benchmarking and the development of more sophisticated efficient fine-tuning techniques will be crucial for bridging the gap between computational innovation and robust, biologically meaningful applications in drug development and personalized medicine [13] [21].

Single-cell foundation models (scFMs) represent a revolutionary advance in computational biology, leveraging large-scale deep learning trained on vast single-cell datasets to interpret complex biological systems [3]. These models, typically built on transformer architectures, learn fundamental principles of cellular biology by processing millions of single-cell transcriptomes, treating individual cells as sentences and genes or genomic features as words or tokens [3] [5]. The potential applications span from identifying novel cell types to predicting drug responses and understanding complex disease mechanisms [1] [5].

However, a persistent challenge limits their real-world utility: a high rate of false positives where sequences or predictions generated by the model fail experimental validation [61]. This critical limitation stems from the sparse sampling of functional sequence space in training data and the models' inherent difficulty in accurately delineating the boundaries of biological functionality [61]. This technical guide explores the framework, methodologies, and experimental protocols for implementing experimental feedback loops to enhance the predictive accuracy of scFMs, ultimately bridging the gap between computational prediction and biological reality.

The Conceptual Framework: Experimental Feedback Loops

The core principle of experimental feedback involves creating a closed-loop system where model predictions are systematically tested and the results are reintegrated to refine the original model. This process transforms a static, one-time model into a dynamic, learning system that continuously improves with each iteration of experimental validation [61].

The False Positive Challenge in Biological Generative Models

Generative probabilistic models for biological sequences, including those based on Direct-Coupling Analysis (DCA), restricted Boltzmann machines, variational autoencoders, and protein language models, have demonstrated notable success in designing artificial biomolecules [61]. Despite this promise, these models often produce a high rate of false positives—sequences predicted as functional that fail experimental tests [61]. This limitation arises fundamentally because these models are trained in an unsupervised manner on multiple-sequence alignments (MSAs) of presumably functional sequences, which provide only a scarce sampling of the viable sequence space [61].

The Reintegration Solution

The proposed solution involves mathematically reintegrating experimental test results directly into the generative model's training procedure [61]. This approach maintains the same model architecture but recalibrates parameters using both the original natural data and newly acquired experimental results [61]. The mathematical implementation involves an updated objective function that incorporates experimental feedback:

Table 1: Components of the Experimental Feedback Objective Function

Component Mathematical Representation Biological Interpretation
Natural Data Likelihood `ℒ(θ∣𝒟_N) = (1/ 𝒟_N ) ∑ ln P(a¯∣θ)` Preserves knowledge learned from original evolutionary data
Reintegration Term `(λ/ 𝒟_T ) ∑ w(b¯) ⋅ ln P(b¯∣θ)` Incorporates experimental validation results
Adjustment Weight w(b¯) < 0 for false positives; w(b¯) > 0 for true positives Decreases probability of non-functional sequences while increasing probability of functional ones

This mathematical framework allows the model to learn from both its successes and failures in experimental validation, effectively refining its understanding of the boundaries of functional sequence space [61].

Methodological Implementation: From Concept to Practice

Workflow for Experimental Feedback Integration

Implementing an effective experimental feedback loop requires a systematic workflow that connects computational and experimental domains. The following diagram illustrates this continuous improvement cycle:

G cluster_computational Computational Domain cluster_experimental Experimental Domain cluster_integration Integration Start Start MSA Natural Sequence Data (MSA) Start->MSA InitialModel Train Initial Foundation Model MSA->InitialModel Generate Generate Candidate Sequences InitialModel->Generate WetLab Experimental Validation Generate->WetLab Classify Classify as True/False Positive WetLab->Classify Reintegrate Reintegrate Experimental Feedback into Model Classify->Reintegrate UpdatedModel Updated Foundation Model (Higher Accuracy) Reintegrate->UpdatedModel Iterative Improvement UpdatedModel->Generate

Diagram 1: Experimental Feedback Workflow

Quantitative Impact of Experimental Feedback

The efficacy of this approach has been demonstrated across both RNA and protein systems. In one notable application focusing on the self-splicing ribozyme from the group I intron RNA family, the integration of experimental feedback dramatically improved model performance [61].

Table 2: Performance Improvement Through Experimental Feedback

Model Stage Functional Sequence Yield Experimental Context
Initial Model 6.7% At 45 mutations from wild-type
After Feedback Integration 63.7% At 45 mutations from wild-type
Improvement Factor ~9.5x Same model architecture

This nearly tenfold improvement in functional sequence generation demonstrates the profound impact that even a single round of experimental feedback can have on model accuracy [61]. The underlying mathematical structure of the model remains unchanged, but the reintegration of experimental data significantly improves parameter learning, highlighting that limitations often stem from insufficient information in original training data rather than model expressivity [61].

Experimental Protocols for Validation

Designing Effective Validation Experiments

Rigorous experimental validation is the cornerstone of effective feedback integration. For scRNA-seq studies that underlie many scFM applications, several critical design considerations must be addressed [62] [63]:

  • Sample Size and Replication: Ensure sufficient cellular coverage and include both technical and biological replicates to account for variability [63]
  • Sample Type Selection: Choose between whole cells or nuclei based on tissue type and research question - nuclei are preferable for difficult-to-dissociate tissues [63]
  • Fresh vs. Fixed Samples: Consider fixation to preserve biological states and minimize stress response artifacts that can skew data [63]
  • Quality Control: Maintain sample viability between 70-90%, minimize debris and aggregation, and ensure accurate cell counting [63]

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Experimental Validation

Reagent / Material Function Application Context
HEPES or Hanks' Buffered Salt Calcium/magnesium-free media to prevent aggregation Cell suspension preparation [63]
Ficoll or Optiprep Density gradient media for separating viable cells from debris Sample purification [63]
Commercial Enzyme Cocktails Tissue-specific dissociation protocols Single-cell suspension generation [63]
gentleMACS Dissociator Automated tissue dissociation Reproducible solid tissue processing [63]
Single-cell RNA-seq kits Library preparation with combinatorial barcoding Fixed sample processing [63]

Computational Methods for Feedback Integration

Model Architectures and Adaptation Strategies

Single-cell foundation models typically employ transformer architectures, with two predominant variants [3]:

  • BERT-like encoder architectures with bidirectional attention mechanisms that learn from all genes in a cell simultaneously [3]
  • GPT-inspired decoder architectures with unidirectional masked self-attention that iteratively predict masked genes conditioned on known genes [3]

The experimental feedback process can be visualized as an enhancement to the standard model training paradigm:

G Standard Standard Training StandardData Training Data: Natural Sequences Only Standard->StandardData Enhanced Feedback-Enhanced Training StandardArch Transformer Architecture (Encoder/Decoder) StandardOutput Foundation Model (High False Positive Rate) StandardArch->StandardOutput StandardData->StandardArch EnhancedData Enhanced Data: Natural Sequences + Experimental Labels Enhanced->EnhancedData EnhancedArch Same Transformer Architecture EnhancedOutput Refined Foundation Model (Improved Specificity) EnhancedArch->EnhancedOutput EnhancedData->EnhancedArch

Diagram 2: Model Training Comparison

Implementation Considerations for scFMs

When implementing feedback loops for single-cell foundation models, several unique considerations emerge:

  • Tokenization Strategies: Genes are typically tokenized using rank-based expression level sorting or binning strategies to create sequential input from non-sequential omics data [3]
  • Multi-modal Integration: Advanced scFMs can incorporate additional modalities like scATAC-seq, spatial sequencing, and proteomics data through specialized tokens [3]
  • Batch Effect Mitigation: Experimental feedback must account for technical variability across different experiments and platforms [3] [1]

The integration of experimental feedback represents a paradigm shift in the development and application of single-cell foundation models. By closing the loop between computational prediction and experimental validation, researchers can transform these powerful but imperfect tools into increasingly accurate models of biological reality. The approaches outlined in this technical guide provide a framework for implementing such feedback systems, with demonstrated efficacy in dramatically improving model performance.

As the field advances, key challenges remain in standardizing feedback protocols, developing user-friendly interfaces for broader adoption, and establishing benchmarks for evaluating improvement across diverse biological contexts [5]. Nevertheless, the systematic reintegration of experimental results stands as a crucial methodology for unlocking the full potential of foundation models in biological research and therapeutic development.

Benchmarking scFMs: A Realistic Look at Performance and Model Selection

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling researchers to extract profound insights from single-cell RNA sequencing (scRNA-seq) data at unprecedented scales. These models, including scGPT, Geneformer, and scFoundation, leverage transformer architectures pretrained on millions of cells to learn fundamental biological principles that can be adapted to various downstream tasks [3]. However, the rapid proliferation of scFMs has created a significant challenge: heterogeneous architectures, coding standards, and evaluation protocols have made meaningful comparison of model performance nearly impossible [6] [24]. This lack of standardization threatens to undermine scientific progress in the field by hindering reproducibility and obscuring the true strengths and limitations of different approaches.

