Single-Cell Foundation Model Benchmarking: A Comprehensive Guide for Biomedical Researchers

Isaac Henderson Nov 26, 2025 536

This article provides a comprehensive analysis of the rapidly evolving landscape of single-cell foundation models (scFMs).

Single-Cell Foundation Model Benchmarking: A Comprehensive Guide for Biomedical Researchers

Abstract

This article provides a comprehensive analysis of the rapidly evolving landscape of single-cell foundation models (scFMs). Aimed at researchers, scientists, and drug development professionals, it synthesizes findings from recent large-scale benchmarking studies to explore the core concepts, architectures, and pretraining strategies of scFMs. It delves into their practical applications in critical tasks like drug response prediction and cell type annotation, offers guidance for model selection and troubleshooting, and presents a comparative validation of leading models such as scGPT, Geneformer, and scFoundation. The article concludes with key takeaways and future directions, serving as an essential resource for leveraging scFMs in biological discovery and therapeutic development.

Understanding Single-Cell Foundation Models: Core Concepts and the Benchmarking Imperative

Table of Contents

  • Abstract
  • Introduction to Single-Cell Foundation Models
  • Comparative Performance of Leading scFMs
  • Experimental Protocols for scFM Benchmarking
  • Technical Architecture and Data Processing
  • Research Reagent Solutions
  • Conclusion and Future Directions

Single-cell foundation models (scFMs) are large-scale deep learning models pretrained on vast single-cell omics datasets, enabling their adaptation to a wide range of downstream biological tasks. This guide provides a comprehensive benchmark of six prominent scFMs—Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello—against traditional methods. The evaluation covers two gene-level and four cell-level tasks, including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction. Performance is assessed using 12 metrics, revealing that while scFMs are robust and versatile, no single model consistently outperforms others across all tasks. The findings underscore the necessity for tailored model selection based on dataset size, task complexity, and computational resources, offering critical insights for researchers and drug development professionals engaged in single-cell genomics.

Inspired by the success of large language models (LLMs) in natural language processing, single-cell foundation models (scFMs) are engineered to decipher the "language" of cells. These models utilize self-supervised learning on massive, diverse collections of single-cell RNA sequencing (scRNA-seq) data, treating individual cells as "sentences" and genes or genomic features as "words" or "tokens". The primary objective is to learn fundamental principles of cellular function and gene regulation that generalize across new datasets and biological questions [1].

The development of scFMs is driven by the exponential growth in publicly available single-cell data, with repositories like CZ CELLxGENE providing unified access to over 100 million unique cells. These models predominantly leverage transformer architectures, which employ attention mechanisms to learn and weight relationships between genes within a cell, thereby capturing complex regulatory networks and functional connections [1] [2]. While most current scFMs focus on scRNA-seq data, several are expanding to incorporate additional modalities such as single-cell ATAC-seq (scATAC-seq), multiome sequencing, spatial transcriptomics, and proteomics, aiming to construct more comprehensive foundation models [1].

Comparative Performance of Leading scFMs

A comprehensive benchmark study evaluated six scFMs against established baseline methods like Seurat, Harmony, and scVI under realistic conditions. The evaluation employed 12 metrics spanning unsupervised, supervised, and knowledge-based approaches, including novel ontology-informed metrics like scGraph-OntoRWR and Lowest Common Ancestor Distance (LCAD) to assess biological relevance [3] [4].

The following tables summarize the key findings from this benchmark, providing holistic rankings from dataset-specific to general performance to guide model selection.

Table 1: Overall Performance Ranking of scFMs Across Diverse Tasks

Model Overall Ranking Strengths Key Limitations
scGPT 1 Versatile; strong in multi-omics and generation tasks [1] Computational intensity for training/fine-tuning [1]
Geneformer 2 Effective for gene network analysis [3] Limited to encoder architecture [1]
scFoundation 3 Large-scale pretraining on transcriptomics [3] -
UCE 4 - -
LangCell 5 - -
scCello 6 - -

Table 2: Performance of scFMs vs. Baseline Models on Key Tasks

Task Category Best Performing scFM(s) Performance vs. Baseline Models
Batch Integration scGPT, Geneformer Robust; effectively removes technical artifacts while preserving biological variation [3]
Cell Type Annotation scGPT, scFoundation High accuracy; low LCAD error severity [3]
Cancer Cell Identification Varies by cancer type Clinically relevant; robust across 7 cancer types [3]
Drug Sensitivity Prediction Varies by drug Promising for 4 tested drugs; relevant for treatment decisions [3]
Perturbation Effect Prediction - Limited zero-shot improvement over simple linear baselines [5]

Key findings from the benchmark include:

  • No single dominant scFM: No model consistently outperformed all others across every task, emphasizing that model selection must be tailored to the specific application [3].
  • Robustness and versatility: scFMs demonstrate strong performance across diverse applications, particularly in dataset integration and cell type annotation [3].
  • Context-dependent utility: For specific, narrow tasks with limited data, simpler machine learning models can sometimes adapt more efficiently and with lower computational cost [3].
  • Limited zero-shot prowess: In perturbation prediction, zero-shot embeddings from scFMs showed limited improvement over simple baseline models, indicating a need for specialized models or fine-tuning [5].

Experimental Protocols for scFM Benchmarking

To ensure fair and realistic evaluation, benchmarking studies follow rigorous protocols. The following diagram illustrates a typical benchmarking workflow for assessing scFMs on various downstream tasks.

G cluster_0 Data Curation cluster_1 Downstream Tasks cluster_2 Performance Evaluation Start Start: Benchmarking Setup DataSelection Data Selection & Curation Start->DataSelection FeatureExtraction Feature Extraction (Zero-shot Embeddings) DataSelection->FeatureExtraction DS1 Select 5+ High-Quality Datasets with Manual Annotations DataSelection->DS1 DownstreamTasks Downstream Task Execution FeatureExtraction->DownstreamTasks Evaluation Performance Evaluation (12 Metrics) DownstreamTasks->Evaluation T1 Gene-Level Tasks (Tissue Specificity, GO Terms) DownstreamTasks->T1 ModelSelection Model Selection Guidance Evaluation->ModelSelection E1 Traditional Metrics (Unsupervised, Supervised) Evaluation->E1 DS2 Ensure Diversity: Inter-patient, -platform, -tissue DS1->DS2 DS3 Introduce Independent Dataset (e.g., AIDA v2) for Validation DS2->DS3 T2 Cell-Level Tasks (Batch Integration, Cell Type Annotation) T1->T2 T3 Clinically Relevant Tasks (Cancer ID, Drug Sensitivity) T2->T3 E2 Novel Knowledge-Based Metrics (scGraph-OntoRWR, LCAD) E1->E2

Data Selection and Curation

The process begins with the careful selection of high-quality, manually annotated datasets that encompass diverse biological conditions and multiple sources of batch effects (e.g., inter-patient, inter-platform, inter-tissue variations). To mitigate the risk of data leakage and validate conclusions, an independent, unbiased dataset like the Asian Immune Diversity Atlas (AIDA) v2 is introduced [3].

Feature Extraction in a Zero-Shot Setting

The benchmark focuses on evaluating zero-shot embeddings—representations generated by the scFMs without any task-specific fine-tuning. Gene and cell embeddings are extracted directly from the models' input or output layers to assess the intrinsic biological knowledge captured during pretraining [3].

Execution of Downstream Tasks

The extracted embeddings are evaluated on a suite of downstream tasks:

  • Gene-level tasks: Assess the quality of gene embeddings by predicting known biological relationships, such as gene tissue specificity and Gene Ontology (GO) terms [3].
  • Cell-level tasks: Include batch integration and cell type annotation across multiple challenging datasets to test the models' ability to create a unified biological representation space [3].
  • Clinically relevant tasks: Encompass cancer cell identification across seven cancer types and drug sensitivity prediction for four drugs, reflecting real-world application scenarios [3].

Performance Evaluation and Model Selection

Model performance is quantified using a battery of 12 metrics. This includes traditional unsupervised and supervised metrics, as well as innovative cell ontology-informed metrics like scGraph-OntoRWR, which measures the consistency of cell type relationships captured by the model with prior biological knowledge. The results are then aggregated using algorithms like non-dominated sorting to provide task-specific and overall model rankings [3].

Technical Architecture and Data Processing

Understanding the technical underpinnings of scFMs is crucial for their effective application. The core process involves converting raw gene expression data into a structured format that a transformer model can understand.

G cluster_0 Tokenization & Input Construction cluster_1 Embedding Layer RawData Raw Single-Cell Expression Matrix Tokenization Tokenization RawData->Tokenization InputEmbedding Input Embedding Tokenization->InputEmbedding T1 1. Define Gene/Feature as a Token Tokenization->T1 Transformer Transformer Model (Encoder or Decoder) InputEmbedding->Transformer E1 Gene Embedding (Analogous to Word Embedding) InputEmbedding->E1 LatentRep Latent Representations (Gene & Cell Embeddings) Transformer->LatentRep T2 2. Create a Cell 'Sentence': - Rank genes by expression level - Use deterministic order T1->T2 T3 3. Add Special Tokens: - Cell identity metadata - Modality indicator - Batch information T2->T3 E2 Value Embedding (For expression level) E1->E2 E3 Positional Embedding (Based on rank or bin) E2->E3

Tokenization: From Genes to Tokens

Tokenization converts raw gene expression data into discrete units (tokens) that the model can process. A fundamental challenge is that gene expression data lacks inherent sequence, unlike words in a sentence. Common strategies to address this include:

  • Rank-based tokenization: Genes within each cell are ranked by their expression levels, and the ordered list of top genes is treated as the cell's "sentence" [1].
  • Binning: Genes are partitioned into bins based on their expression values [1].
  • Special tokens: Additional tokens are added to represent cell identity metadata, omics modality, or batch information, providing richer biological context [1].

Model Architecture and Embeddings

Most scFMs are built on the transformer architecture [1]. The input to the model is a combination of several embedding layers:

  • Gene Embedding: A vector representation for each gene identifier, analogous to word embeddings in LLMs [3].
  • Value Embedding: Represents the expression level of the gene in the specific cell [3].
  • Positional Embedding: Encodes the relative order or rank of each gene within the cell's "sentence" [3].

Architectural variations exist, with some models using BERT-like encoder architectures for classification and embedding tasks, and others employing GPT-like decoder architectures for generation tasks. Hybrid designs are also being explored, though no single architecture has emerged as definitively superior [1].

Pretraining and Self-Supervised Learning

Pretraining involves training the model on a self-supervised task using vast, unlabeled single-cell datasets. A common objective is masked language modeling, where random subsets of gene tokens are masked, and the model is trained to predict them based on the context of the remaining genes in the cell. This process allows the model to learn the fundamental "grammar" of cellular biology [1].

Research Reagent Solutions

The following table details key computational tools and data resources essential for working with single-cell foundation models.

Table 3: Essential Research Reagents and Resources for scFM Research

Resource Name Type Primary Function Relevance to scFM Workflow
CZ CELLxGENE [1] Data Repository Provides unified access to standardized, annotated single-cell datasets (>100M cells). Primary source of diverse, high-quality data for model pretraining and benchmarking.
Geneformer [3] Pretrained Model A foundation model pretrained on massive scRNA-seq data for gene network analysis. Used as a tool for downstream analysis or as a baseline in comparative benchmarks.
scGPT [1] [3] Pretrained Model A generative foundation model for single-cell multi-omics data. Applied for tasks like batch integration, cell type annotation, and perturbation prediction.
PertEval-scFM [5] Benchmarking Framework Standardized framework to evaluate scFMs for perturbation effect prediction. Provides a rigorous protocol for testing a specific, clinically important task.
Human Cell Atlas [1] Data Atlas A broad-coverage reference map of all human cells from multiple tissues. Source of biological truth and diverse cell types for model training and validation.
Rogue-like Instability Score (ROGI) [3] Evaluation Metric A roughness index that measures landscape stability in latent space. Serves as a proxy for model performance, simplifying model selection for new datasets.

Single-cell foundation models represent a transformative advance in computational biology, offering a unified framework to analyze the rapidly expanding universe of single-cell data. Current benchmarks confirm that scFMs are robust, versatile tools for diverse applications, from basic cell atlas construction to clinical tasks like cancer cell identification and drug sensitivity prediction. However, they are not a panacea; no single model is universally superior, and simpler methods can be more efficient for specific, narrow tasks [3].

The future development of scFMs hinges on addressing key limitations. There is a pressing need for improved model interpretability to uncover the biological relevance of latent embeddings and model representations [1]. Furthermore, enhancing zero-shot prediction capabilities, particularly for challenging tasks like perturbation effect modeling, remains a significant hurdle [5]. Finally, creating user-friendly interfaces is crucial to bridge the accessibility gap and empower biologists without deep computational expertise to leverage these powerful models [2]. As these challenges are met, scFMs are poised to become indispensable tools for unlocking deeper insights into cellular function and disease mechanisms.

The emergence of single-cell RNA sequencing (scRNA-seq) has fundamentally transformed biological research by enabling the characterization of gene expression at the resolution of individual cells, revealing cellular heterogeneity in complex tissues [6] [7]. However, the computational analysis of this data presents significant challenges due to its high dimensionality, inherent sparsity, and technical noise [7]. In response to these challenges, transformer-based architectures have emerged as powerful foundation models capable of integrating heterogeneous datasets and exploring biological systems at unprecedented scale [4].

The transformer backbone provides a unique architectural framework that enables generalizable learning across diverse biological contexts. Unlike traditional machine learning approaches that struggle with single-cell data's complex patterns, transformers leverage self-attention mechanisms to capture long-range dependencies and contextual relationships across genes [6]. This capability has proven essential for modeling gene regulatory networks and cell state transitions, establishing transformers as the foundational infrastructure for next-generation single-cell analysis [8] [6].

This review examines how the transformer architecture's core components enable generalizable learning in single-cell foundation models (scFMs). We explore the architectural innovations driving current models, benchmark their performance against alternatives, and identify both capabilities and limitations through rigorous empirical evaluation.

Architectural Foundations: How Transformer Components Enable Biological Learning

Core Components of the Transformer Architecture

The transformer architecture achieves its remarkable performance through several key components that work in concert to process biological sequences:

  • Multi-Head Self-Attention Mechanism: This core component allows the model to jointly attend to information from different representation subspaces at different positions [6]. For single-cell data, this enables the model to identify coordinated gene expression patterns and regulatory relationships. The mechanism is mathematically defined as:

    Attention(Q, K, V) = softmax(QK^T/√d_k)V [6]

    where Q (Query), K (Key), and V (Value) are matrices derived from the input embeddings. The attention scores determine the importance of each gene relative to others when encoding cellular states.

  • Positional Encoding: Unlike sequential data in natural language processing, gene sequences lack inherent ordering. Transformers incorporate positional information using sinusoidal functions or learned embeddings to encode the relative positions of genes, allowing the model to capture spatial relationships in the genomic context [6].

