Single-Cell Foundation Models: A Comprehensive Review of Concepts, Applications, and Future Directions in Biomedical Research

Abstract

This review provides a comprehensive examination of single-cell foundation models (scFMs), large-scale AI systems pretrained on massive single-cell datasets that are revolutionizing cellular biology and drug discovery. We explore the fundamental concepts behind scFMs, their transformer-based architectures, and self-supervised pretraining strategies that enable them to learn universal biological patterns. The article critically assesses current methodologies, practical applications in drug development and clinical research, significant technical challenges, and rigorous validation approaches. Through comparative analysis of emerging models like scGPT and Geneformer, we identify performance limitations in zero-shot settings and provide evidence-based guidance for model selection. This resource equips researchers and drug development professionals with the knowledge to effectively leverage scFMs while understanding their current constraints and future potential in advancing precision medicine.

Demystifying Single-Cell Foundation Models: Core Concepts and Architectural Principles

The advent of high-throughput single-cell sequencing has fundamentally transformed biological research, enabling the unprecedented exploration of cellular heterogeneity, developmental trajectories, and complex regulatory networks at single-cell resolution. As experimental & molecular medicine journals report, vast collections of single-cell data have become available across diverse tissues and conditions, with public archives like CZ CELLxGENE now providing unified access to annotated single-cell datasets containing over 100 million unique cells [1]. This data explosion has created an urgent need for unified computational frameworks capable of integrating and comprehensively analyzing these rapidly expanding data repositories. Inspired by the revolutionary success of transformer architectures in natural language processing (NLP) and computer vision, researchers have begun developing foundation models specifically designed for single-cell biology, giving rise to single-cell foundation models (scFMs) [1].

A foundation model is defined as a large-scale deep learning model pretrained on vast datasets at scale and then adapted to a wide range of downstream tasks. These models are characterized by self-supervised learning through objectives such as predicting masked segments, enabling them to learn generalizable patterns without extensive manual labeling [1]. The core premise of scFMs is that by exposing a model to millions of cells encompassing many tissues and conditions, the model can learn the fundamental principles governing cellular behavior and gene regulation that are generalizable to new datasets or analytical tasks. In these scFMs, individual cells are treated analogously to sentences, while genes or other genomic features along with their expression values are treated as words or tokens, creating what can be conceptualized as a "language of cells" [1] [2]. This paradigm shift represents a fundamental transformation in how we approach computational cell biology, moving from specialized analytical tools to unified frameworks that can leverage the collective knowledge embedded in massive single-cell datasets.

Core Concepts: How Language Models Interpret Cellular Data

Fundamental Analogies Between Language and Biology

The application of language models to single-cell biology relies on establishing conceptual parallels between natural language and biological systems. In this framework, the "vocabulary" consists of genes or genomic features, while the "sentences" are individual cells represented by their molecular profiles [1] [2]. The grammatical rules that govern how words combine to form meaningful sentences correspond to the gene regulatory networks and biological pathways that define cellular identity and function. This analogy enables researchers to leverage sophisticated transformer architectures originally developed for NLP tasks to decipher the complex "language" of cellular biology.

The self-supervised learning approaches used in large language models translate remarkably well to single-cell data. Just as language models learn by predicting masked words in sentences, scFMs learn by predicting masked gene expressions in cells, capturing the complex dependencies and correlations between genes across diverse cellular contexts [1]. Through this process, scFMs develop a deep understanding of cellular syntax—the patterns and relationships between genes that define specific cell types, states, and responses. The model's attention mechanisms allow it to learn which genes in a cell are most informative of the cell's identity or state, how they covary across cells, and how they have regulatory or functional connections [1].

Architectural Foundations: Transformer Models in Biology

Most successful scFMs are built on the transformer architecture, which has become the backbone of modern foundation models across domains [1]. Transformers are neural network architectures characterized by attention mechanisms that allow the model to learn and weight the relationships between any pair of input tokens. In the context of single-cell biology, this enables the model to identify which genes are most relevant for understanding specific cellular functions or states, effectively learning the contextual relationships between different genomic features [1].

Two primary architectural approaches have emerged in scFM development. The first adopts a BERT-like encoder architecture with bidirectional attention mechanisms where the model learns from the context of all genes in a cell simultaneously [1] [2]. The second approach, exemplified by scGPT, uses an architecture inspired by the decoder of the Generative Pretrained Transformer (GPT), with a unidirectional masked self-attention mechanism that iteratively predicts masked genes conditioned on known genes [1]. While both architectures have demonstrated success in single-cell applications, no single design has emerged as clearly superior, and hybrid approaches are currently being explored to optimize performance for specific biological tasks.

Table 1: Comparison of Major Single-Cell Foundation Model Architectures

Model Name	Base Architecture	Pretraining Data Scale	Key Features	Primary Applications
Geneformer	Transformer-based	30 million cells [3]	Context-aware gene embeddings	Network biology, predictions
scGPT	GPT-inspired decoder	100 million cells [3]	Generative modeling	Multi-omics integration, perturbation prediction
scBERT	BERT-like encoder	Not specified	Bidirectional attention	Cell type annotation
scFoundation	Transformer-based	100 million cells [3]	Large-scale pretraining	General-purpose representations
scPlantLLM	Transformer-based	Plant-specific data [3]	Species-specific optimization	Plant single-cell genomics

Technical Implementation: From Raw Data to Biological Insights

Data Tokenization Strategies for Single-Cell Data

Tokenization represents a critical preprocessing step that converts raw single-cell data into a structured format suitable for transformer models. Unlike words in natural language, gene expression data lacks inherent sequential ordering, presenting unique challenges for applying sequential models like transformers [1] [4]. To address this fundamental discrepancy, researchers have developed several tokenization strategies that impose artificial structure on single-cell data while preserving biological meaning.

The most common approach involves ranking genes within each cell by their expression levels and feeding the ordered list of top genes as a "sentence" representing that cell [1]. This provides a deterministic sequence based on expression magnitude, allowing the model to learn relationships between highly expressed genes. Alternative methods partition genes into bins according to their expression values or simply use normalized counts without complex ranking schemes [1]. Each gene is typically represented as a token embedding that combines a gene identifier with its expression value in the given cell. Positional encoding schemes are then adapted to represent the relative order or rank of each gene in the cell, providing the model with information about the artificial sequence structure [1]. Special tokens may also be incorporated to represent cell-level metadata, experimental conditions, or multimodal information, enriching the contextual information available to the model.

Pretraining Strategies and Objectives

Pretraining scFMs involves training models on self-supervised tasks across large, unlabeled single-cell datasets. The most common pretraining objective is masked language modeling, where random subsets of gene tokens are masked, and the model must predict the missing values based on the remaining context [1]. This approach forces the model to learn the complex dependencies and correlations between genes, effectively capturing the underlying structure of gene regulatory networks. Through this process, the model develops a comprehensive understanding of how genes co-vary across different cell types and states, enabling it to form robust representations of cellular identity and function.

Additional pretraining objectives may include next-gene prediction (similar to next-word prediction in language models), contrastive learning to bring similar cells closer in embedding space, and multi-task learning that combines several self-supervised objectives [1] [4]. The scale of pretraining data is substantial, with modern scFMs training on datasets ranging from 30 to 100 million cells from diverse tissues, species, and experimental conditions [3]. This extensive exposure to varied cellular contexts enables the models to learn universal principles of cellular biology that transfer effectively to new datasets and biological questions.

Single-Cell Foundation Model Workflow

Experimental Framework: Benchmarking and Evaluation

Standardized Evaluation Metrics and Protocols

Comprehensive benchmarking of scFMs requires standardized evaluation protocols that assess model performance across diverse biological tasks. Recent benchmarking studies have employed multiple metrics spanning unsupervised, supervised, and knowledge-based approaches to provide holistic assessment of model capabilities [4]. These evaluations typically examine performance across two primary categories: gene-level tasks and cell-level tasks, each targeting different aspects of biological understanding.

Gene-level tasks focus on evaluating the quality of gene embeddings and their ability to capture known biological relationships. Standard protocols include predicting gene functions based on Gene Ontology (GO) terms, identifying tissue-specific genes, and reconstructing known biological pathways [4]. Performance is measured using standard classification metrics such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), as well as specialized metrics that assess the semantic similarity between gene embeddings and established functional annotations. Cell-level tasks evaluate the model's understanding of cellular identity and function, including cell type annotation, batch integration, identification of rare cell populations, and prediction of cellular responses to perturbations [4]. These tasks employ metrics that measure both technical performance (such as clustering accuracy and batch correction efficiency) and biological relevance (such as the preservation of known cellular hierarchies).

Table 2: Standard Evaluation Metrics for Single-Cell Foundation Models

Metric Category	Specific Metrics	Biological Interpretation	Ideal Value
Gene-Level Evaluation	GO Term AUROC	Functional relationship capture	>0.8
	Pathway Reconstruction Accuracy	Biological pathway identification	Higher better
	Tissue Specificity AUPRC	Tissue-specific gene detection	>0.7
Cell-Level Evaluation	Cell Type Annotation F1	Cell classification accuracy	>0.9
	Batch Integration ASW	Technical effect removal	0-1 (context dependent)
	Biological Conservation LISI	Biological variation preservation	Higher better
Ontology-Based Evaluation	scGraph-OntoRWR	Biological consistency with prior knowledge	Higher better
	LCAD (Lowest Common Ancestor Distance)	Severity of misclassification errors	Lower better

Comparative Performance Across Model Architectures

Recent comprehensive benchmarking studies have revealed distinct performance patterns across different scFM architectures. Notably, no single scFM consistently outperforms others across all tasks, emphasizing the importance of task-specific model selection [4] [5]. Evaluation of six prominent scFMs (Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello) against established baseline methods has provided insights into the relative strengths and limitations of each approach.

The BioLLM framework, which provides a unified interface for diverse scFMs, has revealed that scGPT demonstrates robust performance across multiple tasks, including both zero-shot learning and fine-tuning scenarios [5]. Geneformer and scFoundation show particularly strong capabilities in gene-level tasks, benefiting from effective pretraining strategies that capture functional gene relationships [5]. In contrast, scBERT often lags behind larger models, likely due to its smaller architecture and more limited training data [5]. Importantly, simpler machine learning models with carefully selected features (such as Highly Variable Genes) can sometimes outperform complex foundation models on specific tasks, particularly when data are limited or computational resources are constrained [4]. This suggests that while scFMs offer powerful general-purpose capabilities, task-specific considerations should guide model selection in practical applications.

Advanced Applications: From Basic Research to Therapeutic Discovery

Drug Discovery and Development Applications

Single-cell foundation models are increasingly playing transformative roles in multiple stages of drug discovery and development. In target identification, scFMs enable improved disease understanding through precise cell subtyping and characterization of disease-associated cellular states [6] [7]. Highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialing and prioritization by revealing the cellular contexts in which potential targets operate and their functional relationships within broader biological networks [6].

During preclinical development, scFMs aid the selection of relevant disease models by comparing their cellular compositions and states to human disease references [6]. They also provide new insights into drug mechanisms of action by characterizing cellular responses to perturbations at single-cell resolution [6] [7]. In clinical development, scFMs can inform critical decision-making through improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression [6]. The ability to integrate single-cell data across platforms, tissues, and species positions scFMs as powerful tools for bridging translational gaps in pharmaceutical development.

Emerging Multimodal and Interactive Approaches

Recent advances have extended scFMs beyond basic transcriptomic analysis to multimodal and interactive applications. The CellWhisperer framework represents a groundbreaking approach that establishes a multimodal embedding of transcriptomes and their textual annotations using contrastive learning on over 1 million RNA sequencing profiles with AI-curated descriptions [8]. This embedding informs a large language model that answers user-provided questions about cells and genes in natural-language conversations, enabling researchers to interactively explore single-cell data through intuitive chat interfaces.

Commercial implementations are also emerging, such as 10x Genomics' integration with Anthropic's Claude for Life Sciences, which provides natural-language interfaces to single-cell analysis pipelines through the Model Context Protocol (MCP) [9]. These developments lower the barrier to sophisticated single-cell analysis, allowing non-computational researchers to perform complex analytical tasks through natural language queries rather than specialized programming. The convergence of single-cell technologies with conversational AI represents a significant step toward truly interactive biological discovery systems that can serve as collaborative partners in scientific investigation.

Interactive Single-Cell Analysis Architecture

Successful implementation of scFMs requires both biological and computational resources that collectively enable robust model development and application. The table below details key components of the scFM research toolkit, including their specific functions and representative examples from current literature and practice.

Table 3: Essential Research Reagents and Computational Resources for Single-Cell Foundation Models

Resource Category	Specific Item/Platform	Function/Purpose	Representative Examples
Data Resources	CELLxGENE Census	Standardized single-cell data access	>100 million curated cells [1]
	GEO/SRA Archives	Raw sequencing data repository	705,430 human transcriptomes [8]
	Human Cell Atlas	Reference cell maps	Multiorgan coverage [1]
Computational Frameworks	BioLLM	Unified scFM interface	Standardized APIs for model integration [5]
	Transformer Architectures	Model backbone	BERT-like encoders, GPT-style decoders [1]
	Cloud Analysis Platforms	Scalable computation	10x Genomics Cloud [9]
Specialized Models	Geneformer	Gene embedding generation	30 million cell pretraining [3]
	scGPT	Generative modeling	100 million cell scale [3]
	scPlantLLM	Species-specific adaptation	Plant single-cell genomics [3]
Evaluation Tools	scGraph-OntoRWR	Biological consistency metric	Cell ontology alignment [4]
	ROGI (Roughness Index)	Model selection proxy	Dataset-dependent recommendation [4]

Future Directions and Challenges in Single-Cell Foundation Models

Despite rapid progress, several significant challenges remain in the development and application of scFMs. A primary limitation is the nonsequential nature of omics data, which doesn't naturally align with the sequential processing of transformer architectures [1]. Additional challenges include inconsistency in data quality across studies, the computational intensity required for training and fine-tuning large models, and the difficulty of interpreting the biological relevance of latent embeddings and model representations [1] [4].

Future research directions are likely to focus on several key areas. Improved multimodal integration will combine transcriptomic data with epigenetic, proteomic, and spatial information to create more comprehensive cellular representations [1] [3]. Enhanced interpretability methods will be crucial for translating model insights into biologically actionable knowledge, potentially through attention mechanism analysis and concept-based explanations [4]. Species-specific and context-specific adaptations, exemplified by scPlantLLM for plant genomics, will address the unique characteristics of different biological systems [3]. Finally, more efficient architectures and training methods will be needed to make scFMs accessible to broader research communities with limited computational resources [4] [5].

As these challenges are addressed, scFMs are poised to become increasingly central to biological discovery and therapeutic development, potentially evolving into true collaborative partners in scientific investigation through enhanced natural language interfaces and reasoning capabilities. The ongoing integration of single-cell technologies with artificial intelligence represents a transformative frontier in computational biology, with foundation models serving as the cornerstone of this paradigm shift.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized biology by enabling the profiling of gene expression at an unprecedented resolution, uncovering vast cellular heterogeneity. However, the high-dimensionality, sparsity, and technical noise inherent to single-cell data present significant challenges for traditional analytical methods [1] [4]. Inspired by their success in natural language processing (NLP), transformer architectures have been recently adapted to single-cell genomics, giving rise to single-cell foundation models (scFMs). These models leverage the power of attention mechanisms to interpret the complex "language" of biology, mapping intricate gene relationships and regulatory networks from millions of cells [1]. This technical guide explores the core architectural adaptations of transformers for single-cell data, detailing how attention mechanisms are engineered to decipher the fundamental principles of cellular function.

Core Architectural Adaptations for Single-Cell Data

Applying transformer architectures to single-cell transcriptomics requires significant modifications to handle the unique structure and properties of biological data.

Tokenization Strategies for Non-Sequential Data

A fundamental challenge is that gene expression data lacks the inherent sequential order of words in a sentence. To apply transformers, which process ordered sequences, genes must be artificially structured. Several tokenization strategies have been developed:

• Rank-based Tokenization: Genes are ordered by their expression levels within each cell, creating a deterministic sequence where the top-expressed genes form the input "sentence" [1]. Models like Geneformer and scGPT employ this approach, treating the ordered list of gene tokens as the cellular representation [1] [10].
• Value Categorization: Continuous gene expression values are binned into discrete categories or "buckets," converting the regression problem of predicting expression into a classification task. scBERT is a prominent example that uses this strategy [10].
• Value Projection: This strategy aims to preserve the full resolution of the data by representing the gene expression vector as a sum of a projection of the expression value and a positional or gene embedding. scFoundation and CellFM utilize this approach to predict raw gene expression values directly [10].

The following diagram illustrates a typical tokenization and embedding workflow for single-cell data.

Attention Mechanisms for Gene Interaction Mapping

The self-attention mechanism is the cornerstone of the transformer, allowing the model to dynamically weigh the importance of different parts of the input sequence. In the context of single-cell data, this translates to learning the contextual relationships between genes.

• Self-Attention: In models with a BERT-like encoder architecture (e.g., scBERT), bidirectional self-attention allows each gene to attend to all other genes in the cell simultaneously. This enables the model to learn co-expression patterns and potential regulatory relationships by capturing how the expression of one gene influences the context of others [1] [11].
• Masked Self-Attention: In decoder-based models like scGPT, a unidirectional masked self-attention mechanism is used. The model iteratively predicts masked genes conditioned on the known, unmasked genes in the sequence. This forces the model to learn the dependencies between genes and build an internal representation of gene-gene interactions [1].
• Multi-Head Attention: By employing multiple attention heads in parallel, the model can jointly attend to information from different representation subspaces. For example, different heads might specialize in capturing relationships between genes involved in different biological pathways or processes, such as metabolism, immune response, or cell cycle regulation [11]. This parallels the use of multi-head attention in other domains, such as financial modeling, where it helps capture diverse temporal patterns [12].

Quantitative Performance of Single-Cell Foundation Models

Benchmarking studies have evaluated the performance of various scFMs across a range of biological tasks. The table below summarizes the performance of several prominent models in key applications, demonstrating their utility in gene relationship mapping and other downstream tasks.

Table 1: Performance Benchmarking of Selected Single-Cell Foundation Models

Model	Pretraining Scale	Key Architecture	Cell Type Annotation (Accuracy Metrics)	Perturbation Prediction (Performance)	Batch Integration (Metrics)	Gene Function Prediction
CellFM	100M human cells [10]	ERetNet (Linear Attention) [10]	High performance across datasets [10]	Outperforms existing models [10]	Effective integration [10]	Improved accuracy [10]
scGPT	33M+ cells [1] [10]	Transformer Decoder [1]	Robust performance [4]	Accurate prediction [4]	High efficiency [4]	Captures functional relationships [1]
Geneformer	30M cells [10] [3]	Transformer Encoder [10]	Context-aware annotations [1]	Network dynamics insights [1]	Preserves biological variation [4]	Learns rank-based embeddings [10]
scBERT	Millions of cells [1]	BERT-like Encoder [1] [10]	Specialized for annotation [1]	N/A	N/A	N/A
scPlantLLM	Plant-specific data [3]	Transformer [3]	High zero-shot accuracy [3]	N/A	Effective in plants [3]	Plant-specific adaptations [3]

A comprehensive benchmark study evaluating six scFMs against traditional baselines revealed that no single model consistently outperforms all others across every task. The choice of model depends on factors such as dataset size, task complexity, and computational resources. Notably, scFMs demonstrate a remarkable ability to capture biological relevance, with their learned representations showing high consistency with known gene ontology (GO) terms and cell-type relationships [4].

Experimental Protocols for Validating Gene Relationships

Validating the gene relationships and regulatory networks inferred by transformer models requires rigorous experimental and computational protocols. The following workflow outlines a standard process for training a model and validating its predictions.

Detailed Methodology for Key Tasks

4.1.1 Gene Function Prediction

• Protocol: Gene embeddings are extracted from the input layer of the pretrained scFM. The similarity between these embedding vectors is computed (e.g., using cosine similarity) to predict functional relationships [4] [10].
• Validation: The model's predictions are benchmarked against known biological databases, such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Performance is measured by the model's ability to cluster genes with similar functions together in the embedding space and retrieve known functional annotations [4] [10].

4.1.2 Perturbation Response Prediction

• Data Generation: Single-cell CRISPR screening technologies are used to generate knockout or perturbation data. In these experiments, guide RNAs (gRNAs) targeting specific genes are introduced into cells via lentiviral transduction, and the transcriptional outcomes are measured using scRNA-seq [13].
• Modeling Protocol: The pretrained foundation model is fine-tuned or prompted with data from perturbed cells. The model's task is to predict the transcriptional state of a cell given a specific gene perturbation.
• Validation: Predictions are compared to held-out experimental data. Accuracy is assessed by correlating the predicted expression changes with the observed ones for all genes in the genome. Successful models can identify both direct and indirect targets of the perturbed gene [4] [13].

4.1.3 Analyzing Attention Maps for Network Inference

• Protocol: Attention weights from the transformer's self-attention layers are extracted. These weights form an attention map where high scores between two genes indicate a strong model-predicted relationship [1] [14].
• Validation: The inferred relationships are compared to experimentally derived protein-protein interaction networks (e.g., from STRING database) or chromatin interaction data (e.g., from Hi-C). For example, the CREaTor model demonstrated that its attention weights could prioritize functional enhancer-gene interactions validated by CRISPR screens [14].

The development and application of single-cell foundation models rely on a suite of computational tools, datasets, and resources. The following table details key components of the research ecosystem.

Table 2: Key Research Reagent Solutions for scFM Development and Application

Category	Item	Function and Utility
Data Resources	CZ CELLxGENE [1]	Provides unified access to standardized, annotated single-cell datasets; essential for sourcing diverse pretraining data.
	Human Cell Atlas [1]	A broad coverage atlas of cell types and states; serves as a foundational data corpus.
	NCBI GEO / SRA [1] [10]	Public repositories hosting thousands of single-cell studies; primary sources for raw data.
Computational Tools & Models	scGPT [1]	A versatile foundation model based on a generative transformer decoder; used for multi-omic integration and perturbation prediction.
	CellFM [10]	A large-scale foundation model with 800M parameters pretrained on 100M human cells; excels in gene function prediction.
	CREaTor [14]	An attention-based model for zero-shot modeling of cis-regulatory patterns; links CREs to target genes.
Experimental Validation	CRISPR/Cas9 Screens [13]	Enables large-scale gene perturbation; generates ground-truth data for validating model-predicted gene relationships.
	Single-cell Perturbation-seq [13]	Measures transcriptomic readout of CRISPR perturbations in single cells; key for testing causal predictions.
	ChIP-seq & ATAC-seq [14]	Provides data on transcription factor binding and chromatin accessibility; used to validate regulatory insights from models.

The adaptation of transformer architectures and attention mechanisms for single-cell transcriptomics represents a paradigm shift in computational biology. By treating cells as sentences and genes as words, scFMs like scGPT, Geneformer, and CellFM leverage self-supervised learning on massive datasets to infer the complex, context-dependent relationships between genes. The attention mechanism is particularly powerful as it provides a computationally efficient and biologically intuitive way to model gene interactions, potentially uncovering novel regulatory circuits and functional modules. While challenges remain—including the need for better interpretability, handling of multi-omic data, and reduction of computational cost—these models are robust and versatile tools poised to unlock deeper insights into cellular function and disease mechanisms, accelerating discovery in basic research and drug development.

In the development of single-cell foundation models (scFMs), tokenization serves as the critical first step that transforms raw gene expression data into a structured format understandable by deep learning architectures. Single-cell RNA sequencing (scRNA-seq) data presents unique computational challenges, including high dimensionality, significant sparsity, and technical noise [1] [4]. Tokenization addresses these challenges by converting continuous, high-dimensional expression profiles into discrete tokens that preserve biological meaning while enabling efficient model processing. This process draws inspiration from natural language processing (NLP), where words are converted into tokens for language models, but requires specialized adaptations to handle the unique characteristics of biological data [1] [15]. In scFMs, individual cells are treated analogously to sentences, while genes and their expression values become the words or tokens that constitute these cellular sentences [1]. The effectiveness of tokenization directly impacts a model's ability to capture gene-gene interactions, cell-type specificity, and regulatory relationships, making it a fundamental component in building powerful and generalizable scFMs [16].

Key Tokenization Strategies in Single-Cell Analysis

Rank-Based Discretization

Rank-based discretization transforms gene expression values into ordinal rankings within each cell, effectively capturing relative expression levels while maintaining robustness to batch effects and technical noise. This approach, utilized by models including Geneformer and GeneCompass, operates on the biological rationale that the relative ranking of gene importance often carries more information than absolute expression values for determining cell state [17] [1]. The implementation involves normalizing expression values to account for sequencing depth, then ranking genes in descending order based on their normalized expression within each cell. This method naturally deprioritizes universally high-expression housekeeping genes while highlighting genes that distinguish cell states [17]. A key advantage of rank-based discretization is its robustness to technical variations across experiments, as it focuses on relative rather than absolute expression patterns. However, this approach may discard information about the magnitude of expression differences between genes and can be sensitive to the choice of how many top-ranked genes are included for analysis [17] [1].

Bin-Based Discretization

Bin-based discretization groups continuous expression values into predefined categorical bins, preserving aspects of the absolute value distribution while simplifying sequence modeling. This approach is employed by models including scBERT, scGPT, and scMulan [17] [1]. The implementation typically involves establishing expression value thresholds that define bin boundaries, then assigning each gene to a specific bin based on its expression level in a given cell. Bins may represent expression levels such as "unexpressed," "low," "medium," and "high," with the number of bins and their boundaries being key parameters. The primary advantage of bin-based methods is their ability to maintain some information about expression magnitude while still converting continuous values into manageable categories. Limitations include inevitable information loss, particularly for genes with subtle but biologically significant expression differences, and sensitivity to parameter selection which can significantly impact downstream results [17].

Value Projection Methods

Value projection methods represent a hybrid approach that projects gene expression values into continuous embeddings rather than discrete categories. This strategy, adopted by scFoundation and its backbone model xTrimoGene, maintains full data resolution by applying a linear transformation to the gene expression vector, which is then combined with gene-specific embeddings [17] [4]. The implementation typically involves creating separate embeddings for gene identity and expression values, then combining them through element-wise multiplication or concatenation before feeding them into the model architecture. This continuous representation avoids the information loss inherent in discretization methods and can capture more subtle expression patterns. However, value projection diverges from traditional tokenization strategies in NLP and may require more sophisticated model architectures and training approaches to effectively process the continuous embeddings [17].

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

Strategy	Key Models Using This Approach	Advantages	Limitations
Rank-Based Discretization	Geneformer, GeneCompass, LangCell	Robust to batch effects and noise; captures relative expression importance	Discards magnitude information; sensitive to number of genes included
Bin-Based Discretization	scBERT, scGPT, scMulan	Preserves some absolute expression information; simplifies sequence modeling	Introduces information loss; sensitive to bin parameter selection
Value Projection	scFoundation, xTrimoGene	Maintains full expression resolution; avoids discretization artifacts	Diverges from NLP traditions; requires more complex architecture

Experimental Protocols and Methodologies

Data Preprocessing Workflow

A standardized data preprocessing pipeline is essential for effective tokenization across diverse single-cell datasets. The initial processing of single-cell data typically begins with quality control to remove low-quality cells and genes, followed by normalization to account for varying sequencing depths between cells [17]. For rank-based tokenization, the normalized expression matrix is further processed by computing the median of non-zero expression values for each gene across all cells using efficient algorithms like t-digest. The final normalized expression value for gene j in cell i is calculated as M_ij^norm = (M_ij / ∑_k=1ⁿ M_ik) / t-digest{M_kj | M_kj} > 0} [17]. Genes are then ranked within each cell in descending order based on their normalized expression values, with the top k genes typically selected for model input. For bin-based approaches, normalized expression values are mapped to discrete bins based on predefined thresholds, which may be determined empirically or through statistical methods. Value projection methods require careful scaling of expression values to ensure consistent embedding generation across datasets with different expression ranges [17] [4].

Integration with Model Architectures

Tokenization strategies must be carefully aligned with model architectures to optimize performance. Transformer-based models typically incorporate token embeddings along with positional encodings to represent the order of genes in the input sequence [1]. For models using the Mamba architecture (a state space model), such as GeneMamba, tokenized inputs are processed through bidirectional computation to capture both upstream and downstream contextual dependencies [17]. The integration often includes special tokens analogous to those used in NLP, such as [CLS] tokens for cell-level representation or modality indicators for multi-omics data [1] [4]. In graph neural network approaches like scNET, tokenized gene expressions are integrated with protein-protein interaction networks to learn context-specific gene and cell embeddings through a dual-view architecture [18]. These integrations demonstrate how tokenization serves as the bridge between raw biological data and sophisticated model architectures, enabling the capture of complex biological patterns.

Diagram 1: Tokenization Workflow for Single-Cell Foundation Models. This diagram illustrates the comprehensive pipeline from raw single-cell data to model-ready tokenized inputs, highlighting the three major tokenization strategies.

Comparative Analysis of Tokenization Approaches

Performance Across Downstream Tasks

The effectiveness of tokenization strategies must be evaluated through performance on key biological tasks. Recent benchmarking studies have assessed various approaches across multiple applications including cell type annotation, batch integration, and gene-gene relationship identification [4]. Rank-based methods have demonstrated particular strength in capturing cellular hierarchies and developmental trajectories, as their focus on relative expression aligns well with biological processes like differentiation [17] [1]. Bin-based approaches have shown robust performance in cell type classification tasks, where distinct expression categories effectively discriminate between cell states [4]. Value projection methods excel in scenarios requiring fine-grained expression analysis, such as predicting subtle perturbation effects or identifying rare cell populations, where continuous expression information provides critical sensitivity [17] [4]. Notably, no single tokenization strategy consistently outperforms all others across every task, highlighting the importance of selecting approaches based on specific biological questions and data characteristics [4].

Computational Considerations

Tokenization strategies significantly impact computational efficiency and scalability, crucial factors given the rapidly increasing scale of single-cell datasets. Rank-based tokenization typically produces the most compact representations, as only the top k genes are included for each cell, substantially reducing sequence length [17]. Bin-based approaches offer intermediate computational efficiency, with sequence length determined by the number of genes included but requiring additional embedding dimensions to represent different bins [1]. Value projection methods generally have the highest computational demands, as they maintain full gene sets and continuous values, though techniques like gene sampling can mitigate this burden [19]. The computational complexity of subsequent model architectures is directly influenced by tokenization choices; for example, transformer-based models with self-attention mechanisms scale quadratically with sequence length, making reduction of token sequence length particularly important [17] [19]. Emerging architectures like Mamba with linear scaling complexity offer potential to accommodate longer token sequences more efficiently [17].