BioLLM (biological large language model) addresses this critical bottleneck by providing a unified framework for integrating and applying scFMs to single-cell RNA sequencing analysis [6]. By establishing standardized APIs and comprehensive documentation, BioLLM eliminates architectural and coding inconsistencies to enable streamlined model access and consistent benchmarking [24]. This standardized approach is particularly valuable for drug development professionals and researchers who require reliable, comparable performance metrics when selecting models for critical tasks such as drug sensitivity prediction, cancer cell identification, and cell atlas construction [1]. The framework supports both zero-shot evaluation and fine-tuning protocols, allowing for comprehensive assessment of scFMs across diverse application scenarios [6].

The BioLLM Framework: Architecture and Standardization Mechanisms

Core Architectural Components

BioLLM functions as an abstraction layer that harmonizes access to diverse scFMs through a standardized interface. Its architecture consists of several integrated components designed to ensure consistency across evaluations. The Unified Model Interface provides consistent APIs for model loading, inference, and fine-tuning, regardless of the underlying scFM architecture [6]. This eliminates the need for researchers to write model-specific code for each scFM they wish to evaluate. The Standardized Data Preprocessing module ensures that input data undergoes consistent normalization, gene filtering, and tokenization before being fed to any model, removing preprocessing variability as a confounding factor in performance comparisons [24].

The framework incorporates a Configurable Evaluation Pipeline that implements standardized metrics and protocols for benchmarking scFMs across diverse biological tasks [6]. This includes both standard metrics and novel biology-aware evaluation approaches such as scGraph-OntoRWR, which measures the consistency of cell type relationships captured by scFMs with prior biological knowledge [1]. Finally, the Result Aggregation and Visualization component generates comparable outputs and performance summaries across all evaluated models, enabling researchers to make informed decisions based on comprehensive, standardized evidence [24].

Standardization Mechanisms for Fair Comparison

BioLLM implements several technical mechanisms to ensure fair and reproducible model comparisons. Consistent Tokenization Strategies address the fundamental challenge that gene expression data lacks natural sequential ordering, unlike text in natural language processing. BioLLM standardizes how genes are represented as tokens, typically combining gene identifiers with their expression values, and applies uniform positional encoding schemes to represent gene relationships [3]. Uniform Embedding Extraction protocols ensure that cell and gene embeddings are extracted from comparable model components across different scFMs, whether from dedicated cell embedding layers or aggregated gene embeddings [1].

The framework establishes Standardized Benchmarking Tasks that encompass both gene-level and cell-level biological problems. These include gene function prediction, tissue specificity analysis, batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [1] [24]. For each task category, BioLLM implements Comprehensive Evaluation Metrics that span unsupervised, supervised, and knowledge-based approaches. This multi-faceted evaluation strategy captures different dimensions of model performance, from traditional clustering metrics to novel biology-aware measures that assess whether learned representations reflect established biological knowledge [1].

Experimental Benchmarking: Methodology and Protocols

Standardized Evaluation Framework

To ensure comprehensive assessment of scFM capabilities, BioLLM implements a rigorous evaluation protocol encompassing diverse biological tasks and conditions. The benchmarking framework evaluates models across two gene-level tasks and four cell-level tasks under realistic conditions that reflect actual research scenarios [1]. This multi-task approach prevents over-specialization to a single problem type and provides a more holistic view of model capabilities. Evaluations are conducted across multiple datasets with varying biological conditions, including inter-patient, inter-platform, and inter-tissue variations that present distinct challenges for data integration [1].

The framework employs 12 evaluation metrics spanning unsupervised, supervised, and knowledge-based approaches to capture different performance dimensions [1]. Critical to the biological relevance of evaluations is the incorporation of cell ontology-informed metrics that introduce biologically grounded perspectives often overlooked by traditional computational metrics. These include the innovative scGraph-OntoRWR, which measures the consistency of cell type relationships captured by scFMs with established biological knowledge, and the Lowest Common Ancestor Distance (LCAD) metric, which assesses the severity of errors in cell type annotation based on ontological proximity between misclassified cell types [1].

Benchmarking Datasets and Experimental Design

BioLLM's evaluation protocol utilizes carefully selected datasets that represent diverse biological challenges and scenarios. The table below summarizes the key datasets and their characteristics used in comprehensive scFM benchmarking:

Table 1: Benchmarking Datasets for scFM Evaluation

Dataset Biological Context Size Range Batch Effects Evaluation Tasks
Asian Immune Diversity Atlas (AIDA) v2 [1] Immune cell diversity Large-scale Cross-population Cell type annotation, Batch integration
Multi-tissue Atlases [1] Multiple tissue types Moderate to large Inter-tissue, inter-protocol Cross-tissue generalization
Cancer Datasets [1] Seven cancer types Variable Intra-tumor heterogeneity Cancer cell identification, Drug sensitivity
Perturbation Datasets [3] Cellular response to perturbations Moderate Technical variability Perturbation effect prediction

The experimental design incorporates both zero-shot evaluation and fine-tuning protocols to assess different aspects of model capability [6] [24]. Zero-shot evaluation tests the inherent biological knowledge captured during pretraining, while fine-tuning assessment measures how efficiently models adapt to specific tasks with limited additional training. This dual approach provides insights into both the breadth of pretrained knowledge and the adaptability of different scFM architectures.

Key Benchmarking Results and Comparative Analysis

Performance Across Model Architectures

Comprehensive benchmarking through BioLLM has revealed distinct performance patterns across leading scFM architectures, with no single model dominating all tasks. The following table synthesizes key findings from large-scale evaluations:

Table 2: Comparative Performance of Major scFMs Across Task Categories

Model Architecture Type Gene-Level Tasks Cell-Type Annotation Batch Integration Clinical Prediction Computational Efficiency
scGPT [6] [24] GPT-based decoder Strong Excellent Robust Strong across tasks Moderate
Geneformer [6] [24] BERT-like encoder Excellent Good Variable Strong in specific contexts High
scFoundation [6] [24] Custom transformer Strong Good Good Good drug sensitivity prediction Moderate
scBERT [6] [24] BERT-like encoder Limited Moderate Limited Limited High
UCE [1] Ensemble approach Moderate Good Good Variable Low
LangCell [1] Language-cell fusion Good Good Good Emerging capabilities Variable

The results demonstrate that scGPT achieves robust performance across all task categories, particularly excelling in cell type annotation and batch integration [6] [24]. Geneformer and scFoundation show particular strengths in gene-level tasks, benefiting from their effective pretraining strategies on large-scale genomic data [6] [24]. In contrast, scBERT lags behind other models, likely due to its smaller model size and limited training data [6] [24]. Importantly, benchmarking reveals that no single scFM consistently outperforms others across all tasks, emphasizing the need for task-specific model selection [1].

scFMs vs. Traditional Methods

A critical insight from standardized benchmarking is the nuanced performance relationship between scFMs and traditional machine learning methods. While scFMs demonstrate superior performance on complex tasks requiring general biological knowledge, simpler machine learning models with carefully selected features (such as Highly Variable Genes) can be more efficient and effective for specific datasets, particularly under resource constraints [1]. This suggests a complementary relationship where scFMs excel at knowledge-intensive transfer learning scenarios, while traditional methods remain competitive for well-defined problems with sufficient training data.

The evaluation also reveals that pretrained scFM embeddings capture meaningful biological insights into the relational structure of genes and cells, which provides benefits for downstream tasks [1]. Quantitative analysis shows that performance improvements arise from a smoother cell-property landscape in the pretrained latent space, which reduces the difficulty of training task-specific models [1]. This landscape smoothness, measurable through the Roughness Index (ROGI), correlates with downstream task performance and can serve as a proxy for model selection in a dataset-dependent manner [1].

Experimental Protocols for scFM Evaluation

Standardized Workflow for Model Assessment

The BioLLM framework implements a systematic workflow for comprehensive scFM evaluation. The diagram below illustrates the key stages in this standardized assessment protocol:

G Start Start Evaluation SP Standardized Preprocessing Start->SP ME Model & Embedding Extraction SP->ME GLT Gene-Level Tasks ME->GLT CLT Cell-Level Tasks ME->CLT BM Benchmarking Metrics GLT->BM CLT->BM MS Model Selection Guidance BM->MS

Diagram 1: Standardized scFM Evaluation Workflow

Detailed Methodological Protocols

Gene-Level Task Evaluation

Gene-level tasks assess how well scFMs capture functional relationships between genes. The standard protocol involves:

  • Gene Embedding Extraction: Extract gene embeddings from the input layers of scFMs. These embeddings are typically accessed from the gene token representations after model forward passes [1].

  • Functional Similarity Prediction: Evaluate whether functionally similar genes cluster together in the embedding space by benchmarking against known biological relationships, including Gene Ontology (GO) term annotations and tissue specificity patterns [1].

  • Comparison Baseline: Compare scFM gene embeddings against established methods like Functional Representation of Gene Signatures (FRoGS), which learns gene embeddings via random walks on hypergraphs with genes as nodes and GO terms as hyperedges [1].