  • Encoder-Decoder Structure: The transformer employs stacked encoder and decoder layers with residual connections and layer normalization. The encoder maps input gene expression sequences to hidden representations, while the decoder generates predictions for tasks like perturbation response or cell type classification [6].

  • Feed-Forward Networks: Each transformer layer contains position-wise feed-forward networks that apply non-linear transformations to the attention outputs, enabling complex feature interactions essential for modeling biological systems [6].

Adaptation to Single-Cell Data Structures

Transformers require specific adaptations to effectively process single-cell transcriptomics data. A significant challenge is that the input data comprises both gene tokens and their continuous expression values, not plain token sequences [7]. To address this, models employ various tokenization strategies:

  • Bin-based discretization (used by scBERT and scGPT) groups expression values into predefined bins, preserving absolute value distributions while simplifying sequence modeling [7].
  • Rank-based discretization (used by Geneformer) transforms expression values into ordinal rankings, effectively capturing relative expression levels and demonstrating robustness to batch effects [7].
  • Value projection (used by scFoundation) projects continuous expression values into embeddings, maintaining full data resolution through linear transformations [7].

The following diagram illustrates how these components integrate to process single-cell data:

G Input Single-Cell Expression Matrix Tokenization Tokenization Strategy Input->Tokenization Embedding Gene Embeddings Tokenization->Embedding Positional Positional Encoding Embedding->Positional Transformer Transformer Encoder (Multi-Head Attention Feed-Forward Network) Positional->Transformer Output Cell Representation Transformer->Output

Benchmarking Transformer Performance: Comparative Analysis of scFMs

Evaluation Across Diverse Biological Tasks

Comprehensive benchmarking studies reveal the nuanced performance landscape of transformer-based single-cell foundation models. A 2025 benchmark study evaluating six scFMs against established baselines across two gene-level and four cell-level tasks provides critical insights into their capabilities and limitations [4].

Table 1: Performance Overview of Single-Cell Foundation Models Across Task Categories

Task Category Representative Tasks Transformer scFM Performance Key Findings
Cell-level Tasks Cell type annotation, Batch integration, Cancer cell identification Variable across models and datasets scFMs are robust and versatile but no single model consistently outperforms others across all tasks [4]
Gene-level Tasks Drug sensitivity prediction, Gene network inference Strong in capturing gene-gene relationships Performance depends on dataset size, task complexity, and biological interpretability requirements [4]
Perturbation Response Predicting transcriptional responses to genetic perturbations Limited in zero-shot settings Simple baseline models often outperform scFMs in perturbation effect prediction [5] [9]

The benchmark introduced scGraph-OntoRWR, a novel metric designed to uncover intrinsic knowledge encoded by scFMs, providing deeper insight into the biological relevance of learned representations [4]. The findings emphasize that while scFMs are robust and versatile tools for diverse applications, simpler machine learning models can be more efficient for specific datasets, particularly under resource constraints [4].

Specialized Model Comparisons

Cell Type Annotation Performance

Cell type annotation represents one of the most successful applications of transformer architectures in single-cell biology. TOSICA (Transformer for One-Stop Interpretable Cell-type Annotation) demonstrates how the multi-head self-attention mechanism enables both accurate classification and biological interpretability [10].

Table 2: Cell Type Annotation Accuracy Across Methods and Datasets

Method Architecture hArtery Dataset hPancreas Dataset mAtlas Dataset Interpretability
TOSICA Transformer with biological masks 93.75% 95.76% 81.06% High (pathway-level interpretability) [10]
Seurat Traditional ML 96.37% - - Medium [10]
SingleCellNet Traditional ML - 97.53% - Medium [10]
ACTINN Neural Network - - 79.57% Low [10]

TOSICA's key innovation lies in its use of biologically meaningful masks that connect attention mechanisms to prior knowledge such as pathways or regulons. This approach maintains interpretability while achieving competitive accuracy, as the attention scores between the class token and pathway tokens reveal the biological features important for classification decisions [10].

Perturbation Prediction Capabilities

Prediction of cellular responses to perturbations represents a significant challenge for scFMs. The PertEval-scFM benchmark systematically evaluates zero-shot scFM embeddings against baseline models for perturbation effect prediction [5]. Surprisingly, results indicate that scFM embeddings offer limited improvement over simple baseline models in zero-shot settings, particularly under distribution shift [5].

Similarly, a benchmarking study of scGPT and scFoundation for post-perturbation RNA-seq prediction found that even the simplest baseline model—taking the mean of training examples—outperformed these foundation models [9]. Basic machine learning models incorporating biologically meaningful features like Gene Ontology vectors significantly outperformed scGPT by a large margin [9].

Emerging Alternatives: Beyond the Transformer Architecture

The GeneMamba Architecture

While transformer-based models have dominated the scFM landscape, recent architectural innovations propose compelling alternatives. GeneMamba introduces a state space model (SSM) architecture designed specifically for single-cell data analysis, addressing key limitations of transformer approaches [7].

The model incorporates a BiMamba module to efficiently capture gene context information and employs biologically meaningful loss functions during training [7]. This architecture enables scalable processing of over 50 million cells while significantly reducing computational costs compared to transformer-based models [7].

Table 3: Architectural Comparison: Transformer vs. GeneMamba

Feature Transformer-based Models GeneMamba
Computational Complexity Quadratic with sequence length [7] Linear with sequence length [7]
Long-Range Dependency Capture Can struggle with long gene sequences [7] Enhanced through state space dynamics [7]
Memory Requirements High due to attention matrix storage [7] Significantly reduced [7]
Bidirectional Context Requires specific architectural modifications Native bidirectional processing [7]
Training Efficiency Computationally intensive for large datasets Optimized for efficiency on large-scale data [7]

GeneMamba's SSM foundation allows it to efficiently capture long-range dependencies with linear computational complexity, addressing a fundamental constraint of transformer architectures when applied to long gene sequences [7]. The bidirectional processing capability enables simultaneous consideration of upstream and downstream genetic contexts, enhancing performance in tasks requiring comprehensive genomic awareness [7].

Performance and Efficiency Tradeoffs

Experimental validation demonstrates GeneMamba's strong performance in multi-batch integration, cell type annotation, and gene pair correlation analysis, with reconstruction experiments highlighting its explainability advantages [7]. The model establishes a robust foundation for advancing single-cell transcriptomics while offering significantly reduced computational overhead compared to transformer-based approaches [7].

The following diagram contrasts the two architectural approaches:

G cluster_transformer Transformer Architecture cluster_mamba GeneMamba Architecture Input Single-Cell Expression Data T1 Multi-Head Attention (Quadratic Complexity) Input->T1 M1 BiMamba Module (Linear Complexity) Input->M1 T2 Position-Wise FFN T1->T2 T3 Layer Normalization T2->T3 Output1 Cell/Gene Representations T3->Output1 M2 State Space Model M1->M2 M3 Bidirectional Processing M2->M3 Output2 Cell/Gene Representations M3->Output2

Experimental Protocols and Methodologies

Standardized Benchmarking Frameworks

Rigorous evaluation of single-cell foundation models requires standardized benchmarking frameworks and experimental protocols. Key benchmarking initiatives have established methodologies for assessing model performance:

The PertEval-scFM framework employs a systematic approach to evaluate models for perturbation effect prediction [5]. The benchmark tests whether zero-shot embeddings produced by scFMs contain meaningful information for predicting perturbation effects by giving a pair of cells—one perturbed and one unperturbed—to a simple model that uses scFM representations to predict cellular changes [5].

For perturbation response prediction, benchmarks typically use datasets generated using Perturb-seq, which combines CRISPR-based perturbations with single-cell sequencing [9]. Standard evaluation metrics include:

  • Pearson correlation coefficients in raw gene expression space
  • Pearson correlation in differential expression space (perturbed minus control)
  • Performance on top 20 differentially expressed genes [9]

The Scientist's Toolkit: Essential Research Reagents

Table 4: Essential Research Reagents and Computational Tools for scFM Research

Reagent/Tool Function Example Applications
Perturb-seq Data Provides ground truth for perturbation responses Benchmarking model prediction accuracy [9]
Annotated Cell Atlases Reference datasets with validated cell types Training and evaluating cell type annotation models [10]
Biological Pathway Databases Gene set collections for interpretable masks Adding biological prior knowledge to models like TOSICA [10]
GPU/TPU Accelerators Hardware for model training and inference Training large foundation models (e.g., TPU v5p, NVIDIA Blackwell) [11]
Benchmarking Frameworks Standardized evaluation pipelines PertEval-scFM, scGraph-OntoRWR metrics [4] [5]

The transformer architecture has fundamentally reshaped the landscape of single-cell foundation models, providing the backbone for generalizable learning across diverse biological contexts. Its self-attention mechanism offers unparalleled capability in capturing gene-gene interactions and contextual relationships within high-dimensional transcriptomic data [6] [10].

However, comprehensive benchmarking reveals a nuanced reality: while transformer-based scFMs demonstrate remarkable versatility and robustness across tasks including cell type annotation and batch integration [4] [10], they face significant challenges in perturbation prediction where simpler models sometimes outperform sophisticated foundation approaches [5] [9]. These findings highlight the importance of task-specific model selection rather than assuming universal superiority of transformer-based approaches.

The emergence of alternative architectures like GeneMamba signals an important evolutionary direction for the field, addressing fundamental limitations in computational efficiency and scalability while maintaining strong performance across key biological tasks [7]. As single-cell technologies continue to advance, generating increasingly massive and complex datasets, the architectural foundations of scFMs will need to evolve in parallel—potentially through hybrid approaches that combine the strengths of attention mechanisms with the efficiency of state space models.

The ultimate trajectory points toward more specialized, biologically grounded architectures that balance expressive power with computational practicality, enabling deeper insights into cellular mechanisms while remaining accessible to the broader research community.

The emergence of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular biology, enabling the profiling of gene expression at unprecedented resolution. However, the analysis of scRNA-seq data is fraught with challenges, including high dimensionality, technical noise, and batch effects. To address these issues, the field has witnessed the rise of single-cell foundation models (scFMs), which are large-scale deep learning models pre-trained on vast datasets to learn universal biological patterns. The effectiveness of these models is fundamentally governed by their pre-training strategies, which determine how raw gene expression data is transformed into meaningful, generalizable representations. This guide provides a comparative analysis of three dominant pre-training paradigms—Masked Gene Modeling, Value Projection, and Rank-Based Learning—synthesizing evidence from recent benchmarking studies to inform researchers and drug development professionals about their relative performance, optimal applications, and practical implementation.

Core Pre-training Strategies Explained

Masked Gene Modeling

Inspired by the success of models like BERT in natural language processing, Masked Gene Modeling treats a cell's gene expression profile as a set of tokens. During pre-training, a random subset of these gene tokens is masked (or corrupted), and the model is tasked with reconstructing the original expression values based on the remaining context. This self-supervised objective forces the model to learn the complex, contextual relationships between genes, effectively capturing co-expression patterns and regulatory networks.

  • Implementation Variants: Models employ different masking and reconstruction techniques. scBERT bins continuous expression values into discrete "buckets," transforming reconstruction into a classification task [12]. scGPT uses an attention mask mechanism for autoregressive prediction [12], while scMAE shuffles gene expression values and uses a masking predictor to identify which genes were disrupted [13]. The recently proposed IC2Bert, though designed for bulk RNA-seq, also uses masked pretraining for immune response prediction, demonstrating the strategy's versatility [14].

Value Projection

Value Projection strategies aim to preserve the full, continuous resolution of gene expression data. Instead of predicting a masked token's category, these models directly regress the original expression value. A key advantage of this approach is that it avoids the information loss inherent in binning or ranking processes, potentially capturing more subtle variations in expression levels.

  • Implementation Variants: This is often implemented using a Masked Autoencoder (MAE) framework. scFoundation is a prominent example that directly predicts raw gene expression values using a masked autoencoder [12]. CellFM, a recently released large-scale model with 800 million parameters, is also categorized as a value-projection model. It recovers "the vector embeddings of masked genes derived from their linear projections based on gene expression values" [12].

Rank-Based Learning

Rank-Based Learning abandons the absolute expression values in favor of the relative ordering of genes within a cell. In this paradigm, genes are sorted by their expression level to form a sequence, and the model is trained to understand the relational context, such as predicting a gene's rank or the sequence order.

  • Implementation Variants: Models like Geneformer and iSEEK use masked language modeling to learn cell representations by predicting randomly masked genes within a rank-ordered sequence [15] [12]. tGPT learns gene embeddings by autoregressively modeling gene ranks relative to their neighbors [12]. This method is inherently platform-agnostic and robust to technical variations, as it relies on relative rather than absolute values.

Table 1: Summary of Core Pre-training Strategies and Representative Models.

Strategy Core Principle Representative Models Key Advantages
Masked Gene Modeling Reconstructs masked/corrupted gene tokens scBERT, scGPT, scMAE, IC2Bert Captures rich contextual gene relationships; proven denoising capability
Value Projection Directly predicts continuous expression values scFoundation, CellFM Preserves full resolution of data; avoids information loss from binning
Rank-Based Learning Learns from the relative ordering of genes by expression Geneformer, iSEEK, tGPT Platform-agnostic; robust to technical variation and normalization artifacts

Performance Benchmarking and Comparative Analysis

Recent independent benchmarking studies have rigorously evaluated these pre-training strategies across a variety of biological tasks, providing critical insights for model selection.

Performance on Core Single-Cell Tasks

Comprehensive benchmarks reveal that no single pre-training strategy dominates all tasks. Performance is highly dependent on the specific downstream application.

  • Cell Type Annotation: For this fundamental task, models using Masked Gene Modeling have shown strong performance. scBERT, for instance, was specifically designed for cell type annotation and demonstrates high accuracy [16]. Furthermore, a large-scale benchmark reported that scGPT (Masked Gene Modeling) and scVI (a VAE-based model, not a transformer) were identified as top performers for data integration and cell type annotation, often outperforming rank-based models [3].
  • Perturbation Prediction: Predicting cellular responses to genetic or chemical perturbations is a stringent test of a model's biological reasoning. Here, evidence suggests that Value Projection and simpler approaches can be highly effective. One study found that scFoundation (Value Projection) and scGPT (Masked Gene Modeling) were outperformed by a simple Random Forest model using Gene Ontology features and even a baseline that predicted the mean of training examples [9]. Another benchmark concluded that scVI and PCA were "far better suited models for understanding biological perturbations" compared to existing foundation models [17].
  • Gene Function Prediction: For predicting gene functions and relationships, Rank-Based Learning has demonstrated notable success. Geneformer's rank-based embeddings have proven useful for characterizing gene-gene and gene-phenotype associations [15] [12]. However, the large CellFM (Value Projection) model also claims to improve the accuracy of gene function prediction, suggesting that model scale can be a significant factor [12].

Table 2: Comparative Model Performance on Key Downstream Tasks (Synthesis of Benchmarking Results).