Table 2: Computational Characteristics of Tokenization Methods for Large-Scale Single-Cell Data

Tokenization Method	Sequence Length	Memory Usage	Scalability to >1M Cells	Compatibility with Model Architectures
Rank-Based Discretization	Short (top 1,000-5,000 genes)	Low	Excellent	Transformers, Mamba, RNNs
Bin-Based Discretization	Medium (all expressed genes)	Medium	Good	Transformers, RNNs
Value Projection	Long (all genes)	High	Moderate with sampling	Transformers, Specialized architectures

Advanced Tokenization Frameworks and Integrations

Multi-Modal and Integrated Tokenization

Advanced tokenization frameworks have evolved to integrate multiple data types and biological prior knowledge. Multi-omic models incorporate special tokens to indicate modality, such as scATAC-seq for chromatin accessibility or spatial transcriptomics for positional information [1] [20]. The scNET framework demonstrates how protein-protein interaction networks can be integrated with gene expression tokenization through a dual-view architecture that simultaneously learns gene-gene and cell-cell relationships [18]. This approach uses graph neural networks to propagate gene expression information across PPI networks, effectively refining token representations with functional context. Another emerging trend incorporates biological knowledge bases directly into tokenization, such as adding gene ontology terms or pathway information as additional tokens or metadata [1] [18]. These integrated approaches demonstrate how tokenization can evolve beyond simple expression value conversion to incorporate rich biological context, significantly enhancing the biological relevance of model representations.

Emerging Architectures and Tokenization Innovations

Recent architectural innovations have driven corresponding advances in tokenization strategies. The GeneMamba model incorporates a BiMamba module that processes token sequences bidirectionally, capturing both upstream and downstream gene context with linear computational complexity [17]. This approach enables efficient processing of ultra-long sequences, potentially accommodating complete transcriptomes rather than subsets. Another innovation involves dynamic tokenization that adapts to cellular context, similar to how contemporary language models create dynamic token embeddings based on surrounding context [15]. For spatial transcriptomics, tokenization schemes incorporate positional information through absolute or relative coordinate encodings, enabling models to learn spatial expression patterns [15] [20]. These innovations demonstrate the ongoing co-evolution of tokenization strategies and model architectures, working in concert to extract increasingly sophisticated biological insights from single-cell data.

Table 3: Key Research Resources for Implementing Tokenization in Single-Cell Foundation Models

Resource Category	Specific Tools/Datasets	Function in Tokenization Research	Access Information
Benchmark Datasets	CZ CELLxGENE, Human Cell Atlas, PanglaoDB	Provide standardized, annotated single-cell data for developing and evaluating tokenization methods	Publicly available through respective portals
Pre-trained Models	Geneformer, scGPT, scFoundation	Offer pre-trained tokenization modules and embeddings that can be transferred to new datasets	Model weights and code typically available via GitHub repositories
Biological Networks	STRING, BioGRID, Human Protein Reference Database	Source of protein-protein interaction data for integrated tokenization approaches	Publicly available databases with programmatic access
Evaluation Frameworks	BioLLM, scBenchmark, scGraph-OntoRWR	Provide standardized metrics and protocols for assessing tokenization quality	Open-source implementations available
Processing Pipelines	Scanpy, Seurat, scanny	Offer preprocessing workflows that can be adapted for various tokenization strategies	Open-source packages with extensive documentation

Tokenization strategies form the critical foundation upon which single-cell foundation models are built, serving as the essential bridge between raw biological data and powerful computational architectures. The three primary approaches—rank-based discretization, bin-based discretization, and value projection—each offer distinct advantages and limitations, making them suitable for different biological questions and data characteristics. As the field progresses, emerging trends point toward more integrated tokenization schemes that incorporate multiple data modalities, biological prior knowledge, and dynamic context-aware representations. Future developments will likely focus on adaptive tokenization that automatically optimizes strategies based on data characteristics, cross-modal tokenization that seamlessly integrates diverse data types, and interpretable tokenization that provides biological insights into the representation learning process. As single-cell technologies continue to evolve, producing increasingly large and complex datasets, advances in tokenization will remain essential for unlocking the full potential of foundation models to decipher cellular complexity and drive biomedical discovery.

Self-supervised learning (SSL) has emerged as a transformative paradigm in single-cell genomics, enabling researchers to leverage vast, unlabeled datasets to build foundation models with remarkable generalization capabilities. These models learn meaningful biological representations by solving pretext tasks designed to capture inherent structures and relationships within the data. Among these tasks, masked gene prediction has established itself as a cornerstone objective, drawing inspiration from successful applications in natural language processing. However, the biological complexity of single-cell data has spurred the development of numerous complementary pretraining strategies that extend beyond this foundational approach.

This technical guide provides a comprehensive overview of the self-supervised pretraining objectives powering the next generation of single-cell foundation models (scFMs). We examine the technical specifications, implementation considerations, and relative performance of these methods within the context of a broader review of single-cell foundation model concepts. For researchers, scientists, and drug development professionals, understanding these core mechanisms is essential for selecting appropriate models, designing novel architectures, and interpreting results in biological and clinical applications.

Core Pretraining Objectives in Single-Cell Foundation Models

Self-supervised pretraining objectives equip models with generalized biological knowledge before fine-tuning for specific downstream tasks. The table below summarizes the primary objectives used in contemporary single-cell foundation models.

Table 1: Core Self-Supervised Pretraining Objectives in Single-Cell Foundation Models

Objective	Mechanism	Key Variants	Representative Models	Primary Strengths
Masked Gene Prediction	Randomly masks portions of the input gene expression vector and trains the model to reconstruct the original values [21] [10].	Random masking, Gene-programme masking, Isolated masking (GP to GP, GP to TF) [21].	scGPT [1] [20], scFoundation [10] [4], CellFM [10]	Excels in transfer learning; effective for gene-expression reconstruction and cross-modality prediction [21].
Contrastive Learning	Learns representations by maximizing agreement between differently augmented views of the same cell while distinguishing them from other cells [21].	BYOL (Bootstrap Your Own Latent), Barlow Twins [21].	UCE (Universal Cell Embedding) [4]	Addresses data sparsity and batch effects; effective for learning cell-level representations [21].
Gene Ranking Prediction	Treats a cell as a sequence of genes ordered by expression level and trains the model to predict gene rank or position [1] [10].	N/A	Geneformer [10] [4], scBERT [1] [10]	Captures context-dependent gene importance; robust to technical noise [10].
Value Categorization	Bins continuous gene expression values into discrete buckets, transforming regression into a classification task [10].	N/A	scBERT [10], scGPT [10]	Handles high technical variance in expression measurements [10].

Masked Gene Prediction: The Foundational Objective

The masked gene prediction objective, often implemented via a masked autoencoder (MAE) architecture, treats individual cells as sets of genes and their expression values. During pretraining, a random subset of a cell's gene expression values is masked (typically set to zero). The model is then tasked with reconstructing the original values based on the context provided by the remaining, unmasked genes [21] [10]. This forces the model to learn the complex, non-linear dependencies and co-expression patterns that define cellular states.

Evidence suggests that the specific masking strategy influences the quality of the learned representations. While random masking introduces minimal inductive bias, more biologically-informed strategies like gene programme (GP) masking—which masks groups of genes known to function in coordinated pathways—can guide the model toward more meaningful biological insights [21]. Empirical analyses underscore the nuanced role of SSL, showing that models pretrained on large auxiliary datasets (e.g., over 20 million cells) using masked autoencoders demonstrate significant improvements in downstream tasks like cell-type prediction and gene-expression reconstruction, particularly in transfer learning scenarios [21].

Beyond Masking: Complementary Pretraining Strategies

While powerful, masked gene prediction is often combined with or supplemented by other objectives to create more robust foundation models.

• Contrastive Learning: This approach learns effective cell embeddings by creating two augmented views of each cell (e.g., through subsampling, noise addition, or masking) and training an encoder to make their representations agree with each other while disagreeing with representations of other cells. Negative-pair-free methods like BYOL and Barlow Twins have been adapted for single-cell data to avoid the computational expense of defining negative pairs [21].
• Gene Ranking Prediction: Instead of predicting exact expression values, models like Geneformer treat the gene expression profile of a cell as a sequence of genes ranked by expression level. The pretext task involves predicting the rank of masked genes within this sequence, teaching the model about relative gene importance in different cellular contexts [10].
• Value Categorization: This strategy discretizes continuous gene expression values into a finite number of bins or "buckets," converting the reconstruction problem into a classification task. This can improve robustness to technical noise and platform-specific effects [10].

Experimental Protocols and Benchmarking

Rigorous benchmarking is essential for evaluating the performance of different pretraining objectives. The following protocol outlines a standardized workflow for this purpose.

Protocol for Benchmarking Pretraining Objectives

1. Data Curation and Preprocessing

• Data Aggregation: Compile a large-scale, diverse single-cell dataset from public repositories such as CELLxGENE, NCBI GEO, and the Human Cell Atlas [10] [22]. A high-quality corpus, such as the 100 million human cells used to train CellFM, is ideal [10].
• Quality Control: Filter cells and genes based on standard QC metrics (e.g., number of genes per cell, mitochondrial read percentage).
• Gene Name Standardization: Standardize gene nomenclature according to HUGO Gene Nomenclature Committee (HGNC) guidelines [10].
• Normalization: Apply standard normalization and log-transformation to count data.

2. Model Pretraining

• Objective Implementation: Implement the target pretraining objectives (masked prediction, contrastive learning, etc.) using a consistent base architecture (e.g., Transformer) to ensure fair comparison.
• Hyperparameter Setting: Utilize consistent training hyperparameters (learning rate, batch size, masking ratio) across objectives. For masked autoencoders, a masking ratio of 15-40% is common.

3. Downstream Task Evaluation Evaluate the pretrained models in both zero-shot and fine-tuned settings on critical biological tasks:

• Cell-type annotation: Assess prediction accuracy using macro F1 score, which is sensitive to class imbalances [21] [4].
• Batch integration: Use metrics like Local Inverse Simpson's Index (LISI) to evaluate how well the model removes technical artifacts while preserving biological variation [4].
• Perturbation response prediction: Measure the model's ability to predict cellular responses to genetic or chemical perturbations [10] [4].
• Gene function prediction: Evaluate learned gene embeddings on tasks like Gene Ontology term prediction [10] [4].

Table 2: Comparative Performance of Pretraining Objectives on Key Downstream Tasks

Pretraining Objective	Cell-Type Annotation (Macro F1)	Batch Integration (LISI Score)	Perturbation Prediction (Accuracy)	Gene Function Prediction (AUPRC)
Masked Gene Prediction	0.7466 (PBMC) [21]	0.89 (Pancreas) [4]	0.81 (Geneformer) [4]	0.72 (CellFM) [10]
Contrastive Learning	0.7013 (PBMC) [21]	0.85 (Pancreas) [4]	0.76 (UCE) [4]	0.68 (UCE) [4]
Gene Ranking Prediction	0.7310 (Geneformer) [4]	0.87 (Pancreas) [4]	0.83 (Geneformer) [4]	0.75 (Geneformer) [10]
Supervised Baseline	0.7120 (PBMC) [21]	0.82 (Pancreas) [4]	0.79 (MLP) [4]	0.65 (Logistic Regression) [4]

Key Findings from Benchmarking Studies

Recent benchmarking studies have yielded several critical insights:

• Transfer Learning Superiority: The primary strength of self-supervised pretraining, particularly masked autoencoders, emerges in transfer learning scenarios where models pretrained on large auxiliary datasets (e.g., >20 million cells) are applied to smaller target datasets. Performance improvements of 4-13% in macro F1 scores have been observed for cell-type annotation on datasets like PBMC and Tabula Sapiens [21].
• Zero-Shot Capabilities: Models pretrained with masked objectives demonstrate notable zero-shot capabilities, successfully annotating cell types without task-specific fine-tuning [21] [23].
• Objective-Dependent Performance: No single pretraining objective consistently outperforms all others across every task. Masked autoencoders generally excel in gene-expression reconstruction and cross-modality prediction, while contrastive methods can be more effective for certain cell-level representation tasks [21] [4].
• Architectural Considerations: While transformer architectures dominate the scFM landscape, masked autoencoders based on fully connected networks have shown competitive performance, suggesting that pretraining objective may be as important as model architecture [21].

Visualization of Pretraining Workflows and Relationships

The following diagrams illustrate the core workflows for the primary pretraining objectives and their relationships to downstream tasks.

Diagram 1: Masked Gene Prediction Workflow. This objective trains the model to reconstruct randomly masked portions of the gene expression vector, forcing it to learn co-expression patterns and biological dependencies.

Diagram 2: Relationship Between Pretraining Objectives and Downstream Applications. Different self-supervised objectives produce representations with particular strengths for specific biological tasks.

Successfully developing and applying single-cell foundation models requires access to specific data, computational resources, and software tools.

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

Resource Category	Specific Resource	Function/Purpose	Key Features
Data Repositories	CZ CELLxGENE [1] [22]	Provides unified access to standardized single-cell datasets.	Over 100 million unique cells; standardized analysis format [22].
	NCBI GEO / SRA [1] [10]	Archives raw and processed single-cell sequencing data.	Extensive collection of diverse studies and technologies.
	Human Cell Atlas [21] [1]	Reference map of all human cells.	Broad coverage of cell types and states across tissues.
Computational Platforms	BioLLM [20]	Standardized framework for benchmarking scFMs.	Universal interface for >15 foundation models [20].
	DISCO [20]	Single-cell data portal for federated analysis.	Aggregates data from multiple studies with query interface.
	scGPT [1] [20]	End-to-end foundation model framework.	Pretrained on 33M+ cells; supports multiple downstream tasks [20].
Analysis Frameworks	Scanpy [10]	Python-based toolkit for single-cell analysis.	Standard preprocessing, visualization, and analysis workflows.
	Seurat [4]	R toolkit for single-cell genomics.	Comprehensive suite for analysis, integration, and discovery.
	Harmony [4]	Integration method for single-cell data.	Fast, sensitive batch effect correction without compromising biology.

Self-supervised pretraining objectives, with masked gene prediction at the forefront, have fundamentally transformed the analysis of single-cell genomic data. These methods enable models to learn transferable biological knowledge from vast, unlabeled datasets, forming the foundation for powerful, generalizable tools. While masked autoencoding has proven particularly effective, especially in transfer learning scenarios, the diversity of objectives—from contrastive learning to gene ranking—provides researchers with a rich toolkit for addressing specific biological questions.

The ongoing benchmarking of these approaches reveals a nuanced landscape: no single objective dominates across all tasks, emphasizing the importance of task-specific model selection. As the field progresses, the integration of multiple objectives, the development of more biologically-informed pretext tasks, and improved evaluation metrics will further enhance the capabilities of single-cell foundation models. For researchers and drug development professionals, understanding these core mechanisms is no longer optional but essential for leveraging the full potential of single-cell technologies in basic research and translational applications.

The emergence of single-cell genomics has fundamentally transformed biological research by enabling the characterization of cellular heterogeneity at unprecedented resolution. A critical driver of this transformation has been the development of large-scale, publicly accessible data repositories that serve as foundational resources for the scientific community. These repositories provide the vast, diverse datasets necessary for training sophisticated computational models, including single-cell foundation models (scFMs), which require massive amounts of standardized data to learn universal biological patterns. Platforms such as CZ CELLxGENE Discover and initiatives like the Human Cell Atlas (HCA) have become indispensable pillars in this ecosystem, aggregating and standardizing single-cell data from thousands of studies worldwide [24] [25].

The scale of these resources is substantial. As of 2025, CZ CELLxGENE Discover hosts over 33 million unique cells from 436 datasets, characterizing more than 2,700 cell types across healthy human and mouse tissues [24]. Concurrently, the Human Cell Atlas consortium—a global collaborative effort involving over 3,900 members from more than 100 countries—is executing its mission to create comprehensive reference maps of all human cells [25]. These repositories do not merely serve as data archives; they provide standardized, analysis-ready data that has been processed through uniform computational pipelines, enabling robust comparative analyses and meta-analyses across diverse studies and experimental conditions. For researchers developing and applying single-cell foundation models, these resources provide the critical pretraining corpora necessary to build models that can generalize across tissues, species, and biological contexts.

Comprehensive Landscape of Major Data Repositories

Core Repository Specifications and Features

Table 1: Major Public Single-Cell Data Repositories

Repository Name	Primary Content	Scale (as of 2025)	Key Features	Common Applications
CZ CELLxGENE Discover [24]	Standardized single-cell transcriptomics data from healthy human and mouse tissues	33M+ cells, 436 datasets, 2.7K+ cell types [24]	Differential expression tool, Census API, Cell Guide, interactive Explorer, Collections & Datasets	Cell type annotation, differential expression analysis, dataset exploration, model pretraining
Human Cell Atlas (HCA) [25]	Comprehensive reference maps of all human cells from multiple tissues and organs	Global consortium with 3,900+ members from 1,700+ institutes [25]	Open global initiative, organized biological networks, partnership with UNESCO for open science	Reference atlas construction, cross-tissue integration, cell ontology development
DISCO [20]	Aggregated single-cell data across multiple studies and modalities	100M+ cells (aggregated) [20]	Federated analysis capabilities, query interfaces across diverse datasets	Large-scale integrative analysis, cross-study validation
NCBI GEO/SRA [1]	Archival repository for high-throughput sequencing data	Thousands of single-cell sequencing studies [1]	Primary data submission hub, raw and processed data, links to original publications	Data preservation, method benchmarking, reanalysis

Beyond the primary repositories, several specialized resources have emerged to address specific analytical needs. The Census component of CZ CELLxGENE provides programmatic access to any custom slice of standardized cell data through R and Python interfaces, enabling seamless integration into computational workflows [24]. The Cell Guide offers an interactive encyclopedia of over 700 cell types with detailed definitions, marker genes, lineage information, and relevant datasets in one place [24].

For cross-species comparisons and specialized taxonomic groups, resources like scPlantFormer have been pretrained on approximately 1 million Arabidopsis thaliana cells, facilitating plant single-cell omics analysis [20]. The Asian Immune Diversity Atlas (AIDA) v2, available through CELLxGENE, represents an example of population-specific references that are increasingly important for capturing human genetic diversity [4].

These repositories collectively enable the "mosaic integration" approach, where datasets that do not measure identical features can be aligned by leveraging shared cell neighborhoods or robust cross-modal anchors rather than requiring strict feature overlaps [20]. This capability is particularly valuable for integrative analyses across platforms and modalities.

Data Curation Workflows and Standardization Processes

From Raw Data to Analysis-Ready Corpora

The transformation of raw single-cell sequencing data into analysis-ready resources involves multiple curation steps that are critical for ensuring data quality and interoperability. CELLxGENE employs a standardized processing pipeline that performs key harmonization steps including quality control, normalization, batch effect correction, and annotation [24]. This standardized processing is essential for creating the unified corpora required for scFM pretraining, as it mitigates technical variation across different experimental protocols and platforms.

A fundamental challenge in single-cell data curation is the integration of multimodal data—including transcriptomics, epigenomics, proteomics, and spatial information—measured from the same cell [26]. The curation process must preserve the natural biological relationships between these modalities while removing technical artifacts. Methods for this integration include canonical correlation vectorization (CCV), which identifies shared features across modalities by projecting cells into a common basis space, and manifold alignment, which unravels pseudotemporal relationships between different molecular layers such as gene expression and DNA methylation [26].

Table 2: Data Curation and Integration Methods for Single-Cell Repositories

Curation Step	Key Methods/Tools	Purpose	Considerations
Quality Control	scran, scater [27]	Filtering low-quality cells/genes, doublet detection	Dataset-specific thresholds, technology-dependent parameters
Batch Correction	Harmony [27], Seurat CCA [27], scVI [27]	Removing technical variation while preserving biology	Correction strength tuning, biological signal preservation
Multimodal Integration	StabMap [20], TMO-Net [20]	Aligning different omics modalities from same cells	Handling non-overlapping features, preserving inter-modality relationships
Cell Type Annotation	SingleR [28], Azimuth [28]	Assigning cell identities using reference datasets	Resolution levels (broad to detailed), consensus approaches

Metadata Standardization and Ontology Implementation

Effective data curation extends beyond processing molecular measurements to encompass comprehensive metadata standardization. Repositories like CELLxGENE and HCA employ structured ontologies including Cell Ontology (CL), Uberon anatomy ontology, and Gene Ontology (GO) to ensure consistent annotation across datasets [24] [25]. This ontological framework enables precise semantic queries and facilitates cross-dataset integration by establishing common terminologies for cell types, tissues, and biological processes.

The implementation of these ontologies is particularly crucial for scFM development, as it provides the biological grounding necessary for models to learn meaningful representations rather than merely technical artifacts. As noted in benchmark studies, scFMs that incorporate ontological relationships in their training objectives demonstrate superior performance in tasks such as cross-species annotation and rare cell type identification [4].

Diagram 1: Single-cell data curation workflow for repository integration and model pretraining.

Experimental Protocols for Repository-Driven Research

Reference-Based Cell Type Annotation

Leveraging curated public repositories enables robust cell type identification through reference-based annotation, a fundamental application in single-cell analysis. The standard protocol involves:

• In-depth Preprocessing: Rigorous quality control to filter low-quality cells or genes, followed by doublet detection and batch correction to mitigate technical variation from differences in sample preparation or sequencing runs [28].

• Reference Dataset Selection: Careful selection of appropriate reference datasets from repositories based on tissue similarity, species, and experimental protocol. Researchers typically conduct an in-depth review of literature and available cell atlases to identify the most suitable reference datasets [28].

• Annotation Transfer: Using tools such as SingleR or Azimuth to align the gene expression profiles of each single cell with references from similar tissues [28]. The Azimuth project provides cell type annotations at different levels—from broad categories to very detailed subtypes—allowing researchers to choose the appropriate resolution.

• Manual Refinement: Careful review of preliminary annotations against multiple sources of evidence, including verification of canonical marker gene expression patterns, differential gene expression analyses, and consultation of relevant literature [28]. This step integrates biological expertise to interpret ambiguous clusters or edge cases.

This protocol exemplifies how curated repositories serve not merely as data sources but as knowledge bases that transfer biological understanding from well-characterized reference datasets to novel experimental data.

Foundation Model Pretraining Using Repository Data

The development of single-cell foundation models relies on carefully designed pretraining protocols using repository-scale data:

• Data Sourcing and Selection: Compilation of large and diverse datasets from repositories such as CELLxGENE, which provides unified access to annotated single-cell datasets with over 100 million unique cells standardized for analysis [1]. Effective pretraining requires careful selection of datasets, filtering of cells and genes, balancing dataset compositions, and quality controls [1].

• Tokenization Strategy: Conversion of gene expression profiles into discrete tokens that scFMs can process. Common approaches include ranking genes within each cell by expression levels, partitioning genes into expression bins, or using normalized counts directly [1]. Special tokens representing cell identity, metadata, or modality information may be prepended to enrich the input context.

• Model Architecture Configuration: Implementation of transformer-based architectures, typically using either bidirectional encoder representations (BERT-like) for classification tasks or autoregressive decoder architectures (GPT-like) for generation tasks [1]. The attention mechanisms in these architectures enable the model to learn relationships between genes and how they covary across cells.

• Self-Supervised Pretraining: Training models using objectives such as masked gene modeling, where a portion of input genes are masked and the model must predict them based on the remaining context [1]. This approach allows the model to learn fundamental biological patterns without requiring labeled data.

Diagram 2: scFM pretraining protocol using public repository data.

Table 3: Essential Computational Tools for Repository-Based Single-Cell Analysis

Tool Category	Representative Tools	Primary Function	Application in Repository Research
Comprehensive Analysis Platforms	Scanpy [27], Seurat [27]	End-to-end scRNA-seq analysis	Data preprocessing, visualization, and integration of repository datasets
Deep Learning Frameworks	scvi-tools [27], scGPT [20]	Probabilistic modeling and foundation models	Batch correction, imputation, and transfer learning on repository data
Spatial Analysis Tools	Squidpy [27], Nicheformer [20]	Spatially resolved transcriptomics	Integrating spatial context with repository single-cell data
Trajectory Inference	Monocle 3 [27], Velocyto [27]	Pseudotime and cell fate analysis	Mapping developmental trajectories using reference atlases
Multimodal Integration	StabMap [20], TMO-Net [20]	Integrating multiple omics modalities	Combining repository datasets across different molecular layers
Benchmarking Platforms	BioLLM [20]	Standardized model evaluation	Comparing scFM performance across tasks and datasets

Public data repositories have evolved from passive archives to active knowledge platforms that drive discovery in single-cell biology. The continued growth of resources like CELLxGENE and Human Cell Atlas, coupled with advances in computational methods that can leverage these vast data collections, promises to accelerate our understanding of cellular mechanisms in both health and disease. For the field of single-cell foundation models, these repositories provide not only the training data necessary for model development but also the reference frameworks for biological interpretation and validation.

Future developments will likely focus on enhancing multimodal integration, improving cross-species generalization, and developing more efficient data structures for querying and analyzing repository-scale data. As these resources continue to expand, they will play an increasingly central role in enabling researchers to translate cellular-level insights into clinical applications and therapeutic developments.

Practical Implementation: scFM Workflows, Drug Discovery Applications, and Multi-Omic Integration

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the investigation of gene expression at unprecedented resolution, revealing cellular heterogeneity and complex biological processes that are obscured in bulk sequencing data [29]. Concurrently, the field has witnessed the emergence of single-cell foundation models (scFMs)—large-scale deep learning models pretrained on vast datasets—which are revolutionizing data interpretation through self-supervised learning with capacity for various downstream tasks [1]. These technological advances have created an urgent need for unified analytical frameworks capable of integrating and comprehensively analyzing rapidly expanding data repositories.

The volume and complexity of data generated by modern single-cell technologies necessitate robust, standardized, yet flexible end-to-end analysis pipelines that guide researchers from raw sequencing data to meaningful biological insights. These pipelines must address multiple challenges inherent to single-cell data, including high dimensionality, technical noise, batch effects, and the integration of multimodal measurements [30] [31]. This technical guide examines the architecture, components, and implementation of these pipelines within the broader context of single-cell foundation model research, providing researchers and drug development professionals with a comprehensive framework for navigating this rapidly evolving landscape.

Foundational Concepts: From Single-Cell Data to Foundation Models

The Single-Cell Data Landscape

Single-cell technologies have progressed from measuring just the transcriptome to simultaneously capturing multiple molecular layers from the same cells. Modern multi-omics assays can measure gene expression, chromatin accessibility, DNA methylation, and protein abundance in tandem, creating datasets of immense value and complexity [31]. The fundamental computational challenge lies in integrating these different omics layers with distinct feature spaces—for example, accessible chromatin regions in scATAC-seq versus genes in scRNA-seq [32]. Effective pipelines must bridge these modality gaps while preserving biological signals and removing technical artifacts.

A critical characteristic of single-cell data is its high sparsity, high dimensionality, and low signal-to-noise ratio [4]. Gene expression matrices typically contain thousands of cells measured across tens of thousands of genes, with most genes showing zero counts in most cells due to both biological and technical factors. This sparsity presents unique challenges for analytical methods and requires specialized statistical approaches distinct from those used for bulk sequencing data.

The Rise of Single-Cell Foundation Models

Inspired by successes in natural language processing (NLP) and computer vision, researchers have begun developing scFMs that learn from extensive single-cell datasets and can be fine-tuned for various biological analyses [1]. A foundation model is defined as a large-scale, self-supervised artificial intelligence model trained on diverse datasets that can be adapted to a wide range of tasks [1]. These models typically employ transformer architectures that use attention mechanisms to learn relationships between genes, analogous to how language models learn relationships between words [1].

In the scFM paradigm, individual cells are treated analogously to sentences, and genes or other genomic features along with their values are treated as words or tokens [1]. The premise is that by exposing a model to millions of cells encompassing many tissues and conditions, the model can learn fundamental principles of cellular biology that generalize to new datasets or downstream tasks. Early scFMs like scBERT and scGPT appeared around 2022, trained on millions of single-cell transcriptomes in a self-supervised manner [1]. Since then, several large-scale scFMs have been introduced, each leveraging massive single-cell corpora with the goal of learning unified representations that enable diverse biological analyses.

Pipeline Architecture: Components of End-to-End Analysis

Raw Data Processing and Quality Control

The initial stage of any single-cell analysis pipeline involves processing raw sequencing data into gene expression matrices while performing comprehensive quality control. This foundation is critical, as errors introduced at this stage propagate through all downstream analyses.

Sequencing Read Processing: Raw FASTQ files undergo quality assessment, adapter trimming, and alignment to reference genomes. Tools like Cell Ranger (for 10x Genomics data) provide standardized workflows for this process, leveraging the STAR aligner under the hood for accurate and rapid alignment [27]. For specialized applications like allele-specific expression, SNP-tolerant aligners such as GSNAP or WASP-integrated STAR are employed to reduce reference allele bias [33].

Quality Control Metrics: Comprehensive QC assesses multiple aspects of data quality, including reads per cell, genes per cell, mitochondrial read percentage, and complexity measures. Automated pipelines like aPEAch integrate tools like FastQC and Picard to generate standardized QC reports, enabling informed decisions about cell and gene filtering [30]. At this stage, ambient RNA contamination—a common issue in droplet-based technologies—can be addressed using deep learning tools like CellBender that distinguish real cellular signals from background noise [27].

Table 1: Key Quality Control Metrics and Interpretation

Metric	Optimal Range	Indication of Problems	Common Solutions
Reads per cell	Platform-dependent	Low values indicate poor capture	Filter cells with extremely low counts
Genes per cell	>500-1000	Low complexity cells	Filter based on minimum gene detection
Mitochondrial %	<10-20%	High values indicate stressed/dying cells	Filter cells with high mitochondrial content
Ambient RNA	Minimize	Contamination from damaged cells	Computational removal (e.g., CellBender)
Doublet rate	Platform-dependent	Multiple cells in one droplet	Doublet detection algorithms

Normalization, Batch Correction, and Feature Selection

Following quality control, data must be normalized to remove technical biases and make expression values comparable across cells.

Normalization Approaches: Methods range from simple library size normalization (e.g., counts per million) to more sophisticated approaches that account for composition effects (e.g., SCTransform). The choice of normalization method can significantly impact downstream results, particularly for trajectory inference and differential expression testing.

Batch Effect Correction: When integrating datasets across different experiments, platforms, or donors, batch effects must be addressed without removing biological variation. Methods like Harmony efficiently correct batch effects by iteratively clustering cells and correcting embeddings, preserving biological variation while aligning datasets [27]. Similarly, deep learning approaches like those implemented in scvi-tools use variational autoencoders to model the noise and latent structure of single-cell data, providing superior batch correction [27].

Feature Selection: To reduce dimensionality and computational burden, pipelines typically select genes exhibiting high cell-to-cell variation (highly variable genes). The selection method and number of genes retained can significantly impact downstream analyses, with more sophisticated approaches leveraging statistical modeling of the mean-variance relationship.

Dimensionality Reduction and Cell State Identification

The high dimensionality of single-cell data necessitates dimensionality reduction for visualization and analysis. Principal Component Analysis (PCA) is commonly applied initially, followed by nonlinear methods like UMAP (Uniform Manifold Approximation and Projection) or t-SNE for visualization [27].

Cell clustering identifies discrete cell states and types, typically using graph-based methods (e.g., Louvain or Leiden algorithm) applied to a k-nearest neighbor graph constructed in reduced dimension space. The resolution parameter controls the granularity of clustering, with higher values resulting in more fine-grained clusters. Automated cell type annotation then leverages reference datasets to assign biological identities to clusters, with tools like SingleR or automated modules in platforms like Nygen comparing cluster gene expression profiles to annotated reference data [34].

Advanced Analysis: Trajectory Inference and Multi-Omics Integration

Trajectory Inference: Tools like Monocle 3 and Velocyto model dynamic biological processes such as differentiation or response to stimuli [27]. Monocle 3 uses graph-based abstraction to model lineage branching, while Velocyto quantifies spliced and unspliced transcripts to infer future transcriptional states of individual cells through RNA velocity analysis.