Cell-Level Task Evaluation

Cell-level tasks evaluate how well scFMs represent cellular states and relationships:

  • Cell Embedding Extraction: Obtain cell embeddings from scFMs, typically from dedicated cell embedding layers or by aggregating gene embeddings [1].

  • Batch Integration Assessment: Evaluate how well models remove technical batch effects while preserving biological variation using five high-quality datasets with manual annotations and multiple sources of batch effects [1].

  • Cell Type Annotation: Assess annotation accuracy across diverse cell types, with particular attention to challenging scenarios like novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [1].

  • Biological Consistency Evaluation: Apply cell ontology-informed metrics (scGraph-OntoRWR and LCAD) to measure whether learned cell representations reflect established biological knowledge [1].

Key Research Reagent Solutions

The experimental workflows for scFM development and evaluation require both computational and biological resources. The table below details essential components:

Table 3: Essential Research Reagents and Resources for scFM Research

Resource Category Specific Examples Function/Role in Research
Reference Datasets [1] [3] CZ CELLxGENE, Human Cell Atlas, PanglaoDB Provide standardized, annotated single-cell data for model pretraining and benchmarking
Benchmarking Platforms [6] [24] BioLLM framework, PertEval-scFM Enable standardized evaluation across diverse tasks and metrics
Biological Knowledge Bases [1] Gene Ontology (GO), Cell Ontology Provide ground truth for biological relevance evaluation
Computational Infrastructure [3] GPU clusters, High-memory nodes Support training and inference of large-scale transformer models
Specialized Evaluation Metrics [1] scGraph-OntoRWR, LCAD, ROGI Quantify biological relevance and embedding quality beyond standard metrics

Implementation Considerations

Successful implementation of scFM evaluation requires attention to several practical considerations. Computational resource management is crucial, as training and evaluating large transformer models demands significant GPU memory and processing power [3]. The quality and diversity of pretraining data significantly impact model performance, with careful dataset selection, filtering of cells and genes, and balancing of dataset compositions being essential for robust pretraining [3]. Researchers must also implement rigorous validation protocols to mitigate the risk of data leakage and overfitting, including the use of completely independent datasets like the Asian Immune Diversity Atlas (AIDA) v2 for validation [1].

The standardization enabled by frameworks like BioLLM represents a critical advancement for the field of single-cell computational biology. By providing unified interfaces and consistent evaluation protocols, these frameworks allow researchers to make meaningful comparisons across diverse scFM architectures, accelerating methodological progress and enabling more reliable biological discoveries [6] [24]. The comprehensive benchmarking facilitated by BioLLM has yielded crucial insights, particularly that no single scFM dominates all tasks, emphasizing the need for tailored model selection based on specific research questions, dataset characteristics, and computational constraints [1].

Future developments in scFM standardization will likely focus on several key areas. Multimodal integration will expand beyond transcriptomics to incorporate epigenomic, proteomic, and spatial data, requiring new standardization approaches for cross-modal evaluation [3] [64]. Interpretability frameworks will evolve to provide deeper insights into the biological mechanisms captured by scFMs, moving beyond performance metrics to understand what models are actually learning about biological systems [3]. Clinical validation standards will emerge to assess how well scFM predictions translate to real-world biomedical applications, particularly for drug development and personalized medicine [1]. As these advancements unfold, standardization frameworks like BioLLM will play an increasingly vital role in ensuring that progress in single-cell foundation models translates to meaningful biological insights and clinical applications.

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, applying the "pre-train then fine-tune" paradigm, successful in natural language processing, to single-cell transcriptomics data. Trained on millions of single cells, these models aim to learn universal representations of cellular states that can be efficiently adapted to various downstream tasks [3]. The promise of scFMs lies in their potential to capture complex gene-gene interactions and biological principles from massive datasets, thereby providing a powerful, unified framework for analyzing cellular heterogeneity and function [13]. This whitepaper provides a comprehensive technical comparison of four prominent scFMs—scGPT, Geneformer, scFoundation, and scBERT—evaluating their performance across critical applications including perturbation prediction, cell type annotation, and gene function analysis. As these models are increasingly considered for drug development and clinical research, understanding their relative strengths and limitations is paramount for researchers and scientists.

Model Architectures and Pretraining Strategies

The performance of scFMs is fundamentally shaped by their architectural choices and pretraining methodologies. While all four models are based on the Transformer architecture, they employ distinct strategies for tokenization, pretraining objectives, and data handling, leading to different computational profiles and potential applications.

  • scGPT utilizes a decoder-style GPT architecture and employs a value binning strategy, discretizing gene expression values into bins. It is pretrained on over 33 million human cells using a masked gene modeling objective, where the model learns to predict randomly masked expression values based on their context. A key feature of scGPT is its use of an attention mask mechanism, allowing it to handle various downstream tasks including multi-batch integration and perturbation prediction [13] [65].

  • Geneformer uses an encoder-only architecture, similar to BERT. Its distinctive rank-based tokenization approach represents a cell by a sequence of its top 2,048 genes, ordered by expression level. Pretrained on 30 million single-cell transcriptomes, its objective is to predict the identities of randomly masked genes within this ranked list, focusing on learning the relative importance and context of genes rather than their precise expression values [13] [66].

  • scFoundation is a large-scale model with 100 million parameters, based on an asymmetric encoder-decoder architecture. It uses a value projection method, which directly predicts raw gene expression values, thereby preserving the full resolution of the data. Pretrained on approximately 50 million human cells using a read-depth-aware masked autoencoder (MAE) objective, it is designed to model the complete set of human protein-coding genes [67] [13] [68].

  • scBERT also follows an encoder-only BERT-like architecture. It uses value categorization, binning gene expression values into discrete "buckets" and framing expression prediction as a classification problem. Pretrained on millions of human cells, its primary design focus is on accurate cell type annotation, leveraging its deep language model structure to overcome challenges like batch effects and incomplete marker gene lists [13] [69].

Table 1: Architectural and Pretraining Overview of scFMs

Model Architecture Type Primary Tokenization Strategy Pretraining Dataset Size Model Parameters Key Pretraining Objective
scGPT Decoder (GPT-like) Value Binning ~33 million cells 50 million Masked Gene Modeling (MSE loss)
Geneformer Encoder (BERT-like) Gene Ranking ~30 million cells 40 million Masked Gene Modeling (CE loss)
scFoundation Encoder-Decoder Value Projection ~50 million cells 100 million Masked Autoencoding (MSE loss)
scBERT Encoder (BERT-like) Value Categorization Millions of cells Not Specified Masked Gene Modeling (CE loss)

Performance Benchmarking Across Key Biological Tasks

Rigorous benchmarking is essential to determine the practical utility of these models. Independent studies have evaluated them on tasks such as predicting the effects of genetic perturbations, annotating cell types, and inferring gene function, often comparing them against simpler baseline models.

Perturbation Effect Prediction

Predicting transcriptional changes after genetic perturbation is a crucial task for understanding gene function and identifying therapeutic targets. A landmark study benchmarked several scFMs against deliberately simple baselines, such as an "additive model" that sums the effects of single-gene perturbations, and a "no change" model that predicts the control condition [70]. The results were striking: none of the deep learning models consistently outperformed these simple linear baselines in predicting outcomes of double-gene perturbations [70] [71]. Furthermore, when tasked with predicting genetic interactions (e.g., synergistic or buffering effects), no model performed better than the "no change" baseline [70]. A key finding was that even simpler machine learning models, like Random Forest regressors using Gene Ontology features, outperformed finetuned scGPT and scFoundation by a large margin [71]. This suggests that the current pretraining on vast single-cell atlases may not optimally convey the specific biological knowledge required for accurate perturbation prediction.

Table 2: Benchmarking Performance on Perturbation Prediction Tasks

Model / Baseline Double Perturbation Prediction (L2 Distance, lower is better) Genetic Interaction Prediction (vs. No-Change Baseline) Unseen Single Perturbation Prediction (Pearson Delta)
scGPT Underperformed additive baseline [70] Not better [70] 0.641 (Adamson), 0.327 (Replogle K562) [71]
Geneformer Underperformed additive baseline [70] Not better [70] Not Available
scFoundation Underperformed additive baseline [70] Not better [70] 0.552 (Adamson), 0.269 (Replogle K562) [71]
scBERT Underperformed additive baseline [70] Not better [70] Not Available
Additive Baseline Best Performance [70] Not Applicable Not Applicable
Random Forest (GO features) Not Applicable Not Applicable 0.739 (Adamson), 0.480 (Replogle K562) [71]

Cell Type Annotation and Batch Integration

Cell type annotation is a fundamental task in single-cell analysis. Here, scFMs have demonstrated more compelling utility. For instance, scBERT is explicitly designed for this task and has shown strong performance, effectively leveraging its pretrained knowledge of gene-gene interactions to classify cell types even in the presence of batch effects [69]. In a practical case study, Geneformer was fine-tuned to predict donor age from natural killer (NK) cell transcriptomes, achieving an F1-score of 0.63, significantly outperforming a classical Random Forest model (F1-score of 0.47) [66]. This indicates that the contextual gene relationships learned during pretraining can be successfully transferred to subtle phenotypic prediction tasks. Furthermore, a comprehensive benchmark evaluating zero-shot cell embeddings found that scFMs can create latent spaces where cell types separate effectively, and that their performance can be correlated with a "smoother" property landscape, facilitating downstream analysis [13].