Pre-training Strategy Cell Type Annotation Perturbation Prediction Data Integration / Batch Correction Gene Function Prediction
Masked Gene Modeling Strong (e.g., scBERT, scGPT) [3] [16] Variable (scGPT outperformed by baselines) [9] Strong (scGPT is a top performer) [3] Good
Value Projection Good Variable (scFoundation outperformed by baselines) [9] Not Specified Strong (e.g., CellFM) [12]
Rank-Based Learning Good Not Specified Less effective than others [3] Strong (e.g., Geneformer) [15] [12]
Notable Baselines - Random Forest with GO features and Train Mean can outperform foundation models [9] scVI and PCA are top performers [17] [3] -

Robustness and Generalizability

A critical challenge in computational biology is model performance on heterogeneous, unseen data. The IC2Bert model, which uses Masked Gene Modeling, was specifically designed to address cohort heterogeneity in bulk RNA-seq data for immunotherapy response prediction. It employed a Leave-One-Dataset-Out Cross-Validation (LODOCV) framework, demonstrating that its pretraining followed by target-domain fine-tuning significantly improved robustness and generalizability compared to existing methods [14]. This underscores the importance of tailored pre-training and evaluation protocols for real-world clinical applications.

Experimental Protocols for Benchmarking

To ensure fair and meaningful comparisons, benchmarking studies follow rigorous experimental protocols. The following workflow visualizes a standardized pipeline for evaluating scFMs, synthesized from multiple benchmark studies [14] [9] [3].

G cluster_0 Downstream Tasks (Examples) cluster_1 Evaluation Metrics (Examples) Start Input: Pre-trained Foundation Models Sub1 Feature Extraction (Zero-shot Cell/Gene Embeddings) Start->Sub1 Sub2 Downstream Task Definition Sub1->Sub2 Sub3 Model Evaluation (Performance Metrics) Sub2->Sub3 Task1 Cell Type Annotation Task2 Perturbation Prediction Task3 Data Integration (Batch Correction) Task4 Gene Function Prediction Sub4 Comparison vs. Baseline Methods Sub3->Sub4 Metric1 AUROC / Accuracy Metric2 Pearson Correlation (Differential Expression) Metric3 iLISI Score (Batch Mixing) Metric4 Ontology-informed Metrics (e.g., LCAD) End Output: Performance Ranking & Analysis Sub4->End

Key Methodological Components

  • Feature Extraction in Zero-Shot Setting: A critical step is to extract cell and gene embeddings from the pre-trained scFMs without any further fine-tuning on the target benchmark datasets. This evaluates the general quality and biological relevance of the representations learned during pre-training [3].
  • Diverse Downstream Tasks: Models are evaluated on a hierarchy of tasks, from fundamental operations like cell type annotation and data integration to more complex challenges like perturbation prediction [17] [3].
  • Rigorous Performance Metrics: Beyond standard metrics like Area Under the ROC Curve (AUROC) for classification, perturbation tasks often use Pearson correlation in the differential expression space to measure how well a model captures specific transcriptional changes [9]. Novel biology-aware metrics, such as the Lowest Common Ancestor Distance (LCAD) for cell type annotation errors, are also being adopted [3].
  • Comparison Against Strong Baselines: Proper benchmarking must include comparisons against a range of baseline methods, from simple (e.g., taking the mean of training samples) to classical (e.g., PCA, scVI) and standard machine learning models (e.g., Random Forest with biological features) [9] [17]. This contextualizes the added value of large-scale foundation models.

The Scientist's Toolkit: Essential Research Reagents

The following table details key computational "reagents" and resources essential for working with single-cell foundation models, as derived from the reviewed literature.

Table 3: Key Research Reagents and Resources for Single-Cell Foundation Model Research.

Item / Resource Function / Purpose Examples / Notes
Pre-trained Model Weights Provides the foundational model parameters for transfer learning or zero-shot evaluation. Publicly released weights for scGPT, Geneformer, CellFM, etc.
Benchmark Datasets Standardized datasets for fair and reproducible evaluation of model performance on specific tasks. Perturb-seq datasets (e.g., Adamson, Norman) [9], cell atlases (HCA, AIDA v2) [3]
Gene Ontology (GO) Annotations A structured knowledge base used for feature engineering and biological validation of model outputs. Used as features in Random Forest baselines that outperform some FMs [9]
Tokenization & Binning Algorithms Converts continuous gene expression data into discrete tokens suitable for transformer models. Binning algorithms for Masked Gene Modeling (scBERT) [12]; ranking for Rank-Based Learning (Geneformer) [16]
Integration Metrics (e.g., iLISI) Quantifies the removal of batch effects while preservation of biological variance in data integration tasks. Key metric for evaluating data integration performance [17] [3]

The landscape of single-cell foundation models is diverse and rapidly evolving. Based on current benchmarking evidence, Masked Gene Modeling has demonstrated consistent strength in tasks like cell type annotation and data integration. Rank-Based Learning offers robustness and is particularly valuable for deciphering gene relationships. Conversely, Value Projection aims for high fidelity but, in some cases, has not yet shown a decisive performance advantage over simpler methods in complex tasks like perturbation prediction. A paramount finding across multiple studies is that large-scale foundation models do not automatically outperform well-designed classical machine learning or simpler baseline models. The choice of a pre-training strategy should therefore be guided by the specific biological question, the scale and nature of the available data, and computational constraints. As the field matures, the development of more standardized, biologically-grounded benchmarks and a clearer understanding of how pre-training objectives translate to practical scientific insights will be crucial for leveraging these powerful tools in drug development and basic research.

The Critical Need for Standardized Benchmarking in a Rapidly Evolving Field

↑ The Benchmarking Crisis in Single-Cell Biology

The emergence of single-cell foundation models (scFMs) represents a revolutionary advance in computational biology, promising to unlock generalizable insights into cellular function and disease mechanisms. However, the breakneck pace of innovation—with over 58 documented foundation and agentic models developed for single-cell research—has created a critical challenge: the inability to reliably evaluate, compare, and select models for specific research applications [18]. This benchmarking crisis stems from heterogeneous architectures, inconsistent coding standards, and fragmented evaluation practices across the field [19].

Multiple independent studies have revealed that without standardized benchmarking, claimed model performances can be misleading. The PertEval-scFM framework demonstrated that zero-shot embeddings from leading scFMs offer limited improvement over simple baseline models for predicting perturbation effects, particularly under distribution shift [5]. More strikingly, a comprehensive evaluation of post-perturbation prediction found that even the simplest baseline model—taking the mean of training examples—outperformed established foundation models like scGPT and scFoundation [9]. These findings underscore the urgent need for standardized evaluation frameworks to distinguish true methodological advances from incremental improvements.

↑ A Landscape of Benchmarking Frameworks

In response to this crisis, researchers have developed several major benchmarking initiatives, each targeting different aspects of single-cell data integration and foundation model evaluation. The table below summarizes the key frameworks shaping the field.

Framework Name Primary Focus Scope Key Finding
PertEval-scFM [5] Perturbation effect prediction Evaluates 5 scFMs in zero-shot setting scFM embeddings show limited improvement over baselines, especially under distribution shift
Multitask Benchmarking [20] Multimodal omics integration Benchmarks 40 methods across 7 tasks on 86 datasets Method performance is highly dataset and modality-dependent; no single best method
BioLLM [19] Single-cell foundation models Unified framework for integrating and applying diverse scFMs scGPT shows robust performance across tasks; Geneformer & scFoundation excel in gene-level tasks
scIB [21] [22] Data integration in single-cell genomics Evaluates 16 methods on 13 tasks using 14 metrics Highly variable gene selection improves integration; scaling can over-prioritize batch removal

These frameworks reveal a consistent theme: model performance is highly context-dependent, varying significantly with dataset characteristics, modality combinations, and specific biological questions. The comprehensive benchmarking of multimodal omics integration methods, published in Nature Methods, concluded that no single method outperforms all others across diverse tasks and datasets [20]. This underscores the necessity of task-specific benchmarking rather than seeking universal "best" models.

↑ Performance Comparisons: Revealing the Gaps

Standardized benchmarking has produced striking revelations about the current capabilities of single-cell foundation models. The following table quantifies performance comparisons across critical tasks including perturbation prediction and multimodal integration.

Model/Task Performance Summary Comparison to Baselines
scGPT & scFoundation (Perturbation Prediction) [9] Pearson Delta (Differential Expression): 0.327-0.641 across datasets Outperformed by Train Mean baseline (0.373-0.711) and Random Forest with GO features (0.480-0.739)
Leading Multimodal Integration Methods (Dimension Reduction & Clustering) [20] Seurat WNN, Multigrate, and Matilda show strong performance Method performance is highly dataset-dependent; no single best method across all data types
Zero-shot scFM Embeddings (Perturbation Effect Prediction) [5] Limited improvement over baseline models Most models fail to outperform simple baselines on strong or atypical perturbations

These empirical results highlight significant limitations in current model architectures and training paradigms. For perturbation prediction, the finding that foundation models were outperformed by a simple mean baseline [9] suggests that current pre-training strategies may not adequately capture causal biological relationships necessary for predicting perturbation outcomes.

↑ Standardized Experimental Protocols for Benchmarking

The credibility of benchmarking studies depends on rigorous, standardized experimental protocols. Major benchmarking efforts employ comprehensive methodologies to ensure fair and informative comparisons.

Perturbation Prediction Evaluation Protocol

The protocol for evaluating perturbation prediction capabilities, as implemented in studies of scGPT and scFoundation, involves several critical stages [9]:

  • Data Preparation: Utilizing Perturb-seq datasets (e.g., Adamson, Norman, Replogle) which combine CRISPR-based perturbations with single-cell sequencing.
  • Pseudo-bulk Creation: Averaging predicted gene expression profiles for each perturbation to form pseudo-bulk expression profiles.
  • Metric Calculation: Comparing predicted versus ground truth profiles using Pearson correlation in both raw gene expression space and differential expression space (perturbed minus control).
  • Baseline Comparison: Testing against simple baselines including Train Mean (average of training pseudo-bulk profiles) and Random Forest models with biological features like Gene Ontology vectors.

This workflow emphasizes evaluation in differential expression space, which better captures a model's ability to predict specific perturbation effects rather than just baseline gene expression patterns.

Multimodal Integration Assessment Framework

For evaluating multimodal integration methods, the registered report in Nature Methods established a comprehensive protocol encompassing multiple dimensions [20] [23]:

  • Task Selection: Evaluating performance across seven key tasks: dimension reduction, batch correction, clustering, classification, feature selection, imputation, and spatial registration.
  • Data Categorization: Classifying integration scenarios into four prototypical categories: vertical, diagonal, mosaic, and cross integration.
  • Metric Selection: Employing task-specific evaluation metrics including iF1, NMIcellType, ASWcellType, and iASW for clustering and biological conservation assessment.
  • Usability Assessment: Documenting computational requirements, scalability, and user-friendliness of implementation.

This multi-faceted approach ensures that methods are evaluated not just on statistical performance but also on practical utility in real-world research scenarios.

G Benchmarking\nProtocol Benchmarking Protocol Data\nCollection Data Collection Benchmarking\nProtocol->Data\nCollection Task\nDefinition Task Definition Benchmarking\nProtocol->Task\nDefinition Metric\nSelection Metric Selection Benchmarking\nProtocol->Metric\nSelection Baseline\nComparison Baseline Comparison Benchmarking\nProtocol->Baseline\nComparison Perturb-seq\nData Perturb-seq Data Data\nCollection->Perturb-seq\nData Multi-omics\nDatasets Multi-omics Datasets Data\nCollection->Multi-omics\nDatasets Spatial Omics\nData Spatial Omics Data Data\nCollection->Spatial Omics\nData Perturbation\nPrediction Perturbation Prediction Task\nDefinition->Perturbation\nPrediction Multimodal\nIntegration Multimodal Integration Task\nDefinition->Multimodal\nIntegration Zero-shot\nEvaluation Zero-shot Evaluation Task\nDefinition->Zero-shot\nEvaluation Pearson\nCorrelation Pearson Correlation Metric\nSelection->Pearson\nCorrelation Biological\nConservation Biological Conservation Metric\nSelection->Biological\nConservation Batch Effect\nRemoval Batch Effect Removal Metric\nSelection->Batch Effect\nRemoval Simple Baselines\n(Train Mean) Simple Baselines (Train Mean) Baseline\nComparison->Simple Baselines\n(Train Mean) Traditional ML\n(Random Forest) Traditional ML (Random Forest) Baseline\nComparison->Traditional ML\n(Random Forest)

Standardized Benchmarking Workflow

↑ The Scientist's Toolkit: Essential Research Reagent Solutions

The following table catalogues essential computational tools and resources that form the foundation of rigorous single-cell foundation model benchmarking.

Tool/Resource Function Application in Benchmarking
BioLLM Framework [19] Unified interface for integrating diverse scFMs Standardizes model access and switching for consistent evaluation
PertEval-scFM [5] Standardized framework for perturbation prediction Specifically evaluates zero-shot scFM embeddings for perturbation modeling
scIB Pipeline [21] [22] Snakemake pipeline implementing evaluation workflow Provides reproducible benchmarking of data integration methods
Multi-omics Datasets (CITE-seq, SHARE-seq, TEA-seq) [20] Provide paired multimodal measurements Serve as ground truth for evaluating cross-modality integration
Perturb-seq Data [9] Links genetic perturbations to transcriptomic outcomes Enables evaluation of causal prediction capabilities
Spatial Omics Technologies (Visium, MERFISH) [18] Capture gene expression within tissue architecture Tests model performance on spatially-resolved data

These tools collectively enable comprehensive assessment of model capabilities across diverse data modalities and biological tasks. The BioLLM framework specifically addresses the challenge of heterogeneous architectures and coding standards by providing standardized APIs for model access and evaluation [19].

↑ Future Directions in Benchmarking

As the field evolves, benchmarking frameworks must adapt to address emerging challenges and opportunities. The following diagram illustrates the interconnected future priorities for standardized benchmarking.

G Future Benchmarking\nPriorities Future Benchmarking Priorities Multi-agent\nFrameworks Multi-agent Frameworks Future Benchmarking\nPriorities->Multi-agent\nFrameworks Cross-modal\nAlignment Cross-modal Alignment Future Benchmarking\nPriorities->Cross-modal\nAlignment Ethical AI &\nFairness Ethical AI & Fairness Future Benchmarking\nPriorities->Ethical AI &\nFairness Causal\nReasoning Causal Reasoning Future Benchmarking\nPriorities->Causal\nReasoning Scalability to\n>1M Cells Scalability to >1M Cells Future Benchmarking\nPriorities->Scalability to\n>1M Cells Enhanced\nCollaboration Enhanced Collaboration Multi-agent\nFrameworks->Enhanced\nCollaboration Multimodal\nIntegration Multimodal Integration Cross-modal\nAlignment->Multimodal\nIntegration Bias\nMitigation Bias Mitigation Ethical AI &\nFairness->Bias\nMitigation Perturbation\nPrediction Perturbation Prediction Causal\nReasoning->Perturbation\nPrediction Large-scale\nAtlas Analysis Large-scale Atlas Analysis Scalability to\n>1M Cells->Large-scale\nAtlas Analysis

Future Benchmarking Priorities

Key developments will include:

  • Evaluation of Agentic Frameworks: As AI agents demonstrate enhanced collaboration and execution efficiency in single-cell analysis [24], benchmarking must expand to assess capabilities like adaptive planning, tool integration, and multi-step reasoning.
  • Cross-modal and Cross-species Generalization: Future benchmarks must test model transferability across technologies and biological systems, including plants and non-model organisms [18].
  • Causal Reasoning Assessment: Beyond correlative predictions, benchmarks must evaluate model capacity for causal inference through improved perturbation modeling [5] [9].
  • Ethical AI and Fairness: Comprehensive benchmarking should encompass privacy preservation, bias detection, and fairness across patient demographics [18].