Multi-Omics Integration: The integration of different data modalities (e.g., scRNA-seq + scATAC-seq) presents unique computational challenges due to distinct feature spaces. Frameworks like GLUE (Graph-Linked Unified Embedding) address this by modeling regulatory interactions across omics layers explicitly through a knowledge-based guidance graph [32]. Similarly, MOFA+ uses matrix factorization with automatic relevance determination to identify latent factors that represent shared variation across modalities [31].

Single-Cell Foundation Models in the Analytical Pipeline

Architecture and Training of scFMs

Single-cell foundation models typically employ transformer architectures that use self-attention mechanisms to model relationships between genes [1]. Unlike natural language, where words have a natural order, genes lack inherent sequencing, requiring innovative solutions for tokenization and positional encoding.

Tokenization Strategies: In scFMs, tokenization involves defining what constitutes a 'token' from single-cell data, typically representing each gene as a token [1]. Common approaches include ranking genes within each cell by expression levels and feeding the ordered list of top genes as the 'sentence' [1]. Other models partition genes into bins by expression values or simply use normalized counts with positional encoding schemes to represent relative order.

Model Architectures: Most scFMs use variants of the transformer architecture [1]. Some adopt a BERT-like encoder architecture with bidirectional attention, allowing the model to learn from the context of all genes in a cell simultaneously [1]. Others, like scGPT, use decoder-inspired architectures with unidirectional masked self-attention that iteratively predict masked genes conditioned on known genes [1]. The optimal architecture depends on the intended applications, with encoder models generally better for classification and embedding tasks, and decoder models superior for generation.

Pretraining Strategies: scFMs are pretrained on massive collections of single-cell data using self-supervised objectives, often through predicting masked genes or other pretext tasks [1]. Platforms like CZ CELLxGENE provide unified access to annotated single-cell datasets, with over 100 million unique cells standardized for analysis, enabling comprehensive pretraining [1].

Integration of scFMs into Analytical Workflows

scFMs can enhance multiple stages of the analytical pipeline through their learned representations of biological knowledge:

Enhanced Cell Typing: Foundation model embeddings can improve cell type annotation, particularly for rare or novel cell states that might be missed by conventional methods. The contextual understanding learned during pretraining helps recognize cell states even with limited marker information.

Batch Correction and Data Integration: The rich representations learned by scFMs can facilitate more biologically meaningful data integration. For example, scGPT and Geneformer have been applied to batch integration tasks, leveraging their pretrained understanding of biological variation to distinguish technical artifacts from true biological differences [4].

Zero-Shot Analysis: A key advantage of scFMs is their ability to perform zero-shot learning—applying knowledge gained during pretraining to new tasks without additional training [4]. This enables analyses such as predicting cellular responses to perturbation or identifying novel cell states without task-specific training data.

Multi-Omics Integration: scFMs can be extended to incorporate multiple modalities by including modality-specific tokens and embeddings. For example, GLUE uses a guidance graph that explicitly models regulatory interactions between different omics layers, such as connecting accessible chromatin regions to their putative target genes [32].

Experimental Protocols and Benchmarking

Standardized Analytical Protocols

Robust analytical pipelines require standardized protocols for common analytical tasks. Below we outline protocols for key analyses incorporating foundation model approaches:

Protocol 1: Comprehensive scRNA-seq Analysis with Foundation Model Enhancement

• Data Preprocessing: Process raw FASTQ files using Cell Ranger or equivalent aligner to generate count matrices [27].
• Quality Control: Filter cells with <500 genes detected, >20% mitochondrial reads, and potential doublets using DoubletFinder or similar.
• Normalization: Normalize using SCTransform or scGPT's integrated normalization [27] [4].
• Integration: For multiple datasets, integrate using Harmony or scVI, or employ scFM embeddings directly [27] [4].
• Clustering: Perform graph-based clustering (Leiden algorithm) on PCA or scFM embeddings.
• Cell Type Annotation: Use reference-based annotation (SingleR) enhanced by scFM contextual embeddings [4] [34].
• Differential Expression: Identify marker genes using Wilcoxon rank-sum test or model-based approaches.
• Trajectory Analysis: Construct trajectories using Monocle 3 or PAGA on scFM embeddings.

Protocol 2: Multi-Omics Integration Using Graph-Linked Approaches

• Modality-Specific Processing: Process each omics layer independently using appropriate methods (e.g., Signac for scATAC-seq).
• Guidance Graph Construction: Build knowledge graph connecting features across modalities (e.g., linking ATAC peaks to genes based on genomic proximity or chromatin conformation data) [32].
• GLUE Integration: Apply GLUE framework with modality-specific autoencoders linked through the guidance graph [32].
• Joint Visualization: Visualize integrated embeddings using UMAP with modality overlays.
• Regulatory Inference: Extract feature embeddings to predict putative regulatory relationships.
• Biological Validation: Validate predictions using motif enrichment, chromatin interaction data, or functional assays.

Benchmarking and Performance Assessment

Systematic benchmarking is essential for selecting appropriate methods and understanding their limitations. A comprehensive benchmark of six scFMs against established baselines revealed several key insights [4]:

• Task-Dependent Performance: 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 [4].
• Biological Relevance: scFMs demonstrate superior capture of biologically meaningful relationships, as measured by novel metrics like scGraph-OntoRWR, which quantifies consistency between model-derived cell type relationships and established biological knowledge [4].
• Data Efficiency: While scFMs excel with large, diverse datasets, simpler machine learning models can be more efficient for small, focused datasets with limited computational resources [4].

Table 2: Performance Comparison of Single-Cell Analysis Methods

Method	Strengths	Limitations	Optimal Use Cases
Seurat	Versatile, well-documented, supports multiple modalities	R-based, can be memory-intensive for huge datasets	Standard scRNA-seq analysis, CITE-seq, spatial transcriptomics
Scanpy	Scalable to millions of cells, Python ecosystem	Steeper learning curve for beginners	Large-scale integration, advanced development
scVI	Probabilistic modeling, excellent batch correction	Requires GPU for large datasets, more complex implementation	Large dataset integration, probabilistic queries
Harmony	Efficient batch correction, preserves biology	Primarily for batch correction only	Multi-dataset integration, atlas construction
scGPT	Foundation model capabilities, transfer learning	Computational intensity for training/fine-tuning	Novel cell state identification, zero-shot prediction
GLUE	Multi-omics integration, regulatory inference	Complex setup, guidance graph dependency	Integrated scRNA-seq + scATAC-seq analysis

Visualization and Interpretation

Effective visualization is critical for interpreting single-cell data and deriving biological insights. Standard approaches include:

UMAP/t-SNE Visualization: Nonlinear dimensionality reduction techniques project high-dimensional data into two or three dimensions for visualization of cell state relationships [27]. While invaluable for exploration, these visualizations can sometimes create misleading apparent structure, requiring careful interpretation in biological context.

Gene Expression Visualization: Dot plots, violin plots, and feature plots display expression patterns of key genes across clusters or trajectories, enabling identification of marker genes and biological validation of cell states.

Multi-Omics Visualization: Integrated visualization of multiple modalities, such as overlaying chromatin accessibility on transcriptomic embeddings, reveals relationships between different molecular layers [32].

The following diagram illustrates a complete end-to-end single-cell analysis pipeline incorporating foundation models:

Essential Research Reagent Solutions

The computational pipeline relies on a suite of software tools and resources that function as the "research reagents" of bioinformatics. The table below details essential components of the single-cell analytical toolkit:

Table 3: Essential Computational Tools for Single-Cell Analysis

Tool Category	Representative Tools	Primary Function	Key Applications
Raw Data Processing	Cell Ranger, STAR, FastQC	Sequence alignment, quality control	Generating count matrices from FASTQ files
Quality Control	CellBender, Scrublet, SoupX	Ambient RNA removal, doublet detection	Data cleaning, quality assessment
Data Integration	Harmony, Seurat, Scanorama	Batch correction, dataset integration	Combining multiple experiments
Foundation Models	scGPT, Geneformer, scBERT	Pretrained representations, transfer learning	Cell annotation, perturbation prediction
Visualization	UMAP, t-SNE, SCope	Dimensionality reduction, visualization	Data exploration, result presentation
Cell Type Annotation	SingleR, Garnett, scANVI	Automated cell labeling	Cell identity assignment
Trajectory Analysis	Monocle 3, PAGA, Slingshot	Lineage reconstruction, pseudotime	Development, differentiation
Multi-Omics Integration	GLUE, MOFA+, Seurat v4	Integrating different data modalities	Combined RNA+ATAC, CITE-seq analysis
Differential Expression	MAST, DESingle, diffxpy	Identifying marker genes	Cell type signatures, response genes
Pathway Analysis	GSEA, AUCell, Vision	Gene set enrichment, activity scoring	Functional interpretation

End-to-end analysis pipelines represent the critical infrastructure transforming raw single-cell sequencing data into biological insights. The integration of single-cell foundation models into these pipelines marks a significant advancement, offering more unified representations of cellular biology that enhance multiple analytical tasks. However, challenges remain in standardization, interpretability, and computational efficiency.

Future developments will likely focus on several key areas: (1) enhanced multi-omics integration through more sophisticated graph-based approaches that better model regulatory networks; (2) improved interpretability of foundation models to extract novel biological mechanisms from their learned representations; (3) clinical translation through robust biomarker identification and patient stratification; and (4) spatial context integration combining single-cell genomics with spatial transcriptomics for tissue-level understanding.

As these technologies mature, the interplay between experimental biology and computational analysis will deepen, with foundation models potentially guiding experimental design through in silico perturbation predictions. The continued development of standardized, validated, and accessible analytical pipelines will be crucial for realizing the full potential of single-cell technologies in both basic research and therapeutic development.

The drug discovery process is traditionally characterized by extensive timelines, high costs, and alarmingly high failure rates, typically requiring over 12 years and $2.3 billion to bring a new drug to market, with failure rates exceeding 90% [35] [36]. This inefficiency has catalyzed a transformative shift toward artificial intelligence (AI)-driven approaches, particularly leveraging single-cell technologies. Single-cell foundation models (scFMs) represent a revolutionary class of AI tools trained on massive single-cell datasets that are reshaping target identification, perturbation prediction, and compound screening [1] [37]. These models learn fundamental biological principles from millions of cells, enabling researchers to decipher the "language" of biology and make accurate predictions across diverse biological contexts and downstream tasks [1]. By providing a unified framework for analyzing cellular heterogeneity and complex regulatory networks, scFMs are accelerating the translation of cellular insights into therapeutic opportunities.

Single-Cell Foundation Models: Core Concepts and Architectures

Fundamental Architecture and Training Approaches

Single-cell foundation models are large-scale deep learning models pretrained on vast single-cell datasets using self-supervised learning objectives [1]. These models typically employ transformer architectures, which utilize attention mechanisms to weight relationships between genes, allowing the models to learn which genes are most informative of a cell's identity or state and how they covary across cells [1]. The training process involves exposing models to millions of single-cell transcriptomes encompassing diverse tissues, species, and biological conditions, enabling them to capture universal patterns of cellular behavior [1] [37].

In the architecture of scFMs, individual cells are treated analogously to sentences, while genes or other genomic features along with their expression values are treated as words or tokens [1]. Two predominant architectural paradigms have emerged: BERT-like encoder architectures with bidirectional attention mechanisms that learn from all genes in a cell simultaneously, and GPT-like decoder architectures with unidirectional masked self-attention that iteratively predict masked genes conditioned on known genes [1]. Hybrid designs are also being explored, though no single architecture has yet emerged as clearly superior for all single-cell data tasks.

Data Processing and Tokenization Strategies

A critical technical challenge for scFMs is the non-sequential nature of omics data, unlike words in sentences. To address this, researchers have developed various tokenization strategies to convert raw gene expression data into structured inputs that models can process:

• Gene ranking approaches: Genes are ranked within each cell by expression levels, and the ordered list of top genes is treated as the input sequence [1]
• Expression binning: Genes are partitioned into bins based on expression values, with rankings determining positional encoding [1]
• Normalized counts: Some models report no clear advantages for complex ranking strategies and simply use normalized counts [1]
• Special tokens: Additional tokens representing cell identity metadata, experimental batch information, or modality indicators can be prepended to enrich input context [1]

After tokenization, all tokens are converted to embedding vectors that combine gene identifier information with expression values, which are then processed by the transformer layers to generate latent embeddings for each gene token and often a dedicated embedding for the entire cell [1].

Target Identification Through Multi-Omic Integration

Multi-Omic Data Integration Strategies

Target identification has been revolutionized by multi-omics approaches that integrate diverse biological datasets across genomics, transcriptomics, proteomics, and metabolomics [38]. By breaking traditional siloed approaches, multi-omics enables researchers to distinguish causal mutations from inconsequential ones through layered analysis of biological pathways [38]. For example, while genomics can identify disease-associated mutations, transcriptomics and translatomics reveal which mutations actually impact RNA transcription and translation, and proteomics shows the functional protein output, providing crucial context for identifying druggable targets [38].

Advanced computational methods for multi-omic integration include:

• TMO-Net: Pan-cancer multi-omic pretraining that captures cross-modal regulatory patterns [37]
• StabMap: Mosaic integration techniques for harmonizing datasets with non-overlapping features [37]
• PathOmCLIP: Aligns histology images with spatial transcriptomics via contrastive learning [37]
• GIST: Combines histology with multi-omic profiles for 3D tissue modeling [37]

Interpretable Target Discovery with Graph Neural Networks

Graph neural networks (GNNs) have emerged as powerful tools for target discovery by modeling biological systems as networks. The PDGrapher framework exemplifies this approach by solving the inverse problem of identifying which therapeutic targets need perturbation to shift disease states toward healthy states [39]. Unlike traditional methods that learn how perturbations alter phenotypes, PDGrapher directly predicts perturbagens capable of reversing disease phenotypes by embedding disease cell states into protein-protein interaction or gene regulatory networks and learning latent representations of these states [39].

Table 1: Key Target Identification Methods and Applications

Method	Approach	Key Application	Performance Advantage
PDGrapher [39]	Graph neural network with causal inspiration	Predicting combinatorial therapeutic targets	Identifies 13.37% more ground-truth targets in chemical interventions
scGPT [1] [37]	Transformer-based foundation model	Cross-species cell annotation and target discovery	Pretrained on 33+ million cells for zero-shot transfer
BridgeDPI [36]	"Guilt-by-association" principles with network-based learning	Drug-target interaction prediction	Combines network- and learning-based approaches
EpiAgent [37]	Specialized epigenomic pretraining	Capturing regulatory mechanisms	Focuses on epigenetic-level target identification

Perturbation Prediction: From Empirical to Causal Methods

Foundation Model Approaches to Perturbation Modeling

Perturbation prediction involves forecasting how cells respond to genetic or chemical interventions, a capability where scFMs have demonstrated remarkable performance. Models like scGPT and scFoundation employ masked gene modeling during pretraining, where random genes are masked and the model learns to predict their values based on context, inherently learning regulatory relationships and making them well-suited for perturbation prediction [1] [37]. These models can predict outcomes for unseen perturbations by learning fundamental biological principles rather than merely memorizing empirical relationships.

Specialized frameworks have been developed for specific perturbation modeling challenges:

• CRADLE-VAE: Tailored for perturbation modeling, learning how cellular states respond to experimental perturbations [37]
• Nicheformer: Trained on 53-110 million spatially resolved cells to model spatial cellular niches and their perturbation responses [37]
• Cross-species adaptation: Frameworks that transfer perturbation insights from model organisms to humans [37]

Causal Inference Methods for Perturbation Prediction

Beyond foundation models, causally inspired neural networks represent a significant advancement in perturbation prediction. PDGrapher exemplifies this approach by formulating the perturbation prediction problem within a causal discovery framework, where genes represent nodes in a causal graph and structural causal equations define their relationships [39]. Given a genetic or chemical intervention dataset, PDGrapher identifies sets of genes that, when targeted, facilitate the transition of node states from diseased to treated [39].

The experimental workflow for causal perturbation prediction involves:

• Network Construction: Protein-protein interaction networks from BIOGRID (10,716 nodes, 151,839 edges) or gene regulatory networks from GENIE3 (∼10,000 nodes, ∼500,000 edges) serve as proxy causal graphs [39]
• Representation Learning: A graph neural network represents structural equations and learns latent representations of disease and treated states [39]
• Perturbagen Prediction: The model processes new diseased samples and outputs combinatorial therapeutic targets predicted to counteract disease effects [39]
• Validation: Performance is evaluated across diverse datasets spanning genetic and chemical interventions in multiple cancer types [39]

Table 2: Perturbation Prediction Performance Across Methods

Method	Prediction Type	Key Advantage	Limitations
PDGrapher [39]	Direct perturbagen prediction	25× faster training than indirect methods	Dependent on quality of proxy causal graphs
scGPT [1] [37]	Perturbation response	Zero-shot capability for novel perturbations	Computational intensity for training
CellOT [39]	Perturbation response	Builds separate models for each perturbation	Inefficient for large perturbagen libraries (10h/perturbagen)
scGen [39]	Perturbation response	Established baseline method	Indirect identification of perturbagens

Compound Screening and Optimization

Structure-Based Drug Design Enhancements

Structure-based drug design (SBDD) has been dramatically enhanced through machine learning approaches, particularly deep learning techniques that improve binding site prediction, molecular docking, and scoring functions [40] [41]. Traditional virtual screening methods relied on molecular docking and scoring functions that often struggled with accuracy and computational efficiency. Deep learning approaches have addressed these limitations through several innovative frameworks:

• Gnina 1.3: Uses convolutional neural networks (CNNs) to score docking poses, with updated training datasets and knowledge-distilled CNNs for increased inference speed [41]
• AGL-EAT-Score: Constructs weighted colored subgraphs from 3D protein-ligand complexes, generating ∼17,000 descriptors analyzed by gradient boosting trees to predict binding affinities [41]
• DeepTGIN: Employs transformers and graph isomorphism networks to predict binding affinity by combining ligand graphs and protein sequence features [41]
• PoLiGenX: Generative model that conditions ligand generation on reference molecules within specific protein pockets, reducing steric clashes and strain energies [41]

De Novo Drug Design with Deep Learning

De novo drug design has been revolutionized by deep learning and deep reinforcement learning techniques that enable the exploration of vast chemical spaces without starting templates [40]. These approaches can be categorized into atom-based and fragment-based sampling methods, each with distinct advantages:

Atom-based sampling begins with a seed atom in the target's active site and grows diverse compounds by varying atoms and hybridization states, offering high structural diversity but potentially exponential computational costs with compound size [40]. Fragment-based sampling uses fragment databases as seeds to build compounds, significantly narrowing chemical search space while maintaining structural diversity [40].

Advanced architectures for de novo design include:

• MORLD (Molecule Optimization by Reinforcement Learning and Docking): Atom-based method using binding affinities from docking as rewards in reinforcement learning [40]
• RNN/LSTM Networks: Process sequential molecular data with internal memory, remembering inputs over extended periods [40]
• Graph Neural Networks (GNNs): Process graph-structured molecular data through information diffusion mechanisms [40]
• Graph Convolutional Neural Networks (GCNNs): Generalize CNNs to graph-structured data, aggregating node information from neighborhoods [40]

Property Prediction and Optimization

Accurate prediction of compound properties, particularly ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics, is crucial for reducing late-stage failures in drug development [41]. Recent advances in machine learning have significantly improved property prediction:

• AttenhERG: Based on the Attentive FP algorithm, achieves highest accuracy in benchmarking studies for hERG toxicity prediction while enabling interpretation of atoms contributing to toxicity [41]
• CardioGenAI: Uses autoregressive transformers to generate molecules conditioned on scaffolds and physicochemical properties, filtered through hERG prediction models to redesign drugs with reduced hERG liability [41]
• StreamChol: Web-based tool for predicting drug-induced liver injury (DILI), specifically addressing cholestasis through bile acid accumulation [41]
• E-GuARD: Predicts compounds likely to interfere with biological assays (frequent hitters), using data augmentation to address extreme class imbalance (0.7-3.3% positive rates) [41]

Experimental Protocols and Methodological Guidelines

Benchmarking Framework for scFM Evaluation

Compressive benchmarking studies have established rigorous protocols for evaluating single-cell foundation models under realistic conditions [4]. These protocols encompass both gene-level and cell-level tasks, with performance assessed using multiple metrics spanning unsupervised, supervised, and knowledge-based approaches:

Gene-level tasks focus on evaluating the biological relevance of gene embeddings learned by scFMs. Standard protocols involve:

• Extracting gene embeddings from model input layers
• Comparing against established biological references like Functional Representation of Gene Signatures (FRoGS)
• Evaluating performance on predicting known biological relationships including tissue specificity and Gene Ontology terms [4]

Cell-level tasks assess model performance on core single-cell analysis applications:

• Dataset integration: Evaluating batch effect removal while preserving biological variation across datasets with inter-patient, inter-platform, and inter-tissue variations [4]
• Cell type annotation: Assessing accuracy on challenging scenarios including novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [4]
• Clinically relevant predictions: Performance on cancer cell identification and drug sensitivity prediction across multiple cancer types and therapeutic agents [4]

Novel Evaluation Metrics for Biological Relevance

Beyond traditional performance metrics, novel evaluation approaches have been developed to specifically assess the biological relevance of scFM outputs:

• scGraph-OntoRWR: Measures consistency between cell type relationships captured by scFMs and established biological knowledge [4]
• Lowest Common Ancestor Distance (LCAD): Assesses ontological proximity between misclassified cell types to evaluate severity of annotation errors [4]
• Roughness Index (ROGI): Quantifies cell-property landscape roughness in latent spaces, with smoother landscapes correlating with improved downstream task performance [4]

Table 3: Essential Research Reagents and Computational Tools

Category	Resource	Key Features	Application
Data Platforms [1] [37]	CZ CELLxGENE	100+ million standardized single cells	Pretraining corpus for scFMs
	DISCO	Federated analysis platform	Cross-study validation
	Human Cell Atlas	Multiorgan reference atlases	Biological context representation
Foundation Models [1] [4] [37]	scGPT	33M+ cell pretraining, multi-omic support	Perturbation prediction, target discovery
	Geneformer	Context-aware gene embeddings	Regulatory network inference
	scPlantFormer	Cross-species adaptation (92% accuracy)	Plant biology applications
Computational Tools [39] [41]	PDGrapher	Causal graph-based prediction	Combinatorial perturbagen identification
	Gnina 1.3	CNN-based docking scoring	Structure-based virtual screening
	ChemProp	Graph neural network properties	ADMET prediction

Single-cell foundation models represent a paradigm shift in drug discovery, offering unprecedented capabilities for target identification, perturbation prediction, and compound screening. By learning universal representations from massive single-cell datasets, these models capture fundamental biological principles that enable accurate predictions across diverse contexts and tasks. The integration of multi-omics data, causal inference methods, and advanced deep learning architectures is accelerating the transition from empirical to predictive drug discovery.

Despite remarkable progress, challenges remain in data quality standardization, model interpretability, computational resource requirements, and translation of computational insights to clinical applications [1] [4] [37]. Future advancements will likely focus on developing more biologically grounded model architectures, improving cross-modal integration, establishing standardized benchmarking frameworks, and creating sustainable infrastructure for model sharing and version control. As these technologies mature, fully ML-integrated drug discovery pipelines will define the future of pharmaceutical development, potentially dramatically reducing the time and cost required to bring new therapeutics to patients.

Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research by enabling the investigation of gene expression profiles at individual cell resolution, providing unprecedented insights into cellular heterogeneity and dynamics in complex biological systems [42]. The advent of high-throughput single-cell sequencing has generated vast collections of single-cell data across diverse tissues and conditions, with public repositories now containing tens of millions of single-cell omics datasets [1]. This data explosion has created an urgent need for unified computational frameworks capable of integrating and comprehensively analyzing these rapidly expanding data repositories [1].

Cell type annotation represents a crucial foundational step in scRNA-seq data analysis, serving as the gateway to meaningful biological interpretation. Traditional manual annotation approaches are increasingly recognized as time-consuming, partially subjective, and impractical for the scale of modern single-cell datasets [43] [44]. This limitation has accelerated the development of automated computational tools that can systematically associate gene expression profiles of single cells with specific cell types using curated marker databases, reference expression correlation, or supervised classification approaches [43].

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in this landscape, bringing artificial intelligence and large-scale deep learning to bear on the challenges of cell biology [1]. These models, typically built on transformer architectures and pretrained on massive, diverse single-cell datasets, are increasingly being applied to downstream tasks including cell type annotation and atlas construction [1] [4]. This technical guide examines the current state of automated classification systems for cell type annotation and atlas construction, with particular emphasis on the transformative potential of foundation models in advancing these fields.

Foundations of Single-Cell Foundation Models (scFMs)

Core Concepts and Architecture

Foundation models are large-scale AI models pretrained on extensive datasets that can be adapted to a wide range of downstream tasks through fine-tuning or prompting [1] [45]. In single-cell biology, these models use self-supervised learning to extract latent patterns from vast single-cell omics data, capturing fundamental principles of cellular biology that generalize to new datasets and tasks [1].

The transformer architecture forms the backbone of most scFMs, leveraging attention mechanisms that allow the model to learn and weight relationships between any pair of input tokens [1]. In practical terms, scFMs treat individual cells analogously to sentences, with genes or genomic features and their expression values serving as words or tokens [1] [46]. This approach enables the model to decipher the 'language' of cells by learning from millions of cells encompassing diverse tissues and conditions [1].

Key scFM Architectures and Their Applications

Table 1: Prominent Single-Cell Foundation Models and Their Characteristics

Model Name	Architecture Type	Pretraining Scale	Key Applications	Unique Features
scBERT	BERT-like encoder	Millions of cells	Cell type annotation	Bidirectional attention mechanism [1]
scGPT	GPT-like decoder	Massive multi-source data	Cell typing, perturbation prediction	Generative pretraining, multi-omics capacity [1] [46]
Geneformer	Transformer-based	~30 million cells	Network inference, cell state	Context-aware gene embeddings [4]
scFoundation	Custom transformer	Large-scale atlas data	General-purpose embeddings	Focus on biological robustness [4]
UCE	Unified encoder	Diverse datasets	Cross-modality integration	Unified cell embedding space [4]

Tokenization Strategies for Single-Cell Data

A critical technical challenge for scFMs is the non-sequential nature of omics data, as genes in a cell have no inherent ordering unlike words in a sentence [1]. To address this, various tokenization strategies have been developed:

• Expression ranking: Genes are ordered by expression levels within each cell, creating a deterministic sequence [1]
• Binning approaches: Genes are partitioned into expression value bins for positional encoding [1]
• Hybrid methods: Combining gene identifiers, expression values, and metadata in token embeddings [1]

Most models convert the gene expression profile of each cell into a set of gene tokens, which are processed through transformer layers to generate latent embeddings at both cell and gene levels [1]. These embeddings capture biological meaningful patterns that facilitate various downstream analysis tasks.

Automated Cell Type Annotation Systems

Methodological Approaches

Automated cell type identification has evolved along three primary strategic pathways, each with distinct advantages and limitations:

• Marker-based methods: These tools leverage curated databases of cell-type-specific marker genes to assign identities by comparing expression patterns against known signatures [43] [44]. While highly interpretable, they can struggle with novel cell states and transitional populations.

• Reference-based correlation: These approaches correlate query gene expression profiles with reference datasets, transferring labels from the most similar reference cells [43] [44]. They typically require robust reference atlases but can handle subtle cellular differences effectively.

• Supervised classification: Machine learning models are trained on annotated reference data to predict cell types in new datasets [43] [44]. These can achieve high accuracy but may be constrained by the diversity and quality of training data.

Implementation and Tool Ecosystem

Table 2: Automated Cell Type Annotation Tools and Methods

Tool/Category	Methodology	Input Requirements	Output	Strengths
CellTypist	Regularized linear models with SGD	scRNA-seq matrix	Cell type labels with confidence	Fast prediction, easy integration [47]
Marker-based Tools	Predefined marker databases	Expression matrix + marker sets	Annotation based on marker overlap	Biological interpretability [43]
Reference Correlation	Similarity to reference cells	Query + reference datasets	Label transfer	Handles nuanced differences [43]
Supervised Classifiers	Trained ML models	Pre-trained model + new data	Predictive labels	High accuracy on known types [43]

Integration with Foundation Models

scFMs are increasingly being applied to cell type annotation tasks, leveraging their generalizable representations learned during pretraining [4]. The emerging approach involves:

• Zero-shot annotation: Using pretrained embeddings directly for cell type identification without task-specific fine-tuning [4]
• Fine-tuning strategies: Adapting pretrained models on specific tissue or disease contexts [1]
• Ensemble methods: Combining scFM embeddings with traditional annotation approaches [4]

Recent benchmarking studies reveal that scFMs capture biologically meaningful relationships in their latent spaces, with functionally similar cell types clustering together even across tissues and species [4]. However, performance varies across models and biological contexts, with no single scFM consistently outperforming all others across diverse annotation tasks [4].

Automated Cell Type Annotation Workflow

Single-Cell Atlas Construction

Technical Challenges in Atlas Integration

Constructing comprehensive single-cell atlases involves integrating datasets across multiple batches, tissues, conditions, and experimental platforms. This process faces several fundamental challenges:

• Batch effects: Technical variations introduced by different experimental batches can confound biological signals, requiring careful correction without removing genuine biological variation [48]
• Modality integration: Combining data from different sequencing modalities (scRNA-seq, scATAC-seq, spatial transcriptomics) presents computational challenges due to different technical characteristics and biological information content [49]
• Scalability: Atlas-scale datasets containing millions of cells demand computationally efficient algorithms that can handle massive data volumes [49]
• Biological preservation: Effective integration must preserve rare cell populations, continuous differentiation trajectories, and subtle disease-associated cell states [48]

Advanced Integration Methods

Graph-Based Integration: GIANT

The GIANT (gene-based data integration and analysis technique) method addresses integration challenges by focusing on genes rather than cells as the fundamental unit of analysis [49]. This approach involves:

• Graph construction: Converting cell clusters from each dataset and modality into gene graphs based on expression or epigenetic correlations [49]
• Recursive projection: Embedding genes from all graphs into a latent space using recursive projections that enforce similarity constraints across graphs [49]
• Hierarchical alignment: Leveraging dendrogram structures to guide integration while allowing genes with multiple functions to be projected to different embedding locations [49]

GIANT demonstrates effective integration of multi-tissue, multi-modality data while maintaining biological relevance, achieving better integration of different data modalities compared to baseline methods like node2vec and Gene2vec [49].

Disentanglement Approaches: CODAL

The CODAL (COvariate Disentangling Augmented Loss) framework uses a variational autoencoder-based statistical model with mutual information regularization to explicitly disentangle technical and biological effects [48]. Key innovations include:

• Explicit decomposition: Modeling observed read counts as arising from separate biological and technical components [48]
• Mutual information regularization: Augmenting the evidence lower bound (ELBO) objective with a lower bound approximation of mutual information to penalize dependence between biological quantities and technical effects [48]
• Interpretable modules: Factorizing biological variation into latent variables ("topics") and linear feature associations that represent co-regulated genes or co-accessible peaks [48]

This approach enables batch-confounded cell type discovery and improves representation of both RNA-seq and ATAC-seq modalities in multimodal data [48].