Gene Function Prediction and Embedding Quality

The ability of scFMs to generate meaningful gene representations is critical. Newer, larger-scale models like CellFM (an 800M parameter model) report state-of-the-art performance on gene function prediction, suggesting a trend that increasing model and data scale can improve performance on such tasks [67]. Analyses of gene embeddings have shown that representations from models like scGPT and scFoundation contain biologically relevant information. However, when these embeddings were used in simple Random Forest models for perturbation prediction, they still did not consistently outperform models using Gene Ontology features or text-derived gene embeddings from LLMs [71]. This indicates that while the embeddings capture some biological structure, there is room for improvement in their specificity and utility for complex predictive tasks.

Experimental Protocols for Benchmarking

To ensure reproducibility and foster rigorous evaluation of scFMs, this section outlines standard experimental protocols derived from the cited benchmarking studies.

Protocol for Perturbation Prediction Benchmark

This protocol is based on the methodologies described in [70] and [71].

  • Data Preparation:

    • Datasets: Use publicly available Perturb-seq datasets such as Norman et al. (for double-gene perturbations) [70], Adamson et al., and Replogle et al. (for single-gene perturbations) [71].
    • Preprocessing: Follow the original studies' preprocessing steps. This typically includes log-transforming RNA-seq expression values and filtering for highly expressed or highly variable genes (e.g., the top 1,000 genes).
    • Splitting: For double perturbation prediction, split the double-gene perturbations into training and test sets (e.g., 62 for training, 62 for testing across multiple random splits). For unseen single perturbation prediction, use a hold-out set of perturbations.

  • Baseline Models:

    • Implement the "additive baseline": For a double perturbation (A,B), predict the sum of the log-fold changes of the individual perturbations A and B.
    • Implement the "no change" baseline: Predict the control (unperturbed) expression profile.
    • Implement a "mean" baseline: Predict the average expression profile across all training perturbations.
    • Implement a Random Forest model using Gene Ontology (GO) vectors as features for the perturbed genes.

  • Model Fine-Tuning:

    • Fine-tune the scFMs (scGPT, Geneformer, scFoundation) on the training set of perturbations according to their authors' recommended guidelines. This often involves providing the model with the control expression profile and a representation of the perturbation (e.g., a perturbation token).

  • Evaluation:

    • Calculate the L2 distance between the predicted and observed gene expression profiles for the top 1,000 highly expressed genes [70].
    • Compute the Pearson correlation in the differential expression space (Pearson Delta) between the predicted and ground truth pseudo-bulk profiles [71].
    • For genetic interactions, define a null model and calculate the true-positive rate (TPR) and false discovery proportion (FDP) across a range of thresholds [70].

Protocol for Cell Type Annotation

This protocol is based on the application of scBERT [69] and Geneformer [66].

  • Data Preprocessing:

    • Gene Symbol Standardization: Revise gene symbols according to a standard database (e.g., NCBI Gene). Remove unmatched and duplicated genes.
    • Normalization: Normalize the gene expression counts per cell using sc.pp.normalize_total followed by a log1p transformation (sc.pp.log1p) in Scanpy.
    • Tokenization: For scBERT, bin the normalized expression values. For Geneformer, create a ranked list of the top 2,048 genes by expression per cell.

  • Model Fine-Tuning:

    • scBERT: Load the pretrained model and fine-tune it on the annotated dataset using a cross-entropy loss function. The model predicts a cell type label for each input cell.
    • Geneformer: Apply transfer learning by unfreezing only the final few layers of the pretrained model and training it as a classifier on the target task (e.g., donor age group or cell type).

  • Evaluation:

    • Split the dataset into training and test sets.
    • Evaluate performance on the held-out test set using metrics such as Accuracy, F1-score, and Weighted F1-score.
    • For a more biologically informed assessment, use metrics like Lowest Common Ancestor Distance (LCAD) to measure the ontological proximity of misclassifications [13].

G cluster_preprocessing Data Preprocessing cluster_model Model Application cluster_evaluation Evaluation A Raw Single-Cell Data (h5ad/mtx) B Gene Symbol Standardization A->B C Normalization (normalize_total, log1p) B->C D Model-Specific Tokenization C->D E Training/Test Split D->E F Load Pretrained Foundation Model E->F Training Data H Generate Predictions on Test Set E->H Test Data G Fine-tune on Training Set F->G G->H I Calculate Performance Metrics (F1, Accuracy) H->I J Biological Validity Analysis (LCAD, etc.) I->J

Figure 1: scFM Benchmarking Workflow

The Scientist's Toolkit: Essential Research Reagents

The following table details key computational tools and datasets required for working with single-cell foundation models.

Table 3: Essential Research Reagents for scFM Research

Reagent / Resource Type Description / Function Example Source / Access
Perturb-seq Datasets Dataset Provides ground-truth gene expression data following genetic perturbations; essential for benchmarking prediction accuracy. Norman et al. (2019), Adamson et al. (2016), Replogle et al. (2022) [70] [71]
Annotated Cell Atlases Dataset Large-scale collections of single-cell data with cell type labels; used for pretraining and evaluating cell annotation tasks. CELLxGENE, Human Cell Atlas, PanglaoDB [3] [67]
Gene Ontology (GO) Annotations Knowledge Base Provides structured, hierarchical knowledge of gene functions; used for feature engineering in baseline models and validation. Gene Ontology Consortium [71]
scGPT Codebase Software Provides the model architecture, pretrained weights, and scripts for fine-tuning on downstream tasks. GitHub / Original Publication [13] [65]
Geneformer (Hugging Face) Software A pretrained transformer model available on the Hugging Face hub, designed for transfer learning on single-cell data. Hugging Face Model Hub [66]
Scanpy Software A scalable toolkit for single-cell data analysis in Python; used for standard preprocessing (QC, normalization, filtering). GitHub [69]

The current landscape of single-cell foundation models is dynamic and promising, yet our head-to-head comparison reveals a nuanced reality. While models like Geneformer and scBERT excel in specific tasks such as phenotypic prediction and cell type annotation [66] [69], their superiority is not universal. For the critical task of perturbation prediction, simpler baseline models and feature-engineered classical machine learning methods remain highly competitive, and often superior [70] [71]. This underscores that pretraining on vast single-cell atlases does not automatically confer universal capabilities, and highlights the critical importance of rigorous, task-specific benchmarking.

The future of scFMs lies in addressing their current limitations. Promising directions include the development of even larger models trained on curated, species-specific data [67], and the strategic fusion of scFMs with external knowledge sources. Notably, combining the deep representation learning of scFMs like scGPT with the rich, text-based parametric knowledge of Large Language Models (LLMs) has been shown to create synergistic effects, leading to more robust and accurate performance [65]. For researchers and drug development professionals, selecting a model should therefore be a deliberate choice based on the specific task, dataset size, and available computational resources. There is no single "best" model, but a toolkit of specialized options. As the field matures, a focus on biological interpretability, robust benchmarking, and efficient knowledge integration will be key to unlocking the full potential of foundation models in biology and medicine.

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, leveraging large-scale deep learning on massive single-cell transcriptomics datasets to create universal representations of cellular states [3]. These models, typically built on transformer architectures, treat individual cells as "sentences" and genes or genomic features as "words," allowing them to learn the fundamental language of biology through self-supervised pretraining on millions of cells [3] [5]. Despite their rapid development and demonstrated prowess in technical tasks like batch integration and cell type annotation, a critical question remains: to what extent do these models capture biologically meaningful insights rather than merely optimizing for technical metrics? [1]

The current evaluation paradigm for scFMs predominantly relies on computational metrics that assess technical performance but often fail to validate biological relevance. This limitation becomes particularly problematic when models achieve high scores on technical benchmarks but produce biologically implausible results or fail to generalize to real-world biological questions [1] [5]. As scFMs increasingly inform biological discovery and clinical applications, including tumor microenvironment studies and treatment decision-making, establishing evaluation frameworks grounded in biological knowledge becomes essential [1].

This technical guide introduces biology-driven evaluation with Cell Ontology as a rigorous framework to address this gap. By anchoring model assessment in established biological knowledge through structured ontologies, researchers can move beyond technical metrics to ensure scFMs generate biologically credible and clinically actionable insights.

The Cell Ontology: A Biological Ground Truth Framework

Cell Ontology Fundamentals and Structure

The Cell Ontology (CL) is a structured, controlled vocabulary for cell types in animals, serving as a fundamental resource for model organism and bioinformatics databases [72]. With over 2,700 cell type classes, the CL provides a comprehensive classification system that organizes cell types hierarchically based on the "is_a" relation, creating a directed acyclic graph where relationships represent developmental and functional similarities between cell types [73]. The ontology is built on FAIR principles (Findable, Accessible, Interoperable, Reusable) and is tightly integrated with other biological ontologies, including the Uberon multi-species anatomy ontology for recording cell location and the Gene Ontology (GO) for capturing cell function [74] [72].