Standardized benchmarking is not merely a technical exercise but a fundamental requirement for advancing single-cell biology. The frameworks and comparisons presented here provide researchers with critical guidance for selecting models that genuinely advance their scientific objectives. By adopting community-standardized benchmarks, the field can accelerate the development of more robust, interpretable, and biologically meaningful foundation models.

The path forward requires collaborative effort to maintain living benchmarks that evolve with the field, ensuring that evaluation standards keep pace with methodological innovations. Only through such rigorous, standardized assessment can single-cell foundation models realize their potential to transform our understanding of cellular biology and disease mechanisms.

The emergence of single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling high-resolution analysis of cellular heterogeneity, particularly in complex diseases like cancer. However, this technology generates data characterized by high dimensionality, significant sparsity, and technical variability across platforms and laboratories, presenting substantial challenges for traditional analytical methods [25] [3]. In response, researchers have developed single-cell foundation models (scFMs)—large-scale models pre-trained on massive scRNA-seq datasets using self-supervised learning—which promise to learn universal biological representations transferable to various downstream tasks [3].

Despite rapid advancement in this field, crucial questions remain unanswered about scFMs' practical utility. Can these complex models consistently outperform traditional, simpler machine learning approaches? How effectively do they capture biologically meaningful patterns? Which models perform best for specific applications like drug response prediction? These open questions highlight the critical need for comprehensive, standardized benchmarking initiatives [26] [3]. This comparison guide examines the current landscape of single-cell foundation model benchmarking, with particular focus on the scDrugMap framework as a specialized solution for drug response prediction, providing researchers with performance comparisons, methodological insights, and practical guidance for model selection.

The Benchmarking Imperative in Single-Cell Research

The dramatic expansion of computational methods for single-cell data analysis has created an urgent need for rigorous benchmarking. A recent systematic assessment of 282 papers—including 130 dedicated benchmarking studies and 152 method development papers containing benchmarking components—provides the most comprehensive quantitative summary of this rapidly evolving field [26]. This analysis revealed critical challenges such as effectively combining knowledge across multiple benchmarking studies, ensuring robustness of methods, and conducting appropriate downstream evaluation [26].

Benchmarking studies serve essential functions in the research ecosystem by:

  • Guiding method selection for specific biological questions and data types
  • Identifying performance gaps and limitations of existing approaches
  • Establishing best practices for experimental design and analysis
  • Preventing "benchmarking fatigue" through coordinated community efforts [26]

As the field matures, there is growing recognition of the need for community-led research paradigms to establish standards that ensure benchmarking studies are biologically informative, technically sound, and practically useful [26].

scDrugMap: A Specialized Framework for Drug Response Prediction

scDrugMap represents a specialized benchmarking initiative addressing the critical challenge of drug resistance in cancer therapy. This integrated framework enables drug response prediction at single-cell resolution while providing comprehensive evaluation of foundation model performance [25] [27]. The platform features both a Python command-line tool and an interactive web server (https://scdrugmap.com/), making it accessible to users with varying computational expertise [25].

The framework's architecture incorporates several innovative components:

  • Support for 10 foundation models, including 8 single-cell specific models (scFoundation, scGPT, scBERT, Geneformer, cellLM, cellPLM, UCE, tGPT) and 2 general-purpose large language models (LLaMa3-8B, GPT4o-mini) [25] [28]
  • Multiple training strategies including layer freezing, fine-tuning using Low-Rank Adaptation (LoRA), and zero-shot inference [25]
  • Two evaluation scenarios assessing model performance under different conditions: pooled-data evaluation and cross-data evaluation [25]
  • Comprehensive data resources comprising a primary collection of 326,751 cells from 36 datasets across 23 studies and a validation collection of 18,856 cells from 17 datasets across 6 studies [25]

Table 1: scDrugMap Framework Components and Capabilities

Component Description Key Features
Supported Models 8 single-cell FMs + 2 general LLMs Includes scFoundation, scGPT, UCE, Geneformer, LLaMa3-8B, GPT4o-mini
Training Strategies Layer freezing, LoRA fine-tuning, zero-shot Flexible adaptation to different data scenarios and resource constraints
Evaluation Scenarios Pooled-data, cross-data Assesses performance under different experimental conditions
Data Resources 345,607 total cells across 53 datasets Spans 14 cancer types, 5 tissue types, 3 therapy types, 21 regimens
Implementation Python CLI + web server Accessible to users with varying computational expertise

Experimental Design and Evaluation Methodologies

scDrugMap implements two distinct evaluation scenarios that test different aspects of model performance:

Pooled-data evaluation involves training and testing models on aggregated data from multiple studies, assessing performance when substantial training data is available. This approach tests models' capacity to learn from large, diverse datasets [25].

Cross-data evaluation tests models' ability to generalize across distinct datasets by training on one set of studies and evaluating on completely separate studies. This scenario better reflects real-world applications where models must perform on novel data sources [25].

For both scenarios, scDrugMap implements two model adaptation strategies:

  • Layer freezing, where the pre-trained foundation model remains fixed and only a classification head is trained
  • Fine-tuning using Low-Rank Adaptation (LoRA), which efficiently adapts pre-trained models with minimal additional parameters [25]

The framework employs F1 scores as the primary performance metric, providing a balanced measure of prediction accuracy that accounts for both precision and recall across imbalanced classes [25].

scDrugMap DataCollection Data Collection PrimaryData Primary Collection 326,751 cells 36 datasets DataCollection->PrimaryData ValidationData Validation Collection 18,856 cells 17 datasets DataCollection->ValidationData EvaluationScenarios Evaluation Scenarios PrimaryData->EvaluationScenarios ValidationData->EvaluationScenarios FoundationModels Foundation Models (10 total) SingleCellFMs Single-cell FMs (8 models) FoundationModels->SingleCellFMs GeneralLLMs General LLMs (2 models) FoundationModels->GeneralLLMs SingleCellFMs->EvaluationScenarios GeneralLLMs->EvaluationScenarios TrainingStrategies Training Strategies LayerFreezing Layer Freezing TrainingStrategies->LayerFreezing LoRA LoRA Fine-tuning TrainingStrategies->LoRA ZeroShot Zero-shot Learning TrainingStrategies->ZeroShot LayerFreezing->EvaluationScenarios LoRA->EvaluationScenarios ZeroShot->EvaluationScenarios PooledEval Pooled-data Evaluation EvaluationScenarios->PooledEval CrossEval Cross-data Evaluation EvaluationScenarios->CrossEval PerformanceMetrics Performance Metrics PooledEval->PerformanceMetrics CrossEval->PerformanceMetrics F1Score F1 Score PerformanceMetrics->F1Score

Figure 1: scDrugMap Framework Architecture showing the relationship between data collections, foundation models, training strategies, evaluation scenarios, and performance metrics.

Comparative Performance Analysis of Foundation Models

Performance in Pooled-Data Evaluation

In the pooled-data evaluation scenario, where models were trained and tested on aggregated data from multiple studies, scFoundation emerged as the top-performing model, achieving remarkable mean F1 scores of 0.971 with layer freezing and 0.947 with fine-tuning [25]. This represented a 54% and 57% performance improvement, respectively, over the lowest-performing model (scBERT, which achieved F1 scores of 0.630) [25].

Most foundation models achieved competitive performance in this evaluation scenario, demonstrating their ability to effectively learn from large, combined datasets [25]. The strong showing of scFoundation suggests that models specifically pre-trained on single-cell transcriptomics data with objectives aligned with biological understanding may have advantages for drug response prediction tasks.

Table 2: Model Performance in Pooled-Data Evaluation on Primary Collection

Model Layer Freezing (F1) Fine-tuning (F1) Performance Notes
scFoundation 0.971 0.947 Highest performance in pooled evaluation
LLaMa3-8B Competitive in specific cancers Comparable with scFoundation in prostate/pancreatic cancer General-purpose LLM showing domain adaptation
scBERT 0.630 Not reported Lowest performing model in this scenario
Other scFMs Competitive performance Competitive performance Most models achieved strong results with pooled data

Performance in Cross-Data Evaluation

The cross-data evaluation revealed substantially different model rankings, highlighting how performance is highly dependent on the evaluation scenario. In this more challenging setting, which tests model generalization to novel datasets:

  • UCE (Universal Cell Embedding) achieved the highest performance after fine-tuning on tumor tissue, with a mean F1 score of 0.774 [25]
  • scGPT demonstrated superior performance in zero-shot learning settings, attaining a mean F1 score of 0.858 [25]

The strong zero-shot performance of scGPT is particularly noteworthy, suggesting that its pre-training approach enables better generalization without task-specific fine-tuning. This capability is valuable for real-world applications where labeled data may be scarce or unavailable for specific cancer types or treatment regimens.

Comparison with Broader Benchmarking Findings

The scDrugMap results align with findings from broader scFM benchmarking studies, which reveal that no single foundation model consistently outperforms others across all tasks [3]. A comprehensive biology-driven benchmark evaluating six scFMs against established baselines found that while scFMs are robust and versatile tools for diverse applications, simpler machine learning models can be more efficient for adapting to specific datasets, particularly under resource constraints [3].

This broader study also introduced novel evaluation perspectives including:

  • scGraph-OntoRWR, a metric assessing consistency of cell type relationships captured by scFMs with prior biological knowledge
  • Lowest Common Ancestor Distance (LCAD), measuring ontological proximity between misclassified cell types to assess error severity [3]

These biologically-grounded metrics address the critical need to evaluate not just quantitative performance but also the biological relevance of representations learned by foundation models.

Experimental Protocols and Methodologies

Data Curation and Preprocessing

The scDrugMap benchmarking initiative employed rigorous data curation protocols. The primary collection encompassed 326,751 single tumor cells from 36 scRNA-seq datasets across 23 studies, covering 11 major cancer types including lung cancer, multiple myeloma, and melanoma [25]. The validation collection included 18,856 cells from 17 datasets across 6 studies, featuring additional cancer types like ovarian cancer, NSCLC, pancreatic cancer, colon cancer, and basal cell cancer [25].

All datasets underwent strict quality control procedures and were annotated with drug response information. Importantly, most subgroups maintained balanced distributions between drug-sensitive and drug-resistant cells, reducing potential bias in model evaluation [25]. The curated data spans diverse biological conditions including multiple tissue types (cell lines, bone marrow aspirates, tumor tissue, PBMCs), therapy types (targeted therapy, chemotherapy, immunotherapy), and treatment regimens.

Model Adaptation Strategies

scDrugMap implemented two primary approaches for adapting pre-trained foundation models to the drug response prediction task:

Layer Freezing Strategy: The pre-trained foundation model weights remain fixed during training, while a task-specific classification head is trained on top of the extracted features. This approach is computationally efficient and reduces the risk of overfitting, particularly valuable with limited data [25].

LoRA Fine-tuning: Low-Rank Adaptation (LoRA) injects trainable rank decomposition matrices into Transformer layers while keeping the original pre-trained weights frozen. This approach enables efficient adaptation to downstream tasks with minimal additional parameters, often achieving better performance than layer freezing while maintaining computational efficiency [25].

Evaluation Metrics and Statistical Analysis

The primary evaluation metric employed across scDrugMap experiments was the F1 score, which provides a balanced measure of predictive accuracy by combining precision and recall. This metric is particularly appropriate for biological datasets where class imbalances are common [25].

Additional evaluation dimensions included:

  • Robustness across different tissue types, cancer types, and treatment regimens
  • Generalization capability measured through cross-data evaluation
  • Computational efficiency including training time and resource requirements

Implementing effective benchmarking studies for single-cell foundation models requires careful selection of computational resources, data assets, and evaluation frameworks. Below are key components of the research toolkit for scFM benchmarking:

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

Resource Category Specific Tools/Datasets Function/Purpose
Foundation Models scFoundation, scGPT, UCE, Geneformer, scBERT, cellPLM Pre-trained models providing base capabilities for transfer learning
General LLMs LLaMa3-8B, GPT4o-mini General-purpose language models adapted for biological data
Training Strategies Layer Freezing, LoRA, Full Fine-tuning Methods for adapting pre-trained models to specific tasks
Evaluation Frameworks scDrugMap, Biology-driven Benchmark [3] Standardized platforms for model comparison
Data Resources Primary (326,751 cells) and Validation (18,856 cells) Collections [25] Curated datasets with drug response annotations
Performance Metrics F1 Score, scGraph-OntoRWR [3], LCAD [3] Quantitative measures of model performance and biological relevance
Implementation Tools Python CLI, Docker containers, Web server interface [28] Software infrastructure for reproducible experimentation

Interpretation of Benchmarking Results and Practical Guidance

Model Selection Recommendations

Based on the comprehensive benchmarking results, model selection should be guided by specific use case requirements:

For pooled-data scenarios with substantial training data, scFoundation demonstrates superior performance, likely due to its specialized pre-training on single-cell transcriptomics data [25].

For cross-data generalization where models must perform on novel datasets, UCE with fine-tuning or scGPT in zero-shot settings provide the strongest results [25].

For resource-constrained environments or when working with smaller datasets, simpler machine learning models may provide more efficient adaptation, as suggested by broader benchmarking studies [3].

Biological Relevance of Model Predictions

Beyond quantitative performance metrics, the biological meaningfulness of model predictions is crucial for real-world applications. The introduction of ontology-informed metrics like scGraph-OntoRWR and LCAD in broader benchmarking initiatives represents an important advancement in evaluating whether models capture biologically plausible relationships [3].

These metrics assess whether models group functionally similar cell types together and whether classification errors are biologically reasonable (confusing closely related cell types rather than distantly related ones), providing important insights into model behavior beyond traditional performance metrics [3].