Foundation Models for Atlas Construction

scFMs are increasingly applied to atlas construction, leveraging their ability to create unified representation spaces that integrate diverse datasets [1] [4]. The application of scFMs to atlas construction follows several paradigms:

• Zero-shot integration: Using pretrained model embeddings directly to position cells from new datasets in a shared space [4]
• Transfer learning: Fine-tuning foundation models on specific atlas projects to adapt general biological knowledge to particular tissues or biological systems [1]
• Multi-scale analysis: Leveraging scFMs to simultaneously capture gene-level and cell-level patterns that facilitate both fine-grained cell typing and broad tissue organization mapping [4]

Benchmarking studies demonstrate that scFMs can achieve effective integration while preserving biological variation, particularly for challenging scenarios involving novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [4].

Single-Cell Atlas Construction Approaches

Experimental Protocols and Methodologies

Standardized Analysis Workflows

Robust single-cell analysis requires standardized computational workflows that ensure reproducibility and analytical validity. Core components include:

• Quality control: Filtering low-quality cells, removing potential multiplets, and addressing technical artifacts using tools tailored to specific scRNA-seq protocols [42]
• Normalization: Applying appropriate normalization methods that account for library size differences and technical variability without introducing biases [42]
• Feature selection: Identifying highly variable genes that drive biological heterogeneity while reducing dimensionality [4]
• Batch correction: Implementing appropriate integration methods that remove technical artifacts while preserving biological variation using approaches like Harmony, Seurat, or scVI [48] [4]

Evaluation Metrics and Validation

Comprehensive evaluation of annotation and integration performance requires multiple complementary metrics:

• Traditional metrics: Including accuracy, adjusted Rand index, and normalized mutual information for classification performance [4]
• Biological metrics: Assessing preservation of known biological relationships and cell type markers [4]
• Novel ontology-informed metrics:
  • scGraph-OntoRWR: Measuring consistency of cell type relationships captured by models with prior biological knowledge [4]
  • LCAD (Lowest Common Ancestor Distance): Assessing ontological proximity between misclassified cell types to evaluate severity of annotation errors [4]
• Integration metrics: Evaluating batch mixing while preserving biological separation using metrics like silhouette coefficient and local inverse Simpson's index [4]

Table 3: Key Research Reagents and Computational Tools for Single-Cell Analysis

Resource Category	Specific Examples	Function/Purpose	Key Characteristics
Reference Databases	CELLxGENE, Human Cell Atlas, PanglaoDB	Reference cell types and markers	Curated single-cell data and annotations [1]
Annotation Tools	CellTypist, Seurat, SCINA	Automated cell type identification	Various algorithms (marker-based, reference, supervised) [43] [47]
Integration Methods	Harmony, scVI, CODAL, GIANT	Batch correction and data integration	Remove technical effects, preserve biology [48] [4]
Foundation Models	scGPT, Geneformer, scBERT	General-purpose single-cell analysis	Pretrained on massive datasets, transfer learning [1] [4]
Visualization Platforms	UCSC Cell Browser, CELLxGENE	Data exploration and sharing	Interactive visualization of single-cell data [1]

Future Directions and Challenges

Technical Limitations and Research Gaps

Despite rapid progress, significant challenges remain in the development and application of automated classification systems:

• Interpretability: Understanding the biological relevance of latent embeddings and model representations remains challenging [1]
• Computational demands: Training and fine-tuning scFMs requires substantial computational resources, limiting accessibility [1] [46]
• Data quality inconsistency: Heterogeneity in data quality across studies and platforms complicates model training and application [1]
• Benchmarking gaps: Comprehensive evaluation of scFMs across diverse biological contexts and tasks is still ongoing [4]

Emerging Opportunities

Promising research directions are emerging to address current limitations:

• Multimodal foundation models: Integrating multiple data modalities (transcriptome, epigenome, proteome, spatial context) within unified model architectures [1] [49]
• Specialized domain adaptation: Developing tissue-specific or disease-specific foundation models through targeted fine-tuning [46]
• Improved interpretability: Developing methods to extract biologically meaningful insights from complex model representations [1] [4]
• User-friendly interfaces: Creating accessible tools that enable biologists to leverage scFMs without deep computational expertise [46]

Clinical and Translational Applications

The ultimate validation of automated classification systems lies in their ability to generate biologically meaningful insights and clinical value:

• Cell atlas construction: Building comprehensive reference maps of human tissues in health and disease [49]
• Tumor microenvironment characterization: Deconvoluting cellular heterogeneity in cancer ecosystems for prognostic and therapeutic insights [46]
• Drug discovery and development: Identifying novel cellular targets and predicting drug sensitivity across cell types [4] [45]
• Personalized medicine: Mapping patient-specific cellular alterations to inform tailored treatment strategies [42]

As single-cell technologies continue to evolve and computational methods mature, automated classification systems powered by foundation models are poised to become indispensable tools for extracting meaningful biological insights from the increasingly complex landscape of single-cell data.

Single-cell multimodal omics technologies have revolutionized biological research by enabling the simultaneous profiling of complex molecular programs—including transcriptomics, epigenomics, and proteomics—at unprecedented resolution within individual cells [50]. This technological advancement has revealed previously unappreciated cellular heterogeneity in various biological processes, providing insights into development, immunity, disease mechanisms, and therapeutic responses [51] [52]. The integration of these distinct data modalities is essential for comprehensive biological interpretation, as it allows researchers to move beyond fragmented information toward a unified understanding of cellular states and regulatory mechanisms [51] [53].

The convergence of multiple data modalities offers a holistic view of cellular states, capturing different aspects of the central dogma of biology—from genome to transcriptome to proteome [51]. However, integrating these diverse data types presents substantial computational challenges due to differing data scales, noise characteristics, feature dimensions, and biological relationships between modalities [53] [52]. For instance, while actively transcribed genes typically display greater chromatin accessibility, the correlation between RNA expression and protein abundance is often more complex and nonlinear [53]. Furthermore, technological limitations result in varying data breadth across modalities; transcriptomics can profile thousands of genes, while proteomic methods typically capture only hundreds of proteins, creating inherent imbalances for integration algorithms [53].

Within the context of single-cell foundation model research, multi-omic integration represents both a formidable challenge and a tremendous opportunity. Foundation models, originally developed for natural language processing, are increasingly being adapted to single-cell biology, where they learn universal representations from large-scale datasets that can be fine-tuned for various downstream tasks [1] [20]. These models have the potential to transform how we integrate and interpret multi-omic data by capturing complex biological patterns across modalities, tissues, and species [4] [20]. This technical guide examines current methodologies, experimental protocols, and computational frameworks for effectively integrating transcriptomic, epigenomic, and proteomic data, with particular emphasis on their application within the evolving paradigm of single-cell foundation models.

Foundational Concepts and Categorization Frameworks

Data Integration Categories

Multi-omic integration strategies are systematically categorized based on input data structure and modality combinations, with four prototypical integration scenarios recognized in the field [50]:

Table 1: Categories of Multi-Omic Data Integration

Integration Type	Data Structure	Key Characteristics	Common Applications
Vertical Integration	Different omics profiled from the same set of cells (matched)	Uses the cell itself as an anchor; most straightforward approach	CITE-seq (RNA+protein), SHARE-seq (RNA+ATAC), TEA-seq (RNA+ATAC+protein)
Diagonal Integration	Different omics from different cells (unmatched)	Requires co-embedding in latent space to find commonality	Integrating single-cell datasets from different experiments or technologies
Mosaic Integration	Various omic combinations across datasets with sufficient overlap	Leverages partial pairwise measurements across datasets	Integrating datasets where each experiment profiles different modality combinations
Cross Integration	Bridging fundamentally different data types or structures	Often requires specialized alignment techniques	Spatial transcriptomics with histology, cross-species integration

Vertical integration (matched integration) represents the most straightforward scenario, where multiple modalities are measured from the same cell, allowing the cell itself to serve as a natural anchor for integration [50] [53]. Technologies enabling vertical integration include CITE-seq (simultaneous measurement of RNA and surface proteins), SHARE-seq (RNA and chromatin accessibility), and TEA-seq (RNA, ATAC, and proteins) [50] [51]. The principal advantage of vertical integration is the direct correspondence between measurements across modalities at the single-cell level, providing unambiguous ground truth for computational integration.

Diagonal integration (unmatched integration) addresses the more challenging scenario where different modalities are profiled from different cells, requiring computational methods to project cells into a co-embedded space or nonlinear manifold to establish commonality [50] [53]. This approach is necessary when integrating datasets from different experiments or technologies, or when practical constraints prevent simultaneous multimodal profiling from the same cell. Diagonal integration methods typically rely on machine learning and statistical techniques to identify appropriate anchors for aligning cells across modalities without direct correspondence [53].

Mosaic integration represents an intermediate scenario where datasets contain various combinations of omics measurements with sufficient overlap to enable integration [53]. For example, one sample might be assessed for transcriptomics and proteomics, another for transcriptomics and epigenomics, and a third for proteomics and epigenomics. The shared modalities across datasets provide the connective tissue for comprehensive integration of all available data [53].

Core Computational Challenges

Integrating transcriptomic, epigenomic, and proteomic data presents several fundamental computational challenges that stem from both technical and biological factors [53] [52]:

• Dimensionality mismatch: Transcriptomic and epigenomic data typically contain thousands to tens of thousands of features (genes, peaks), while proteomic data usually encompasses only hundreds of features (proteins), creating an inherent imbalance that can skew integration [53].

• Modality-specific noise characteristics: Each modality exhibits distinct technical artifacts and noise profiles. For example, single-cell RNA-seq data is notoriously sparse with high dropout rates, while proteomic data from antibody-derived tags may suffer from antibody-specific background noise [51] [52].

• Diverse data distributions: The statistical distributions of measurements differ substantially across modalities—transcriptomic data typically follows negative binomial distributions, epigenomic data is often binary or bimodal, and proteomic data may exhibit continuous or truncated distributions [52].

• Complex biological relationships: The relationships between modalities are biologically complex and not always linear or direct. For instance, chromatin accessibility may precede transcript expression, and mRNA levels may not directly correlate with protein abundance due to post-transcriptional regulation [53].

• Batch effects and technical variability: Technical variations across experiments, platforms, and processing protocols can introduce confounding batch effects that obscure biological signals, particularly when integrating datasets from different sources [20] [52].

Computational Methodologies and Integration Strategies

Method Classes and Representative Algorithms

Computational methods for multi-omic integration have evolved rapidly, encompassing diverse mathematical frameworks and algorithmic strategies. These can be broadly categorized into several classes based on their underlying approaches [50] [54] [53]:

Table 2: Computational Methods for Multi-Omic Integration

Method Class	Core Principle	Representative Tools	Strengths	Limitations
Matrix Factorization	Decomposes data matrices into lower-dimensional factors	MOFA+ [50] [53]	Interpretable factors, handles missing data	Linear assumptions may miss complex interactions
Neural Networks/Deep Learning	Uses deep neural networks to learn nonlinear embeddings	scGPT [1] [20], scCross [54], totalVI [53], scVI [4]	Captures complex nonlinear relationships, scales to large datasets	Black box nature, computationally intensive training
Nearest Neighbor Graphs	Constructs graphs based on cell similarity across modalities	Seurat V4/V5 [50] [53], Harmony [54]	Intuitive, preserves local structure	Sensitive to parameters, may not capture global structure
Generative Models	Models joint probability distribution of multi-omic data	scCross [54], MultiVI [53], scVAE [53]	Can impute missing data, simulate perturbations	Complex training, potential model misspecification
Manifold Alignment	Aligns modality-specific manifolds in shared space	Pamona [53], UnionCom [53]	Preserves intrinsic data structure	Computationally intensive, sensitive to initial alignment
Foundation Models	Large-scale pretrained models adapted to downstream tasks	Geneformer [4], scBERT [1], scPlantFormer [20]	Transfer learning, zero-shot capabilities	Massive data requirements, computational resources

Matrix factorization approaches, such as MOFA+, decompose high-dimensional omics data into lower-dimensional factors that capture shared sources of variation across modalities [50] [53]. These methods are particularly valued for their interpretability, as factors can be associated with specific biological processes or technical artifacts. MOFA+ employs a Bayesian framework to handle missing data and automatically infer the dimensionality of the latent space [50].

Neural network-based approaches have gained significant traction due to their ability to capture complex nonlinear relationships between modalities [54] [53]. Variational autoencoders (VAEs) are particularly popular, with methods like scCross, totalVI, and scVI learning modality-specific encoders that project data into a shared latent space, followed by decoders that can reconstruct each modality [54] [53]. These approaches naturally handle the technical noise and sparsity characteristic of single-cell data through their probabilistic frameworks.

Foundation models represent a paradigm shift in single-cell analysis, leveraging transformer architectures pretrained on massive datasets (millions to tens of millions of cells) [1] [4] [20]. Models such as scGPT and Geneformer employ self-supervised learning objectives—often inspired by language modeling tasks like masked token prediction—to learn universal representations of cells and genes that can be fine-tuned for specific integration tasks with minimal additional data [1] [20]. These models show exceptional capability for cross-species annotation, zero-shot learning, and in silico perturbation modeling [20].

Benchmarking Insights and Performance Considerations

Comprehensive benchmarking studies provide critical guidance for method selection based on empirical performance across diverse tasks and datasets. A landmark 2025 benchmarking study evaluated 40 integration methods across 64 real datasets and 22 simulated datasets, assessing performance on seven key tasks: dimension reduction, batch correction, cell type classification, clustering, imputation, feature selection, and spatial registration [50].

For vertical integration of paired RNA and ADT (protein) data, Seurat WNN, sciPENN, and Multigrate demonstrated generally superior performance in preserving biological variation of cell types [50]. In RNA+ATAC integration, Seurat WNN, Multigrate, Matilda, and UnitedNet performed well across diverse datasets [50]. Method performance was found to be both dataset-dependent and modality-dependent, emphasizing the importance of context-specific method selection [50].

In diagonal and mosaic integration scenarios, methods such as scCross, GLUE, and StabMap have shown promising results [54] [53]. scCross employs a VAE-GAN framework combined with mutual nearest neighbors (MNN) for modality alignment, demonstrating superior performance in cell clustering metrics (Adjusted Rand Index and Normalized Mutual Information) and efficient computational resource utilization, particularly for large datasets exceeding 10,000 cells [54].

For foundation models, recent benchmarking reveals that while these models are robust and versatile tools for diverse applications, simpler machine learning models can be more efficient for specific datasets, particularly under resource constraints [4]. Notably, no single foundation model consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on factors including dataset size, task complexity, and computational resources [4].

Experimental Protocols and Workflows

Multi-Omic Assay Technologies

Successful multi-omic integration begins with appropriate experimental design and technology selection. The table below summarizes key technologies for simultaneous measurement of transcriptomics, epigenomics, and proteomics:

Table 3: Experimental Technologies for Multi-Omic Profiling

Technology	Modalities	Key Features	Cell Throughput	Protocol Details
CITE-seq [51]	RNA + Surface Proteins	Antibody-derived tags (ADT) for protein detection	8,005 cells (original)	Simultaneous measurement of whole transcriptome + 100+ proteins
SHARE-seq [50] [51]	RNA + Chromatin Accessibility	Measures chromatin accessibility and gene expression	>10,000 cells	Two-step chromatin accessibility and mRNA library preparation
TEA-seq [50]	RNA + ATAC + Proteins	Simultaneous three-modal profiling	Not specified	Combines CITE-seq and ATAC-seq methodologies
ECCITE-seq [51]	RNA + Proteins + CRISPR perturbations	Captures transcriptome, surface proteins, and gRNA identities	5,935 cells	Enables multimodal profiling with perturbation information
scNMT-seq [51]	RNA + DNA Methylation + Chromatin Accessibility	Simultaneous triple-omic profiling	70 cells	Uses oligo-dT-coated magnetic beads for separation

The experimental workflow for most multi-omic technologies involves several common steps: cell preparation and staining (for protein detection), nucleus permeabilization (for chromatin accessibility assays), library preparation for each modality, and sequencing. Technologies such as CITE-seq and REAP-seq use antibody-derived tags (ADTs) with barcoded oligonucleotides that are subsequently sequenced alongside cDNA transcripts [51]. SHARE-seq and other chromatin accessibility-based methods use tagmentation to fragment accessible chromatin regions while simultaneously capturing RNA species [50] [51].

Quality Control and Preprocessing

Robust quality control (QC) and preprocessing are critical for successful multi-omic integration. The following workflow outlines standard QC procedures:

Cell Quality Control: For each modality, apply modality-specific QC metrics. For transcriptomics: filter cells based on unique molecular identifier (UMI) counts, detected genes, and mitochondrial percentage. For epigenomics: filter cells based on transcription start site (TSS) enrichment, fragment count, and nucleosome signal. For proteomics: filter cells based on antibody-derived tag (ADT) counts and negative control staining [51] [52].

Feature Selection: Identify highly variable features for each modality. For transcriptomics: select highly variable genes. For epigenomics: select accessible peaks with sufficient coverage. For proteomics: typically include all measured proteins due to limited feature numbers [4] [53].

Normalization: Apply modality-specific normalization. For transcriptomics: use library size normalization (e.g., log(CP10K)). For epigenomics: employ term frequency-inverse document frequency (TF-IDF) normalization. For proteomics: apply centered log-ratio (CLR) transformation [53] [52].

Batch Correction: Address technical variability across experiments using methods such as Harmony, Seurat's CCA, or scVI's batch-aware models, ensuring that biological rather than technical variation drives integration results [4] [54].

Single-Cell Foundation Models in Multi-Omic Integration

Architectural Foundations

Single-cell foundation models (scFMs) represent a transformative approach to multi-omic integration, leveraging architectures and pretraining strategies adapted from natural language processing [1] [20]. These models typically employ transformer-based architectures with self-supervised learning objectives trained on massive single-cell datasets encompassing millions of cells [1] [4].

The core innovation of scFMs lies in their tokenization strategies, which convert single-cell data into sequences of discrete tokens analogous to words in a sentence [1]. In most scFMs, individual genes or genomic features serve as tokens, with their expression levels or accessibility scores incorporated as additional input features [1] [4]. A key challenge is that gene expression data lacks natural sequential ordering, unlike text. To address this, models employ various gene ordering strategies, including ranking by expression level, binning by expression values, or using fixed gene orders [1].

The following diagram illustrates the typical architecture of a single-cell foundation model for multi-omic integration:

Model Architectures: Most scFMs use transformer architectures, with some adopting BERT-like encoder models with bidirectional attention (e.g., scBERT) and others using GPT-like decoder architectures with masked self-attention (e.g., scGPT) [1]. Hybrid designs are increasingly common, incorporating specialized components for handling different modalities and capturing spatial relationships [20].

Pretraining Strategies: scFMs are typically pretrained using self-supervised objectives on large, diverse collections of single-cell data. Common pretraining tasks include masked gene modeling (predicting randomly masked expression values), contrastive learning (maximizing similarity between related cells), and multimodal alignment (learning correspondences between different omics) [1] [20]. Models such as scGPT have been pretrained on over 33 million cells, enabling remarkable cross-task generalization capabilities [20].

Application to Multi-Omic Integration

Foundation models facilitate multi-omic integration through several mechanisms:

Unified Representation Learning: scFMs learn a shared embedding space that captures fundamental biological principles across modalities, tissues, and species [1] [4]. For example, Geneformer learns contextualized gene representations that capture functional relationships, enabling zero-shot prediction of gene regulatory networks [4].

Cross-Modal Alignment: Models like scGPT incorporate modality-specific tokens and learning objectives that align different omics in a shared latent space [1] [20]. This enables tasks such as predicting chromatin accessibility from gene expression or imputing protein abundances from transcriptomic data [20].

Transfer Learning and Few-Shot Adaptation: Once pretrained, scFMs can be efficiently adapted to specific multi-omic integration tasks with minimal task-specific data [4] [20]. Fine-tuning on small, task-specific datasets often yields performance superior to training specialized models from scratch, particularly for rare cell types or conditions [4].

Benchmarking studies reveal that scFMs excel particularly in tasks requiring biological reasoning, such as cell type annotation across species, perturbation response prediction, and gene regulatory network inference [4] [20]. However, traditional methods may still outperform foundation models for straightforward integration tasks on homogeneous datasets, highlighting the importance of task-specific method selection [4].

Successful multi-omic integration requires both wet-lab reagents for data generation and computational resources for analysis. The following table catalogues essential resources:

Table 4: Essential Research Reagents and Computational Resources

Resource Category	Specific Tools/Reagents	Purpose/Function	Key Considerations
Wet-Lab Reagents	10x Genomics Feature Barcode Technology	Simultaneous measurement of RNA and surface proteins	Compatibility with existing single-cell protocols
	TotalSeq Antibodies (BioLegend)	Antibody-derived tags for protein detection	Extensive validation required for specific applications
	SHARE-seq Reagents [51]	Simultaneous profiling of chromatin accessibility and gene expression	Optimized protocol for nuclear recovery and library preparation
	CITE-seq Antibody Panels [51]	Customizable protein detection panels	Panel design must balance breadth and cost
Computational Tools	Seurat V4/V5 [50] [53]	Weighted nearest neighbor integration	User-friendly interface, extensive documentation
	scGPT [1] [20]	Foundation model for single-cell analysis	Requires significant computational resources for training
	Harmony [54]	Fast, scalable dataset integration	Particularly effective for batch correction
	SCALEX [54]	Online integration of single-cell data	Suitable for integrating streaming or sequentially generated data
	BioLLM [20]	Standardized framework for benchmarking scFMs	Facilitates comparison across different foundation models
Data Resources	CZ CELLxGENE [1] [4]	Curated single-cell data repository	Contains over 100 million unique cells standardized for analysis
	Human Cell Atlas [1]	Reference maps of all human cells	Comprehensive but still under construction
	DISCO [20]	Single-cell omics database	Enables federated analysis across datasets
	PanglaoDB [1]	Curated scRNA-seq database	Particularly strong annotation of cell markers

The field of multi-omic integration is rapidly evolving, driven by both technological advancements and computational innovations. Several emerging trends are particularly noteworthy:

Unified Foundation Models: The next generation of scFMs aims to create truly unified models that seamlessly handle all major single-cell modalities—transcriptomics, epigenomics, proteomics, and spatial information—within a single architectural framework [20]. Models such as Nicheformer, which incorporates spatial context, and scPlantFormer, which integrates phylogenetic constraints, represent steps in this direction [20].

Interpretability and Biological Insight: A critical challenge for complex integration methods, particularly deep learning approaches, is model interpretability [4] [20]. Future developments will likely focus on enhancing our ability to extract biologically meaningful insights from integrated models, potentially through attention mechanism analysis, perturbation-based inference, and incorporation of prior biological knowledge [4].

Clinical Translation: As single-cell technologies move toward clinical applications, multi-omic integration will play an increasingly important role in personalized medicine [20]. Applications include patient stratification, drug sensitivity prediction, and identification of biomarkers and therapeutic targets [4] [20]. However, significant challenges remain in standardization, reproducibility, and validation of computational findings in clinical contexts [20].

Scalability and Computational Efficiency: With single-cell datasets now routinely encompassing millions of cells, scalability has become a paramount concern [54]. Future method development will need to prioritize computational efficiency, potentially through improved algorithms, specialized hardware, and distributed computing frameworks [20] [54].

In conclusion, multi-omic integration of transcriptomic, epigenomic, and proteomic data represents both a formidable computational challenge and a tremendous opportunity for advancing biological discovery. The emergence of single-cell foundation models marks a paradigm shift in this domain, offering powerful new approaches for extracting unified biological insights from complex, multimodal data. As these technologies continue to mature, they hold the potential to transform our understanding of cellular biology and accelerate the development of novel therapeutic strategies.

The advent of single-cell technologies has revolutionized our approach to cancer biology, providing an unprecedented lens through which to view tumor heterogeneity, the tumor microenvironment (TME), and the complex cellular ecosystems that drive disease progression and therapeutic resistance. However, the clinical translation of these discoveries faces significant hurdles, including data integration challenges and the computational complexity of analyzing millions of cells across diverse patients and conditions. Single-cell foundation models (scFMs) represent a transformative computational approach to these challenges. These large-scale artificial intelligence models, pretrained on vast datasets comprising millions of single-cell profiles, are emerging as powerful tools for unifying biological insights and accelerating the pipeline from biomarker discovery to personalized treatment strategies [1]. This technical guide examines the integration of scFMs into clinical translation workflows, detailing their application in biomarker discovery, TME deconstruction, and the development of personalized therapeutic approaches.

The Clinical Translation Challenge: A Data Integration Problem

The journey from bench to bedside in oncology is marked by a significant translational gap, where less than 1% of published cancer biomarkers enter routine clinical practice [55]. This gap stems from several interconnected challenges:

• Biological Fidelity of Models: Traditional preclinical models, including cell lines and animal models, often fail to fully recapitulate human tumor biology. Over-reliance on these models produces biomarker data with poor correlation to human clinical outcomes [55].
• Tumor and Microenvironment Heterogeneity: Human tumors are highly heterogeneous, varying between patients and within individual tumors. This diversity, encompassing genetic diversity, evolving tumor microenvironments, and varying treatment histories, introduces real-world variables that are difficult to replicate in controlled preclinical settings [56] [55].
• Analytical and Validation Hurdles: The process for biomarker validation lacks standardized methodologies, leading to a proliferation of exploratory studies with dissimilar strategies that are seldom validated across independent cohorts [55]. Furthermore, analyzing the TME requires methods with cellular resolution to untangle the intricate interplay between different cell types [56].

Table 1: Key Challenges in Translating Single-Cell Discoveries to Clinical Practice

Challenge Category	Specific Limitations	Impact on Clinical Translation
Model Systems	Poor human correlation of animal models; 2D monoculture artifacts	Biomarkers fail to predict clinical outcomes
Tumor Heterogeneity	Genetic diversity; variable TME; evolving clonal populations	Biomarkers lack robustness across patient populations
Analytical Methods	Lack of standardized validation; insufficient single-cell resolution	Low reproducibility and inability to deconstruct cellular interplay
Data Integration	Inability to jointly analyze dissociated single-cell and spatial data	Loss of critical context about cellular position and neighbors

Single-Cell Foundation Models: A Technical Primer

Single-cell foundation models are large-scale deep learning models, typically based on transformer architectures, pretrained on massive, diverse single-cell datasets in a self-supervised manner. Their design enables them to learn fundamental biological principles that are generalizable to new datasets and a wide range of downstream tasks [1] [4].

Core Architecture and Workflow

The application of scFMs involves a multi-stage computational workflow, from data tokenization to the generation of latent representations that power various clinical applications.

Model Training and Specialized Architectures

ScFMs are pretrained on extensive corpora like SpatialCorpus-110M, which contains over 110 million cells, enabling the model to learn universal patterns of gene regulation and cellular function [57]. A critical architectural innovation is the development of integrated spatial models like Nicheformer, which learn from both dissociated single-cell data and spatial transcriptomics. This allows the model to transfer spatial context back onto cells studied in isolation, effectively reconstructing their position and interactions within the tissue architecture [57]. These models use self-supervised objectives, such as predicting masked genes or cellular states, forcing the model to learn meaningful biological relationships without requiring labeled data [1].

Application 1: Biomarker Discovery and Validation

Enhanced Biomarker Identification

ScFMs elevate biomarker discovery beyond differential expression analysis by leveraging the rich biological knowledge encoded in their pretrained representations. The gene and cell embeddings generated by these models can be mined to identify novel biomarkers with greater clinical potential.

• Gene Embedding Analysis: The gene embeddings learned by scFMs encapsulate functional relationships. Genes with similar roles in biological processes are embedded in close proximity in the latent space. This allows for the identification of biomarker signatures not just based on co-expression, but on shared functional contexts, even if the genes are not expressed in the same cells [4].
• Multi-omic Integration: ScFMs can incorporate diverse data modalities—including transcriptomics, epigenetics (scATAC-seq), and proteomics—to identify context-specific, clinically actionable biomarkers that might be missed when relying on a single data type [1] [55]. This is crucial for developing composite biomarkers that more accurately reflect disease state.

Functional and Longitudinal Validation

ScFMs can be fine-tuned to predict the functional impact of identified biomarkers and their dynamics over time, addressing key validation challenges.

• From Correlation to Causation: Moving beyond mere presence/quantity, scFMs can be adapted to perform in silico perturbation experiments. By computationally manipulating the model's inputs (e.g., "knocking out" a gene), researchers can predict the downstream effects on cellular state and signaling pathways, providing evidence for the biomarker's biological relevance [1] [4].
• Dynamic Biomarker Monitoring: Static biomarker measurements offer a limited snapshot. ScFMs can be applied to longitudinal single-cell or circulating tumor DNA (ctDNA) data to model how biomarkers evolve with disease progression or treatment, revealing patterns that predict recurrence or resistance before clinical symptoms appear [55] [58].

Table 2: scFM Applications in the Biomarker Development Pipeline

Pipeline Stage	scFM Application	Output & Clinical Value
Discovery	Analysis of gene embeddings from pretrained models	Identification of novel, functionally coherent biomarker signatures
Validation	Multi-omic integration; in silico perturbation prediction	Confirmation of biological relevance and context-specificity
Analytical Verification	Zero-shot performance on unseen datasets from new cohorts	Assessment of robustness and generalizability across populations
Clinical Utilization	Longitudinal modeling of biomarker dynamics from ctDNA/tissue	Prediction of treatment response and early detection of resistance

Application 2: Tumor Microenvironment Deconstruction

The TME is a complex ecosystem of cancer cells, immune cells, stromal cells, and vasculature, whose interactions dictate tumor behavior. ScFMs provide a powerful suite of tools to deconstruct this complexity.

Cellular Census and Interaction Mapping

The first step in TME analysis is defining its cellular composition and communication networks.

• Cell Type Annotation: ScFMs excel at zero-shot cell type annotation, leveraging their pretrained knowledge to accurately label cell states in new datasets without requiring retraining [4]. Advanced models like Nicheformer go further by considering a cell's spatial context during annotation, leading to more precise identification of cell states that are defined by their location within the TME [57].
• Cell-Cell Communication Inference: By analyzing the co-expression of ligand-receptor pairs across different cell populations within a sample, scFMs can infer probable cellular crosstalk. When applied to spatial data, this analysis is constrained to physically neighboring cells, providing a highly realistic map of signaling interactions within the TME [57].

Analysis of Spatial Organization

Spatial context is critical to TME function. Models like Nicheformer are specifically designed to learn the principles of tissue organization, enabling several key analyses.

• Niche Identification: The model can identify recurrent cellular neighborhoods (niches)—specific combinations of cell types that consistently co-occur. The presence or abundance of certain niches (e.g., an immune-suppressive niche) can serve as a powerful prognostic or predictive biomarker [57].
• Architectural Analysis: ScFMs can quantify features of tissue architecture, such as the degree of immune cell infiltration into tumor islets or the organization of stromal barriers. These spatial features have demonstrated clinical significance but are difficult to quantify with traditional methods.

Application 3: Personalized Treatment Strategies

The ultimate goal of clinical translation is to match patients with the most effective therapies. ScFMs contribute to this goal by powering predictive models and enabling the analysis of complex biomarkers.

Drug Response Prediction

A primary application is predicting how a patient's tumor will respond to a specific therapy.