A key advantage of the Cell Ontology is its ability to represent cell type relationships in a computationally tractable form. The graph structure inherently encodes biological similarity—cell types that are closer in the ontology graph typically share more similar functions, developmental origins, and gene expression profiles [73]. This property enables guilt-by-association reasoning, where nearby nodes in the graph are expected to have similar features, providing a biological foundation for transferring annotations from known to novel cell types [73].

Cell Ontology in Single-Cell Research

The Cell Ontology has been widely adopted by major single-cell initiatives as a standard for consistent cell type annotation. Platforms including CZ CELLxGENE, the Human Cell Atlas (HCA), HuBMAP, the Single Cell Expression Atlas, and the BRAIN Initiative Cell Census Network (BICCN) utilize CL to annotate cell types in their reference maps and databases [72]. This widespread adoption has established CL as a community standard for representing cellular diversity, making it an ideal foundation for biology-driven evaluation of scFMs.

The critical challenge that CL addresses is the inconsistent terminology used to describe cell types across independent research groups [73]. Without a controlled vocabulary, joint analysis of multiple datasets becomes problematic, and comparisons between models lack standardization. By providing a consistent framework for cell type representation, CL enables reproducible annotations and facilitates the benchmarking of scFMs against established biological knowledge.

Novel Evaluation Metrics Leveraging Cell Ontology

scGraph-OntoRWR: Measuring Biological Consistency

The scGraph-OntoRWR metric evaluates how well the relational structure of cell types learned by an scFM aligns with the known biological relationships encoded in the Cell Ontology [1]. This metric operates on the principle that if a model has captured biologically meaningful representations, cell types that are closely related in the Cell Ontology should be positioned proximally in the model's latent space.

The experimental protocol for scGraph-OntoRWR involves:

  • Cell Embedding Extraction: Generate latent representations for a diverse set of cell types with known CL annotations using the scFM in zero-shot mode (without task-specific fine-tuning).

  • Similarity Graph Construction: Calculate pairwise similarities between all cell type representations to construct a model-derived cell type similarity graph.

  • Ontology Graph Processing: Extract the relevant subgraph from the Cell Ontology containing all evaluated cell types and their relationships.

  • Random Walk with Restart (RWR) Execution: Perform RWR on both the model-derived graph and the CL ontology graph to obtain probability distributions over cell types for each starting cell type.

  • Distribution Comparison: Compute the similarity between the RWR distributions from the model and CL graphs using a statistical measure (e.g., Jensen-Shannon divergence or cosine similarity).

A higher scGraph-OntoRWR score indicates better alignment between the model's internal representation of cell type relationships and established biological knowledge, suggesting the model has learned biologically relevant features rather than technical artifacts.

Lowest Common Ancestor Distance (LCAD): Quantifying Annotation Error Severity

The Lowest Common Ancestor Distance (LCAD) metric provides a biologically informed assessment of cell type annotation errors by evaluating not just whether a classification is incorrect, but how biologically unreasonable the error is [1]. Traditional accuracy metrics treat all misclassifications equally, but from a biological perspective, confusing two closely related cell types (e.g., different T-cell subsets) is less severe than confusing biologically distant types (e.g., a neuron and a hepatocyte).

The LCAD protocol operates as follows:

  • Cell Type Prediction: Obtain predicted cell type labels from the scFM for a test dataset with ground truth CL annotations.

  • Error Identification: Identify all misclassified cells where the predicted cell type does not match the ground truth.

  • LCA Calculation: For each misclassification, find the Lowest Common Ancestor (LCA) of the predicted and actual cell types within the Cell Ontology graph.

  • Distance Computation: Calculate the ontological distance between the misclassified cell type and its ground truth, typically measured as the number of edges or the semantic similarity between the two types in the CL hierarchy.

  • Error Severity Scoring: Compute an aggregate LCAD score across all misclassifications, with lower scores indicating that errors occur primarily between biologically similar cell types.

Table 1: Comparison of Biology-Driven Evaluation Metrics

Metric Name Evaluation Target Underlying Principle Interpretation
scGraph-OntoRWR Global cell type relationships Random walk with restart on similarity graphs Higher scores indicate better alignment with biological knowledge
LCAD Cell type annotation errors Ontological distance in Cell Ontology Lower scores indicate more biologically plausible errors
OnClass Accuracy Unseen cell type classification Graph-based knowledge transfer Measures generalizability to novel cell types

OnClass: Benchmarking Unseen Cell Type Classification

The OnClass algorithm provides a powerful framework for evaluating an scFM's ability to classify cells into cell types not present in the training data [73]. This capability is crucial for real-world applications where researchers encounter novel cell types not represented in existing annotated datasets. Remarkably, even comprehensive atlases like Tabula Muris Senis cover less than 5% of all cell types described in the Cell Ontology, making this an essential evaluation dimension [73].

The OnClass evaluation protocol:

  • Data Splitting with Unseen Terms: Split annotated datasets into training and test sets such that a controlled proportion of Cell Ontology terms in the test set are "unseen" (not present in training).

  • Model Projection: Project both the single-cell transcriptomes and the Cell Ontology terms into the same low-dimensional space using OnClass's nonlinear transformation.

  • Classification: Classify cells in the test set using their proximity to Cell Ontology terms in the embedded space, leveraging the ontology graph structure.

  • Performance Assessment: Evaluate classification performance using metrics like AUROC, Accuracy@3, and Accuracy@5 specifically for the unseen cell types.

OnClass substantially outperforms traditional classification methods on this task, with reported AUROC scores of 0.87 compared to 0.67 for other methods when 70% of cell types are unseen in the training data [73].

Implementation Framework and Experimental Protocols

Integrated Evaluation Workflow

Implementing a comprehensive biology-driven evaluation requires a structured workflow that integrates traditional metrics with the novel Cell Ontology-informed approaches. The following diagram illustrates the complete experimental workflow:

G Figure 1: Biology-Driven Evaluation Workflow cluster_inputs Input Data Sources cluster_evaluation Evaluation Modules cluster_bio Evaluation Modules ScFM Single-Cell Foundation Model Embedding Embedding Extraction ScFM->Embedding CL Cell Ontology BioMetrics Biology-Driven Metrics CL->BioMetrics Atlas Annotated Reference Atlas Atlas->Embedding Traditional Traditional Metrics (Batch Correction, Clustering) Embedding->Traditional Embedding->BioMetrics Results Holistic Model Assessment Traditional->Results RWR scGraph-OntoRWR BioMetrics->RWR LCAD_metric LCAD Analysis BioMetrics->LCAD_metric OnClass OnClass Evaluation BioMetrics->OnClass RWR->Results LCAD_metric->Results OnClass->Results

Detailed Experimental Protocol for scGraph-OntoRWR

Data Preparation and Preprocessing

  • Reference Dataset Curation: Select diverse, high-quality annotated datasets encompassing multiple tissues, species, and experimental conditions. Recommended sources include:

    • CZ CELLxGENE Discover [72]
    • Tabula Muris/Senis [73]
    • Human Cell Atlas [3] [72]
    • Asian Immune Diversity Atlas (AIDA) v2 for unbiased validation [1]

  • Cell Ontology Alignment: Map all cell type annotations to standard CL terms using natural language processing approaches to ensure consistent terminology [73].

  • Quality Control: Apply stringent quality control metrics appropriate for each dataset, including thresholds for detected genes, mitochondrial content, and potential doublets.

Feature Extraction from scFMs

  • Zero-Shot Embedding Generation: Extract cell embeddings from each scFM without task-specific fine-tuning to evaluate the intrinsic biological knowledge captured during pretraining.

  • Gene Embedding Extraction: For gene-level evaluation, extract gene embeddings from the input layers of scFMs to assess whether functionally related genes cluster together in the latent space.

  • Metadata Association: Associate each embedding with corresponding CL annotations and experimental metadata for downstream analysis.

Similarity Graph Construction

  • Distance Calculation: Compute pairwise distances between all cell type centroids in the embedding space using appropriate distance metrics (cosine distance recommended for high-dimensional embeddings).

  • Graph Formation: Convert distance matrices to similarity graphs using kernel transformations or k-nearest neighbor approaches.

  • Parameter Optimization: Determine optimal graph construction parameters through sensitivity analysis to ensure robust results.

Random Walk with Restart Execution

  • Transition Matrix: Construct the transition probability matrix for both the model-derived similarity graph and the Cell Ontology graph.

  • Restart Probability: Set the restart probability parameter (typically 0.1-0.3) based on graph density and preliminary experiments.

  • Convergence Check: Run RWR until convergence (stationary distribution achieved) or for a fixed number of iterations with early stopping.

Statistical Analysis and Interpretation

  • Distribution Comparison: Calculate similarity between RWR distributions using multiple measures (cosine similarity, Jaccard index, Wasserstein distance).