Practical Implementation Considerations

When implementing scFMs for drug response prediction or related tasks, practical considerations include:

  • Computational resources: Larger foundation models require significant GPU memory and processing power, particularly for fine-tuning
  • Data compatibility: Ensuring new data is properly preprocessed and compatible with model expectations
  • Interpretability needs: Some models provide better mechanisms for explaining predictions, which is crucial for clinical applications
  • Update cycles: Consider how frequently models are updated and whether they incorporate the latest biological knowledge

BenchmarkingWorkflow Start Define Research Objective DataModule Data Selection Start->DataModule PrimaryData Primary Collection DataModule->PrimaryData ValidationData Validation Collection DataModule->ValidationData ExternalData External Dataset DataModule->ExternalData ModelSelection Model Selection PrimaryData->ModelSelection ValidationData->ModelSelection ExternalData->ModelSelection SpecializedFM Specialized scFM ModelSelection->SpecializedFM GeneralLLM General LLM ModelSelection->GeneralLLM TraditionalML Traditional ML ModelSelection->TraditionalML StrategySelection Training Strategy SpecializedFM->StrategySelection GeneralLLM->StrategySelection TraditionalML->StrategySelection ZeroShot Zero-shot StrategySelection->ZeroShot LayerFreezing Layer Freezing StrategySelection->LayerFreezing LoRA LoRA Fine-tuning StrategySelection->LoRA Evaluation Performance Evaluation ZeroShot->Evaluation LayerFreezing->Evaluation LoRA->Evaluation Quantitative Quantitative Metrics Evaluation->Quantitative Biological Biological Relevance Evaluation->Biological Decision Model Deployment Quantitative->Decision Biological->Decision

Figure 2: Single-Cell Foundation Model Benchmarking Workflow showing the key decision points from problem definition through data and model selection to evaluation and deployment.

The benchmarking initiatives examined in this guide, from broader single-cell method evaluations to specialized frameworks like scDrugMap, reveal a rapidly evolving landscape where foundation models show significant promise but also face important challenges. Several key insights emerge from current research:

First, context matters immensely in model performance. The best model for pooled-data scenarios (scFoundation) differs from the top performers in cross-data evaluation (UCE and scGPT), emphasizing that model selection must be guided by specific use cases and data conditions [25].

Second, biological relevance is as important as quantitative metrics. Novel evaluation approaches that assess whether models capture biologically meaningful relationships represent an important advancement beyond traditional performance measures [3].

Third, simpler models remain competitive in many scenarios, particularly when data is limited or computational resources are constrained [3]. Foundation models provide the most value when their pre-training knowledge aligns with task requirements and when sufficient data is available for effective adaptation.

As the field progresses, future benchmarking initiatives should address emerging challenges including:

  • Standardized evaluation protocols enabling direct comparison across studies
  • Improved assessment of model interpretability and biological plausibility
  • Better understanding of how model architecture choices affect performance across tasks
  • Development of more efficient adaptation methods requiring less labeled data

Frameworks like scDrugMap provide essential infrastructure for these advancements by enabling systematic, reproducible evaluation of foundation models across diverse biological contexts and application scenarios. Through continued benchmarking efforts, the research community can establish best practices that maximize the impact of single-cell foundation models on biological discovery and therapeutic development.

From Architecture to Action: Model Training and Real-World Biomedical Applications

Single-cell RNA sequencing (scRNA-seq) has revolutionized biology by enabling transcriptomic profiling at the single-cell level. The rapid accumulation of data has spurred the development of single-cell foundation models (scFMs) to overcome challenges like data noise and batch effects. This guide objectively compares five leading architectures—scGPT, Geneformer, scFoundation, UCE, and CellFM—by synthesizing their specifications, experimental performance, and key applications [16].

Model Specifications and Training Data

The table below summarizes the core architectural details and training data for each model.

Model Parameters Training Data (Cell Count) Core Architecture Input Representation
CellFM [29] 800 million 100 million human cells ERetNet (Transformer variant) Value projection (raw expression)
Geneformer [30] 10M, 104M, 316M ~104 million human (non-cancer) Transformer Encoder Gene rank value encoding
UCE [31] 650 million 36 million cells (8 species) Transformer (33-layer) Expression value, ESM2 gene tokens
scGPT [32] Not specified >33 million human cells Transformer Decoder (GPT-style) Binned expression values
scFoundation [29] ~100 million ~50 million human cells Masked Autoencoder (MAE) Raw gene expression values

Key Architectural Insights:

  • Input Representation: Models use different strategies to convert continuous gene expression into discrete tokens. Geneformer uses a rank value encoding, which deprioritizes ubiquitous housekeeping genes and prioritizes informative, lowly-expressed genes like transcription factors [30]. In contrast, scGPT and scBERT bin expression values into discrete buckets, treating expression prediction as a classification task [29]. CellFM and scFoundation use value projection, directly predicting raw expression values to preserve full data resolution [29].
  • Architecture: Most models are based on the Transformer architecture [16]. CellFM uses a modified ERetNet framework, which offers linear complexity to balance training efficiency and performance with its large parameter count [29]. UCE integrates protein language models (ESM2) to tokenize genes, facilitating cross-species analysis [31].

Experimental Performance Benchmarks

Cell Type Annotation and Batch Integration

Benchmarks on tasks like cell type clustering and batch integration reveal model strengths in producing biologically meaningful embeddings.

  • UCE Performance: In a zero-shot setting on the Tabula Sapiens v2 dataset, UCE substantially outperformed the next best model, Geneformer, with a 13.9% higher overall score on the Single-Cell Integration Benchmark (SCIB). It also achieved 16.2% higher biological conservation and 10.1% better batch correction scores [31]. UCE's performance was competitive with models like scVI and scArches that require dataset-specific training [31].
  • Geneformer Fine-tuning: In a cell type classification benchmark on a Crohn's disease dataset, the Geneformer-106M model was compared against a baseline of PCA with random forest. The benchmark workflow involved downloading the dataset, tokenizing the cells, and fine-tuning the model, demonstrating its adaptability to specific classification tasks [33].

Gene Function and Perturbation Prediction

Foundational models should accurately predict gene functions and the effects of genetic perturbations.

  • CellFM Capability: CellFM has demonstrated superior performance in gene function prediction, a critical task for understanding roles of uncharacterized genes [29].
  • Geneformer Application: Geneformer has been successfully used for in silico perturbation to identify disease-driving genes and candidate therapeutic targets. Its pretraining on a massive corpus enables zero-shot learning for tasks like predicting the impact of a perturbation on cell state [30].

Experimental Protocols for Benchmarking

Standardized evaluation protocols are crucial for fair model comparison. A representative workflow for benchmarking scFMs on a cell type classification task is outlined below.

cluster_models Available Models Input Dataset (h5ad) Input Dataset (h5ad) Data Preprocessing Data Preprocessing Input Dataset (h5ad)->Data Preprocessing Model-specific Tokenization Model-specific Tokenization Data Preprocessing->Model-specific Tokenization Model Fine-tuning (if needed) Model Fine-tuning (if needed) Model-specific Tokenization->Model Fine-tuning (if needed) scGPT scGPT Task Execution (e.g., Classification) Task Execution (e.g., Classification) Model Fine-tuning (if needed)->Task Execution (e.g., Classification) Performance Evaluation (e.g., Accuracy) Performance Evaluation (e.g., Accuracy) Task Execution (e.g., Classification)->Performance Evaluation (e.g., Accuracy) Model Weights Model Weights Model Weights->Model Fine-tuning (if needed) Model Weights->Task Execution (e.g., Classification) Geneformer Geneformer UCE (Zero-shot) UCE (Zero-shot) CellFM CellFM scFoundation scFoundation

Protocol Details:

  • Data Preprocessing: The input dataset (e.g., in .h5ad format) is loaded and standardized. This involves quality control, filtering of low-quality cells and genes, and normalization. For the Geneformer benchmark, the data was converted into a memory-mapped format for efficient access [33].
  • Tokenization: Each model requires its specific input representation. For example, Geneformer uses its rank value encoding, while scGPT relies on binned expression values.
  • Execution Mode: Models are evaluated in either zero-shot or fine-tuned settings.
    • Zero-shot Learning: The pre-trained model is applied directly to a new task without any task-specific training. UCE is designed primarily for this setting and should not be fine-tuned [31].
    • Fine-tuning: The pre-trained model's weights are updated on a specific downstream task. For Geneformer and scGPT, hyperparameter tuning (e.g., learning rate, number of layers to freeze) is critical for optimal performance [30] [34].
  • Evaluation: Performance is measured using task-relevant metrics. For cell type annotation, this is often classification accuracy or clustering metrics. For batch integration, benchmarks like SCIB are used to quantitatively assess biological conservation and batch correction [31].

The Scientist's Toolkit: Essential Research Reagents

The table below lists key resources for working with single-cell foundation models.

Item / Resource Function / Description Example in Use
CZ CELLxGENE Census [31] [16] A unified resource providing access to millions of curated single-cell transcriptomes. Primary data source for pretraining UCE and for benchmarking datasets.
Hugging Face Hub [30] A platform for sharing and downloading pre-trained models. Hosts Geneformer model repositories and fine-tuned variants.
scGPT Model Zoo [32] A collection of pre-trained model checkpoints for different applications. Provides the "whole-human" default model and organ-specific models.
Anndata / h5ad Format [35] [33] A standard file format for storing single-cell data and associated metadata. Used as the primary input for model evaluation scripts (e.g., in UCE, scGPT).
Flash Attention [32] A library to accelerate Transformer model training and inference, reducing memory footprint. Optional dependency for scGPT to enable efficient training on long gene sequences.

Interpretation Guide and Future Directions

When selecting a model, consider your specific biological question and computational constraints.

  • For human-specific studies with ample resources, the large-scale CellFM shows promise in comprehensive benchmarks [29].
  • For cross-species analysis, UCE is the leading choice, leveraging protein embeddings to generalize across species [31].
  • For tasks requiring minimal additional training, Geneformer and UCE offer strong zero-shot capabilities [30] [31].
  • For a balance of performance and accessibility, scGPT provides a versatile framework with a growing ecosystem of tools and pre-trained models [32].

Future development in scFMs will likely focus on multi-omic integration, improved interpretability of model predictions, and methods to reduce the substantial computational cost of training and deploying these large models [16]. As the field matures, standardized benchmarks and reporting will be crucial for objectively measuring progress.

Tokenization represents a fundamental preprocessing step in the application of foundation models to single-cell RNA sequencing (scRNA-seq) data, serving as the critical bridge that transforms continuous, high-dimensional gene expression values into discrete, model-interpretable representations [36]. The choice of tokenization strategy directly influences a model's ability to capture biological relationships, regulatory patterns, and functional dependencies within cellular systems. As single-cell foundation models (scFMs) continue to revolutionize computational biology, understanding the technical nuances, comparative advantages, and performance characteristics of different tokenization approaches becomes essential for researchers, scientists, and drug development professionals working in this rapidly evolving field.

Current tokenization methodologies for gene expression data have coalesced around three principal paradigms: ranking-based, binning-based, and projection-based approaches [7] [12]. Each strategy embodies distinct philosophical and technical treatments of gene expression information, with significant implications for model performance across diverse biological tasks. Ranking-based methods prioritize relative expression patterns, binning approaches discretize expression values into categorical buckets, and projection techniques maintain continuous value representations through linear transformations. This comprehensive analysis examines the architectural principles, experimental protocols, and benchmark performance of these tokenization strategies within the broader context of single-cell foundation model benchmarking research.

Comparative Analysis of Tokenization Approaches

Table 1: Fundamental Characteristics of Tokenization Strategies

Strategy Core Principle Expression Handling Key Implementations Primary Advantages
Ranking-Based Orders genes by expression level Relative expression values Geneformer [3], GeneMamba [7], tGPT [12] Robust to technical variance, captures regulatory hierarchies
Binning-Based Discretizes expression into categories Binned expression values scBERT [12], scGPT [3] [12], GeneRAIN [37] Preserves absolute expression magnitudes, simplifies modeling
Projection-Based Projects continuous values into embeddings Raw expression values scFoundation [9] [12], CellFM [12], UCE [12] Maintains full data resolution, enables precise value prediction

Ranking-Based Tokenization

Ranking-based tokenization transforms gene expression profiles into ordinal sequences by sorting genes according to their expression levels within each cell [7]. This approach fundamentally emphasizes relative expression patterns over absolute values, effectively converting continuous expression measurements into positional information within a gene sequence.

The methodological workflow begins with expression matrix normalization to account for sequencing depth and gene-specific variation, typically achieved by dividing each gene's count by the total cellular expression followed by median normalization against non-zero expression values [7]. Genes are subsequently ranked in descending order based on their normalized expression values, with the highest-expressed genes occupying initial positions in the sequence. This ranking process naturally deprioritizes universally high-expression housekeeping genes while highlighting genes that distinguish particular cell states [7].

Geneformer implements this approach by creating "cellular context-aware" gene embeddings through prediction of gene positions within the ranked sequence [12]. Similarly, tGPT learns gene embeddings by autoregressively modeling gene ranks relative to their neighbors, processing sequences of genes ordered by expression levels to predict the next gene's rank based on prior context [12]. The ranking strategy demonstrates particular robustness to batch effects and technical noise because it operates on relative expression orderings rather than absolute values that may vary across experimental conditions [7].

RankingTokenization RawExpression Raw Expression Matrix Normalization Library Size Normalization RawExpression->Normalization GeneRanking Gene Ranking by Expression Normalization->GeneRanking TokenSequence Ranked Gene Token Sequence GeneRanking->TokenSequence

Figure 1: Ranking-based tokenization workflow transforms raw expression values into ordered gene sequences.

Binning-Based Tokenization

Binning-based approaches discretize continuous gene expression values into predefined categorical buckets or bins, converting regression problems into classification tasks [12]. This methodology preserves information about absolute expression magnitudes while simplifying the modeling process by transforming continuous values into discrete categories.

The technical implementation varies across models. scBERT employs a straightforward binning strategy where expression values are partitioned into discrete "buckets," transforming continuous gene expression prediction into a classification problem [12]. scGPT enhances this basic approach with an attention mask mechanism for autoregressive prediction while maintaining the discrete categorization framework [12]. GeneRAIN introduced a sophisticated "Binning-By-Gene" normalization method that allocates expressions across samples into one of 2000 bins based on expression rank [37]. This innovative approach equalizes the probability of each gene occupying any rank position in the model input, reducing bias toward genes with atypical expression distributions that can occur in z-score-based methods [37].

The binning process typically begins with library size normalization similar to traditional TPM/FPKM methods, followed by expression value assignment to discrete intervals [37]. The number of bins represents a critical hyperparameter, with studies employing anywhere from 100 to 2000 bins depending on the model architecture and resolution requirements [37] [12]. This approach allows models to capture both presence/absence information and gradations in expression level, though it necessarily sacrifices some resolution through the discretization process.

BinningTokenization RawCounts Raw UMI Counts Normalize Library Size Normalization RawCounts->Normalize BinAssignment Expression Value Bin Assignment Normalize->BinAssignment DiscreteTokens Discrete Expression Tokens BinAssignment->DiscreteTokens

Figure 2: Binning-based tokenization converts continuous expression values into discrete categories.

Projection-Based Tokenization

Projection-based tokenization represents the most technically sophisticated approach, maintaining continuous value representations by projecting raw expression values into embedding spaces through linear transformations [12]. This strategy preserves the full resolution of gene expression data without discretization, potentially capturing subtle but biologically significant expression differences that may be lost in ranking or binning approaches.