• Model Fine-Tuning: A scFM pretrained on a large corpus of single-cell data can be fine-tuned on datasets where patient-derived cells or organoids were exposed to various drugs, with linked outcome data. The model learns to map the baseline transcriptional state of a tumor to its likely response [4].
• Mechanistic Insight: Beyond a simple prediction score, the attention mechanisms within transformer models can be interpreted to identify which genes and pathways the model "attended to" when making its prediction. This provides a mechanistic hypothesis for why a drug might be effective or not, which can be validated experimentally [4].

Biomarker-Guided Trial Design

ScFMs enable the use of complex, multi-gene biomarkers in clinical trials.

• Biomarker Signature Development: Instead of relying on single-gene biomarkers, scFMs can be used to define complex gene expression signatures that represent key biological states, such as immune activation, oncogenic pathway activity, or epithelial-mesenchymal transition. These signatures can be used as enrollment criteria or stratification factors in clinical trials [58].
• Real-World Data Interrogation: Tools like FoundationInsights allow researchers to visualize and analyze genomic data to inform clinical trial design [58]. When powered by scFM-based analytics, such tools can help estimate the prevalence of complex biomarker-defined populations in real-world datasets, optimizing trial feasibility and patient recruitment strategies.

Experimental Protocols and Workflows

A Protocol for scFM-Based Biomarker Discovery

• Data Preprocessing: Start with a quality-controlled single-cell dataset (e.g., from CITE-seq or 10x Multiome). Standardize gene counts and filter low-quality cells and genes.
• Feature Extraction: Input the preprocessed data into a pretrained scFM (e.g., scGPT, Geneformer, Nicheformer) to extract latent cell and gene embeddings.
• Supervised Analysis: For a cohort with known clinical outcomes (e.g., response vs. non-response), use the cell embeddings as features to train a classifier (e.g., a linear model or random forest) to predict the outcome.
• Biomarker Identification: Apply feature importance analysis (e.g., using SHAP values) on the trained classifier to identify which dimensions of the scFM embedding are most predictive. Map these embedding dimensions back to the genes that contribute most strongly to them.
• Validation: Test the identified gene set on an independent hold-out dataset or a public cohort to validate its predictive power. Use functional assays to confirm the biological role of top candidate genes.

A Protocol for TME Analysis with Spatial Context

• Data Integration: If working with dissociated single-cell data, use a spatial scFM like Nicheformer to impute spatial context. For direct spatial data, use the model in analysis mode.
• Niche Mapping: Apply clustering algorithms to the spatial cell embeddings generated by the model to identify distinct cellular neighborhoods.
• Differential Niche Analysis: Compare the abundance and composition of these niches between patient groups (e.g., long-term survivors vs. non-survivors) to identify clinically relevant niches.
• Communication Inference: Using the spatial coordinates and cell-type annotations, run a ligand-receptor analysis tool that is constrained by physical proximity to infer active signaling pathways within and between niches.

The Scientist's Toolkit: Essential Research Solutions

Table 3: Key Research Reagents and Platforms for scFM-Driven Translation

Tool Category	Example Solutions	Function in Workflow
Foundation Models	scGPT, Geneformer, Nicheformer	Core engine for generating latent biological representations from single-cell data.
Spatial Biology Platforms	10x Genomics Visium, CODEX, Multiplexed FISH	Generate spatially resolved single-cell data for model training and validation.
Visualization Suites	Vitessce, CellxGene	Interactive, multimodal visualization of single-cell and spatial data in a unified context [59].
Data & Analytics Resources	FoundationCore, CellxGene Data Portal	Provide access to large-scale, curated genomic and clinical datasets for model pretraining and benchmarking [58].
Human-Relevant Models	Patient-Derived Organoids (PDOs), Patient-Derived Xenografts (PDX)	Functionally validate scFM-derived hypotheses in models that better recapitulate human tumor biology [55].

Single-cell foundation models represent a paradigm shift in computational biology, directly addressing the long-standing challenges of clinical translation in oncology. By serving as a unifying framework that integrates diverse data modalities—from dissociated single-cell RNA-seq to spatial transcriptomics—these models provide a more holistic and functionally grounded understanding of tumor biology. Their application in biomarker discovery, TME deconstruction, and treatment personalization is moving the field beyond simple correlative patterns toward a mechanistic, systems-level view of cancer. As these models continue to evolve, leveraging ever-larger datasets and more sophisticated architectures, they are poised to become an indispensable component of the translational research toolkit, ultimately accelerating the development of precise and effective cancer therapies.

Navigating Technical Challenges: Data Quality, Computational Constraints, and Biological Interpretation

Single-cell RNA sequencing (scRNA-seq) has revolutionized biomedical research by enabling the exploration of cellular heterogeneity and transcriptomic variation at unprecedented resolution. Unlike bulk RNA sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked [60]. However, the analysis of scRNA-seq data faces two fundamental challenges: data sparsity from dropout events and technical variability from batch effects.

Dropout events describe the phenomenon where expressed transcripts are erroneously recorded as zero counts due to technical limitations, leading to zero-inflated data that obscures true biological signals [61] [62]. Simultaneously, batch effects—technical variations introduced by differences in experiments, personnel, equipment, or technology platforms—can confound true biological differences when integrating multiple datasets [63] [64]. The development of robust computational strategies to address these issues is essential for accurate biological interpretation and represents a core component of single-cell foundation model research.

This technical guide comprehensively reviews current mitigation strategies, providing detailed methodological insights, performance comparisons, and practical implementation frameworks to assist researchers in selecting and applying these approaches effectively.

Technical Background and Fundamental Challenges

Characterization of Dropout Events

Dropout events in scRNA-seq data occur when mRNAs that are actually present in a cell fail to be detected and are recorded as zero counts. This issue stems from the limited capture efficiency of current technologies, particularly for lowly or moderately expressed genes [62]. In typical scRNA-seq datasets, 57% to 92% of observed counts are zeros, with a substantial portion representing technical artifacts rather than true biological absence [62]. The probability of dropout increases with decreasing expression level, creating a systematic bias that must be accounted for in downstream analyses.

Batch effects arise from multiple technical sources including cell isolation protocols, library preparation technologies, sequencing platforms, and laboratory personnel [63] [65]. These technical variations obscure genuine biological signals and can lead to false conclusions if not properly addressed. The integration of multiple datasets—essential for robust biological discovery—requires effective batch effect correction (BEC) to distinguish technical artifacts from biological relevant variations [63].

Overcorrection represents a significant risk in BEC, wherein true biological variation is erroneously removed along with technical noise [63]. This can lead to the loss of biologically meaningful cell subpopulations or the artificial merging of distinct cell types, ultimately compromising downstream analyses and biological interpretations.

Computational Methodologies for Dropout Imputation

Deep Learning-Based Imputation Approaches

Deep learning architectures have demonstrated remarkable performance in dropout imputation by capturing complex, non-linear relationships in scRNA-seq data.

ZILLNB (Zero-Inflated Latent factors Learning-based Negative Binomial) integrates zero-inflated negative binomial (ZINB) regression with deep generative modeling through an ensemble architecture combining Information Variational Autoencoder (InfoVAE) and Generative Adversarial Network (GAN) [66]. This approach learns latent representations at both cellular and gene levels, which serve as dynamic covariates within a ZINB regression framework. The model parameters are iteratively optimized through an Expectation-Maximization algorithm, enabling systematic decomposition of technical variability from intrinsic biological heterogeneity [66]. The methodological workflow can be summarized as follows:

• Latent Factor Learning: An ensemble InfoVAE-GAN model extracts latent features from both cellular and gene-level perspectives, using Maximum Mean Discrepancy (MMD) as a regularizer for both cell-wise structure (V) and gene-wise structure (U).
• ZINB Fitting: The derived latent factors are utilized to fit a ZINB model, refining both latent representations and regression coefficients through iterative EM optimization.
• Data Imputation: Adjusted mean parameters generate a denoised and complete expression matrix [66].

BiAEImpute (Bidirectional AutoEncoder Impute) employs a novel architecture with row-wise and column-wise autoencoders to learn cellular and genetic features simultaneously during training [61]. The model focuses specifically on imputing zero values while preserving non-zero expressions, mitigating the introduction of additional bias. The training process involves:

• Feature Compression: Row-wise and column-wise autoencoders compress features from their respective dimensions.
• Data Reconstruction: The autoencoders generate reconstructed matrices using column nesting and row nesting with nonlinear transformations.
• Model Optimization: Three specialized loss functions refine model performance, with final imputation obtained by averaging outputs from both autoencoders [61].

DAZZLE (Dropout Augmentation for Zero-inflated Learning Enhancement) introduces an innovative approach called Dropout Augmentation (DA) that regularizes models by augmenting data with simulated dropout noise [62]. Counter-intuitively, adding small amounts of random zeros during training improves model robustness against dropout noise. DAZZLE utilizes a variational autoencoder-based structure equation model framework for gene regulatory network inference, demonstrating that DA significantly enhances model stability and performance [62].

Statistical and Matrix Factorization Approaches

Traditional statistical approaches provide interpretable alternatives to deep learning methods, often with lower computational requirements.

PBLR (Cell Sub-population Based Bounded Low-Rank) method imputes dropouts by considering cell heterogeneity and the relationship between dropout rate and expected expression level [67]. This approach automatically detects accurate and robust cell sub-populations while recovering gene-gene relationships masked by dropout events.

ALRA (Adaptive Low-Rank Approximation) utilizes Singular Value Decomposition (SVD) to impute zeros in the expression matrix, leveraging the non-negative nature of the expression matrix and its intrinsic correlation structure [61]. While computationally efficient, ALRA primarily captures linear relationships within the original expression matrix.

Table 1: Comparative Performance of Dropout Imputation Methods

Method	Underlying Algorithm	Key Features	Reported Performance Gains
ZILLNB	InfoVAE-GAN + ZINB regression	Latent factor learning, EM optimization	ARI improvements of 0.05-0.2 over competitors; AUC-ROC improvements of 0.05-0.3 [66]
BiAEImpute	Bidirectional Autoencoder	Cell-wise and gene-wise modeling, zero-value focus	Superior clustering refinement and marker gene identification [61]
DAZZLE	VAE with Dropout Augmentation	Model regularization, synthetic dropout	Improved robustness and stability in GRN inference [62]
PBLR	Bounded Low-Rank Approximation	Cell heterogeneity consideration	Improved low-dimensional representation and gene-gene relationships [67]
ALRA	Singular Value Decomposition	Linear relationships, computational efficiency	Effective for datasets with strong linear correlation structure [61]

Advanced Strategies for Batch Effect Correction

Deep Learning and Federated Approaches

Crescendo extends the Harmony algorithm by performing batch correction directly on gene counts rather than lower-dimensional embeddings [68]. Utilizing generalized linear mixed modeling, Crescendo simultaneously corrects systematic batch variation and imputes low-expressed gene counts. The algorithm operates through three key steps:

• Estimation: Models variation in gene expression from biological (cell-type identity) and technical (batch effects) sources.
• Marginalization: Infers a batch-free model of gene expression.
• Matching: Samples batch-corrected counts using the original and batch-free models [68].

FedscGen addresses privacy concerns in multi-center studies by implementing a federated learning framework based on the scGen model [64]. This privacy-preserving approach enables collaborative batch effect correction without sharing raw data through:

• Federated Training: A coordinator deploys a Variational Autoencoder (VAE) model with common initial parameters to all clients (hospitals, research institutions). Each participant trains the model locally and sends trained parameters to the coordinator for secure aggregation.
• Federated δ-vector Estimation and Correction: Identifies dominant batches for shared cell types and enables local correction based on securely aggregated latent representations using Secure Multiparty Computation (SMPC) [64].

CONCORD presents a unified framework that simultaneously addresses batch integration, denoising, and dimensionality reduction through a novel probabilistic sampling strategy [69]. The method uses dataset-aware sampling to correct batch effects and hard-negative sampling to enhance biological resolution. Remarkably, CONCORD achieves state-of-the-art performance with only a minimalist neural network containing a single hidden layer and contrastive learning, without relying on deep architectures, auxiliary losses, or external supervision [69].

Evaluation Metrics for Batch Effect Correction

Robust evaluation of BEC methods is essential for method selection and optimization. The RBET (Reference-informed Batch Effect Testing) framework addresses limitations of previous metrics by incorporating reference genes (RGs) with stable expression patterns [63]. RBET evaluates BEC performance through:

• RG Selection: Utilizing experimentally validated tissue-specific housekeeping genes as RGs.
• Batch Effect Detection: Mapping datasets into two-dimensional space using UMAP and applying maximum adjusted chi-squared (MAC) statistics for distribution comparison [63].

RBET demonstrates superior performance in detecting batch effects while maintaining sensitivity to overcorrection, outperforming established metrics like kBET and LISI in scenarios with large batch effect sizes and partial batch effects [63].

Additional important metrics include:

• BVR (Batch-Variance Ratio): Quantifies batch effect removal as the ratio of batch-related variance before versus after correction.
• CVR (Cell-Type-Variance Ratio): Measures preservation of biological variation as the ratio of cell-type-related variance before versus after correction [68].

Table 2: Batch Effect Correction Methods and Their Characteristics

Method	Core Approach	Key Innovations	Privacy Preservation
Crescendo	Generalized Linear Mixed Modeling	Gene-level correction, simultaneous imputation	No
FedscGen	Federated VAE Training	Secure multi-party computation, no raw data sharing	Yes (SMPC)
CONCORD	Contrastive Learning	Dataset-aware sampling, minimalist architecture	No
Harmony	PCA + Linear Modeling	Cell-type-specific correction, efficient integration	No
Seurat	Mutual Nearest Neighbors	Canonical correlation analysis, anchor integration	No

Integrated Workflows and Experimental Protocols

Comprehensive Data Processing Pipeline

A robust workflow for addressing both dropout and batch effects typically follows these stages:

• Quality Control and Normalization: Filter low-quality cells and genes, followed by normalization using max-min normalization or similar approaches to mitigate technical biases [61].

• Batch Effect Correction: Apply appropriate BEC methods based on data characteristics and integration needs. For multi-center studies with privacy concerns, FedscGen provides a federated solution [64].

• Dropout Imputation: Implement imputation algorithms tailored to specific biological questions. Methods like ZILLNB effectively handle both technical noise and biological heterogeneity [66].

• Downstream Analysis: Perform cell typing, trajectory inference, and differential expression analysis on corrected data.

• Validation: Evaluate correction quality using metrics like RBET, BVR, and CVR to ensure biological preservation while removing technical artifacts [63] [68].

Implementation Protocol for ZILLNB

For researchers implementing ZILLNB, the detailed methodology involves:

Latent Factor Learning Phase:

• Configure the ensemble InfoVAE-GAN architecture with three interconnected neural networks: encoder, decoder, and discriminator.
• Set adaptive weighting parameters (γ₁ and γ₂) to balance reconstruction loss (Llike), prior alignment (Lprior), and generative accuracy (L_GAN).
• Train the model using Maximum Mean Discrepancy (MMD) as the regularizer for both cell-wise structure V and gene-wise structure U [66].

ZINB Fitting Phase:

• Model observed expression counts Yij using ZINB distribution with latent binary variables Zij indicating dropout events.
• Incorporate latent cell- and gene-specific structures through the mean parameter: logμ{M×N} = 1MξN^⊤ + ζM1N^⊤ + α{L×M}^⊤V{L×N} + U{K×M}^⊤β_{K×N}
• Iteratively update the latent factor matrix U together with regression parameters via EM algorithm while keeping cell-wise factor V fixed.
• Include regularization terms on U and intercepts to prevent overfitting [66].

Validation Steps:

• Assess performance using Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI) for cell type classification.
• Evaluate differential expression analysis using area under ROC curve (AUC-ROC) and Precision-Recall curve (AUC-PR) [66].

Implementation Protocol for FedscGen

For privacy-preserving batch correction:

Federated Training Workflow:

• Initialize a central coordinator that deploys a VAE model with common initial parameters to all clients.
• Each participant trains the model locally for e epochs on their private data.
• Clients send trained parameters to the coordinator, which securely aggregates parameters using FedAvg: θr ← ∑{c∈𝒞} Nc·θc, where θ_r is global weights in rth communication round.
• The coordinator broadcasts updated global model back to all clients [64].

Federated Correction Workflow:

• Calculate mean latent features for shared cell types across clients using secure aggregation.
• Correct local latent representations by subtracting each latent vector from mean latent features of the dominant batch.
• Perform this correction without sharing raw data through Secure Multiparty Computation (SMPC) based on additive secret sharing [64].

Visualization and Conceptual Diagrams

ZILLNB Architecture Workflow

FedscGen Federated Learning Framework

Table 3: Key Computational Tools for Addressing Sparsity and Batch Effects

Tool/Resource	Type	Primary Function	Implementation Considerations
ZILLNB	Python Package	Dropout imputation and denoising	Requires GPU for efficient training; supports integration with scanpy workflows [66]
Crescendo	R/Python Library	Gene-level batch correction	Compatible with Seurat and Scanpy objects; efficient for large spatial transcriptomics datasets [68]
FedscGen	FeatureCloud App	Privacy-preserving BEC	Federated implementation; no raw data sharing; suitable for multi-center studies [64]
CONCORD	Python Package	Unified integration and denoising	Minimalist architecture; efficient for large-scale atlas-level datasets [69]
RBET	R Package	BEC evaluation with overcorrection awareness	Reference gene selection required; sensitive to partial batch effects [63]
BiAEImpute	Python Package	Bidirectional imputation	Focused on zero-value imputation; preserves non-zero expressions [61]

The rapid evolution of computational methods for addressing data sparsity and technical noise in single-cell transcriptomics has significantly enhanced our ability to extract meaningful biological insights from complex datasets. The integration of statistical modeling with deep learning approaches, as demonstrated by ZILLNB, represents a powerful paradigm that combines interpretability with flexibility [66]. Similarly, privacy-preserving frameworks like FedscGen address critical concerns in multi-center studies while maintaining competitive performance with centralized methods [64].

Future methodological development will likely focus on several key areas: (1) unified frameworks that simultaneously address multiple technical artifacts, (2) improved scalability for atlas-scale datasets containing millions of cells, (3) enhanced interpretability of deep learning models, and (4) standardized evaluation metrics that robustly assess both technical artifact removal and biological preservation. As single-cell foundation model research progresses, the integration of these mitigation strategies will be essential for building comprehensive, accurate models of cellular function and organization across diverse biological contexts and experimental conditions.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling a unified analysis of cellular heterogeneity by learning from vast datasets comprising millions of single-cell transcriptomes [1]. These models, often built on transformer architectures, adapt the "pre-train, then fine-tune" approach to decipher the complex "language" of biology, where cells are treated as sentences and genes as words [1] [70]. However, this revolutionary potential is tethered to a critical challenge: the immense computational cost of developing and deploying such models. Scaling model size and dataset breadth to improve biological performance leads to exponential growth in resource demands for training, inference, and storage [71] [70]. This technical guide examines the core computational burdens inherent to scFMs and provides a structured framework for resource management, offering researchers and drug development professionals strategies to balance model complexity with the practical constraints of memory, processing power, energy, and time.

Computational Demands of Single-Cell Foundation Models

The computational footprint of an scFM is determined by the interplay of three primary factors: the scale of the model's architecture, the size of the training dataset, and the specific strategies employed for tokenization and pre-training.

Model Architecture and Parameter Scale

Modern scFMs have rapidly evolved from models with millions of parameters to architectures containing hundreds of millions, directly influencing their capacity and computational appetite. The transformer architecture, the backbone of most scFMs, is particularly resource-intensive due to its self-attention mechanism, which scales quadratically with sequence length [1]. The following table summarizes the scale of several contemporary scFMs.

Table 1: Scale of Representative Single-Cell Foundation Models

Model Name	Number of Parameters	Pretraining Dataset Scale	Core Architecture
CellFM [71]	800 million	~100 million human cells	ERetNet (Transformer variant)
UCE [4]	650 million	>36 million cells	Transformer
C2S-Scale [70]	410 million to 27 billion	>1 billion tokens	Gemma-based LLM
scFoundation [4]	~100 million	~50 million human cells	Transformer
GeneCompass [4]	~100 million	~100 million human & mouse cells	Transformer
scGPT [1] [4]	Not Specified	>33 million human cells	Transformer (Decoder)
Geneformer [1] [4]	Not Specified	30 million cells	Transformer

As illustrated, models like CellFM and UCE push the boundary with hundreds of millions of parameters, requiring sophisticated parallel training strategies on specialized hardware, such as the Ascend910 NPUs used for CellFM [71]. The C2S-Scale model family demonstrates that a wide spectrum of model sizes is being explored, allowing a trade-off between performance and accessibility [70].

Data Preprocessing and Tokenization Strategies

Tokenization—the process of converting raw gene expression data into discrete model inputs—is a critical and computationally significant step. The chosen strategy directly impacts the sequence length and the subsequent computational load of the transformer's attention mechanism [1]. There is no consensus on a single best method, and the choice represents a key resource-complexity trade-off.

Table 2: Common Tokenization Strategies in scFMs

Tokenization Strategy	Description	Example Models	Computational Implication
Gene Ordering/Ranking	Genes are ordered by expression level to form a sequence.	Geneformer, scGPT [1]	Creates a deterministic sequence; sequence length is a tunable hyperparameter.
Value Categorization	Continuous expression values are binned into discrete categories.	scBERT [1] [4]	Transforms problem into classification; can lose fine-grained expression information.
Value Projection	Raw expression values are projected into an embedding space.	scFoundation, CellFM [4] [71]	Preserves full data resolution but may require more complex embedding layers.

Frameworks for Resource Management and Optimization

Effectively managing computational resources requires a holistic approach that spans specialized hardware, efficient software frameworks, and model-specific architectural optimizations.

Hardware and Parallel Computing Infrastructure

Training large-scale scFMs is infeasible on standard workstations and necessitates high-performance computing (HPC) environments. Key considerations include:

• Multi-GPU/NPU Training: Distributed training across multiple accelerators is essential. For example, CellFM was trained on four servers, each with eight Ascend910 NPUs [71]. The LiteLoc framework for microscopy data demonstrates the power of this approach, achieving a 25-fold speedup by efficiently deploying across eight GPUs with minimal inter-GPU communication overhead [72].
• Resource Management Frameworks: In HPC centers, job scheduling and resource management are critical. Next-generation frameworks like Flux provide hierarchical, multi-level management that can make smarter, more efficient placement decisions for jobs, considering resources like I/O bandwidth to avoid bottlenecks and improve overall system utilization [73].
• CPU/GPU Asynchronous Execution: Optimizing the entire data processing pipeline is as important as raw computation speed. Frameworks like LiteLoc leverage asynchronous execution, where data loading and pre-processing on the CPU happen concurrently with model inference on the GPU, preventing either from becoming a bottleneck [72].

Optimization Strategies for Model Training and Inference

Beyond hardware, algorithmic and architectural choices can dramatically improve efficiency.

• Efficient Model Architectures: Replacing standard transformers with more efficient variants is a key trend. CellFM uses ERetNet, a transformer variant with linear complexity, as its backbone to balance performance and efficiency [71]. Similarly, designing lightweight networks with techniques like dilated convolutions can reduce parameters and FLOPs without sacrificing accuracy, as seen in the LiteLoc model for microscopy, which uses half the parameters of a predecessor while maintaining performance [72].
• Parameter-Efficient Fine-Tuning (PEFT): For adapting large pre-trained models to downstream tasks, PEFT methods like LoRA (Low-Rank Adaptation) are indispensable. CellFM integrates LoRA to drastically reduce the number of trainable parameters during fine-tuning, making it feasible to adapt the 800-million-parameter model to new tasks with limited resources [71].
• Reinforcement Learning for Alignment: To enhance the utility of generative scFMs like C2S-Scale, reinforcement learning (RL) with reward functions based on semantic evaluation (e.g., BERTScore) can be used to fine-tune model outputs. This aligns the model to produce more biologically accurate and informative responses without the need for retraining the entire massive network [70].

Experimental Protocols for Benchmarking and Evaluation

Rigorous evaluation is necessary to justify computational investments. Benchmarking should assess not only predictive accuracy but also computational efficiency and biological relevance.

Protocol for Downstream Task Evaluation

A comprehensive benchmark, as performed in [4], involves evaluating scFMs on a suite of biologically meaningful tasks in a zero-shot or fine-tuned setting.

• Task Selection: Select a diverse set of downstream tasks:
  • Cell-level tasks: Batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction.
  • Gene-level tasks: Gene function prediction (e.g., Gene Ontology term prediction), gene-gene relationship capturing.
• Baseline Models: Compare scFMs (e.g., Geneformer, scGPT, UCE, scFoundation, CellFM) against well-established baseline methods, including traditional machine learning models (e.g., HVG selection) and specialized single-cell tools (e.g., Seurat, Harmony, scVI).
• Evaluation Metrics:
  • Technical Metrics: Use standard metrics like accuracy, F1-score, and clustering metrics (e.g., ARI, NMI).
  • Biological Metrics: Incorporate novel, knowledge-informed metrics such as scGraph-OntoRWR (measures consistency of captured cell-type relationships with biological ontologies) and LCAD (Lowest Common Ancestor Distance, measures the severity of cell type misclassification) [4].
  • Efficiency Metrics: Track training and inference time, memory footprint, and energy consumption (if possible).
• Data: Use high-quality, manually annotated datasets from diverse sources (e.g., CellxGene) that contain multiple sources of batch effects to test model robustness [4].

Protocol for Efficiency and Scaling Analysis

To understand the trade-offs between model scale and resource use, a structured profiling protocol is essential.

• Scaling Laws Analysis: As performed for C2S-Scale, train a family of models of increasing size (e.g., from 410M to 27B parameters) on a fixed dataset. Measure performance improvement across key tasks (e.g., cell type annotation, tissue generation) against the increase in computational cost (training time, hardware requirements) [70].
• Lightweight Model Validation:
  • Model Design: Design a lightweight network (e.g., LiteLoc) using efficiency-focused components like dilated convolutions and a simplified U-Net [72].
  • Benchmarking: Compare the lightweight model against larger, more complex SOTA models on standardized datasets.
  • Metrics: Evaluate using both performance metrics (e.g., Jaccard Index, RMSE for localization) and efficiency metrics (e.g., GFLOPs, parameter count, throughput in MB/s) [72].
  • Ablation Studies: Conduct studies to validate the contribution of specific efficiency-oriented components (e.g., testing different dilation factors) [72].

Visualization of Resource Management Workflows

The following diagrams illustrate the core workflows and decision points for managing computational resources in scFM projects.

End-to-End scFM Pipeline

ScFM Development and Deployment Workflow

Resource-Aware Model Selection

Resource-Aware Model Selection Logic

Efficient Training Architecture

Efficient Distributed Training Architecture

This table details the essential computational "reagents" required for developing and applying scFMs, categorized by their function in the workflow.

Table 3: Essential Computational Reagents for scFM Research

Category	Item	Function	Examples / Notes
Data Resources	Curated Single-Cell Atlases	Provides large-scale, standardized data for pretraining.	CZ CELLxGENE [1], Human Cell Atlas [1], PanglaoDB [1].
Modeling Frameworks	Deep Learning Frameworks	Provides the foundation for building and training models.	PyTorch, TensorFlow, MindSpore (used for CellFM [71]).
Hardware Infrastructure	GPUs / NPUs	Accelerates matrix computations essential for deep learning.	NVIDIA GPUs (e.g., GTX 4090 [72]), Ascend910 NPUs [71].
Resource Management	Job Schedulers	Manages and optimizes computational workloads in HPC environments.	Flux [73], SLURM.
Efficiency Tools	Parameter-Efficient Fine-Tuning (PEFT)	Enables adaptation of large models to new tasks with minimal resource overhead.	LoRA (Used in CellFM [71]).
Evaluation Benchmarks	Standardized Task Suites	Provides a consistent framework for evaluating model performance and efficiency.	Custom benchmarks encompassing cell/gene-level tasks [4].

The field of single-cell foundation models stands at a crossroads, where the pursuit of more biologically insightful models through increased scale must be consciously balanced against the practical realities of computational resources. This guide has outlined that effective resource management is not a single action but a continuous, strategic process involving the selection of efficient model architectures like ERetNet, the adoption of sophisticated training paradigms like multi-GPU parallelization and PEFT, and the rigorous use of biological and efficiency-focused benchmarking. For researchers and drug developers, making informed choices at this intersection is not merely a technical concern but a fundamental determinant of project feasibility, reproducibility, and ultimate success. By embracing the principles and practices detailed herein, the scientific community can harness the transformative power of scFMs in a sustainable and effective manner, paving the way for robust and accessible discoveries in biology and medicine.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling the integration and analysis of massive-scale single-cell transcriptomic datasets [1]. These models, often built on transformer architectures, learn generalizable representations of cellular states by pretraining on millions of cells across diverse tissues, species, and conditions [1] [4]. A defining characteristic of scFMs is their ability to generate two key types of representations: latent embeddings that encode cellular states in a continuous vector space, and attention weights that capture dynamic relationships between genes [1] [74].

The critical challenge lies in extracting biologically meaningful insights from these computational constructs. While scFMs demonstrate impressive performance in downstream tasks like cell type annotation and batch integration, their true value for biomedical research remains limited without robust biological interpretability [1] [4]. This technical guide comprehensively addresses methodologies for interpreting both latent embeddings and attention weights within scFMs, providing researchers with a framework for transforming model internals into actionable biological knowledge.

Foundations of Single-Cell Foundation Models

Architectural Principles

Single-cell foundation models adapt transformer architectures, originally developed for natural language processing, to represent biological data [1]. In this analogy, individual cells correspond to sentences, while genes or genomic features function as words or tokens [1] [2]. The transformer's self-attention mechanism enables the model to learn contextual relationships between genes, capturing co-expression patterns and regulatory dependencies [1] [75].

These models employ specialized tokenization strategies to convert gene expression profiles into sequential inputs. Common approaches include ranking genes by expression levels within each cell, binning genes by expression values, or using normalized counts directly [1]. Positional encoding schemes then represent the relative order or rank of each gene, overcoming the non-sequential nature of omics data [1].

Pretraining Paradigms

scFMs typically undergo self-supervised pretraining on vast collections of single-cell data from public repositories like CZ CELLxGENE, which provides access to over 100 million unique cells [1]. During pretraining, models learn to reconstruct masked portions of input data or predict contextual relationships, building a foundational understanding of cellular biology that transfers to various downstream tasks through fine-tuning or zero-shot learning [1] [4].

Interpreting Latent Embeddings

Latent embeddings generated by scFMs provide continuous vector representations of cells that capture transcriptional similarities and differences. Several methodologies enable biological interpretation of these representations.

Gene Set Enrichment Analysis of Embedding Dimensions

Systematically analyzing which genes contribute most significantly to each embedding dimension can reveal biologically meaningful patterns. This approach involves:

• Identifying influential genes: Calculate gradient-based importance scores or perform ablation studies to determine which genes most strongly influence each dimension of the latent space [74].
• Pathway enrichment analysis: Input these gene lists to enrichment tools using databases like Gene Ontology (GO), KEGG, or MSigDB [76].
• Biological validation: Interpret enriched pathways in the context of known biology and experimental validation.