  • Significance Testing: Assess statistical significance through permutation testing by comparing observed similarity scores against null distributions generated from randomized graphs.

  • Benchmarking: Compare scGraph-OntoRWR scores across multiple scFMs and baseline methods to establish performance rankings.

Cross-Dataset Validation Protocol

To mitigate the risk of data leakage and ensure robust evaluation, implement cross-dataset validation using completely independent datasets not included in scFM pretraining corpora [1]. The Asian Immune Diversity Atlas (AIDA) v2 serves as an ideal independent validation set for this purpose. The protocol involves:

  • Model Application: Apply scFMs to the independent dataset in zero-shot mode to generate cell embeddings.

  • Performance Assessment: Evaluate all biology-driven metrics on this held-out dataset.

  • Consistency Analysis: Compare performance patterns between main benchmark datasets and independent validation sets to identify potential data leakage or overfitting.

Essential Research Reagents and Computational Tools

Successful implementation of biology-driven evaluation requires specific computational tools and resources. The following table details essential components of the evaluation toolkit:

Table 2: Research Reagent Solutions for Biology-Driven Evaluation

Tool/Resource Type Function in Evaluation Access Information
Cell Ontology Biological Knowledge Base Provides structured vocabulary and relationships for cell types cell-ontology.github.io [74]
OnClass Python Package Classifies cells into seen and unseen Cell Ontology terms GitHub Repository [73]
scGraph-OntoRWR Custom Metric Implementation Measures alignment between model representations and biological knowledge Custom implementation based on benchmark [1]
CZ CELLxGENE Data Platform Source of standardized, CL-annotated single-cell datasets cellxgene.cziscience.com [72]
scFMs (Geneformer, scGPT, etc.) Foundation Models Target models for biological evaluation Various repositories and platforms [1]
AIDA v2 Independent Validation Dataset Provides unbiased validation to mitigate data leakage concerns CellxGene Platform [1]

Interpreting Results and Model Selection Guidance

Holistic Performance Assessment

Biology-driven evaluation generates multidimensional assessment data that requires integrated interpretation. The following diagram illustrates the decision framework for model selection based on comprehensive evaluation:

G Figure 2: Model Selection Decision Framework cluster_criteria Selection Criteria cluster_recommendations Model Selection Recommendations Evaluation Comprehensive Evaluation Results Biological Biological Relevance (scGraph-OntoRWR, LCAD, OnClass) Evaluation->Biological Technical Technical Performance (Integration, Annotation Accuracy) Evaluation->Technical Practical Practical Considerations (Resources, Dataset Size) Evaluation->Practical Complex Complex scFM (Large datasets, Complex tasks, High interpretability needs) Biological->Complex Technical->Complex Simple Simpler Alternative (Small datasets, Specific tasks, Resource constraints) Technical->Simple Practical->Simple

Task-Specific Model Recommendations

Current benchmarking reveals that no single scFM consistently outperforms others across all tasks and datasets [1]. Model selection must therefore be guided by specific use cases and requirements:

  • For novel cell type discovery and annotation: Prioritize models with high OnClass accuracy and low LCAD scores, indicating strong performance on unseen cell types and biologically reasonable errors.

  • For clinical applications and treatment decision-making: Emphasize biological relevance metrics (scGraph-OntoRWR) alongside traditional performance measures to ensure clinically plausible results.

  • For large-scale atlas construction: Select models demonstrating robust performance across diverse tissues and conditions in cross-dataset validation.

  • For resource-constrained environments: Consider simpler alternatives when dataset size is limited or computational resources are constrained, as scFMs may not provide sufficient advantages in these scenarios to justify their computational costs [1].

Roughness Index (ROGI) as Selection Proxy

The Roughness Index (ROGI) provides a computationally efficient proxy for predicting model performance on specific datasets without exhaustive evaluation [1]. ROGI measures the smoothness of the cell-property landscape in the pretrained latent space, with smoother landscapes generally correlating with better downstream task performance. Calculating ROGI for a candidate scFM on a target dataset can guide model selection when comprehensive evaluation is infeasible.

Biology-driven evaluation with Cell Ontology represents a paradigm shift in assessing single-cell foundation models, moving beyond technical metrics to ensure biological relevance and clinical utility. The framework presented in this guide—centered on scGraph-OntoRWR, LCAD, and OnClass evaluation—provides researchers with robust methodologies to answer critical questions about whether scFMs genuinely capture biological insights or merely optimize technical benchmarks.

As the field of single-cell genomics continues to generate increasingly complex and large-scale datasets, and as scFMs grow in architectural sophistication and pretraining data volume, rigorous biological validation becomes increasingly crucial. By adopting the biology-driven evaluation framework outlined in this guide, researchers and drug development professionals can make informed decisions in model selection and application, ultimately accelerating biological discovery and therapeutic development through more reliable and interpretable computational models.

The integration of structured biological knowledge through Cell Ontology bridges the gap between computational performance and biological meaning, ensuring that single-cell foundation models fulfill their promise as transformative tools for understanding cellular function and disease mechanisms.

Single-cell foundation models (scFMs) represent a transformative approach in computational biology, leveraging large-scale deep learning models pretrained on vast single-cell datasets to create versatile tools adaptable to various downstream tasks [3]. These models are trained on millions of single-cell transcriptomes through self-supervised learning objectives, learning fundamental biological principles that enable generalization to new datasets and tasks [3] [5]. The core premise draws inspiration from natural language processing, where individual cells are treated analogously to sentences, and genes or genomic features along with their expression values serve as words or tokens [3]. Despite their promising capabilities, practical implementation requires careful consideration of when these complex models provide genuine advantages over simpler, established methods—a decision that must be guided by specific task requirements, dataset characteristics, and available computational resources [1] [13].

Understanding Single-Cell Foundation Models (scFMs)

Architectural Foundations and Pretraining Strategies

scFMs typically employ transformer-based architectures, which utilize attention mechanisms to learn and weight relationships between genes within a cell [3]. The development of these models involves several critical components:

  • Tokenization: Raw gene expression data is converted into discrete tokens. Genes become input tokens, and their combinations collectively represent a single cell. A key challenge is that gene expression data lacks natural sequencing, requiring strategies like ranking genes by expression levels or binning expression values to create deterministic sequences for transformer processing [3].

  • Model Architectures: Most scFMs implement variants of transformer architectures. Some adopt BERT-like encoder architectures with bidirectional attention mechanisms, allowing the model to learn from all genes in a cell simultaneously. Others utilize GPT-inspired decoder architectures with unidirectional masked self-attention that iteratively predicts masked genes based on known genes. Hybrid designs are also emerging, though no single architecture has demonstrated clear superiority for single-cell data [3].

  • Pretraining Objectives: Models are trained using self-supervised tasks, primarily masked gene modeling (MGM) where the model learns to predict randomly masked genes based on the context of other genes in the cell. This process enables the model to capture fundamental biological relationships and patterns from diverse cellular contexts without requiring labeled data [1] [3].

Commercially Available scFMs and Their Specifications

Current scFMs vary in their architectural details, pretraining data, and intended applications. The table below summarizes key characteristics of prominent models:

Table: Comparison of Single-Cell Foundation Models

Model Name Omics Modalities Model Parameters Pretraining Dataset Size Key Architectural Features
Geneformer [1] scRNA-seq 40 million 30 million cells 2048 ranked genes; encoder architecture with masked gene modeling
scGPT [1] [3] scRNA-seq, scATAC-seq, CITE-seq, spatial 50 million 33 million cells 1200 HVGs; decoder architecture with iterative MGM
UCE [1] scRNA-seq 650 million 36 million cells Incorporates protein embeddings from ESM-2; genomic position-based ordering
scFoundation [1] scRNA-seq 100 million 50 million cells 19,264 genes; asymmetric encoder-decoder; read-depth-aware MGM
LangCell [1] scRNA-seq 40 million 27.5 million cells 2048 ranked genes; incorporates cell type labels during pretraining

Decision Framework: When to Choose scFMs vs. Simpler Models

Key Decision Factors and Practical Considerations

Benchmarking studies reveal that no single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on specific factors [1] [13]. The decision framework below outlines critical considerations:

D Start Model Selection Decision DatasetSize Dataset Size & Complexity Start->DatasetSize TaskNature Task Nature & Requirements Start->TaskNature Resources Computational Resources Start->Resources Interpretability Interpretability Needs Start->Interpretability SmallData Small datasets (<10,000 cells) DatasetSize->SmallData LargeData Large, complex datasets (>100,000 cells) DatasetSize->LargeData StandardTask Standard tasks: batch correction, basic cell annotation TaskNature->StandardTask ComplexTask Complex tasks: perturbation prediction, novel cell discovery TaskNature->ComplexTask LimitedResources Limited computational resources Resources->LimitedResources AmpleResources Ample computational resources Resources->AmpleResources HighInterpretability High interpretability required Interpretability->HighInterpretability BlackBoxAcceptable Black-box models acceptable Interpretability->BlackBoxAcceptable SimpleModel Choose Simpler Models (Seurat, Harmony, scVI) ComplexModel Choose scFMs (Geneformer, scGPT, scFoundation) SmallData->SimpleModel LargeData->ComplexModel StandardTask->SimpleModel ComplexTask->ComplexModel LimitedResources->SimpleModel AmpleResources->ComplexModel HighInterpretability->SimpleModel BlackBoxAcceptable->ComplexModel