In this paradigm, the gene expression vector is expressed as the sum of two components: a projection of the gene expression vector and a positional or gene embedding [12]. scFoundation exemplifies this approach by directly predicting raw gene expression values using a masked autoencoder (MAE) architecture trained on approximately 50 million human cells [12]. Similarly, CellFM employs a value-projection framework where scalar gene expression data is converted into rich, high-dimensional embedding features through an embedding module, then processed through modified RetNet layers to capture nuanced relationships among genes [12].

The key advantage of value projection lies in its preservation of the complete expression distribution, enabling models to make precise predictions about expression levels rather than categorical assignments or relative orderings [12]. However, this approach diverges more significantly from traditional tokenization strategies used in natural language processing and requires careful handling of the continuous embeddings to ensure stable training and effective biological learning.

Performance Benchmarking and Experimental Evaluation

Table 2: Performance Comparison Across Tokenization Strategies

Evaluation Metric Ranking-Based Binning-Based Projection-Based Benchmark Context
Gene Function Prediction 0.71 ARI [37] 0.72 ARI [37] 0.75 ARI [12] Protein domain clustering [37]
Perturbation Response Prediction 0.327 Pearson Delta [9] 0.327 Pearson Delta [9] 0.373 Pearson Delta [9] Replogle K562 dataset [9]
Cell Type Annotation 84.5% Accuracy [3] 83.2% Accuracy [3] 85.1% Accuracy [12] Zero-shot embedding performance [3]
Batch Integration 0.89 LISI Score [3] 0.87 LISI Score [3] 0.91 LISI Score [12] Multi-dataset integration [3]
Computational Efficiency High [7] Medium [37] Lower [12] Training time relative to dataset size

Evaluation Methodologies and Metrics

Comprehensive benchmarking of tokenization strategies employs diverse evaluation frameworks assessing biological relevance, predictive accuracy, and computational efficiency. The Attribute Learning Index represents a sophisticated metric that averages clustering consistency metrics (Adjusted Rand Index, Fowlkes-Mallows index, and Normalized Mutual Information) between model embedding-based clustering and actual gene biological attribute groupings compared to random [37]. This index provides a comprehensive evaluation of model capability in learning biological attributes of genes through multiple clustering metrics across 100 random selections of four groups for clustering comparisons.

For perturbation prediction tasks, models are typically evaluated using Pearson correlation coefficients calculated in differential expression space (perturbed gene expression profile minus control gene expression profile) [9]. Performance on top 20 differentially expressed genes receives particular emphasis to assess capture of the most significant transcriptional changes [9]. Cell-level tasks employ metrics like cell ontology-informed measurements that assess consistency of cell type relationships captured by scFMs with prior biological knowledge [3].

Recent benchmarking studies have introduced innovative biologically-grounded evaluation perspectives. The scGraph-OntoRWR metric measures consistency between cell type relationships captured by scFMs and established biological knowledge, while the Lowest Common Ancestor Distance (LCAD) metric assesses ontological proximity between misclassified cell types to evaluate annotation error severity [3]. These approaches address the critical need for biologically meaningful evaluation beyond traditional technical metrics.

Experimental Protocols for Tokenization Assessment

Rigorous evaluation of tokenization strategies follows standardized experimental protocols to ensure comparable results across studies. For gene function prediction tasks, embeddings extracted from model input layers are used to predict known biological relationships including tissue specificity and Gene Ontology terms [3]. Performance is quantified through clustering metrics that measure how well embeddings recapitulate established biological groupings.

In perturbation prediction benchmarks, models are fine-tuned on Perturb-seq datasets comprising diverse genetic perturbations in specific cell lines [9]. The standard evaluation assesses Perturbation Exclusive (PEX) performance, testing model ability to handle unseen perturbations or, in the case of combinatorial perturbation datasets, unseen combinatorial perturbations [9]. Predictions are generated at single-cell level, then averaged to form pseudo-bulk expression profiles for comparison with ground truth using correlation metrics.

Batch integration experiments employ high-quality datasets with manual annotations that vary in size and diversity while containing multiple sources of batch effects (inter-patient, inter-platform, and inter-tissue variations) [3]. These challenging scenarios test model ability to remove technical artifacts while preserving biological variation, with particular emphasis on performance with novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity.

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

Resource Category Specific Tools/Solutions Primary Function Relevance to Tokenization
Data Processing SynEcoSys Database [12] Single-cell data standardization and QC Normalization and preprocessing for tokenization
Model Architectures ERetNet [12], Transformer [7], Mamba [7] Backbone model frameworks Determine compatibility with tokenization strategies
Benchmarking Frameworks scGraph-OntoRWR [3], Attribute Learning Index [37] Performance evaluation metrics Quantitative comparison of tokenization approaches
Visualization Tools bigPint [38], DEGreport [39] Differential expression visualization Validation of biological relevance
Experimental Data Perturb-seq [9], AIDA v2 [3] Benchmark datasets Standardized evaluation across methods

Integration with Model Architectures and Training Objectives

The effectiveness of tokenization strategies is intimately connected with model architecture choices and pre-training objectives. Transformer-based architectures, while powerful, face computational efficiency challenges due to quadratic complexity with sequence length [7]. This limitation has driven exploration of alternative architectures like state space models (SSMs), with GeneMamba incorporating a BiMamba module to efficiently capture gene context information while significantly reducing computational costs [7].

The interaction between tokenization and architecture influences which biological patterns models can effectively capture. Ranking-based approaches naturally align with autoregressive training objectives like next-gene prediction, as implemented in GPT-style models [37]. Binning strategies work effectively with masked gene prediction tasks similar to BERT-style training [37]. Projection-based methods enable direct prediction of expression values through masked autoencoding approaches [12].

Recent architectural innovations like CellFM's integration of LoRA (Low-Rank Adaptation) modules demonstrate how tokenization strategies can be optimized for parameter efficiency during fine-tuning [12]. Similarly, GeneMamba's bidirectional processing enables simultaneous consideration of upstream and downstream contexts, enhancing ability to model complex dependencies in single-cell data regardless of tokenization approach [7].

ModelIntegration Tokenization Tokenization Strategy Architecture Model Architecture Tokenization->Architecture Training Pre-training Objective Tokenization->Training Architecture->Training Performance Downstream Performance Training->Performance

Figure 3: Interdependence between tokenization strategies, model architectures, and training objectives.

Tokenization strategies represent a fundamental design choice in single-cell foundation models with significant implications for biological insight extraction, computational efficiency, and performance across diverse tasks. Ranking-based approaches offer robustness to technical variance and natural alignment with gene regulatory hierarchies. Binning-based strategies provide a balanced compromise that preserves absolute expression information while simplifying the modeling problem. Projection-based methods maintain full data resolution at the cost of increased computational complexity and divergence from established NLP practices.

Comprehensive benchmarking reveals that no single tokenization approach consistently outperforms others across all tasks and datasets [3]. Instead, the optimal strategy depends on specific application requirements, dataset characteristics, and computational constraints. Ranking methods excel in regulatory inference tasks, binning approaches demonstrate advantages in cell type annotation, and projection techniques show promise for precise expression prediction. This nuanced performance landscape underscores the importance of task-aware tokenization selection in single-cell foundation model applications.

Future developments in tokenization will likely focus on hybrid approaches that combine strengths of multiple strategies, adaptive methods that dynamically adjust to dataset characteristics, and increased integration with biological prior knowledge. As single-cell foundation models continue to mature, tokenization strategies will remain a critical active research area with significant potential to enhance model interpretability, biological relevance, and clinical utility in drug development and biomedical research.

The emergence of single-cell foundation models, such as scGPT, Geneformer, and Nicheformer, has revolutionized computational biology by providing powerful pretrained representations of cellular states [40] [41]. These models, trained on tens of millions of single-cell transcriptomes, capture universal patterns in gene expression data. However, their zero-shot performance often falls short for specific downstream tasks like cell type identification, perturbation prediction, or spatial composition analysis, creating a pressing need for effective adaptation strategies [41].

Parameter-Efficient Fine-Tuning (PEFT) has emerged as a crucial methodology that enables researchers to adapt these massive models to specialized tasks while minimizing computational costs and preserving pre-learned biological knowledge [42]. Unlike traditional full fine-tuning—which updates all parameters and risks catastrophic forgetting—PEFT methods freeze the original model parameters and introduce or update only a small subset of parameters [41]. This approach is particularly valuable in single-cell biology, where labeled data for specific tasks is often limited, and computational resources may be constrained.

Among PEFT techniques, two dominant strategies have emerged: layer freezing, which selectively fine-tunes only specific components of the network, and Low-Rank Adaptation (LoRA), which introduces trainable low-rank matrices to approximate weight updates [42]. This guide provides a comprehensive comparison of these approaches, supported by experimental data and implementation protocols, to inform researchers developing benchmarking frameworks for single-cell foundation models.

Theoretical Foundations and Methodologies

Layer Freezing: Selective Parameter Updates

Layer freezing operates on the principle that different layers in a neural network capture different types of information. In transformer-based single-cell foundation models, earlier layers often learn general gene interaction patterns, while later layers capture more task-specific features [43]. Strategic freezing preserves generally useful representations while allowing specialization in higher layers.

Implementation Spectrum:

  • Full Freezing: Only the task-specific head (e.g., classifier) is trainable
  • Partial Freezing: Selective layers (typically earlier ones) remain frozen
  • Adaptive Freezing: Gradual freezing during training based on convergence metrics

The core challenge lies in determining which layers to freeze and when. As noted in benchmarking studies, improper freezing strategies can significantly degrade model performance, particularly when the target task diverges substantially from the pretraining domain [43].

Low-Rank Adaptation: Efficient Parameter Updates

LoRA exploits the hypothesis that weight updates during fine-tuning have low "intrinsic rank" [44]. Instead of modifying the original weight matrices ( W \in \mathbb{R}^{d \times k} ), LoRA represents weight updates with a low-rank decomposition ( BA ), where ( B \in \mathbb{R}^{d \times r} ), ( A \in \mathbb{R}^{r \times k} ), and ( r \ll min(d,k) ). The forward pass becomes:

[ h = Wx + BAx ]

where ( W ) remains frozen, and only ( A ) and ( B ) are trainable [45]. For single-cell foundation models, this approach preserves the pretrained biological knowledge while efficiently adapting to new tasks.

Advanced LoRA Variants for Single-Cell Applications

Recent research has developed sophisticated LoRA variants specifically enhancing single-cell model adaptation:

AFLoRA (Adaptive Freezing of Low-Rank Adaptation) introduces incremental freezing of LoRA matrices during fine-tuning based on a novel freezing score, reducing computation and alleviating overfitting [44]. The method incorporates trainable feature transformation vectors alongside the projection matrices, with the complete operation for a layer ( l ) described as:

[ Y = W0^l X + \Lambdab^l B^l \Lambda_d^l A^l X ]

where ( \Lambdab^l ) and ( \Lambdad^l ) are the trainable transformation vectors [44].

La-LoRA (Layer-wise Adaptive Low-Rank Adaptation) dynamically allocates ranks to different layers based on their contribution to the overall performance, employing a Dynamic Contribution-Driven Parameter Budget (DCDPB) and Truncated Norm Weighted Dynamic Rank Allocation (TNW-DRA) [46]. This approach recognizes that uniform rank allocation across layers is suboptimal, as different layers contribute unequally to final performance.

Experimental Comparison and Performance Analysis

Quantitative Benchmarking on Foundation Models

Experimental evaluations across multiple single-cell tasks demonstrate the comparative advantages of different PEFT approaches. The following table summarizes key performance metrics from recent studies:

Table 1: Performance Comparison of PEFT Methods on Single-Cell Foundation Models

Method % Trainable Parameters Cell Type Annotation (Accuracy) Perturbation Prediction (AUPRC) Spatial Label Prediction (F1) Training Efficiency (Relative Speed)
Full Fine-Tuning 100% 94.2% 0.891 0.872 1.0×
Layer Freezing (Top-2) 18% 93.8% 0.885 0.869 1.7×
Standard LoRA 0.5-2% 95.1% 0.902 0.891 2.3×
AFLoRA 0.07% 96.2% 0.919 0.901 3.2×
La-LoRA 0.05-0.1% 96.8% 0.925 0.910 3.5×

Data compiled from [44] [41] [46]

Table 2: Task-Specific Performance on GLUE Benchmark for NLP-Based Single-Cell Models

Method #Params. (M) CoLA (Matthew's corr) SST-2 (Acc) MRPC (F1) RTE (Acc) Avg. Score
Full Fine-Tuning 184 69.21 95.64 89.22 82.49 87.82
LoRA (r=8) 1.33 69.73 95.57 89.71 85.32 88.38
AdaLoRA 1.27 70.86 95.95 90.22 87.36 88.83
AFLoRA (r=4) 0.14 72.01 96.22 91.91 88.09 89.23

Reproduced from [44]

Computational Efficiency and Resource Requirements

For researchers working with large-scale single-cell data, computational efficiency is paramount. Recent benchmarking reveals significant differences in resource utilization:

Table 3: Computational Requirements for Different Fine-Tuning Approaches

Method Memory Usage (GB) Training Time (Hours) Storage Overhead (MB) Inference Latency (ms)
Full Fine-Tuning 15.8 4.2 1200 12.3
Layer Freezing 9.3 2.5 1200 12.3
Standard LoRA 5.1 1.8 15 12.5
AFLoRA 4.7 1.3 12 12.4

Data from [44] [41] [12]

AFLoRA demonstrates particularly impressive efficiency gains, yielding up to ( 1.86\times ) improvement in runtime and ( 2.96\times ) reduction in FLOPs compared to alternatives while requiring ( 9.5\times ) fewer average trainable parameters than standard LoRA [44].

Experimental Protocols and Implementation Guidelines

Standardized Benchmarking Workflow

To ensure reproducible comparisons between fine-tuning strategies, researchers should adhere to standardized experimental protocols. The following diagram illustrates a comprehensive benchmarking workflow:

G Figure 1: Single-Cell Foundation Model Fine-Tuning Benchmarking Workflow cluster_Training Training Phase cluster_Evaluation Evaluation Phase Start Start: Select Pretrained Foundation Model (scGPT, Geneformer, Nicheformer) DataPrep Data Preparation & Splitting (Task-specific single-cell datasets) Start->DataPrep MethodConfig Method Configuration (Layer Freezing, LoRA, AFLoRA, La-LoRA) DataPrep->MethodConfig FreezingSetup Layer Freezing Setup (Determine frozen/trainable layers) MethodConfig->FreezingSetup LoRASetup LoRA Configuration (Rank selection, matrix placement) MethodConfig->LoRASetup TrainingExec Training Execution (Fixed epochs, early stopping) FreezingSetup->TrainingExec LoRASetup->TrainingExec Metrics Comprehensive Metrics Collection (Accuracy, Efficiency, Resource Use) TrainingExec->Metrics Comparison Comparative Analysis (Statistical testing, effect sizes) Metrics->Comparison Documentation Results Documentation & Reporting Comparison->Documentation

Key Configuration Parameters

Successful implementation requires careful attention to method-specific parameters:

For Layer Freezing:

  • Freezing Strategy: Top-layer only, bottom-layer only, or alternating patterns
  • Unfreezing Scheduling: Progressive unfreezing vs. static freezing
  • Learning Rate Differentiation: Different rates for frozen vs. unfrozen layers

For LoRA and Variants:

  • Rank Selection: Typically ranges from 4-32 for single-cell models
  • Matrix Placement: Attention layers (query, value, key, output) and/or MLP layers
  • Alpha Parameter: Scaling factor for low-rank updates (often set to rank)
  • Dropout: Regularization within LoRA components (typically 0.1)

For Advanced Variants:

  • AFLoRA: Requires setting initial training epochs before freezing and freezing score threshold
  • La-LoRA: Needs contribution measurement interval and rank reallocation schedule

Task-Specific Implementation Considerations

Different single-cell tasks benefit from specialized configurations:

Cell Type Identification: LoRA typically outperforms layer freezing, with optimal rank between 8-16 applied to attention mechanisms and MLP layers [41].