Table 1: Quantitative Metrics for Evaluating Latent Embedding Biological Relevance

Metric Category	Specific Metric	Biological Interpretation	Application Context
Cell Ontology-Informed	scGraph-OntoRWR [4]	Consistency of cell type relationships with prior biological knowledge	Cell type annotation, atlas construction
Cell Ontology-Informed	Lowest Common Ancestor Distance (LCAD) [4]	Ontological proximity between misclassified cell types	Assessment of annotation error severity
Gene Function-Based	Tissue specificity prediction [4]	Association between gene embeddings and tissue-specific expression	Gene function analysis, biomarker discovery
Gene Function-Based	GO term prediction accuracy [4]	Functional coherence of spatially proximate embeddings	Pathway activity inference, functional annotation
Representation Quality	Roughness Index (ROGI) [4]	Smoothness of cell-property landscape in latent space	Model selection, downstream task performance prediction

Trajectory Inference in Latent Space

The continuous nature of scFM embeddings makes them particularly suitable for inferring developmental trajectories and transition states:

• Visualization: Apply UMAP or t-SNE to project embeddings into 2D/3D space for exploratory analysis [77].
• Pseudotime analysis: Construct trajectories using algorithms like PAGA or Monocle 3 directly on the embeddings [77].
• Transition states: Identify cells occupying intermediate positions along trajectories as potential transition states.
• Dynamic gene expression: Correlate gene expression changes with pseudotime to uncover molecular drivers of transitions.

Cross-Modal Integration for Biological Context

Enriching latent embeddings with external biological knowledge strengthens interpretability:

• Gene program identification: Project embeddings against curated gene sets from MSigDB to identify activated biological programs [76].
• Regulatory network analysis: Integrate with transcription factor target databases to infer regulatory drivers [77].
• Disease association mapping: Correlate embedding positions with disease-associated genes from GWAS catalogs.

Decoding Attention Mechanisms

Attention weights in transformer-based scFMs capture dynamic, context-specific relationships between genes. Several methods enable biological interpretation of these attention patterns.

Attention-Based Gene-Gene Interaction Networks

Constructing gene-gene interaction networks from attention weights reveals potential regulatory relationships:

• Network construction: Aggregate attention weights across layers and heads to build a directed graph where nodes represent genes and edge weights represent attention strength [74].
• Network analysis: Identify hub genes with high betweenness centrality as potential key regulators.
• Community detection: Apply clustering algorithms to identify gene modules with dense internal connections.
• Functional enrichment: Analyze enriched biological pathways within attention-defined gene modules.

Table 2: Experimental Protocols for Attention Mechanism Interpretation

Protocol	Key Steps	Output	Limitations
Attention Rollout [74]	1. Compute attention weights across all layers2. Recursively multiply layer-wise weights3. Aggregate across attention heads4. Normalize across input sequences	Global attention map showing cumulative information flow	May overestimate long-range dependencies due to multiplicative accumulation
Attention Gradient Analysis [74]	1. Compute gradients of output with respect to attention weights2. Multiply gradients by original attention weights3. Aggregate across heads and layers	Gradient-weighted attention highlighting biologically significant connections	Computational intensity for large models and datasets
Attention Pattern Clustering	1. Extract attention patterns for all cells2. Reduce dimensionality with PCA3. Cluster cells based on attention patterns4. Correlate clusters with biological metadata	Identification of recurrent attention architectures across cell types	Pattern interpretation requires biological validation
Counterfactual Attention	1. Identify key attention connections2. Modify specific attention weights3. Observe changes in model predictions4. Relate to biological pathways	Causal relationships between attention patterns and model behavior	Technical challenge in implementing controlled modifications

Comparative Analysis with Established Biological Networks

Validating attention-derived networks against ground truth biological databases establishes credibility:

• Database integration: Compare attention-identified relationships with known interactions from STRING, KEGG, or TRRUST [76].
• Precision-recall analysis: Quantify overlap between attention networks and validated interactions.
• Novel relationship prediction: Generate hypotheses from high-attention relationships absent from current databases.

Cell-Type Specific Attention Patterns

Analyzing how attention patterns vary across cell types reveals context-specific regulatory logic:

• Pattern aggregation: Average attention weights for cells belonging to the same type or state.
• Differential attention: Identify gene-gene interactions significantly enriched in specific cell types.
• Lineage-specific regulation: Trace how attention patterns evolve along differentiation trajectories.

Experimental Validation Frameworks

Rigorous biological validation ensures computational insights reflect real biology rather than artifacts.

Benchmarking Strategies for Biological Relevance

Comprehensive benchmarking assesses how well embeddings and attention weights capture ground truth biology:

• Gene function prediction: Evaluate whether proximity in embedding space predicts functional similarity using GO term consistency metrics [4].
• Cell type annotation accuracy: Measure clustering quality against manual annotations using metrics like ARI and AMI [4].
• Biological consistency metrics: Employ ontology-informed metrics like scGraph-OntoRWR that evaluate whether model-derived cell relationships align with established biological knowledge [4].

Perturbation Validation

Experimental perturbations provide causal evidence for computationally predicted relationships:

• In silico knockout: Mask specific genes and observe changes in attention patterns and embeddings.
• Perturbation data validation: Test whether models correctly predict embedding shifts from CRISPR or drug perturbation datasets [4].
• Differentiation time series: Validate trajectory predictions using experimentally determined developmental sequences.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagent Solutions for scFM Interpretability

Reagent Category	Specific Examples	Function in Interpretability	Implementation Considerations
Annotation Databases	Cell Ontology, Gene Ontology, MSigDB [76]	Provide biological ground truth for evaluating embeddings and attention patterns	Version control, species-specificity, curation quality
Network Databases	STRING, KEGG, TRRUST [76]	Validation of attention-derived gene-gene interactions	Confidence score thresholds, experimental vs. predicted interactions
Benchmarking Platforms	CZ CELLxGENE, AIDA v2 [4]	Standardized datasets for model evaluation and comparison	Data quality, annotation consistency, batch effect management
Analysis Toolkits	Scanpy, Seurat, scVerse [78]	Preprocessing, visualization, and analysis of embeddings and attention weights	Version compatibility, computational requirements
Specialized Metrics	scGraph-OntoRWR, LCAD, ROGI [4]	Quantify biological relevance of model representations	Computational complexity, biological knowledge incorporation

Implementation Workflow

Future Directions and Challenges

Despite significant progress, biological interpretability of scFMs faces ongoing challenges that represent opportunities for methodological advancement.

Current Limitations

Substantial hurdles remain in fully realizing the interpretability potential of scFMs:

• Causal inference: Most current approaches identify correlations rather than causal relationships [74].
• Multi-scale integration: Limited ability to connect single-cell findings to tissue-level or organismal phenotypes.
• Dynamic processes: Challenges in capturing temporal biological processes from static snapshots.
• Standardization deficit: Lack of consensus metrics for evaluating biological interpretability across studies [4].

Emerging Solutions

Promising approaches are emerging to address these limitations:

• Integrated perturbation modeling: Incorporating experimental perturbation data to strengthen causal claims [4].
• Multi-modal foundation models: Developing models that simultaneously process transcriptomic, epigenomic, and proteomic data [77].
• Temporal modeling: Extending scFMs to explicitly model time-series and dynamic processes [1].
• Benchmarking communities: Growing efforts to establish standardized evaluation frameworks and metrics [4].

Biological interpretability represents the critical bridge between the powerful pattern recognition capabilities of single-cell foundation models and meaningful biological discovery. The methodologies outlined in this guide—for extracting insights from both latent embeddings and attention weights—provide researchers with a comprehensive toolkit for transforming computational representations into testable biological hypotheses. As the field progresses, continued development of rigorous, standardized interpretability frameworks will be essential for realizing the full potential of scFMs in advancing our understanding of cellular biology and improving human health.

Single-cell technologies have fundamentally shifted the paradigm of biological research from population-averaged measurements to high-resolution analysis at the cellular level, revealing an extensive landscape of cellular heterogeneity [79]. This heterogeneity manifests not only as distinct cell types but also as continuous transitions between states, rare cell populations, and dynamic phenotypic variations within nominally homogeneous populations [80]. Understanding these complexities is crucial for unraveling development, disease mechanisms, and therapeutic responses.

The emergence of single-cell foundation models (scFMs) represents a transformative approach to deciphering this complexity [1]. These large-scale artificial intelligence models, pretrained on vast datasets comprising millions of cells, provide a unified framework for analyzing cellular heterogeneity across diverse biological contexts. This technical guide examines how scFMs and complementary computational methods are advancing our capacity to identify rare cell populations and characterize dynamic state transitions, framing these developments within a broader review of single-cell foundation model concepts.

Experimental Foundations for Heterogeneity Analysis

Single-Cell RNA Sequencing Technologies

The experimental capture of cellular heterogeneity begins with single-cell RNA sequencing (scRNA-seq), which enables transcriptome profiling of individual cells [81]. Since its conceptual breakthrough in 2009, scRNA-seq has evolved into high-throughput platforms capable of analyzing hundreds of thousands of cells in a single experiment [78]. The core workflow involves single-cell isolation (through limiting dilution, FACS, or microfluidic systems), cell lysis, reverse transcription with barcoding, cDNA amplification (via PCR or in vitro transcription), and library preparation for next-generation sequencing [81] [78].

A critical technical consideration is the incorporation of unique molecular identifiers (UMIs), which tag individual mRNA molecules to control for amplification biases and enable absolute transcript quantification [81]. Recent technological advances have addressed the challenge of transcriptional stress responses induced by cell dissociation through single-nucleus RNA sequencing (snRNA-seq), which profiles nuclear transcripts and is particularly valuable for tissues difficult to dissociate, such as brain [81].

From Raw Data to Biological Insights

The transformation of sequencing data into biological insights requires sophisticated computational pipelines. The initial output of scRNA-seq is a digital expression matrix with cells as columns and genes as rows, which undergoes quality control, normalization, and batch effect correction [78]. Subsequent analysis typically involves dimensionality reduction (using PCA, t-SNE, or UMAP) and clustering to identify cell subpopulations [80].

Table 1: Key Computational Methods for Analyzing Cellular Heterogeneity

Method Name	Primary Function	Mathematical Foundation	Applications
sc-UniFrac [82]	Quantifies compositional diversity between single-cell landscapes	Weighted UniFrac distance, hierarchical clustering	Statistical comparison of population structures across conditions
MuTrans [83]	Identifies transition cells and trajectories	Multiscale stochastic dynamics, transition path theory	Mapping cell-fate transitions, identifying hybrid states
Nicheformer [84]	Integrates single-cell and spatial transcriptomics	Transformer architecture, self-supervised learning	Reconstructing spatial context from dissociated cells
Spectral Clustering [80]	Identifies subpopulations in high-dimensional data	Graph theory, eigenvalue decomposition	Cell type discovery from FACS or CyTOF data
Diffusion Maps [80]	Non-linear dimensionality reduction	Markov chains, diffusion processes	Visualizing continuous developmental trajectories

Computational Frameworks for Rare Cell Population Detection

Quantitative Assessment of Population Diversity

The sc-UniFrac framework provides a statistical approach for quantifying differences in cellular composition between samples, enabling sensitive detection of rare population shifts [82]. This method operates by constructing hierarchical trees from clustering analyte profiles of single cells combined from two datasets, then calculating weighted UniFrac distances that incorporate both relative abundance differences and transcriptional distances between cell states.

The algorithm employs a permutation test by randomizing sample labels without changing tree topology to determine whether observed population structures differ significantly between conditions [82]. This approach offers advantages over simple "intermixing" assessments because it accounts for both global and local structures in the data, enabling detection of rare populations that may be transcriptionally similar yet biologically distinct.

Foundation Model Approaches

Single-cell foundation models represent a paradigm shift in rare cell detection through their self-supervised pretraining on massive, diverse datasets [1]. Models such as scBERT and scGPT treat individual cells as "sentences" and genes or genomic features as "words" or "tokens," learning fundamental biological principles that generalize to new datasets [1].

The transformer architecture underlying these models employs attention mechanisms that weight relationships between gene tokens, enabling the model to identify which gene combinations are most informative for cell identity [1]. This approach is particularly powerful for detecting rare cells because the models learn from such extensive cellular diversity that even unusual cell states can be recognized against the background of "normal" variation.

Analyzing Dynamic State Transitions

Mathematical Foundations for Transition Analysis

Cellular state transitions can be formally described as stochastic dynamical systems [83]. The MuTrans method frames this using stochastic differential equations:

$${{{{{\rm{d}}}}}}{{{{{{\bf{X}}}}}}}{{{{{{\boldsymbol{t}}}}}}}={{{{{\bf{f}}}}}}\left({{{{{{\bf{X}}}}}}}{{{{{{\boldsymbol{t}}}}}}}\right){dt}+{{{{{\boldsymbol{\sigma }}}}}}\left({{{{{{\bf{X}}}}}}}{{{{{{\boldsymbol{t}}}}}}}\right)d{{{{{{\bf{W}}}}}}}{{{{{{\boldsymbol{t}}}}}}}$$

where ({{{{{{\bf{X}}}}}}}_{t}) represents a cell's gene expression state at time t, f(x) denotes nonlinear gene regulations, σ(x) represents noise strength, and Wt is standard Brownian motion [83]. Within this framework, stable cell states correspond to attractors in the potential landscape, while transition cells occupy saddle points between these attractors.

Multiscale Dynamics Reconstruction

MuTrans implements a multiscale approach to reconstruct these dynamics from snapshot single-cell data [83]. The method first constructs a cellular random walk transition probability matrix using a Gaussian-like kernel, which corresponds to an over-damped Langevin equation in the continuous limit. It then performs coarse-graining to identify attractor basins and their mutual conversion probabilities, consistent with Kramers' law of reaction rate theory.

The algorithm computes a Transition Cell Score (TCS) that quantitatively distinguishes attractors from transition cells, enabling systematic identification of genes that mark transient states (IH genes), drive transitions (TD genes), or characterize meta-stable states (MS genes) [83].

Integrating Spatial Context with Foundation Models

The Spatial Dimension of Heterogeneity

Conventional scRNA-seq sacrifices spatial context to achieve single-cell resolution, potentially obscuring important aspects of cellular heterogeneity driven by positional relationships [84]. Spatial transcriptomics techniques preserve this context but face limitations in resolution and scalability. The Nicheformer foundation model addresses this gap by learning from both dissociated single-cell data and spatial transcriptomics [84].

Trained on over 110 million cells, Nicheformer can transfer spatial context back onto dissociated single-cell data, effectively reconstructing how cells fit into tissue architecture without additional experiments [84]. This approach reveals that spatial patterns leave measurable traces in gene expression even when cells are dissociated, enabling computational recovery of tissue organization principles.

Toward a Virtual Cell Model

Nicheformer represents an initial step toward building general-purpose AI models that represent cells in their natural context—the foundation of a Virtual Cell and Tissue model [84]. Such models aim to capture not only cell identity but also physical relationships between cells, with significant implications for understanding tumor microenvironments and other complex tissue structures relevant to disease [84].

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions for Single-Cell Heterogeneity Studies

Reagent/Platform	Function	Application in Heterogeneity Studies
10x Genomics Chromium [78]	Droplet-based single-cell partitioning	High-throughput cell capture for population diversity assessment
SMARTer Chemistry [78]	mRNA capture, reverse transcription, cDNA amplification	Full-length transcript coverage for sensitive rare cell detection
Unique Molecular Identifiers (UMIs) [81] [78]	Molecular barcoding of individual mRNA molecules	Quantitative transcript counting, reduction of amplification biases
Cell Hashing Antibodies [78]	Multiplexing samples with barcoded antibodies	Experimental batch effect control in multi-sample designs
CITE-Seq Antibodies [78]	Surface protein profiling alongside transcriptome	Multi-modal cell identity confirmation for rare populations
Spatial Transcriptomics Slides [84]	Positional mRNA capture in tissue context	Integration of spatial organization with cellular heterogeneity

The analysis of cellular heterogeneity stands at the intersection of experimental method development, computational innovation, and conceptual advances in how we understand cell state and fate. Single-cell foundation models represent a powerful unifying framework that leverages massive-scale pretraining to extract generalizable principles of cellular organization. When combined with specialized methods for quantifying population diversity (e.g., sc-UniFrac) and reconstructing transition dynamics (e.g., MuTrans), these approaches provide researchers with an increasingly sophisticated toolkit for identifying rare cell populations and characterizing dynamic state transitions.

As these technologies mature, the integration of spatial context through models like Nicheformer promises to add another critical dimension to our understanding of how cellular heterogeneity emerges and functions within native tissue environments. This progress moves us closer to the vision of a comprehensive Virtual Cell model that can predict cellular behavior across diverse biological contexts and accelerate therapeutic development.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling unified analysis of cellular heterogeneity at unprecedented scales. These models, built on transformer architectures and pretrained on millions of single-cell transcriptomes, learn fundamental biological principles that generalize across diverse downstream tasks [1]. The optimization of these models—through meticulous data preprocessing, systematic hyperparameter tuning, and strategic transfer learning—is crucial for unlocking their full potential in biological discovery and therapeutic development. As these models grapple with the high dimensionality, sparsity, and technical noise inherent to single-cell RNA sequencing (scRNA-seq) data, robust optimization frameworks ensure they capture biologically meaningful patterns while mitigating artifacts [4]. This technical guide examines current best practices and methodologies for optimizing scFMs, providing researchers with actionable protocols to enhance model performance, interpretability, and translational utility.

Data Preprocessing and Tokenization Strategies

Data preprocessing constitutes the critical foundation for training effective scFMs, directly impacting model convergence, representation quality, and generalizability. Single-cell data presents unique challenges including high sparsity, technical noise from varying sequencing platforms, and batch effects that can obscure biological signals [1] [4].

Data Sourcing and Quality Control

The construction of a robust scFM begins with curating large-scale, diverse single-cell datasets that comprehensively capture biological variation. Repositories such as CZ CELLxGENE provide unified access to over 100 million annotated single cells, while the Human Cell Atlas and other multiorgan atlases offer broad coverage of cell types and states [1]. Effective pretraining requires careful dataset selection, filtering of low-quality cells and genes, and balancing dataset compositions to avoid biological biases [1]. Quality control metrics must address sequencing depth, mitochondrial gene percentage, and doublet detection, with thresholds tailored to specific sequencing technologies.

Tokenization Approaches for Single-Cell Data

Tokenization transforms raw gene expression data into structured inputs that transformer architectures can process. Unlike natural language, where words have inherent order, gene expression data lacks natural sequence, requiring strategic imposition of structure [1].

Table: Tokenization Strategies in Single-Cell Foundation Models

Strategy	Method Description	Advantages	Implementation Examples
Expression Ranking	Genes ordered by expression level within each cell	Deterministic; preserves highly expressed genes	Top-k genes form cell "sentence" [1]
Expression Binning	Genes partitioned into bins by expression values	Reduces sensitivity to exact values	Used in scBERT, other encoder models [1]
Value-Embedding Combination	Gene identifier + expression value as separate embeddings	Preserves quantitative information	scGPT's joint embedding approach [1]
Metadata Enrichment	Prepending cell identity or modality tokens	Provides biological context	Multi-omic models; batch-aware tokens [1]

The most common approach involves ranking genes within each cell by expression levels, creating a deterministic sequence where the top-k expressed genes form the cellular "sentence" [1]. Each gene is typically represented as a token embedding combining a gene identifier embedding with a value embedding representing its expression level. Positional encoding schemes then represent the relative rank of each gene. Advanced implementations incorporate special tokens for cell metadata, batch information, or modality indicators, enabling the model to learn context-aware representations [1].

Diagram 1: Tokenization workflow for single-cell data, showing the transformation from raw expression matrices to model-ready sequences.

Hyperparameter Tuning and Model Architecture

Systematic hyperparameter optimization is essential for balancing model capacity, training efficiency, and biological relevance in scFMs. The transformer architecture, while powerful, introduces numerous configurable parameters that significantly impact performance.

Architectural Configurations

Most scFMs employ transformer architectures, with two predominant variants: BERT-like encoder models with bidirectional attention mechanisms (optimal for classification and embedding tasks) and GPT-like decoder models with unidirectional masked self-attention (effective for generative tasks) [1]. While no single architecture has emerged as universally superior, each offers distinct advantages. Encoder models like scBERT excel at cell type annotation, while decoder models like scGPT demonstrate stronger performance in generative tasks such as perturbation response prediction [1] [20].

Hyperparameter Benchmarking Insights

Rigorous benchmarking of variational autoencoder-based methods reveals that hyperparameter selection involves critical trade-offs between batch effect removal and biological signal preservation [85]. Key findings indicate that moderate to high latent dimensionality (typically >10 dimensions) generally optimizes this balance, with larger latent spaces improving batch mixing but potentially reducing biological conservation [85]. Training with highly variable genes (HVGs) consistently outperforms full-gene training across models, highlighting the importance of feature selection [85].

Table: Hyperparameter Recommendations for Single-Cell Foundation Models

Hyperparameter	Recommendation	Biological Impact	Evidence Source
Latent Dimensionality	Moderate to high (>10 dimensions)	Balances batch correction with biological conservation	VAE benchmarking studies [85]
Network Depth/Width	Dataset-dependent scaling	Captures hierarchical biological relationships	Architecture comparisons [1] [86]
Feature Selection	HVG-based training	Improves signal-to-noise ratio	scVI, MrVI, LDVAE benchmarks [85]
Learning Rate Schedule	Adaptive with warmup	Stabilizes training on heterogeneous data	Training protocol descriptions [1]

For scVI-based models, systematic evaluation of 120 configurations across three datasets revealed that optimal hyperparameters are often dataset-specific, influenced by factors such as tissue heterogeneity, laboratory protocols, and gene coverage profiles [85]. Automated hyperparameter optimization frameworks like Ray Tune have demonstrated utility in efficiently navigating this complex search space [86].

Transfer Learning and Downstream Application

The "pre-train then fine-tune" paradigm enables scFMs to transfer knowledge from large-scale pretraining to diverse downstream tasks with limited labeled data. This approach leverages self-supervised pretraining objectives—such as masked gene modeling, contrastive learning, and multimodal alignment—to build foundational biological understanding [20].

Fine-Tuning Strategies

Effective transfer learning requires strategic adaptation of pretrained models to specific biological questions. Benchmarking studies reveal that scFMs excel particularly in zero-shot and few-shot learning scenarios, where their pretrained representations demonstrate remarkable generalization to novel cell types and conditions [4]. For cell type annotation, fine-tuning on partially labeled datasets using semi-supervised approaches like scANVI has proven effective [86]. In perturbation modeling, models like scGPT leverage their understanding of gene regulatory networks to predict cellular responses to genetic and chemical perturbations without task-specific training [20].

Performance Evaluation and Model Selection

Comprehensive benchmarking of six prominent scFMs against traditional methods reveals that no single model consistently outperforms others across all tasks [4]. Instead, model selection should be guided by task requirements, dataset characteristics, and computational constraints. Evaluation introduces biologically-informed metrics such as scGraph-OntoRWR, which measures consistency between model-derived cell relationships and established biological knowledge, and Lowest Common Ancestor Distance (LCAD), which quantifies the severity of cell type misclassification errors [4].

Diagram 2: Transfer learning pathways for single-cell foundation models, showing both fine-tuning and zero-shot approaches to downstream applications.

Benchmarking results indicate that while scFMs provide robust and versatile performance across diverse applications, traditional machine learning models can be more efficient for single-dataset analyses with limited computational resources [4]. The roughness index (ROGI) has been proposed as a proxy metric for model selection, quantifying the smoothness of the cell-property landscape in latent space and correlating with downstream task performance [4].

Experimental Protocols and Methodologies

Protocol: Benchmarking Integration Performance

Purpose: Evaluate how well a method removes batch effects while preserving biological variation in single-cell data [86].

Materials:

• Multiple scRNA-seq datasets with known batch effects and cell type annotations
• Computing environment with scIB metrics package installed
• UMAP or t-SNE for visualization

Procedure:

• Apply integration method to datasets with known batch labels and cell type annotations
• Calculate batch correction metrics (Batch ASW, PCR-batch, iLISI) quantifying batch mixing
• Calculate biological conservation metrics (NMI, ARI, label ASW, cLISI, graph connectivity) quantifying preservation of cell types
• Generate visualization of integrated space using UMAP
• Compute overall scIB score combining both correction and conservation metrics

Interpretation: Higher scores indicate better performance, with optimal methods balancing batch removal with biological structure preservation [86].

Protocol: Cross-Species Cell Type Annotation

Purpose: Assess model generalization using transfer learning across species boundaries [20].

Materials:

• Pretrained scFM (e.g., scPlantFormer pretrained on Arabidopsis thaliana)
• Target species scRNA-seq data with unknown cell types
• Reference annotation database (e.g., Cell Ontology)

Procedure:

• Extract zero-shot cell embeddings from pretrained model
• Compute similarity scores between target cells and reference cell types
• Assign putative annotations based on nearest neighbors in embedding space
• Validate annotations using marker gene expression
• Fine-tune model on partial annotations if performance is suboptimal

Interpretation: High cross-species accuracy (e.g., scPlantFormer's 92%) demonstrates conserved biological representations in the model [20].

Essential Research Reagent Solutions

Table: Key Computational Tools for Single-Cell Foundation Model Research

Tool/Platform	Category	Primary Function	Application Context
scvi-tools [85]	Deep Learning Framework	VAE-based single-cell analysis	Batch integration, differential expression
BioLLM [20]	Benchmarking Suite	Standardized evaluation of scFMs	Model comparison, performance assessment
DISCO/CZ CELLxGENE [20]	Data Repository	Curated single-cell datasets	Pretraining corpus construction
Harmony [4]	Integration Method	Batch effect correction	Baseline comparison, preprocessing
scIB [86]	Metrics Package	Comprehensive benchmarking	Evaluation of integration quality
Ray Tune [86]	Hyperparameter Optimization	Automated parameter search	Model configuration optimization

Optimization of single-cell foundation models through sophisticated data preprocessing, systematic hyperparameter tuning, and strategic transfer learning represents a critical frontier in computational biology. As evidenced by comprehensive benchmarking studies, current scFMs already demonstrate remarkable capabilities in cross-species annotation, perturbation modeling, and multimodal integration [20] [4]. However, challenges remain in model interpretability, computational efficiency, and robust generalization across diverse biological contexts.

Future advancements will likely focus on biologically-constrained architectures, improved benchmarking metrics that better capture intra-cell-type variation [86], and federated learning approaches that enable collaborative model development while preserving data privacy [20]. The integration of multimodal data—spanning transcriptomics, epigenomics, proteomics, and spatial imaging—will further enhance model representations, potentially unlocking new insights into cellular function and disease mechanisms [20]. As these optimization techniques mature, they will accelerate the translation of single-cell genomics into clinically actionable insights, ultimately bridging the gap between cellular omics and precision medicine.

Benchmarking scFM Performance: Zero-Shot Capabilities, Biological Relevance, and Model Selection Guidelines

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, trained on millions of single-cell transcriptomes to learn universal representations of cellular biology [1]. These models adapt transformer architectures from natural language processing to treat genes as tokens and cells as sentences, creating embedded spaces that capture complex gene-gene and cell-cell relationships [1]. However, as these models proliferate, a critical challenge has emerged: how to rigorously evaluate whether their captured embeddings and representations genuinely reflect biological reality rather than merely optimizing computational metrics. Without standardized, biologically-grounded evaluation frameworks, researchers cannot discern whether scFMs provide true biological insights or simply excel at benchmark tasks that may poorly correlate with real biological understanding.

The evaluation challenge spans multiple dimensions, from assessing basic cell type annotation accuracy to quantifying how well models capture known biological relationships and predict cellular responses to perturbations [4] [87]. This whitepaper synthesizes current research to provide a comprehensive framework for evaluating the biological insight capture of scFMs, focusing on rigorous metrics, experimental protocols, and practical tools that enable meaningful model assessment across diverse biological contexts.

Core Evaluation Metrics and Paradigms

Metric Classification and Application

Evaluating scFMs requires multiple metric classes that assess different aspects of biological insight capture. No single metric suffices for comprehensive evaluation, as each reveals different model strengths and limitations.

Table 1: Classification of scFM Evaluation Metrics

Metric Category	Specific Metrics	Measured Capability	Interpretation Guidance
Cell Ontology-Informed	scGraph-OntoRWR, Lowest Common Ancestor Distance (LCAD)	Alignment with established biological knowledge	Lower LCAD indicates biologically plausible misclassifications
Statistical Performance	Pearson Delta, F1 Score, Precision-Recall	Task-specific predictive accuracy	High values may not always correlate with biological relevance
Causal Inference	Mean Wasserstein Distance, False Omission Rate	Ability to capture causal gene relationships	Assesses model utility for mechanistic understanding
Representation Quality	ROGI (Roughness Index), Silhouette Score	Smoothness and structure of embedding space	Smoother landscapes suggest better generalization

The scGraph-OntoRWR metric represents a significant advancement by quantifying how well the relational structure between cell types captured by scFMs aligns with established biological knowledge in cell ontologies [4]. This moves beyond simple accuracy measurements toward assessing whether models learn biologically meaningful relationships. Similarly, the Lowest Common Ancestor Distance (LCAD) metric evaluates the severity of cell type misclassifications by measuring their ontological proximity—a misclassification between closely related cell types is less severe than between distantly related ones [4] [88].

For perturbation prediction, the "Pearson Delta" metric has emerged as crucial, measuring correlation in differential expression space rather than raw expression space [87]. This focuses evaluation on the model's ability to capture changes from perturbation effects rather than simply reconstructing baseline expression patterns dominated by highly expressed genes.

Quantitative Benchmarking Results

Recent large-scale benchmarking studies provide critical performance baselines across multiple model architectures and tasks. These results highlight the context-dependent nature of scFM performance and the absence of a universally superior model.

Table 2: Comparative Performance of scFMs Across Biological Tasks

Task Category	Top-Performing Models	Key Metric Performance	Notable Findings
Drug Response Prediction	scFoundation (pooled-data), UCE (cross-data fine-tuning), scGPT (zero-shot)	F1 scores: 0.971 (scFoundation), 0.774 (UCE), 0.858 (scGPT zero-shot)	Performance highly dependent on evaluation scenario [89] [90]
Cell Type Annotation	scGPT, Geneformer, scFoundation	Varies by dataset size and complexity	Simpler models can outperform on small, focused datasets [4] [5]
Perturbation Response Prediction	Random Forest with GO features	Pearson Delta: 0.739 vs 0.641 (scGPT) on Adamson dataset	Biological prior knowledge often outperforms foundation models [87]
Network Inference	Mean Difference, Guanlab	Superior F1 scores on biological evaluation	Simple methods can outperform complex causal inference approaches [91]

A critical finding across multiple studies is that scFMs do not consistently outperform simpler baseline methods, particularly when task-specific data is limited or when biological prior knowledge is incorporated into traditional machine learning approaches [4] [87]. For example, in perturbation response prediction, a simple Random Forest model using Gene Ontology features significantly outperformed both scGPT and scFoundation, with Pearson Delta metrics of 0.739 versus 0.641 and 0.552 respectively on the Adamson dataset [87].

Experimental Protocols for Biological Validation

Gene-Level Evaluation Protocol

Gene-level evaluations assess how well scFMs capture functional gene relationships and biological pathways, providing insights into the model's understanding of fundamental biological mechanisms.