Diagram: Decision Framework for Model Selection

Performance Comparison Across Task Types

Comprehensive benchmarking studies evaluating six scFMs against established baselines across multiple tasks provide quantitative insights into performance patterns [1] [13]. The following table summarizes typical performance relationships:

Table: Performance Characteristics of scFMs vs. Simpler Models by Task Type

Task Category Representative Tasks When scFMs Excel When Simpler Models Excel
Cell-level Tasks Batch integration, cell type annotation Large, diverse datasets with multiple batch effects; cross-tissue homogeneity challenges [1] Smaller datasets (<50,000 cells); single-batch or minimal technical variation [1] [13]
Gene-level Tasks Gene function prediction, tissue specificity Capturing complex gene relationships; leveraging pretrained biological knowledge [1] Specific, well-defined gene sets with established functional annotations [1]
Clinical Prediction Drug sensitivity prediction, cancer cell identification Multi-cancer analyses; leveraging transfer learning from diverse cellular contexts [1] Single cancer type with abundant training data; resource-constrained environments [1] [13]
Perturbation Modeling In silico perturbation prediction, treatment response Novel target identification; rare disease applications with limited data [75] Well-studied pathways with extensive prior knowledge; validation-focused studies [5]

Experimental Protocols and Evaluation Metrics

Standardized Benchmarking Methodology

To ensure fair comparison between scFMs and simpler models, recent benchmarking studies have established rigorous evaluation protocols [1] [76]. The general workflow encompasses:

  • Feature Extraction:

    • For scFMs: Extract zero-shot gene and cell embeddings from pretrained models without task-specific fine-tuning
    • For baseline models: Apply standard preprocessing (HVG selection) and generate embeddings using established methods (Seurat, Harmony, scVI)
    • Implementation note: Code for applying scGPT, Geneformer, and LangCell is available in the scFM-Bench GitHub repository [76]

  • Downstream Task Evaluation:

    • Cell-level tasks: Batch integration, cell type annotation, cancer cell identification, drug sensitivity prediction
    • Gene-level tasks: Gene function prediction, tissue specificity analysis
    • Cross-dataset validation: Use independent datasets like Asian Immune Diversity Atlas (AIDA) v2 to mitigate data leakage risks [1]

  • Performance Quantification:

    • Employ 12 metrics spanning unsupervised, supervised, and knowledge-based approaches
    • Include novel biology-aware metrics: scGraph-OntoRWR and Lowest Common Ancestor Distance (LCAD)
    • Calculate roughness index (ROGI) as proxy for dataset-specific model recommendation [1]

Essential Research Reagents and Computational Tools

Table: Key Research Reagents and Computational Tools for scFM Implementation

Resource Category Specific Tools/Datasets Function and Application
Pretraining Data Repositories CZ CELLxGENE [3], Human Cell Atlas [3], PanglaoDB [3] Provide standardized, annotated single-cell datasets for model pretraining and fine-tuning
Baseline Methods Seurat [1], Harmony [1], scVI [1] Established computational methods serving as performance benchmarks for standard tasks
Evaluation Frameworks scFM-Bench [76], scGraph-OntoRWR [1] Standardized benchmarking pipelines and biology-informed evaluation metrics
Model Implementations scGPT [76], Geneformer [76], LangCell [76] Prebuilt scFM architectures with available code and pretrained weights for downstream applications

Resource Considerations and Practical Implementation

Computational Requirements and Infrastructure

The implementation of scFMs demands significant computational resources, creating practical barriers for many research settings [5]:

  • Hardware Requirements: Training scFMs typically requires high-end GPU clusters with substantial memory (often 16GB+ VRAM per GPU), while inference on pretrained models can be accomplished with more modest resources
  • Model Size Considerations: scFMs range from 40 million to 650 million parameters, with larger models generally requiring more specialized infrastructure for both training and deployment [1]
  • Time Investment: Fine-tuning scFMs for specific tasks requires less time than full pretraining but still demands considerable computational time compared to traditional methods

Accessibility Challenges and Emerging Solutions

Current limitations in scFM accessibility present significant hurdles for widespread adoption [5]:

  • Technical Barriers: Most scFMs are built using unfamiliar repositories and programming languages not commonly used by biologists
  • Interpretability Concerns: Models often function as "black boxes" with limited biologically intuitive construction and results interpretation
  • Validation Gaps: Novel predictions generated by scFMs frequently lack proper experimental validation, limiting trust in clinical applications

Emerging solutions focus on developing user interfaces to make these tools accessible to biologists without deep computational expertise, alongside improved interpretation frameworks to enhance biological relevance of outputs [5].

The field of scFMs continues to evolve rapidly, with several promising directions emerging:

  • Closed-Loop Frameworks: Recent approaches enable scFMs to incorporate experimental perturbation data during fine-tuning, creating iterative improvement cycles that enhance prediction accuracy [75]
  • Multi-Modal Integration: Next-generation scFMs are incorporating additional data modalities including scATAC-seq, spatial transcriptomics, and proteomics to create more comprehensive cellular representations [3]
  • Specialized Foundation Models: Domain-specific scFMs tailored to particular biological contexts (e.g., tumor immunology, neurobiology) may overcome limitations of general-purpose models [5]

Strategic Recommendations for Researchers

Based on current evidence and benchmarking studies, researchers should:

  • Conduct Pilot Comparisons: Implement both scFM and simpler baseline methods on representative data subsets before committing to full-scale analysis
  • Prioritize Biological Validation: Regardless of model complexity, prioritize experimental validation of computational predictions to ensure biological relevance
  • Consider Hybrid Approaches: Leverage scFMs for exploratory analysis and hypothesis generation, then apply simpler, more interpretable models for validation studies
  • Monitor Rapid Developments: Acknowledge that the field is evolving rapidly, with new models and approaches emerging frequently that may change performance relationships

The strategic selection between scFMs and simpler models ultimately depends on carefully balancing task requirements, data characteristics, available resources, and interpretability needs. As the field matures and accessibility improves, scFMs hold tremendous potential to transform single-cell research by providing deeper biological insights and enabling more accurate predictions of cellular behavior.

Single-cell foundation models (scFMs) are revolutionizing the analysis of cellular heterogeneity by providing a unified framework for interpreting complex biological data. Trained on millions of single-cell transcriptomes using self-supervised learning, these models learn universal representations of genes and cells, which can be adapted to various downstream tasks such as cell type annotation, batch integration, and perturbation prediction [3]. The performance of scFMs on these tasks hinges critically on three interdependent pillars: the biological fidelity of cell embeddings, the effectiveness of batch correction, and the model's ability to generalize across diverse datasets and biological contexts. This technical guide synthesizes recent benchmarking studies to provide a comprehensive evaluation of current scFMs, offering structured protocols and metrics to assess their strengths and limitations in real-world applications.

Core Evaluation Metrics for scFM Performance

Benchmarking studies employ a multifaceted set of metrics to quantitatively assess scFM performance across different tasks and data modalities. These metrics span unsupervised, supervised, and biology-informed categories to provide a holistic view of model capabilities [1].

Table 1: Key Performance Metrics for Evaluating scFMs

Metric Category Metric Name Description Interpretation
Cell Embedding Quality scGraph-OntoRWR Measures consistency of cell-type relationships in embeddings with prior biological knowledge (Cell Ontology) [1]. Higher values indicate embeddings better capture known biological relationships.
Lowest Common Ancestor Distance (LCAD) Assesses ontological proximity between misclassified cell types [1]. Lower severity errors (smaller LCAD) indicate better annotation quality.
Shannon Entropy Quantifies specificity of gene/protein expression across cell clusters [77]. Lower entropy indicates more specific, higher-quality markers.
Batch Correction Batch ASW Average silhouette width of batches; measures batch mixing [78]. Lower absolute values indicate better batch integration.
Cell-type ASW Average silhouette width of cell types; measures biological preservation [78]. Higher values indicate cell-type separation is better preserved.
Graph Connectivity Assesses connectivity of the k-nearest neighbor graph based on cell labels [78]. Higher values indicate better preservation of local biology.
Generalization Zero-shot Accuracy Performance on novel tasks (e.g., cell annotation) without task-specific fine-tuning [1]. Higher values indicate stronger generalization from pretraining.
kNN Probing Accuracy Accuracy of a k-Nearest Neighbor classifier on learned embeddings for a task like cell typing [78]. Higher values indicate more informative embeddings for downstream analysis.

Experimental Protocols for Benchmarking scFMs

Rigorous, standardized evaluation is paramount for assessing scFMs. The following protocols, derived from large-scale benchmarking efforts, provide a blueprint for reproducible testing.