Perturbation Prediction: AFLoRA shows particular advantages, with adaptive freezing preventing overfitting to limited perturbation data [47].

Spatial Composition Prediction: Integrated approaches that combine LoRA with minimal layer unfreezing deliver optimal performance for spatially-aware tasks [40].

The Scientist's Toolkit: Essential Research Reagents

Table 4: Essential Computational Tools for Single-Cell PEFT Research

Tool/Resource Type Primary Function Application in PEFT Research
scGPT Foundation Model Single-cell representation learning Base model for PEFT evaluations and benchmarking
Hugging Face PEFT Library Software Library PEFT method implementations Provides standardized LoRA, prefix tuning, and other PEFT methods
CellFM Foundation Model Human cell transcriptomics Large-scale model (800M parameters) for testing scalability
Nicheformer Foundation Model Spatial single-cell analysis Evaluating spatial task adaptation
Scanpy Data Processing Single-cell data analysis Dataset preprocessing and evaluation metrics calculation
LoRA Matrix Modules Custom Code Low-rank adaptation layers Modifying foundation model architectures for efficient tuning

Compiled from [44] [40] [41]

Based on comprehensive experimental evidence, we recommend:

  • For most single-cell classification tasks (cell type identification, disease state prediction): Implement LoRA or AFLoRA with rank 8-16, as these methods consistently outperform layer freezing while requiring significantly fewer trainable parameters.

  • For resource-constrained environments or extremely small datasets: La-LoRA provides the optimal balance of performance and efficiency, dynamically allocating parameters where they provide greatest impact.

  • When adapting to fundamentally novel domains: Consider hybrid approaches that combine selective layer unfreezing with LoRA, particularly when the target task significantly diverges from the pretraining domain.

  • For production systems requiring multiple specialized models: Standard LoRA offers the best balance of performance, efficiency, and implementation simplicity.

The rapid evolution of PEFT methodologies continues to enhance our ability to adapt single-cell foundation models to specialized tasks. AFLoRA and La-LoRA represent the cutting edge, demonstrating that adaptive, dynamic approaches outperform static fine-tuning strategies across most biological applications. As single-cell foundation models grow in size and complexity, these parameter-efficient approaches will become increasingly essential tools in computational biology.

Drug resistance remains a significant barrier to improving the effectiveness of cancer therapies, with many treatments showing modest response rates. [25] Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in drug responses but introduces challenges due to its high dimensionality, sparsity, and technical variability. [25] [3] Single-cell foundation models (scFMs), pre-trained on massive datasets, offer a promising solution by learning universal biological knowledge, enabling them to adapt to various downstream tasks like drug response prediction through transfer learning. [25] [3] [48] However, with multiple scFMs now available, their relative performance remains unclear. This guide provides an objective, data-driven comparison of leading scFMs, detailing their performance, optimal use cases, and practical experimental protocols to inform researchers and drug development professionals.

Comparative Performance of Leading scFMs

The table below synthesizes key performance metrics from major benchmarking studies, evaluating top scFMs on drug response prediction and related tasks.

Table 1: Benchmarking Performance of Single-Cell Foundation Models

Model Name Primary Task Evaluated Reported Performance (F1 Score/Correlation) Key Strengths Noted Limitations
scFoundation [25] Drug Response Prediction (Pooled-data) 0.971 (mean F1, layer-freezing); 0.947 (mean F1, fine-tuning) Excels in pooled-data evaluation scenarios. Performance can vary in cross-data evaluation. [25]
scGPT [25] Drug Response Prediction (Zero-shot) 0.858 (mean F1, zero-shot) Superior zero-shot learning capabilities; useful for multi-omics integration. [25]
UCE [25] Drug Response Prediction (Cross-data, fine-tuned) 0.774 (mean F1, fine-tuned on tumor tissue) High performance after fine-tuning on specific tissues like tumor. [25]
Geneformer [3] [48] General Cell-level & Perturbation Tasks Competitive, but no single model dominates all tasks. [3] Proven capability in predicting gene dosage sensitivity and chromatin dynamics. [25] Zero-shot embeddings show limited improvement for perturbation prediction in some benchmarks. [5]
scBERT [25] Drug Response Prediction ~0.630 (mean F1, lowest performer in one benchmark) Effective for cell type annotation. [3] Lower performance in certain drug response prediction tasks. [25]
CRISP Framework [48] Perturbation Response in Unseen Cell Types 41% improvement in Pearson correlation vs. baselines Specialized for zero-shot prediction on unseen cell types/drugs; integrates various scFMs. A specialized framework, not a base scFM.

Experimental Protocols and Evaluation Methodologies

Understanding the experimental design behind these benchmarks is crucial for interpreting the results and applying them to new research.

scDrugMap Benchmarking Framework

The scDrugMap framework conducted a comprehensive evaluation of ten foundation models (eight single-cell specific, two LLMs) under distinct scenarios. [25]

  • Data Curation: The study used a primary collection of 326,751 cells from 36 datasets and a validation collection of 18,856 cells from 17 datasets, spanning diverse cancer types, tissues, and treatment regimens. [25]
  • Evaluation Scenarios:
    • Pooled-data evaluation: Models were trained and tested on aggregated data from multiple studies. This tests a model's ability to discern signal in a large, heterogeneous dataset.
    • Cross-data evaluation: Models were trained on one set of studies and tested independently on datasets from held-out studies. This tests generalizability and robustness to batch effects and unseen biological conditions. [25]
  • Training Strategies:
    • Layer Freezing: Using the pre-trained model as a fixed feature extractor.
    • Fine-tuning with LoRA: Applying Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, to adapt the pre-trained weights to the specific task. [25]

Biology-Driven Benchmarking

Another large-scale benchmark assessed six scFMs against traditional baselines using biologically informed metrics. [3] [4]

  • Tasks: Included both gene-level (e.g., predicting gene function) and cell-level tasks (e.g., batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction). [3]
  • Novel Metrics:
    • scGraph-OntoRWR: Measures the consistency of cell-type relationships captured by the model with prior biological knowledge from cell ontologies.
    • Lowest Common Ancestor Distance (LCAD): Assesses the severity of cell type annotation errors by measuring their proximity in the ontology hierarchy. [3]
  • Key Finding: No single scFM consistently outperformed all others across every task. The choice of the best model depends on the specific task, dataset size, and the need for biological interpretability. [3] [4]

The CRISP Framework for Unseen Cell Types

The CRISP framework was specifically designed to predict drug responses in previously unseen cell types, a major challenge in drug repurposing. [48]

  • Core Methodology: CRISP uses an scFM to encode control cell states and a chemical model to represent drugs. It then learns a cell-type-specific transformation map to predict the perturbed state from the control state embedding. [48]
  • Training: Employs a specialized strategy with cell-type-specific classifiers and contrastive learning to capture divergent drug responses across different cell types. [48]
  • Evaluation: Was tested on predicting responses for held-out cell types and drugs, showing a 24.5% average performance improvement over existing methods. [48]

The following diagram illustrates the core workflow of the CRISP framework for predicting perturbation responses in unseen cell types.

G Control Cells\n(Unseen Type) Control Cells (Unseen Type) scFM\n(e.g., scGPT, Geneformer) scFM (e.g., scGPT, Geneformer) Control Cells\n(Unseen Type)->scFM\n(e.g., scGPT, Geneformer) Drug Compound Drug Compound Chemical Embedding\nModel Chemical Embedding Model Drug Compound->Chemical Embedding\nModel Control Cell\nPre-embedding Control Cell Pre-embedding scFM\n(e.g., scGPT, Geneformer)->Control Cell\nPre-embedding Drug Embedding Drug Embedding Chemical Embedding\nModel->Drug Embedding CRISP Core\n(Transformation Map) CRISP Core (Transformation Map) Control Cell\nPre-embedding->CRISP Core\n(Transformation Map) Drug Embedding->CRISP Core\n(Transformation Map) Predicted Perturbed\nCell State Predicted Perturbed Cell State CRISP Core\n(Transformation Map)->Predicted Perturbed\nCell State

The Scientist's Toolkit: Essential Research Reagents

This table details the key computational tools and data resources central to benchmarking scFMs for drug response prediction.

Table 2: Key Reagents for scFM Drug Response Research

Tool / Resource Type Primary Function in Research
scDrugMap [25] Integrated Framework Provides a unified platform (CLI & web server) for benchmarking and applying multiple scFMs to drug response prediction.
CRISP [48] Prediction Framework A specialized framework designed for zero-shot prediction of drug responses in unseen cell types by leveraging scFMs.
LoRA (Low-Rank Adaptation) [25] Fine-tuning Method A parameter-efficient method for adapting large pre-trained models to specific tasks without full fine-tuning.
Curated Primary Dataset (scDrugMap) [25] Data Resource A collection of 326,751 single cells from 23 studies, used for training and pooled-data evaluation.
Curated Validation Dataset (scDrugMap) [25] Data Resource An external set of 18,856 cells from 6 studies, used for testing model generalizability.
PertEval-scFM [5] Benchmarking Framework A standardized framework for evaluating zero-shot scFM embeddings on perturbation effect prediction.
scGraph-OntoRWR [3] Evaluation Metric A novel biology-driven metric that evaluates scFMs by comparing learned cell relationships to established ontologies.

Decision Workflow and Future Directions

The following diagram summarizes the key decision points for researchers when selecting and applying an scFM for drug response prediction, based on the benchmarking insights.

G Start Start: Drug Response Prediction Task Q_Data Is your primary goal to predict responses in UNSEEN cell types? Start->Q_Data Q_Data_Avail Is a large, aggregated training dataset available? Q_Data->Q_Data_Avail No Model_CRISP Consider the CRISP framework built on a suitable scFM (e.g., scGPT-cancer) Q_Data->Model_CRISP Yes Q_ZeroShot Is a zero-shot prediction capability required? Q_Data_Avail->Q_ZeroShot No Model_scFoundation Select scFoundation (for pooled-data scenario) Q_Data_Avail->Model_scFoundation Yes Model_scGPT Select scGPT (for zero-shot capability) Q_ZeroShot->Model_scGPT Yes Model_Test Test multiple scFMs (e.g., UCE, Geneformer) with fine-tuning (LoRA) on your data Q_ZeroShot->Model_Test No

The Path Forward

Future development of scFMs must address several key areas. There is a need for specialized models and higher-quality datasets that capture a broader range of cellular states to improve performance, particularly in zero-shot and perturbation prediction settings. [5] Furthermore, the development and adoption of standardized, biologically meaningful evaluation metrics—like scGraph-OntoRWR and pathway impact metrics—are crucial to ensure that model improvements translate to real biological and clinical insights. [3] [49] As the field matures, collaboration between computational scientists and biological domain experts will be essential to build the next generation of scFMs that are not only powerful but also truly interpretable and reliable for critical drug discovery applications. [49]

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the characterization of gene expression at the individual cell level. This high-resolution view reveals cellular heterogeneity, identifies rare cell populations, and elucidates developmental trajectories that are obscured in bulk sequencing approaches. However, the analysis of scRNA-seq data presents unique computational challenges, particularly in two critical areas: accurate cell type annotation and effective batch integration. Cell type annotation involves classifying individual cells into known biological categories based on their gene expression profiles, while batch integration addresses unwanted technical variations that arise when combining datasets from different experiments, protocols, or laboratories.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology. These large-scale models, pre-trained on millions of cells, aim to learn universal representations of cellular states that can be adapted to various downstream tasks. Unlike traditional methods designed for specific analytical tasks, scFMs leverage transfer learning to apply knowledge gained from vast datasets to new, smaller-scale experiments. This review provides a comprehensive comparison of these innovative approaches against established computational methods, focusing specifically on their performance in cell type annotation and batch integration tasks within the broader context of single-cell foundation model benchmarking research.

Performance Benchmarking of Analytical Methods

Performance Metrics for Method Evaluation

Rigorous benchmarking requires multiple complementary metrics to evaluate different aspects of performance. For batch integration, key metrics include the k-nearest-neighbor batch effect test (kBET), which quantifies batch mixing; graph connectivity, which assesses whether similar cell types from different batches form connected neighborhoods; and average silhouette width (ASW), which measures separation between batches versus within batches [50]. Biological conservation is equally important and can be evaluated using metrics such as normalized mutual information (NMI) for cell-type label conservation, trajectory conservation scores for developmental processes, and cell-cycle variance conservation [50].

For cell type annotation, standard metrics include overall accuracy, weighted accuracy (accounting for similarity between cell types), and F1 scores (balancing precision and recall) [51]. Particularly important is performance on rare cell populations, which can be evaluated using isolated label scores that measure how well methods identify cell types with limited representation [50].

Benchmarking Results for Batch Integration

Table 1: Performance Comparison of Batch Integration Methods

Method Category Representative Methods Best For Key Strengths Performance Notes
Global Models ComBat Simple batch correction Fast, proven track record with bulk RNA-seq Tends to overcorrect with complex batch effects [52]
Linear Embedding Models Harmony, Seurat, Scanorama Simple to moderate complexity tasks Good balance of speed and performance Harmony performs well on less complex tasks [50] [52]
Graph-based Methods BBKNN Large datasets Computational efficiency, fast runtime May struggle with highly nested batch effects [52]
Deep Learning Approaches scVI, scANVI, scGen Complex integration tasks Handle nested batch effects, large datasets scANVI (with labels) and scVI perform best on complex atlas-level tasks [50] [52]
Foundation Models scGPT, CellFM Diverse tasks with transfer learning Leverage pre-training on massive datasets Robust and versatile but not always superior to traditional DL approaches [4]

Recent large-scale benchmarking studies have provided crucial insights into method selection. A comprehensive evaluation of 16 integration methods across 13 integration tasks representing over 1.2 million cells found that performance varies significantly with task complexity [50]. For simpler tasks with minimal biological confounding, Harmony and Seurat consistently perform well. However, for complex integration challenges such as atlas-level data with nested batch effects (where batches contain different cell type compositions), deep learning methods like scVI and its supervised counterpart scANVI demonstrate superior performance, particularly when cell-type labels are available [50] [52].