Objective: Quantify how accurately gene embeddings from scFMs reflect known biological relationships, including gene functionality, pathway membership, and tissue specificity [4].

Methodology:

• Embedding Extraction: Extract gene embeddings from the input layers of scFMs, typically from the gene token representations before transformer layers [4].
• Similarity Calculation: Compute cosine similarities between all gene pairs within the embedding space to establish relationship strengths.
• Ground Truth Definition: Establish known biological relationships using authoritative databases:
  • Gene Ontology (GO) terms for functional similarities [4] [87]
  • KEGG and REACTOME for pathway co-membership [87]
  • Tissue-specific expression patterns from reference databases
• Metric Calculation:
  • Perform gene function prediction using k-nearest neighbors in embedding space
  • Calculate precision-recall curves for retrieving known functional relationships
  • Compare against baseline embeddings (e.g., FRoGS) using area under curve metrics [4]

Interpretation: Effective gene embeddings should show high precision in retrieving known functional relationships, with functionally similar genes (e.g., same protein complex) clustering closely in the embedded space.

Cell-Level Evaluation Protocol

Cell-level evaluations assess how well scFMs capture cellular identities, states, and relationships, crucial for applications like cell type annotation and atlas construction.

Objective: Determine how accurately cell embeddings preserve biological variation while removing technical artifacts, and how well they align with established biological knowledge of cell type relationships [4].

Methodology:

• Embedding Generation: Generate cell embeddings using the scFM's cell representation (typically a special [CLS] token or aggregated gene embeddings) [1].
• Batch Integration Assessment:
  • Utilize datasets with known batch effects (inter-patient, inter-platform, inter-tissue)
  • Apply unsupervised clustering metrics (Silhouette Score, ARI) to assess biological preservation
  • Calculate batch mixing metrics (e.g., Graph Connectivity) to quantify technical artifact removal [4]
• Biological Alignment Validation:
  • Apply scGraph-OntoRWR to measure consistency with cell ontology relationships
  • For misclassifications, calculate LCAD to assess biological plausibility of errors [4] [88]
• Task-Specific Fine-Tuning:
  • Implement few-shot learning for cell type annotation on novel cell types
  • Assess cross-tissue generalization capabilities

Interpretation: High-quality cell embeddings should show strong biological structure preservation (high clustering metrics) while effectively removing technical batch effects, with relationship patterns that align with established biological ontologies.

Perturbation Response Prediction Protocol

Perturbation response prediction evaluates how well scFMs can forecast cellular behavior under genetic or chemical perturbations, with significant implications for drug discovery and disease modeling.

Objective: Assess the model's ability to accurately predict post-perturbation gene expression profiles, particularly for unseen perturbations or novel cellular contexts [87] [92].

Methodology:

• Data Preparation:
  • Utilize Perturb-seq datasets (e.g., Adamson, Norman, Replogle)
  • Implement proper dataset splits: Perturbation Exclusive (PEX) for unseen perturbations, Cell Exclusive (CEX) for unseen cell types [87]
• Model Configuration:
  • For foundation models: implement both fine-tuning and feature extraction approaches
  • Incorporate perturbation representations (e.g., perturbation tokens in scGPT)
  • Compare against baselines: Train Mean, Random Forest with GO features, kNN Regression [87]
• Evaluation Metrics:
  • Primary: Pearson correlation in differential expression space (Pearson Delta)
  • Secondary: Performance on top 20 differentially expressed genes
  • Comparative: F1 scores for classification approaches [87]
• Specificity Analysis:
  • Assess model performance across different perturbation types (single, combinatorial)
  • Evaluate generalization to novel biological contexts

Interpretation: Effective perturbation models should significantly outperform simple baselines (especially Train Mean) and show robust performance across different perturbation types and cellular contexts, indicating genuine understanding of causal biological mechanisms rather than pattern recognition.

Implementing rigorous evaluation frameworks for scFMs requires both computational resources and biological reference data. The table below details essential components for establishing a comprehensive evaluation pipeline.

Table 3: Essential Resources for scFM Evaluation

Resource Category	Specific Tools/Databases	Primary Function	Application Context
Benchmarking Frameworks	BioLLM, scDrugMap, scFME, CausalBench	Standardized model evaluation and comparison	Platform-specific model assessment [5] [89] [92]
Data Resources	CZ CELLxGENE, Human Cell Atlas, PanglaoDB	Curated single-cell data for training and testing	Model pretraining and biological validation [1]
Biological Reference	Gene Ontology, Cell Ontology, KEGG, REACTOME	Ground truth for biological relationship validation	Metric calculation (scGraph-OntoRWR, functional prediction) [4] [87]
Perturbation Datasets	Adamson, Norman, Replogle datasets	Benchmarking perturbation response prediction	Evaluation of causal inference capabilities [87] [91]
Baseline Models	Seurat, Harmony, scVI, Random Forest with GO	Performance comparison baselines	Contextualizing scFM performance [4] [87]

The BioLLM framework deserves particular note as it provides a unified interface that integrates diverse scFMs, eliminating architectural and coding inconsistencies to enable streamlined model access and comparative evaluation [5]. Similarly, CausalBench provides specialized evaluation suites for network inference from single-cell perturbation data, incorporating both biologically-motivated metrics and distribution-based interventional measures [91].

Rigorous evaluation of single-cell foundation models requires multi-faceted approaches that extend beyond traditional performance metrics to include biological plausibility, causal understanding, and practical utility. The frameworks and metrics outlined in this whitepaper provide a roadmap for researchers to assess whether these models genuinely capture biological insights or simply excel at benchmark tasks.

Future evaluation methodologies will need to address several emerging challenges. First, as multi-modal single-cell data becomes increasingly available, evaluation frameworks must expand to assess how well models integrate information across transcriptomics, epigenomics, proteomics, and spatial contexts. Second, the field requires more sophisticated causal evaluation paradigms that can discern whether models truly understand mechanistic biology rather than recognizing correlative patterns. Finally, as these models move toward clinical applications, evaluation frameworks must incorporate metrics relevant to drug discovery and therapeutic development, such as candidate target prioritization accuracy and clinical outcome prediction.

The rapid evolution of scFMs necessitates equally rapid advancement in their evaluation methodologies. By adopting the comprehensive framework presented here—encompassing gene-level, cell-level, and perturbation-response assessments with biologically-grounded metrics—researchers can more accurately discern model capabilities and limitations, ultimately accelerating the development of more biologically insightful and clinically valuable foundation models.

Single-cell foundation models (scFMs), such as Geneformer and scGPT, represent a transformative paradigm in computational biology, promising to learn universal patterns from vast single-cell transcriptomics data. However, rigorous evaluation of their zero-shot performance—where models are applied without any task-specific fine-tuning—reveals significant limitations. Empirical evidence demonstrates that these complex models frequently underperform simpler, established methods in critical tasks like cell type clustering and batch integration. This in-depth technical analysis synthesizes recent benchmarking studies to outline the performance gaps, explore the underlying causes, and provide standardized protocols for evaluation. The findings underscore that despite their theoretical promise, the current generation of scFMs has not yet achieved the robust, generalizable biological understanding necessary for reliable zero-shot application in discovery-driven research.

Foundation models are large-scale machine learning models pretrained on extensive datasets, with the goal of capturing universal patterns that can be adapted to various downstream tasks [1]. In single-cell biology, the exponential growth of single-cell RNA sequencing (scRNA-seq) data has spurred the development of scFMs, which aim to learn fundamental biological principles from millions of cellular profiles [4] [1]. These models typically employ transformer-based architectures and are trained using self-supervised objectives, such as masked gene expression prediction, where the model learns to predict randomly masked genes based on the context of other genes in a cell [1].

A crucial yet underexplored aspect of scFMs is their zero-shot capability—the ability to generate meaningful insights on new data without additional training. This capability is particularly vital for biological discovery settings where labels are unknown and fine-tuning is impractical [93] [94]. While scFMs are often evaluated through fine-tuning on specific benchmarks, this approach can mask fundamental limitations in the biological knowledge actually learned during pretraining [93]. Recent rigorous evaluations in zero-shot settings have exposed surprising performance gaps, challenging claims about these models' generalizability and biological understanding [93] [94] [95].

This technical review synthesizes evidence from multiple systematic benchmarks to assess the real-world zero-shot capabilities of current scFMs. We analyze their performance across key biological tasks, compare them against simpler baselines, and provide methodological frameworks for rigorous evaluation. The cumulative findings suggest that the single-cell research community should maintain cautious skepticism toward claims of emergent biological understanding in scFMs until more robust evaluation standards are established and met.

Performance Benchmarks: scFMs vs. Established Methods

Cell Type Clustering

Cell type clustering represents a fundamental task in single-cell analysis where models must group cells based on biological function rather than technical artifacts. In zero-shot evaluation, both scGPT and Geneformer demonstrate inconsistent performance compared to established methods.

Table 1: Zero-shot Cell Type Clustering Performance (AvgBIO Score)

Method	Pancreas Dataset	PBMC (12k) Dataset	Tabula Sapiens	Immune Dataset
HVG	0.74	0.71	0.76	0.73
Harmony	0.72	0.69	0.74	0.71
scVI	0.75	0.70	0.77	0.74
scGPT	0.68	0.73	0.70	0.67
Geneformer	0.62	0.64	0.63	0.61

Note: AvgBIO score averages multiple clustering metrics (ARI, NMI, ASW). Higher scores indicate better performance. Data compiled from [93].

Notably, the simple approach of selecting highly variable genes (HVG) consistently outperforms both foundation models across most datasets and metrics [93] [94]. scGPT shows a performance advantage only in the PBMC (12k) dataset, while generally underperforming scVI and Harmony on other datasets [93]. The variability in performance across datasets persists even when evaluation datasets partially overlap with pretraining corpora, suggesting an unclear relationship between pretraining objectives and effective cell type representation [93].

Batch Integration

Batch integration—removing technical artifacts while preserving biological variation—is critical for combining datasets from different sources. scFMs struggle significantly with this task in zero-shot settings.

Table 2: Batch Integration Performance Across Datasets

Method	Pancreas	PBMC	Tabula Sapiens	Immune
HVG	0.81	0.79	0.83	0.80
Harmony	0.78	0.76	0.72	0.77
scVI	0.82	0.78	0.81	0.75
scGPT	0.71	0.74	0.79	0.78
Geneformer	0.58	0.61	0.59	0.60

Note: Scores represent batch integration metrics (average across multiple measures). Higher scores indicate better batch mixing while preserving biological variation. Data compiled from [93].

Qualitative assessment of embedding spaces reveals that Geneformer's representations often fail to retain meaningful cell type information, with clustering primarily driven by batch effects rather than biology [93]. While scGPT offers some cell type separation, the dominant structure in its embeddings still reflects batch effects rather than biological signals [93]. Across quantitative metrics, Geneformer consistently ranks last in batch integration capability, sometimes explaining more variance through batch effects than the original data [93].

Perturbation Effect Prediction

The ability to predict transcriptome changes after genetic perturbations represents a key claim of several scFMs. However, recent benchmarks reveal startling limitations.

Table 3: Perturbation Prediction Performance (L2 Distance)

Method	Double Perturbations	Unseen Single Perturbations
Additive Baseline	0.41	-
No Change Baseline	0.52	0.51
Linear Model	-	0.45
GEARS	0.55	0.49
scGPT	0.58	0.52
scFoundation	0.54	-
Geneformer*	0.61	0.55

Note: Lower L2 distances indicate better performance. Asterisk denotes models repurposed with linear decoders. Data from [95].

In predicting double perturbation effects, all foundation models performed worse than a simple additive baseline that sums individual logarithmic fold changes [95]. Similarly, for unseen single perturbations, none consistently outperformed a simple linear model or even the "no change" baseline that always predicts control condition expression [95]. When researchers extracted gene embeddings from scFoundation and scGPT and used them in simple linear models, performance matched or exceeded that of the models' built-in decoders, suggesting the pretrained representations provide limited predictive value [95].

Experimental Protocols for Zero-Shot Evaluation

Standardized Evaluation Framework

Rigorous zero-shot evaluation requires standardized protocols to ensure comparable and reproducible assessments across models and tasks. The following methodology outlines a comprehensive framework adapted from recent benchmarks [93] [4].

Data Preparation and Preprocessing

• Select diverse evaluation datasets representing different tissues, species, and experimental conditions
• Ensure no overlap between evaluation datasets and pretraining corpora, or explicitly account for potential overlaps
• Apply consistent normalization and quality control procedures across all models
• For cell-level tasks, utilize datasets with manual annotations and multiple batch effects sources (inter-patient, inter-platform, inter-tissue)

Embedding Extraction

• For transformer-based scFMs, extract the [CLS] token embedding or mean pool all gene token embeddings as the cell representation
• Process all cells through the model without any parameter updates
• For gene-level tasks, extract gene token embeddings from the input layer

Performance Assessment

• Apply task-specific evaluation metrics without any model fine-tuning
• For clustering: use AvgBIO score (combining ARI, NMI, and ASW)
• For batch integration: employ multiple complementary metrics (PCR, batch ASW, etc.)
• For perturbation prediction: calculate L2 distance between predicted and observed expression
• Compare against established baselines (HVG, Harmony, scVI) and simple models (linear regression, additive models)

This protocol emphasizes the critical importance of using multiple datasets and metrics to obtain a comprehensive view of model performance, as results can vary significantly across biological contexts and evaluation measures [93] [4].

Novel Evaluation Metrics

Recent research has introduced biologically-grounded metrics that move beyond technical assessments to evaluate how well scFMs capture meaningful biological relationships:

Cell Ontology-Informed Metrics

• scGraph-OntoRWR: Measures consistency between cell type relationships in the embedding space and established biological knowledge from cell ontologies [4]
• Lowest Common Ancestor Distance (LCAD): Quantifies ontological proximity between misclassified cell types, providing biological context for annotation errors [4]

Roughness Index (ROGI)

• Estimates model performance correlation with cell-property landscape roughness in the latent space
• smoother landscapes typically indicate better generalization and easier fine-tuning [4]

These novel metrics help bridge the gap between technical performance and biological relevance, addressing concerns that scFMs might optimize for mathematical abstractions rather than biologically meaningful representations.

Technical Analysis: Why Do scFMs Underperform?

Limitations in Pretraining Objectives

The dominant pretraining approach for scFMs—masked language modeling—may be fundamentally mismatched to the characteristics of single-cell data. While this method has proven highly successful in natural language processing, gene expression data lacks the inherent sequential structure of language [1]. The arbitrary ordering of genes by expression magnitude creates artificial sequences that may not reflect biological reality, potentially limiting the model's ability to learn genuine gene-gene relationships [1].

More critically, evaluation of the pretraining task itself reveals concerning limitations. When assessing scGPT's ability to predict held-out gene expression, the model frequently defaults to predicting median expression values regardless of true expression levels [94]. Only when conditioning on cell embeddings does performance slightly improve, and even then primarily for highly expressed "housekeeping" genes rather than context-specific variable genes [94]. This suggests that scFMs may be learning superficial statistical patterns rather than the deeper regulatory relationships necessary for robust biological understanding.

Data Representation Challenges

Single-cell data presents unique challenges that may complicate learning transferable representations:

High Sparsity and Noise

• Single-cell transcriptomic data exhibits significant technical noise and dropout events
• The signal-to-noise ratio is particularly challenging at the single-cell level [96]

Non-Sequential Nature

• Unlike language, gene-gene relationships are not sequential but involve complex, dynamic networks
• Current positional encoding schemes may impose artificial structure on biological data [1]

Batch Effects and Technical Variability

• Substantial technical artifacts across experiments complicate learning invariant biological representations
• Models may overfit to technical covariates rather than biological signals

These characteristics may explain why simpler, more specialized methods often outperform foundation models that are trained on massive but heterogeneous datasets.

Visualization of Evaluation Frameworks

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Computational Tools for scFM Evaluation

Tool/Resource	Type	Primary Function	Application in Evaluation
CELLxGENE	Data Platform	Provides standardized access to annotated single-cell datasets	Source of pretraining data and evaluation benchmarks [93] [1]
Highly Variable Genes (HVG)	Feature Selection	Identifies genes with highest cell-to-cell variation	Simple baseline for clustering and batch integration [93] [94]
Harmony	Integration Algorithm	Iterative clustering-based batch correction	Established baseline for data integration tasks [93]
scVI	Probabilistic Model	Deep generative model for scRNA-seq analysis	Performance benchmark for clustering and integration [93]
AvgBIO Score	Evaluation Metric	Combines ARI, NMI, and ASW clustering metrics	Comprehensive assessment of clustering performance [93] [97]
scGraph-OntoRWR	Biological Metric	Measures consistency with cell ontology relationships	Evaluation of biological relevance in embeddings [4]
Linear Baselines	Simple Models	Additive and "no change" prediction models	Critical controls for perturbation prediction tasks [95]

Comprehensive zero-shot evaluation of single-cell foundation models reveals significant limitations in their current state of development. Despite their theoretical promise and massive parameter counts, models like scGPT and Geneformer frequently underperform simpler, established methods in critical tasks including cell type clustering, batch integration, and perturbation prediction. The consistency of these findings across multiple independent studies suggests fundamental challenges in how these models learn and represent biological knowledge.

The performance gaps likely stem from multiple factors: potentially misaligned pretraining objectives that prioritize token-level prediction over cellular understanding, the non-sequential nature of biological data that mismatches with transformer architectures originally designed for language, and the inherent noisiness and sparsity of single-cell measurements. Rather than capturing deep biological principles, current scFMs may be learning superficial patterns that fail to generalize in true zero-shot settings.

These findings carry important implications for researchers and drug development professionals. First, practitioners should maintain healthy skepticism toward claims of emergent biological understanding in scFMs and continue employing established methods alongside any foundation model approaches. Second, the research community must develop more biologically meaningful evaluation frameworks that assess genuine understanding rather than task-specific optimization. Finally, future scFM development should prioritize architectural innovations and pretraining objectives specifically designed for biological data rather than directly transplanting approaches from natural language processing.

While single-cell foundation models represent an exciting direction for computational biology, their current limitations in zero-shot settings highlight the substantial work needed before they can reliably function as virtual cells or generalized biological reasoners. Rigorous, critical benchmarking remains essential to guide this rapidly evolving field toward genuinely impactful advances.

The advent of single-cell RNA sequencing (scRNA-seq) has fundamentally transformed biological research by enabling the decoding of gene expression profiles at the individual cell level, thereby revealing cellular heterogeneity and complex biological processes previously obscured in bulk analyses [29]. This technological revolution has generated an explosion of high-dimensional, sparse, and noisy transcriptomic data, presenting substantial computational challenges for analysis and interpretation [98]. Traditional machine learning (ML) methods have served as cornerstone computational tools for clustering, dimensionality reduction, and trajectory inference in single-cell transcriptomics [29]. However, the exponential growth of scRNA-seq data has catalyzed the development of more sophisticated analytical paradigms, particularly single-cell foundation models (scFMs) trained on millions of cells using self-supervised learning approaches [98].

Two pioneering scFMs have emerged at the forefront of this revolution: Geneformer, a context-aware, attention-based deep learning model pretrained on approximately 30 million human single-cell transcriptomes [99] [100], and scGPT, a generative pre-trained transformer designed to integrate and analyze large-scale single-cell multi-omics data across over 33 million cells [101] [102]. These models promise to learn universal biological representations that can be efficiently adapted to diverse downstream tasks through transfer learning, potentially surpassing the capabilities of traditional ML methods [101] [100].

This review presents a comprehensive technical analysis comparing these emerging foundation models against established traditional ML methods, examining their architectural foundations, performance characteristics, and practical applicability within single-cell genomics research. By synthesizing evidence from recent benchmarking studies and technical specifications, we aim to provide researchers and drug development professionals with a nuanced framework for selecting appropriate computational strategies based on specific research contexts, dataset characteristics, and analytical objectives.

Architectural Foundations and Training Methodologies

scGPT: A Multi-Omic Generative Foundation Model

scGPT employs a generative pre-trained transformer architecture specifically designed for single-cell multi-omics data integration. The model configuration consists of 12 transformer blocks with an embedding size of 512 and 8 attention heads per block, totaling approximately 53 million parameters [102]. Pretraining utilizes a diverse corpus of over 33 million non-cancerous human cells from the CZ CELLxGENE Discover Census, incorporating both single-cell transcriptomic and multi-omic data [101] [102].

A distinctive feature of scGPT is its value binning technique for processing raw gene expression counts into relative values, treating each gene as a distinct token with unique identifiers [102]. The model employs an iterative masked gene modeling pretraining objective with mean squared error (MSE) loss, combining both gene-prompt and cell-prompt approaches [98]. This generative framework enables scGPT to learn context-aware representations of genes and cells that can be fine-tuned for diverse downstream applications including multi-batch integration, multi-omic integration, cell-type annotation, genetic perturbation prediction, and gene network inference [102].

Geneformer: A Context-Aware Encoder Model

Geneformer utilizes a Transformer Encoder architecture pretrained on approximately 30 million human scRNA-seq profiles, employing a masked gene modeling objective with cross-entropy loss for gene identity prediction [98] [100]. Unlike scGPT, Geneformer incorporates a rank value encoding system that represents transcriptomes based on gene expression rankings rather than absolute values, creating "cellular sentences" where genes are ordered by expression level [100]. The model processes 2,048 ranked genes as input and generates embeddings of either 256 or 512 dimensions depending on the specific configuration (6-layer or 12-layer architecture) [98].

Geneformer's pretraining emphasizes learning context-dependent genetic network dynamics through its attention mechanism, enabling in silico simulations of gene manipulation experiments and advancing understanding of genetic networks and disease mechanisms [100]. The model has demonstrated particular strength in predicting disease-causing genes and modeling transcriptome-scale dose-dependent effects of perturbations [100].

Traditional Machine Learning Methods

Traditional ML approaches for single-cell analysis encompass a diverse ecosystem of algorithms optimized for specific analytical tasks. These include:

• Dimensionality Reduction: Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualization and downstream analysis [29].
• Clustering: Hierarchical, graph-based, and model-based clustering methods for identifying cell types or states [29].
• Batch Correction: Anchor-based methods like Seurat and clustering-based approaches like Harmony that integrate multiple datasets by correcting for technical variations [98] [93].
• Generative Modeling: Probabilistic frameworks such as scVI (single-cell Variational Inference) that explicitly model count-based gene expression data [98] [93].

These traditional methods typically employ specialized architectures tailored to specific analytical tasks rather than the general-purpose foundation model paradigm, with many operating on carefully selected highly variable genes (HVGs) to reduce dimensionality and computational complexity [93].

Table 1: Architectural Comparison of scGPT, Geneformer, and Traditional ML Methods

Feature	scGPT	Geneformer	Traditional ML
Architecture Type	Generative Transformer	Transformer Encoder	Task-specific algorithms
Parameters	53 million	40 million	Varies by method
Pretraining Data	33+ million cells (multi-omic)	30 million human cells	Not pretrained
Input Representation	Value binning of expression	Rank value encoding	HVGs, normalized counts
Pretraining Objective	Masked gene modeling (MSE loss)	Masked gene modeling (CE loss)	Not applicable
Multi-omic Support	Yes (RNA, ATAC, CITE-seq, spatial)	Primarily scRNA-seq	Limited to specialized methods
Primary Use Cases	Multi-omic integration, batch correction, perturbation prediction	Cell type classification, in silico perturbation, network inference	Specific tasks (clustering, visualization, etc.)

Performance Benchmarking Across Key Applications

Cell Type Annotation and Classification

Cell type annotation represents a fundamental application in single-cell genomics where foundation models theoretically excel due to their comprehensive biological knowledge. Benchmarking studies reveal a complex performance landscape dependent on evaluation protocols. When fine-tuned on specific datasets, both scGPT and Geneformer demonstrate enhanced accuracy in cell type classification, leveraging their pretrained representations to achieve superior performance with limited task-specific data [101] [100].

However, in zero-shot settings where models are applied without any task-specific fine-tuning, recent evaluations indicate limitations. Both scGPT and Geneformer underperform compared to simpler baselines like highly variable genes (HVG) selection combined with established methods such as Harmony and scVI for cell type clustering, as measured by average BIO (AvgBio) score and average silhouette width (ASW) metrics [93]. This performance gap highlights the critical distinction between fine-tuned and zero-shot applications, particularly relevant for exploratory research where cell composition may be unknown and fine-tuning infeasible.

Batch Integration and Data Harmonization

Batch integration presents substantial challenges in single-cell analysis due to technical variations across experiments, platforms, and laboratories. scGPT exhibits robust performance in multi-batch integration, effectively correcting for batch effects while preserving biological variance, particularly when datasets share similarities with its pretraining corpus [101] [102]. Quantitative evaluations demonstrate that scGPT competes favorably with specialized batch correction methods like Harmony and scVI on complex datasets containing both technical and biological batch effects, though its performance varies across different evaluation metrics [93].

Geneformer shows more limited effectiveness in batch integration tasks, with its embedding spaces often retaining substantial batch-specific information and sometimes failing to preserve meaningful biological separation between cell types [93]. In comprehensive benchmarking, Geneformer consistently ranked below scGPT, Harmony, and scVI in batch mixing scores across multiple datasets, with its embeddings sometimes explaining more variance from batch effects than the original data [93].

Surprisingly, the simple approach of selecting highly variable genes (HVG) achieved competitive or superior batch integration scores compared to all foundation models and specialized integration algorithms in certain evaluations, particularly when assessed in full dimensionality rather than reduced spaces [93].

Perturbation Response Prediction

Predicting cellular responses to genetic perturbations represents an area where foundation models demonstrate distinctive capabilities. Both scGPT and Geneformer enable in silico simulation of gene manipulation experiments, offering powerful alternatives to costly and time-consuming laboratory interventions [101] [100].

Geneformer has proven effective in identifying disease-causing genes validated through in vivo experiments, demonstrating its capacity to model context-dependent genetic interactions [100]. Similarly, scGPT shows proficiency in predicting effects of genetic perturbations on gene expression patterns, leveraging its generative architecture to model complex regulatory relationships [102].

Traditional ML methods like scGen have previously shown capability in predicting single-cell perturbation responses [101], but foundation models offer the advantage of generalizable knowledge transfer across diverse biological contexts without requiring task-specific architectural redesign.

Cross-Species Generalization

The transferability of foundation models across species represents a particularly promising application. Recent development of mouse-Geneformer, trained on approximately 21 million mouse scRNA-seq profiles, demonstrates the architecture's adaptability across organisms [99] [100]. Remarkably, mouse-Geneformer exhibits cross-species utility, achieving cell type classification accuracy comparable to human Geneformer when applied to human data after ortholog-based gene name conversion [99] [100].

This cross-species capability varies by biological context, with mouse-Geneformer performing well for myocardial infarction models but showing only partial consistency for human-specific conditions like COVID-19, reflecting fundamental physiological differences between species [99] [100]. Such cross-species applications offer particular value for human research involving tissues inaccessible for ethical or technical reasons, such as embryonic samples [100].

Table 2: Performance Comparison Across Key Tasks

Analytical Task	scGPT	Geneformer	Traditional ML
Cell Type Annotation (Fine-tuned)	Superior with limited data	Enhanced accuracy after fine-tuning	Requires specialized algorithms for each cell type
Cell Type Annotation (Zero-shot)	Inconsistent vs. baselines	Underperforms HVG+Harmony/scVI	HVG selection performs strongly
Batch Integration	Robust, especially on complex batches	Limited effectiveness	Harmony and scVI generally strong
Perturbation Prediction	Strong capabilities demonstrated	Identifies validated disease genes	Specialized methods exist (e.g., scGen)
Multi-omic Integration	Native capability	Limited support	Requires specialized frameworks
Cross-species Transfer	Not explicitly evaluated	Effective with ortholog conversion	Method-specific implementation needed
Computational Resources	High during pretraining, moderate for fine-tuning	High during pretraining, moderate for fine-tuning	Generally lower requirements

Experimental Protocols and Methodologies

Benchmarking Framework Design

Rigorous evaluation of single-cell foundation models requires carefully designed benchmarking protocols that account for diverse application scenarios and potential data leakage. Comprehensive benchmarks should assess both zero-shot performance and fine-tuned capabilities across biologically meaningful tasks [98] [93]. The recently proposed benchmarking framework evaluates foundation models against traditional baselines using multiple metrics spanning unsupervised, supervised, and knowledge-based approaches [98].

Critical considerations for benchmarking include:

• Task Selection: Encompassing gene-level tasks (gene network inference, gene function prediction) and cell-level tasks (cell type annotation, batch integration, perturbation response prediction, drug sensitivity forecasting) [98].
• Dataset Diversity: Including datasets with varying biological conditions, technical platforms, and species origins to assess generalizability [98].
• Evaluation Metrics: Employing multiple complementary metrics including traditional clustering scores (ASW, ARI) and novel biology-aware measures like scGraph-OntoRWR that evaluate consistency with established biological knowledge [98].
• Data Leakage Prevention: Implementing rigorous separation between pretraining and evaluation datasets, with some benchmarks introducing completely independent datasets like the Asian Immune Diversity Atlas (AIDA) v2 for validation [98].

Zero-Shot Evaluation Protocol

Zero-shot evaluation has emerged as a critical assessment paradigm, particularly for exploratory research where labeled data for fine-tuning is unavailable [93]. The standard protocol involves:

• Embedding Extraction: Generating cell embeddings using pretrained foundation models without any task-specific fine-tuning.
• Baseline Comparison: Comparing against traditional methods including HVG selection, Seurat, Harmony, and scVI.
• Dimensionality Reduction: Applying standard techniques (UMAP, t-SNE) for visualization and qualitative assessment.
• Quantitative Assessment: Calculating multiple metrics including batch integration scores (LISI, PCR) and cell type separation metrics (ASW, ARI) [93].
• Biological Validation: Assessing whether identified patterns align with established biological knowledge.

This evaluation strategy has revealed that foundation models do not consistently outperform simpler methods in zero-shot settings, underscoring the importance of rigorous validation before deployment in discovery research [93].

Fine-Tuning Methodologies

When labeled data is available, fine-tuning represents the most powerful approach for adapting foundation models to specific tasks. Standard fine-tuning protocols for scGPT and Geneformer include:

• Learning Rate Strategy: Initial learning rates of 0.0001 with decay after each epoch (10% for scGPT) [102].
• Masking Strategies: For scGPT, mask ratios of 0.4 for gene expression prediction tasks with specific weighting schemes for different loss components [102].
• Training-Testing Splits: Standard 90%-10% splits for training and evaluation with 30 epochs of fine-tuning for most tasks [102].
• Task-Specific Heads: Adding lightweight neural network layers on top of frozen or partially fine-tuned foundation model embeddings.

Fine-tuning typically requires substantially less computational resources and data than pretraining, making foundation models accessible to researchers without extensive computational infrastructure [101] [100].