Protocol for Evaluating Cell Embedding Quality

Objective: To determine if cell embeddings generated by an scFM accurately reflect known biological hierarchies and cell-type definitions.

  • Embedding Extraction: Process a hold-out dataset (e.g., from the Asian Immune Diversity Atlas v2 [1]) using the scFM in zero-shot mode to extract cell embeddings.
  • Cell Ontology Alignment (scGraph-OntoRWR):
    • Construct a knowledge graph from the Cell Ontology, connecting cell types via "isa" and "partof" relationships.
    • Compute a similarity matrix of cell embeddings from the scFM output.
    • Perform Random Walk with Restart (RWR) on the Cell Ontology graph, seeded with the embedding-based similarity matrix.
    • Quantify the alignment between the steady-state probability distribution of RWR and the original embedding similarities. A higher alignment score indicates superior biological relevance [1].
  • Cell-type Specificity (Shannon Entropy):
    • Obtain cell cluster definitions using the def_clust() function (e.g., via Seurat) [77].
    • For a given gene or protein, calculate its normalized Shannon entropy across the clusters: H_normalized = -1/log2(N) * Σ(p_i * log2(p_i)), where N is the number of clusters and p_i is the expression proportion in cluster i.
    • A lower entropy value signifies that the feature is a specific marker for a smaller number of clusters, indicating high embedding quality [77].

Protocol for Assessing Batch Correction

Objective: To evaluate an scFM's ability to integrate data from different experimental batches while preserving meaningful biological variation.

  • Data Preparation: Use a dataset with known batch effects (e.g., from different patients, platforms, or laboratories) and high-quality cell-type annotations. The benchmark should include at least five datasets of varying sizes and diversity [1].
  • Integration and Embedding: Generate integrated cell embeddings using the scFM's built-in integration function or by mapping batches to a common latent space.
  • Metric Calculation:
    • Batch ASW: Compute the silhouette width where the "cluster" label is the batch identifier. Values range from -1 to 1. Scores close to 0 indicate successful batch mixing, while scores approaching 1 indicate strong batch separation [78].
    • Cell-type ASW: Compute the silhouette width using the cell-type labels. Scores close to 1 indicate that cells of the same type are tightly grouped and well-separated from other types, confirming biological preservation [78].
    • Graph Connectivity: Construct a k-nearest neighbor graph (k=15) on the integrated embeddings using cell-type labels. The metric reports the proportion of cell labels that are connected in the graph. A value of 1 indicates all cells of the same type form a connected component [78].

Protocol for Testing Model Generalization

Objective: To probe the model's ability to perform well on unseen data, novel cell types, and across species.

  • Zero-shot Cell Annotation:
    • Task: Annotate cell types in a completely new dataset (query) by comparing its scFM embeddings to a reference dataset with known labels.
    • Method: Map query and reference data to a joint latent space using the scFM. Annotate query cells using a k-NN classifier (e.g., k=30) trained on the reference embeddings [78].
    • Evaluation: Calculate annotation accuracy and the LCAD metric to assess the biological reasonableness of any misclassifications [1].
  • Cross-species and Cross-tissue Generalization:
    • Task: Apply a model trained on one species (e.g., mouse) to data from another species (e.g., human), or across different tissues.
    • Method: Use a model with cross-species capabilities (e.g., scPlantFormer [79]). Perform zero-shot embedding and annotation as above, potentially using orthologous gene mapping.
    • Evaluation: Report accuracy and kNN Probing Accuracy on the target species/tissue to quantify transferability [79].

G cluster_metrics Core Evaluation Metrics start Start Benchmarking data Input scRNA-seq Data (Multiple Batches) start->data model scFM (Zero-shot or Fine-tuned) data->model eval Performance Evaluation model->eval embed Cell Embedding Quality • scGraph-OntoRWR • Shannon Entropy eval->embed batch Batch Correction • Batch ASW • Cell-type ASW eval->batch gen Generalization • Zero-shot Accuracy • kNN Probing eval->gen report Final Model Ranking & Selection Guidance embed->report Biological Relevance batch->report Technical Robustness gen->report Transferability

Figure 1: A standardized workflow for benchmarking Single-Cell Foundation Models (scFMs), assessing three core performance aspects to guide model selection.

Performance Landscape of Leading Single-Cell Foundation Models

Comprehensive benchmarking reveals that no single scFM dominates across all tasks. Performance is highly dependent on the specific application, dataset size, and available computational resources [1] [6]. The table below synthesizes findings from major studies to guide model selection.

Table 2: Comparative Analysis of Leading Single-Cell Foundation Models

Model Name Pretraining Scale Key Strengths Key Weaknesses / Limitations Recommended Tasks
scGPT [6] [79] ~33 million cells [6] Robust performance across all tasks (zero-shot & fine-tuning); supports multi-omic data [6]. High computational requirements [1]. Batch correction, cross-species annotation, perturbation prediction.
Geneformer [1] [6] ~30 million cells [1] Strong gene-level task performance; effective pretraining strategy [6]. May be outperformed on specific cell-level tasks [1]. Gene embedding analysis, regulatory network inference.
scFoundation [1] [6] ~50 million cells [1] Strong performance on gene-level tasks; large model capacity [6]. High computational intensity [1]. Large-scale cell atlas construction, gene function prediction.
scBERT [3] [6] Not specified Early pioneer for cell type annotation using transformer architecture [3]. Lags in performance likely due to smaller size and limited training data [6]. Educational purposes, baseline comparisons.
UCE [1] ~36 million cells [1] Incorporates protein sequence information via ESM-2 embeddings [1]. Specialized architecture; general performance not top-ranked [1]. Tasks linking gene expression to protein function.
Specialized Frameworks (scVI, CLAIRE) [78] Varies Excel at uni-modal batch correction, often outperforming foundation models on this specific task [78]. Less versatile; not designed for the wide range of tasks supported by scFMs [78]. Dedicated batch effect removal in scRNA-seq data.

Successfully applying and benchmarking scFMs requires a suite of computational tools and data resources.

Table 3: Essential Toolkit for scFM Research and Application

Tool/Resource Name Type Function & Purpose
BioLLM [6] Software Framework Provides a unified interface for integrating and applying diverse scFMs, enabling standardized benchmarking and streamlined model switching.
CITESeQC [77] Quality Control Tool The first software package for multi-layered, quantitative quality control of CITE-Seq data, assessing RNA, protein, and their interactions.
CellxGene / CZ CELLxGENE Discover [1] [79] Data Repository Provides unified access to millions of curated and standardized single-cell datasets, essential for pretraining and unbiased evaluation.
scSSL-Bench [78] Benchmarking Suite An open-source benchmark that evaluates self-supervised learning methods, including scFMs, on tasks like batch correction and cell type annotation.
VICE [80] Quality Assessment Tool Evaluates scRNA-seq data quality and estimates the true positive rate of differential expression results based on sample size and noise.
Seurat [1] [77] Analysis Toolkit A standard R toolkit for single-cell analysis, often used for clustering, visualization, and as a baseline method in benchmarks.

The field of single-cell foundation models is dynamic, with different architectures excelling in specific areas. The key to successful application lies in task-driven model selection.

G start Define Your Task q1 Is your primary goal batch correction? start->q1 q2 Do you require strong generalization (zero-shot)? q1->q2 No m1 Consider Specialized Frameworks (scVI, CLAIRE) q1->m1 Yes q3 Are computational resources constrained? q2->q3 No m2 Select scGPT q2->m2 Yes q4 Is it a gene-level analysis task? q3->q4 No m4 A simpler ML model may be more efficient q3->m4 Yes q4->m2 No m3 Select Geneformer or scFoundation q4->m3 Yes

Figure 2: A decision framework for selecting the most appropriate single-cell analysis model based on research goals and constraints.

As outlined in the decision framework, practitioners should choose models strategically. scGPT is the most versatile for generalized zero-shot applications, while specialized tools like scVI can be superior for dedicated batch correction. For gene-centric analyses, Geneformer and scFoundation are powerful choices. Importantly, for smaller, focused datasets with limited resources, simpler machine learning models can sometimes adapt more efficiently than large foundation models [1]. Ultimately, leveraging unified frameworks like BioLLM can significantly streamline the process of accessing, evaluating, and deploying these powerful tools, accelerating discovery in single-cell biology [6].

Conclusion

Single-cell foundation models represent a paradigm shift in computational biology, offering a unified framework to analyze cellular systems at an unprecedented scale. They have demonstrated significant promise in critical areas like drug response prediction, target identification for rare diseases, and the creation of in-silico models for perturbation studies. However, their journey from powerful tools to indispensable assets in biomedical research hinges on addressing key challenges: improving interpretability, enhancing computational efficiency, and standardizing benchmarking. Future progress will likely involve the development of more biologically intuitive models, the seamless integration of multi-modal data, and the establishment of robust 'closed-loop' systems that continuously learn from experimental validation. For researchers and clinicians, this promises a future where foundation models accelerate the path from genomic data to actionable biological insights and effective therapeutic strategies, ultimately paving the way for truly personalized medicine.

References