Research Reagent Solutions: Essential Materials for Single-Cell Foundation Model Research

Table 3: Essential Research Reagents and Computational Tools

Resource Category	Specific Solutions	Function and Application
Data Resources	CZ CELLxGENE Census [101] [102]	Primary data source for pretraining foundation models, containing millions of single-cell transcriptomes
	PanglaoDB, Single Cell Expression Atlas [99] [100]	Curated databases for compiling species-specific training data
	Asian Immune Diversity Atlas (AIDA) v2 [98]	Independent benchmarking dataset for validating model performance
Computational Frameworks	Scanpy, Scikit-learn [100]	Traditional ML pipelines for single-cell data analysis
	Harmony, Seurat [98] [93]	Specialized algorithms for batch integration and data harmonization
	scVI [98] [93]	Probabilistic generative model for single-cell transcriptomics
Foundation Model Implementations	scGPT (PyPI package) [103]	User-friendly implementation of scGPT model for downstream applications
	Geneformer (HuggingFace) [99]	Accessible version of Geneformer for transfer learning
	Mouse-Geneformer [99] [100]	Species-specific adaptation for mouse transcriptomics
Experimental Platforms	10x Genomics Chromium [99] [104]	High-throughput single-cell RNA sequencing platform
	MGI, Oxford Nanopore [104]	Emerging sequencing technologies for single-cell genomics
	Parse Biosciences, Scale Biosciences [104]	Commercial solutions for scalable single-cell profiling

Decision Framework and Future Directions

Model Selection Guidelines

The comparative analysis reveals that no single approach consistently outperforms others across all tasks and datasets. Model selection should be guided by specific research objectives, dataset characteristics, and computational resources:

• Choose Foundation Models When:

  • Analyzing datasets with limited labeled examples for supervised tasks
  • Requiring multi-task capabilities across different analytical domains
  • Investigating perturbation responses or genetic interactions
  • Working with data similar to foundation model pretraining corpora
  • Sufficient computational resources are available for fine-tuning

• Prefer Traditional ML Methods When:

  • Conducting zero-shot exploratory analysis of novel cell types
  • Processing datasets substantially different from pretraining distributions
  • Computational resources are constrained
  • Specific well-defined tasks (e.g., batch correction) dominate requirements
  • Interpretability and computational efficiency are primary concerns

• Consider Hybrid Approaches:

  • Using foundation model embeddings as input to traditional ML algorithms
  • Applying traditional methods for initial data exploration followed by focused foundation model application
  • Employing ensemble strategies that leverage strengths of multiple approaches

Emerging Challenges and Research Opportunities

Despite rapid progress, several challenges persist in the development and application of single-cell foundation models:

• Biological Interpretability: Current foundation models often function as "black boxes," with limited mechanistic insight into how they derive biological conclusions [29] [98]. Developing interpretability frameworks that extract biologically meaningful insights from model attention patterns and embeddings represents a critical research direction.
• Data Representation: The optimal representation of single-cell data for foundation model pretraining remains an open question, with competing approaches including rank-based encoding (Geneformer), value binning (scGPT), and absolute expression values [98].
• Multimodal Integration: While scGPT incorporates multi-omic capabilities, comprehensive integration of transcriptomic, epigenomic, proteomic, and spatial information within a unified foundation model architecture remains largely unrealized [101] [102].
• Resource Efficiency: The computational demands of foundation model pretraining and deployment limit accessibility for researchers without substantial computational infrastructure. Developing more efficient architectures and training strategies represents an important equity consideration [98].

Future research directions likely to shape the field include the development of more biologically grounded pretraining objectives, incorporation of explicit biological knowledge through knowledge graphs, and creation of specialized foundation models for clinical applications including drug development and personalized medicine [29] [98].

Single-Cell Analysis Decision Framework

The comparative analysis of scGPT, Geneformer, and traditional machine learning methods reveals a rapidly evolving landscape in single-cell genomic analysis. Foundation models represent a paradigm shift toward general-purpose biological intelligence, offering unprecedented capabilities for knowledge transfer across diverse analytical tasks through pretraining on massive single-cell datasets. However, traditional ML methods maintain important advantages in specific contexts, particularly for well-defined analytical tasks and zero-shot exploratory research.

The optimal analytical strategy depends critically on specific research contexts, with foundation models excelling in scenarios benefiting from transfer learning and traditional methods maintaining superiority in resource-constrained environments or highly specialized applications. Rather than a simple replacement narrative, the future of single-cell analysis likely involves synergistic integration of both paradigms, leveraging the complementary strengths of foundation models and specialized traditional algorithms.

As the field matures, addressing challenges related to interpretability, resource efficiency, and biological grounding will be essential for realizing the full potential of foundation models in both basic research and clinical translation. Through rigorous benchmarking and thoughtful model selection guided by the framework presented here, researchers can effectively harness these powerful tools to advance our understanding of cellular biology and accelerate therapeutic development.

The emergence of single-cell foundation models (scFMs) represents a transformative development in computational biology, leveraging large-scale deep learning on massive single-cell datasets to create versatile tools for biological discovery [1]. These models, typically built on transformer architectures, learn universal biological knowledge during pretraining on millions of single-cell transcriptomes, enabling adaptation to various downstream tasks from cell type annotation to drug sensitivity prediction [98] [4]. However, as the field progresses, critical questions have emerged about how to effectively evaluate these models' ability to capture meaningful biological insights beyond technical performance metrics [98].

Traditional evaluation metrics for single-cell analysis often focus on technical aspects like clustering quality or batch integration efficiency, but fail to assess whether models truly learn the underlying biological relationships that reflect established knowledge [98] [4]. This limitation has prompted the development of novel evaluation frameworks that incorporate biological prior knowledge, particularly through cell ontology-informed metrics that measure consistency with known biological hierarchies and relationships [98] [4]. These approaches address a crucial gap in the benchmarking pipeline by ensuring that computational models not only perform well statistically but also generate biologically plausible and interpretable results.

This technical guide explores the emerging paradigm of biology-driven evaluation metrics for single-cell foundation models, with particular focus on scGraph-OntoRWR—a novel metric designed to quantify the biological relevance of learned cell representations—and related cell ontology-informed assessment frameworks. These methodologies represent a significant advancement toward bridging the gap between computational performance and biological meaning in the age of foundation models for single-cell biology.

Background: Single-Cell Foundation Models and Evaluation Challenges

The Architecture and Training of Single-Cell Foundation Models

Single-cell foundation models are typically built on transformer architectures and trained on massive collections of single-cell RNA sequencing (scRNA-seq) data [1]. The fundamental concept draws an analogy to natural language processing: individual cells are treated as "sentences" while genes or genomic features along with their expression values serve as "words" or "tokens" [1]. Through self-supervised pretraining on diverse datasets encompassing numerous cell types, tissues, and conditions, these models learn fundamental principles of cellular biology that generalize to new datasets and tasks [1].

A key challenge in applying transformer architectures to single-cell data lies in the non-sequential nature of gene expression information. Unlike words in a sentence, genes have no inherent ordering [98] [4]. Different models employ various strategies to address this challenge, including ranking genes by expression levels within each cell, binning genes by expression values, or using normalized counts without specific ordering [1]. These approaches enable the model to process gene expression profiles through attention mechanisms that learn which genes are most informative of a cell's identity or state and how they covary across cells [1].

Limitations of Conventional Evaluation Approaches

Traditional evaluation metrics for single-cell analysis methods face significant limitations when applied to foundation models. Conventional approaches typically measure technical performance aspects such as:

• Clustering quality metrics (e.g., silhouette score, adjusted Rand index)
• Batch correction efficiency (e.g., batch mixing scores)
• Classification accuracy for cell type annotation

While these metrics provide valuable information about technical performance, they offer limited insight into whether the model has learned biologically meaningful representations that align with established knowledge [98] [4]. This creates a critical gap in evaluation methodology, as models might achieve high technical performance while learning representations that contradict known biological relationships or fail to capture important functional associations.

The limitations of conventional evaluation approaches have become particularly apparent as scFMs are applied to increasingly complex biological and clinical questions, such as cancer cell identification, drug sensitivity prediction, and tumor microenvironment characterization [98]. In these contexts, biological plausibility becomes as important as technical performance for generating trustworthy insights that can inform experimental validation and clinical decision-making.

scGraph-OntoRWR: A Novel Metric for Biological Relevance

Conceptual Framework and Theoretical Basis

The scGraph-OntoRWR metric represents a groundbreaking approach to evaluating the biological relevance of cell representations learned by single-cell foundation models [98] [4]. This metric is specifically designed to measure the consistency between the relational structure of cell types captured by scFMs and prior biological knowledge encoded in cell ontologies [98].

The theoretical foundation of scGraph-OntoRWR rests on the premise that functionally similar cell types should be positioned closer together in the latent space learned by a foundation model, analogous to how semantically similar words cluster together in language model embeddings [98] [4]. By formalizing this principle, the metric provides a quantitative measure of how well a model's internal representations align with established biological knowledge about cell type relationships, going beyond what can be captured by technical performance metrics alone.

The "OntoRWR" component of the name refers to the "Ontology-based Random Walk with Restart" algorithm that forms the computational core of the method [98]. This approach leverages the hierarchical structure of cell ontologies to inform the evaluation process, embedding biological knowledge directly into the metric calculation.

Methodological Implementation

The implementation of scGraph-OntoRWR involves several key steps that transform both the model embeddings and ontological information into a comparable framework:

• Embedding Extraction: Cell embeddings are extracted from the scFM in a zero-shot manner, without task-specific fine-tuning, to evaluate the intrinsic biological knowledge captured during pretraining [98].

• Graph Construction: A cell-cell similarity graph is constructed from the model embeddings using k-nearest neighbors or similar approaches, representing the relational structure of cell types as learned by the model [98].

• Ontology Processing: Relevant cell ontology structures are processed into a comparable graph format, capturing known biological relationships between cell types [98] [4].

• Random Walk with Restart: The core algorithm performs random walks with restart on both the embedding-derived graph and the ontology graph, comparing the visitation patterns to quantify consistency between learned representations and biological knowledge [98].

• Consistency Quantification: The similarity between random walk distributions on the model-derived graph and ontology graph provides the final scGraph-OntoRWR score, with higher values indicating better alignment with biological prior knowledge [98].

Table 1: Key Components of scGraph-OntoRWR Implementation

Component	Description	Implementation Considerations
Embedding Extraction	Zero-shot cell embeddings from scFM	Ensures evaluation of intrinsic model knowledge rather than task-specific adaptation
Graph Construction	k-NN graph from embedding space	Graph construction parameters (e.g., k value) require careful selection
Ontology Source	Cell Ontology structures	Requires mapping between model cell types and standard ontology terms
RWR Parameters	Restart probability, walk length	Parameters affect sensitivity to local vs. global graph structure
Similarity Metric	Comparison of node visitation distributions	Choice of distance metric between distributions affects scoring

Experimental Protocols and Validation

The original benchmark study that introduced scGraph-OntoRWR implemented a comprehensive validation framework to demonstrate its utility [98]. The experimental protocol encompassed:

Dataset Curation: Five high-quality datasets with manual annotations were selected, varying in size and diversity and containing multiple sources of batch effects (inter-patient, inter-platform, and inter-tissue variations) to present realistic challenges [98]. These datasets provided the biological ground truth for evaluation.

Model Selection: Six prominent scFMs with different pretraining settings were evaluated, including Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello [98]. These represented the current state-of-the-art in single-cell foundation modeling.

Benchmarking Pipeline: The evaluation employed a standardized pipeline for feature extraction, downstream tasks, and performance assessment using 12 metrics spanning unsupervised, supervised, and knowledge-based approaches [98]. This multi-faceted evaluation ensured comprehensive assessment of model capabilities.

Biological Significance Testing: The scGraph-OntoRWR scores were correlated with biological plausibility of model outputs and performance on clinically relevant tasks to establish practical utility [98].

The validation demonstrated that scGraph-OntoRWR provides unique insights into model performance not captured by conventional metrics, particularly in measuring how well models preserve biological relationships in challenging scenarios such as novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [98].

Figure 1: scGraph-OntoRWR Calculation Workflow. The diagram illustrates the key steps in computing the scGraph-OntoRWR metric, from embedding extraction to final consistency score calculation.

Additional Cell Ontology-Informed Assessment Metrics

Lowest Common Ancestor Distance (LCAD)

The Lowest Common Ancestor Distance (LCAD) metric complements scGraph-OntoRWR by focusing on the biological plausibility of cell type misclassifications [98]. Unlike conventional accuracy metrics that treat all misclassifications equally, LCAD incorporates ontological proximity to assess the severity of errors [98].

Methodological Approach: LCAD operates on the principle that not all misclassifications are equally problematic from a biological perspective. Misclassifying a cell into a closely related cell type (e.g., confusing CD4+ and CD8+ T cells) is less severe than misclassifying it into a completely different lineage (e.g., confusing a T cell with a neuron) [98]. The metric quantifies this by:

• Identifying the lowest common ancestor of the true and predicted cell types within the cell ontology hierarchy
• Calculating the ontological distance between the true cell type and this common ancestor
• Using this distance to weight the severity of the classification error

Implementation Considerations: The calculation of LCAD requires a well-structured and comprehensive cell ontology, as well as accurate mapping between model-predicted cell types and standard ontology terms [98]. The metric is particularly valuable for evaluating models in scenarios where cell type granularity varies or when dealing with novel or closely related cell populations.

Integration with Conventional Evaluation Frameworks

Cell ontology-informed metrics are designed to complement rather than replace conventional evaluation approaches [98]. The comprehensive benchmark study that introduced these metrics employed a holistic evaluation strategy incorporating:

• 12 different metrics spanning unsupervised, supervised, and knowledge-based approaches [98]
• Multiple task types including gene-level tasks (tissue specificity and GO term prediction) and cell-level tasks (batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction) [98]
• Both preclinical datasets with diverse biological conditions and clinically relevant tasks across seven cancer types and four drugs [98]

This integrated approach provides a more complete picture of model capabilities, balancing technical performance with biological plausibility [98].

Table 2: Comparison of Cell Ontology-Informed Evaluation Metrics

Metric	Primary Function	Key Advantages	Implementation Complexity
scGraph-OntoRWR	Measures consistency of learned cell relationships with ontology	Captures global relational structure; Applicable to zero-shot embeddings	High (requires graph construction and RWR implementation)
LCAD	Assesses biological severity of misclassifications	Provides nuanced error analysis; Useful for granular cell type distinctions	Medium (requires ontology integration and distance calculation)
Cell Type ASW	ontology-aware variation of average silhouette width	Integrates ontology with clustering quality assessment; Standardized implementation	Low (modification of existing silhouette width metric)

Experimental Applications and Case Studies

Benchmarking Single-Cell Foundation Models

The primary application of scGraph-OntoRWR and related ontology-informed metrics has been in comprehensive benchmarking of single-cell foundation models [98] [4]. The original study that introduced these metrics applied them to evaluate six prominent scFMs across multiple tasks and datasets, revealing several key insights:

No Single Model Dominance: No single scFM consistently outperformed others across all tasks, emphasizing the need for tailored model selection based on factors such as dataset size, task complexity, biological interpretability requirements, and computational resources [98].

Biological Insight Extraction: The pretrained zero-shot scFM embeddings indeed captured meaningful biological insights into the relational structure of genes and cells, which proved beneficial for downstream tasks [98].

Performance Correlates with Landscape Smoothness: Model performance improvement correlated with cell-property landscape roughness in the pretrained latent space, verifying that performance gains arise from a smoother landscape that reduces the difficulty of training task-specific models [98].

These findings demonstrate how ontology-informed metrics provide unique insights into model characteristics that extend beyond what can be learned from conventional evaluation approaches alone.

Practical Implementation Guidelines

Implementing scGraph-OntoRWR and related metrics in practice requires careful attention to several methodological considerations:

Ontology Selection and Processing: The choice of cell ontology and its processing significantly impacts metric calculation. Researchers should:

• Use comprehensive, actively maintained cell ontologies with broad coverage of relevant cell types
• Ensure consistent mapping between model-predicted cell types and ontology terms
• Consider ontology versioning and updates that might affect reproducibility

Parameter Sensitivity Analysis: Key parameters in scGraph-OntoRWR implementation require careful tuning and sensitivity analysis:

• k value in k-NN graph construction balances local and global structure capture
• Restart probability in RWR algorithm affects the balance between local neighborhood exploration and global graph exploration
• Random walk length influences how extensively the graph structure is explored

Integration with Existing Benchmarks: Researchers should integrate ontology-informed metrics with established evaluation frameworks to provide comprehensive model assessment [98]. This includes combining them with conventional metrics for clustering quality, batch correction, and classification accuracy.

Figure 2: Integrated Evaluation Framework. The diagram shows how ontology-informed metrics complement conventional technical performance metrics in a comprehensive model assessment strategy.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for Implementing Ontology-Informed Evaluation

Resource Category	Specific Tools/Solutions	Function in Evaluation Pipeline
Cell Ontology Resources	Cell Ontology (CL) from OBO Foundry	Provides standardized cell type definitions and hierarchical relationships for metric calculation
Annotation Tools	CELLxGENE, Cell Annotation Explorer	Facilitates mapping between model-predicted cell types and standard ontology terms
Graph Analysis Libraries	NetworkX, igraph, GraphTool	Enables graph construction, random walk implementation, and topological analysis
Single-Cell Analysis Frameworks	Scanpy, Seurat, SCimilarity	Provides foundational infrastructure for single-cell data processing and embedding extraction
Model Implementation Frameworks	PyTorch, TensorFlow, JAX	Supports implementation and modification of foundation model architectures
Benchmarking Platforms	scIB, OpenProblems, SingleCellBench	Offers standardized environments for comparative model evaluation

Emerging Applications and Methodological Extensions

The development of scGraph-OntoRWR and related cell ontology-informed metrics represents an important step toward biologically-grounded evaluation of single-cell foundation models, but several promising directions for future work remain:

Multi-Ontology Integration: Current approaches primarily focus on cell type ontologies, but extension to incorporate additional biological ontologies (e.g., Gene Ontology, Disease Ontology, Anatomy Ontology) could provide even more comprehensive biological grounding [105].

Dynamic Ontology Alignment: As biological knowledge evolves, evaluation frameworks need to adapt accordingly. Developing approaches that can handle ontology updates and revisions without requiring complete recalibration would enhance long-term utility.

Cross-Species Applicability: Extending these metrics to enable meaningful evaluation across species boundaries would facilitate research in model organisms and comparative biology [105].

Integration with Spatial Metrics: As spatially resolved transcriptomics becomes increasingly important, developing spatial analogues of ontology-informed metrics could address the unique challenges of spatial data analysis [106].

The introduction of scGraph-OntoRWR and other cell ontology-informed assessment metrics marks a significant paradigm shift in the evaluation of single-cell foundation models [98] [4]. By explicitly incorporating biological prior knowledge into the evaluation process, these approaches address a critical gap in conventional benchmarking frameworks that focus primarily on technical performance.

The comprehensive benchmark studies implementing these metrics have demonstrated their utility in revealing aspects of model capability that would otherwise remain hidden [98]. They provide crucial insights into whether models are learning biologically meaningful representations that align with established knowledge, rather than merely optimizing for statistical performance metrics.

As single-cell foundation models continue to evolve and find applications in increasingly complex biological and clinical contexts, the importance of biologically-grounded evaluation will only grow. scGraph-OntoRWR and related metrics represent an essential step toward ensuring that these powerful computational tools generate not just statistically sound but biologically meaningful and clinically actionable insights [98] [4]. Their continued development and refinement will play a crucial role in bridging the gap between computational innovation and biological discovery in the era of foundation models for single-cell biology.

Within the broader context of single-cell foundation model (scFM) research, the paradigm of biological data analysis is shifting from purely exploratory studies to structured, model-informed discovery. The rapid accumulation of single-cell transcriptomics data has provided an unprecedented resource for training sophisticated machine learning models [1]. However, the transition from traditional analytical methods to foundation models presents researchers with a complex selection problem: how to choose the most appropriate model for specific scientific tasks amid competing considerations of performance, computational efficiency, and biological interpretability.

Single-cell foundation models represent a class of large-scale deep learning models pretrained on vast single-cell datasets using self-supervised objectives [1]. These models typically employ transformer architectures to process single-cell data by treating individual cells as "sentences" and genes or genomic features as "words" or "tokens" [1]. The fundamental promise of scFMs lies in their ability to learn universal biological principles from massive, heterogeneous datasets, which can then be adapted to various downstream tasks through fine-tuning or zero-shot learning [4].

Despite their theoretical advantages, practical applications reveal that no single scFM consistently outperforms all others across diverse tasks [4]. This reality necessitates an evidence-based approach to model selection that carefully balances task requirements, dataset characteristics, and resource constraints. This technical guide synthesizes recent benchmarking studies to provide a structured framework for selecting optimal models across common single-cell analysis scenarios, with particular emphasis on performance trade-offs and practical implementation considerations.

Fundamental Concepts in Model Selection

Taxonomy of Single-Cell Analysis Tasks

Model selection strategies must be aligned with specific analytical objectives. Single-cell research encompasses distinct task categories, each with unique requirements and evaluation criteria:

• Cell-level tasks: These include fundamental analyses such as cell type annotation, batch integration, and identification of novel cell states. Performance in these tasks depends on the model's ability to create biologically meaningful representations that preserve authentic biological variation while removing technical artifacts [4].
• Gene-level tasks: These involve understanding gene functions, interactions, and regulatory relationships. Effective models must capture functional similarities between genes and reflect known biological pathways [4].
• Clinical prediction tasks: These encompass clinically relevant applications such as cancer cell identification, drug sensitivity prediction, and treatment outcome forecasting. Models must demonstrate robustness across diverse patient populations and experimental conditions [4].

Model Selection Considerations

Evidence-based model selection requires simultaneous optimization across multiple, often competing, dimensions:

• Performance metrics: Task-specific evaluation criteria must be selected to measure success meaningfully. These may include traditional machine learning metrics (accuracy, F1-score) alongside biologically informed metrics that assess concordance with established biological knowledge [4].
• Computational efficiency: Model selection must account for practical constraints, including training time, inference speed, memory requirements, and hardware dependencies [4].
• Data requirements: Models vary in their pretraining data composition and volume, affecting their performance on specific tissue types, species, or experimental conditions [1].
• Interpretability: The ability to extract biologically meaningful insights from model outputs varies significantly across different architectural approaches [4].

Performance Landscape of Single-Cell Foundation Models

Comparative Framework and Evaluation Metrics

Recent benchmarking studies have established comprehensive frameworks for evaluating scFMs against traditional methods and each other. These evaluations typically employ a diverse set of metrics spanning multiple performance categories:

Table 1: Evaluation Metrics for Single-Cell Foundation Models

Metric Category	Specific Metrics	Measurement Focus
Integration (Batch)	Batch PCR, CMS, iLISI	Effectiveness of technical batch effect removal
Integration (Biological)	Isolated Label ASW, Isolated Label F1, bNMI, cLISI	Preservation of biological variation
Mapping Quality	Cell Distance, Label Distance, mLISI, qLISI	Accuracy of query-to-reference mapping
Classification Performance	F1 (Macro), F1 (Micro), F1 (Rarity)	Cell type annotation accuracy
Biological Consistency	scGraph-OntoRWR, LCAD	Concordance with established biological knowledge
Unseen Population Detection	Milo, Unseen Cell Distance	Identification of novel cell states

These metrics collectively assess a model's ability to generate representations that are both technically robust and biologically meaningful. The introduction of ontology-informed metrics such as scGraph-OntoRWR represents a significant advance, enabling quantitative assessment of whether model-derived cell type relationships reflect established biological hierarchies [4].

Task-Specific Model Performance

Comprehensive benchmarking across six prominent scFMs (Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello) and well-established baseline methods reveals a complex performance landscape with clear task-dependent patterns:

Table 2: Model Performance Across Task Categories

Task Category	Top-Performing Models	Key Performance Differentiators
Batch Integration	scGPT, scVI, Harmony	Handling of inter-patient, inter-platform, and inter-tissue variations
Cell Type Annotation	scBERT, XGBoost, SVM	Accuracy on rare cell types and cross-tissue generalization
Gene Function Prediction	Geneformer, scGPT	Capture of functional gene relationships and tissue specificity
Clinical Prediction	scFoundation, UCE	Robustness across patient populations and prediction accuracy
Novel Cell Type Detection	LangCell, scCello	Identification of unseen populations in query data

Benchmarking results consistently demonstrate that while scFMs provide robust and versatile performance across diverse applications, simpler machine learning models often adapt more efficiently to specific datasets, particularly under resource constraints or when working with homogeneous data [4]. For example, in cell type annotation tasks, tree-based models like XGBoost combined with appropriate feature selection can achieve competitive performance with significantly lower computational requirements [107].

The Simplicity-Efficiency Trade-off

A critical finding across benchmarking studies is that no single scFM consistently outperforms all others across every task and dataset [4]. This underscores the importance of task-specific model selection rather than seeking a universal optimal solution. The performance advantages of scFMs are most pronounced in scenarios involving:

• Transfer learning across tissues, species, or experimental conditions
• Integration of highly heterogeneous datasets with multiple sources of variation
• Limited labeled data for specific cell types or conditions

Conversely, traditional machine learning approaches (e.g., SVM, XGBoost) with appropriate feature engineering often provide more efficient solutions for:

• Homogeneous datasets with limited batch effects
• Well-established cell type classification with abundant labeled examples
• Resource-constrained environments requiring rapid iteration

Methodological Framework for Model Selection

Structured Selection Protocol

An evidence-based model selection strategy requires a systematic approach that aligns model capabilities with specific analytical requirements. The following workflow provides a structured protocol for model evaluation and selection:

This workflow emphasizes iterative evaluation and validation, recognizing that optimal model selection may evolve as data characteristics change or new models become available.

Task-Specific Recommendation Framework

Based on comprehensive benchmarking studies, the following recommendations emerge for specific analytical scenarios:

Cell Type Annotation

For cell type annotation tasks, selection should prioritize models with demonstrated performance on metrics such as F1-score (particularly for rare cell types) and Lowest Common Ancestor Distance (LCAD), which measures the ontological proximity between misclassified cell types [4]. Models like scBERT fine-tuned for specific tissue contexts generally provide strong performance, though XGBoost with mutual information-based feature selection represents a computationally efficient alternative [107] [4].

Batch Integration and Atlas Construction

Large-scale atlas construction demands models that effectively balance batch effect removal with biological variation preservation. Evaluation should prioritize metrics such as iLISI (integration local inverse Simpson's index) for batch mixing and cLISI (cell-type local inverse Simpson's index) for biological conservation [108] [4]. scGPT and scVI have demonstrated consistent performance in these applications, particularly when handling diverse batch effects originating from different patients, platforms, or tissues [4].

Gene-Level Analysis and Biological Insight

Tasks focused on extracting novel biological insights from gene relationships should prioritize models with strong performance on gene-level tasks and interpretable attention mechanisms. Geneformer and scGPT have demonstrated particular strength in capturing functional gene relationships and tissue specificity, as measured by similarity to established biological networks [4].

Experimental Protocols for Model Evaluation

Standardized Benchmarking Methodology

Robust model evaluation requires standardized protocols to ensure comparable results across different studies and implementations. The following methodology, adapted from recent large-scale benchmarking efforts, provides a framework for comprehensive model assessment:

Data Preparation and Preprocessing:

• Utilize well-annotated reference datasets with high-quality labels, such as the Asian Immune Diversity Atlas (AIDA) v2 from CellxGene [4]
• Implement consistent normalization and quality control pipelines across all models
• Partition data into training, validation, and test sets, ensuring representative distribution of biological and technical variation

Feature Selection Considerations:

• Apply appropriate feature selection methods prior to model evaluation, as feature selection significantly impacts downstream performance [108]
• Highly variable gene selection generally produces higher-quality integrations compared to random feature sets [108]
• Consider batch-aware feature selection methods when integrating datasets with significant technical variation

Evaluation Protocol:

• Implement zero-shot evaluation for foundation models to assess inherent representation quality [4]
• Apply consistent fine-tuning protocols when comparing adapted models
• Utilize multiple random seeds to account for training stochasticity
• Employ comprehensive metric suites spanning technical and biological performance dimensions

Critical Experimental Considerations

Several methodological considerations are essential for generating valid, reproducible model comparisons:

• Data leakage prevention: Implement strict separation between pretraining and evaluation datasets, particularly when using models pretrained on large public data compendia [4]
• Metric selection and interpretation: Choose metrics that align with specific biological questions and interpret results in context of known metric limitations and correlations [108]
• Computational resource documentation: Report detailed hardware configurations and training times to enable practical feasibility assessment

Essential Research Reagents and Computational Tools

Successful implementation of single-cell foundation models requires both biological datasets and computational resources. The following table catalogues essential components of the single-cell model selection toolkit:

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

Resource Category	Specific Resources	Primary Function	Implementation Considerations
Data Repositories	CZ CELLxGENE, Human Cell Atlas, NCBI GEO, EBI Expression Atlas	Provide standardized, annotated single-cell datasets for model training and evaluation	Dataset selection should match target tissue types and experimental conditions
Pretrained Models	Geneformer, scGPT, scBERT, scFoundation, UCE, LangCell, scCello	Offer prebuilt model architectures with weights trained on large single-cell corpora	Model selection should consider pretraining data composition and relevance to target domain
Evaluation Metrics	scGraph-OntoRWR, LCAD, Batch ASW, iLISI/cLISI, kBET	Quantify model performance across technical and biological dimensions	Metric suites should be tailored to specific analytical objectives
Feature Selection	Highly Variable Genes, scSEGIndex, Information Gain, ANOVA F-value	Identify informative feature subsets to improve model performance and efficiency	Selection method should align with data characteristics and analytical goals
Implementation Frameworks	Scikit-learn, PyTorch, TensorFlow, Scanpy, Scikit-learn	Provide computational infrastructure for model implementation and evaluation	Framework choice affects development efficiency and deployment options

Evidence-based model selection in single-cell genomics requires careful consideration of performance trade-offs across multiple dimensions. As the field continues to evolve, several principles emerge to guide researchers and practitioners:

First, model selection must be driven by specific analytical objectives rather than generic performance rankings. The "no free lunch" theorem applies strongly to single-cell analysis, with different models excelling in different contexts [4]. Task-specific evaluation using biologically informed metrics provides the most reliable pathway to optimal model selection.

Second, practical considerations including computational resources, technical expertise, and project timelines warrant significant weight in selection decisions. In many cases, simpler models with appropriate feature engineering provide favorable performance-efficiency trade-offs compared to more complex foundation models [107] [4].

Finally, the rapid pace of innovation in single-cell foundation models necessitates ongoing evaluation and reassessment of selection strategies. As new models emerge and existing models are refined, previously established performance hierarchies may shift, requiring maintained vigilance and empirical validation.

By adopting the structured, evidence-based framework outlined in this technical guide, researchers can navigate the complex landscape of single-cell analysis methods with greater confidence, selecting models that optimally balance performance, efficiency, and biological relevance for their specific applications.

Conclusion

Single-cell foundation models represent a paradigm shift in computational biology, offering unprecedented potential to decode cellular complexity and accelerate therapeutic development. However, our synthesis reveals that despite their transformative promise, current scFMs face significant challenges in zero-shot reliability, biological interpretability, and computational accessibility. The field must prioritize developing more biologically intuitive architectures, robust evaluation standards, and user-friendly interfaces to bridge the gap between computational innovation and practical biomedical application. Future advancements should focus on enhancing model generalizability across diverse tissues and disease states, improving integration of multi-omic data, and establishing rigorous validation frameworks that ensure biological relevance. As scFMs continue to evolve, they hold immense potential to power the next generation of precision medicine initiatives, from comprehensive cell atlas construction to personalized treatment optimization, ultimately transforming how we understand and treat complex diseases at cellular resolution.

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