Single-cell foundation models have shown particular promise in batch integration tasks. A 2025 benchmark evaluating six scFMs against established baselines found that these models are "robust and versatile tools for diverse applications" [4]. However, the study also noted that "simpler machine learning models are more adept at efficiently adapting to specific datasets, particularly under resource constraints," highlighting the importance of context-dependent method selection [4].

Benchmarking Results for Cell Type Annotation

Table 2: Performance Comparison of Cell Type Annotation Methods for scATAC-seq Data

Method Modality Overall Accuracy Handling of ATAC-specific Cell Types Scalability
Bridge Integration Cross-modality (requires multiome data) High for human tissues Robust performance Moderate [51]
scJoint Cross-modality High for mouse tissues Tends to assign cells to similar types Good [51]
Seurat v3 Intra-modality Moderate Moderate performance Good [51]
scGCN Intra-modality Variable Poor performance for unique types Time-consuming [51]
Conos Intra-modality Lower than alternatives Not specified Most time and memory efficient [51]

Cell type annotation methods demonstrate more variable performance across different tissues and species. A benchmark of five annotation tools for scATAC-seq data revealed that Bridge integration, which uses multi-modal data as a "bridge" between scRNA-seq and scATAC-seq datasets, generally achieves the highest accuracy for human tissues, while scJoint performs best for mouse tissues [51]. Notably, the performance of methods that transfer labels from scRNA-seq to scATAC-seq data (such as Seurat v3 and Conos) depends heavily on accurate gene activity estimation from chromatin accessibility data, introducing a potential source of error [51].

Single-cell foundation models have demonstrated competitive performance in cell type annotation tasks. Models like scBERT and scGPT leverage transfer learning from large-scale pre-training to generate context-aware cell representations that can be fine-tuned for annotation with limited labeled data [4]. However, benchmarking reveals that "no single scFM consistently outperforms others across all tasks," emphasizing the need for researchers to select models based on specific factors such as dataset size, biological interpretability requirements, and computational resources [4].

Experimental Protocols for Benchmarking Studies

General Benchmarking Framework

Reproducible benchmarking of computational methods requires standardized protocols across several key phases. The workflow begins with data collection and preprocessing, where datasets with known ground truth (through simulation or expert annotation) are gathered. For batch integration benchmarks, this typically includes both simulated data, where the true biological signals and batch effects are explicitly defined, and real datasets with carefully annotated cell identities [50]. Preprocessing steps like highly variable gene selection and appropriate normalization have been shown to significantly impact method performance [50].

The integration phase involves running each method with multiple preprocessing combinations (e.g., with/without scaling, with/without highly variable gene selection) to ensure fair comparison. For a comprehensive assessment, methods should be evaluated across diverse integration tasks varying in complexity, number of batches, and cell-type composition [50].

The evaluation phase employs multiple complementary metrics assessing both batch effect removal and biological conservation. As emphasized in the scIB pipeline, "integration accuracy was evaluated using 14 performance metrics divided into two categories: removal of batch effects and conservation of biological variance" [50]. This dual focus prevents overcorrection, where batch effects are removed at the expense of genuine biological signal.

Specialized Protocols for Perturbation Prediction

For evaluating perturbation response prediction, specialized benchmarks like PertEval-scFM have been developed. This framework specifically assesses "zero-shot single-cell foundation model embeddings against baseline models to assess whether these contextualized representations enhance perturbation effect prediction" [5]. The protocol involves obtaining embeddings from pre-trained scFMs without additional fine-tuning, then training simple models on these representations to predict transcriptional responses to genetic or chemical perturbations.

Recent results from such benchmarks indicate that "scFM embeddings offer limited improvement over simple baseline models in the zero-shot setting, particularly under distribution shift" [5]. This highlights the importance of specialized evaluation protocols that test model capabilities under realistic conditions, including out-of-distribution predictions that simulate real-world scenarios where models encounter cell types or conditions not present in their training data.

G cluster_0 Data Preparation Phase cluster_1 Method Application Phase cluster_2 Evaluation Phase D1 Public Data Collection (Simulated & Real) D2 Ground Truth Definition (Expert Annotation) D1->D2 D3 Data Preprocessing (Normalization, HVG Selection) D2->D3 M1 Method Execution (Multiple Preprocessing Combinations) D3->M1 D3->M1 M2 Output Generation (Embeddings, Corrected Matrices) M1->M2 E1 Batch Effect Removal Metrics (kBET, iLISI, ASW) M2->E1 M2->E1 E2 Biological Conservation Metrics (ARI, NMI, Trajectory) M2->E2 M2->E2 E3 Overall Performance Scoring (Weighted Combination) E1->E3 E2->E3

Figure 1: Workflow for Benchmarking Single-Cell Analysis Methods. The process involves three main phases: data preparation with ground truth establishment, method application with multiple preprocessing combinations, and comprehensive evaluation using both batch removal and biological conservation metrics.

Research Reagent Solutions for Single-Cell Analysis

The computational methods discussed rely on various "research reagents" in the form of software tools, packages, and frameworks. Understanding this ecosystem is crucial for implementing the analytical approaches described in this review.

Table 3: Essential Research Reagent Solutions for Single-Cell Analysis

Tool/Package Primary Function Key Features Access
scIB Python Module [50] Integration benchmarking 14 performance metrics, standardized pipeline Open source
PertEval-scFM [5] Perturbation prediction evaluation Zero-shot scFM evaluation framework Open source (GitHub)
Scanorama [50] [52] Batch integration High performance on complex tasks, embedding output Open source
scVI/scANVI [50] [52] Deep learning integration Handles nested batch effects, uses cell labels (scANVI) Open source
Bridge Integration [51] Cross-modality annotation Leverages multiome data, avoids gene activity calculation Open source (Seurat)
Trailmaker [53] End-to-end analysis platform Cloud-based, no coding required, automated workflow Free for academics
CellxGene VIP [54] Data visualization Interactive exploration, quality control plots Open source

The table above highlights key computational tools that serve as essential reagents in single-cell analysis workflows. Platforms like Trailmaker and CellxGene VIP provide user-friendly interfaces that democratize access to advanced analytical capabilities for researchers without extensive computational backgrounds [53] [54]. These tools typically support standard data formats such as 10X Genomics outputs, H5 files, and Seurat objects, ensuring compatibility with most experimental pipelines.

For method developers and advanced users, benchmarking pipelines like scIB provide critical infrastructure for rigorous method evaluation [50]. This Python module implements 14 distinct metrics for assessing integration performance and has been used in large-scale benchmarking studies evaluating up to 68 integration method and preprocessing combinations [50]. Similarly, specialized frameworks like PertEval-scFM enable standardized assessment of perturbation prediction capabilities, an increasingly important task in therapeutic development [5].

G cluster_0 Input Data Types cluster_1 Annotation Approaches cluster_2 Output & Evaluation D1 scRNA-seq Data M1 Reference-Based (Label Transfer) D1->M1 M3 Foundation Models (e.g., scGPT, scBERT) D1->M3 D2 scATAC-seq Data M2 Cross-Modality (e.g., Bridge Integration) D2->M2 D3 Multiome Data (RNA + ATAC) D3->M2 O1 Cell Type Labels M1->O1 O2 Annotation Confidence Scores M1->O2 M2->O1 M2->O2 M3->O1 M3->O2 O3 Accuracy Assessment (Overall, Rare Populations) O1->O3 O2->O3

Figure 2: Cell Type Annotation Methods and Evaluation Framework. This diagram illustrates the three main approaches to cell type annotation (reference-based, cross-modality, and foundation models), their required input data types, and the evaluation metrics used to assess annotation quality.

The benchmarking studies summarized in this review demonstrate that both traditional methods and emerging foundation models have distinct strengths and optimal application scenarios for cell type annotation and batch integration. While single-cell foundation models show remarkable versatility and robustness across diverse tasks, they do not consistently outperform well-established traditional methods in all scenarios. The selection of an appropriate method should be guided by multiple factors, including dataset size, computational resources, task complexity, and the need for biological interpretability.

As the single-cell field continues to evolve with increasingly complex datasets and analytical challenges, rigorous benchmarking remains essential for guiding methodological development and application. Future advances will likely come from specialized models tailored to specific biological questions and improved integration of multi-modal data types. The computational "reagent solutions" outlined in this review provide researchers with essential tools to implement these advanced analytical approaches and drive discoveries in basic biology and therapeutic development.

Navigating Practical Challenges: A Guide to scFM Selection and Performance Optimization

The emergence of single-cell RNA sequencing (scRNA-seq) has revolutionized biology by enabling transcriptomic profiling at unprecedented resolution, uncovering cellular heterogeneity with remarkable precision [55]. This technological advancement has prompted the development of computational tools specifically designed to analyze the complex, high-dimensional data generated. However, single-cell data analysis suffers from inherent technical challenges, including substantial noise, batch effects, and significant sparsity [55]. To address these limitations, the field has recently turned to foundation models—large-scale machine learning models pre-trained on massive datasets—with the promise of providing a unified framework for analyzing cellular states.

While these single-cell foundation models (scFMs) represent a significant breakthrough, a crucial theoretical concept from computational learning theory tempers expectations about their universal applicability: the No-Free-Lunch (NFL) Theorem. Originally formulated by David Wolpert and William Macready, the NFL theorem states that for certain types of mathematical problems, the computational cost of finding a solution, averaged over all problems in the class, is the same for any solution method [56]. In essence, this means that no single algorithm can outperform all others across every possible problem domain. When applied to scFMs, this theorem provides a mathematical foundation for understanding why, despite their impressive capabilities, no single foundation model can possibly dominate across all analytical tasks in single-cell biology.

Understanding the No-Free-Lunch Theorem

Theoretical Foundation

The No-Free-Lunch theorem, in its most general form, establishes that when averaged across all possible problems, all optimization algorithms perform equally well [57]. Wolpert and Macready's seminal 1997 paper demonstrated that "any two optimization algorithms are equivalent when their performance is averaged across all possible problems" [58]. This counterintuitive result has profound implications for machine learning and optimization, suggesting that without prior knowledge of the problem domain, no algorithm has inherent superiority.

The theorem's mathematical formulation reveals that for any pair of algorithms a₁ and a₂: [ \sum{f}P(d{m}^{y} \mid f,m,a{1}) = \sum{f}P(d{m}^{y} \mid f,m,a{2}) ] where (d{m}^{y}) represents a sequence of (m) values in the course of optimization, and (P(d{m}^{y} \mid f,m,a)) is the probability of observing that sequence given objective function (f), iteration step (m), and algorithm (a) [58]. This equality holds when summing over all possible objective functions (f), leading to the conclusion that all algorithms have identically distributed performance when objective functions are drawn uniformly at random.

Implications for Machine Learning and Biological Applications

For machine learning practitioners, the NFL theorem translates to a sobering reality: there is no universally best learning algorithm [57]. As philosopher David Hume pointed out centuries earlier, inductive reasoning from past observations does not guarantee future predictive accuracy without making assumptions about the problem structure [59]. In the context of single-cell biology, this means that the performance of any scFM is inherently tied to characteristics of the training data and the specific biological questions being asked.

The NFL theorem does not render algorithm development futile but rather emphasizes that superior performance on one class of problems must be paid for with inferior performance on another class [56]. This "conservation of performance" across problem domains has direct relevance for scFM development, as it suggests that models optimized for specific biological contexts (e.g., specific tissues, species, or experimental conditions) will inevitably underperform on tasks outside their training distribution.

The Landscape of Single-Cell Foundation Models

The rapid advancement of scRNA-seq technologies has spurred development of numerous foundation models with varied architectural approaches and training strategies. Current models can be broadly categorized into three paradigms based on how they represent gene expression data:

  • Gene-ranking-based models (e.g., Geneformer [55], tGPT [55]) treat single-cell data as sequences of genes ordered by expression levels, leveraging transformer architectures to learn contextual relationships.
  • Value categorization models (e.g., scBERT [55], scGPT [55]) discretize continuous gene expression values into "buckets" or categories, transforming regression problems into classification tasks.
  • Value projection models (e.g., CellFM [55], scFoundation [55]) preserve the full resolution of expression data by using projection layers to embed raw counts or normalized values.

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

Model Parameters Training Data Architecture Type Key Features
CellFM [55] [60] 800 million 100 million human cells Value Projection Modified RetNet framework; MindSpore implementation
scGPT [55] Not specified 33 million human cells Value Categorization Attention mask mechanism; self-supervised learning
Geneformer [55] Not specified 30 million human cells(human & mouse) Gene Ranking Pretrained on gene ranks; transfer learning
scFoundation [55] ~100 million ~50 million human cells Value Projection Masked autoencoder; predicts raw expression
UCE [55] 650 million 36 million cells(multiple species) Value Categorization Cross-species integration; protein language models

Case Study: CellFM - Scale and Limitations

CellFM represents one of the most ambitious scFM efforts to date, with 800 million parameters trained on a massive dataset of 100 million human cells [55]. The model employs a modified RetNet framework designed to balance computational efficiency with performance, utilizing ERetNet Layers with Gated Multi-head Attention and Simple Gated Linear Units [55]. During pre-training, CellFM aims to recover vector embeddings of masked genes derived from linear projections based on gene expression values, categorizing it as a value-projection approach [55].

Despite its impressive scale, CellFM's developers acknowledge limitations common to many foundation models. The model struggles with data quality issues, batch effects, and generalizability to rare cell types or disease states not well-represented in its training corpus [55]. These limitations align with NFL predictions—even models of unprecedented scale cannot escape the fundamental tradeoffs between performance on different problem types.

Benchmarking scFMs: Quantitative Evidence for the No-Free-Lunch Phenomenon

The PertEval-scFM Benchmark

Recent systematic benchmarking efforts provide empirical validation of the NFL theorem in the context of scFMs. The PertEval-scFM framework was specifically designed to evaluate models for perturbation effect prediction—a crucial task in drug development and functional genomics [61]. This standardized benchmark assesses zero-shot scFM embeddings against simpler baseline models to determine whether these contextualized representations genuinely enhance predictive performance.

The results from PertEval-scFM reveal a striking pattern: scFM embeddings do not provide consistent improvements over baseline models for perturbation effect prediction [61]. Furthermore, all models struggled with predicting strong or atypical perturbation effects, and performance degradation was particularly pronounced under distribution shift—when test conditions differed substantially from training data [61]. This finding directly demonstrates the NFL principle in action, as scFMs optimized for general single-cell analysis fail to maintain superiority on specialized tasks like perturbation prediction.

Cross-Model Performance Comparison

Comprehensive evaluation across multiple analytical tasks reveals the variable performance that NFL predicts. While CellFM reportedly outperforms existing models in cell annotation, gene function prediction, and gene-gene relationship capturing [55], this superiority comes with tradeoffs. The PertEval findings indicate that for perturbation prediction, simpler models often compete effectively with or even surpass foundation models, particularly in data regimes with limited samples or strong distribution shifts [61].

Table 2: Relative Model Performance Across Different Task Types

Task Type Best Performing Model Type Key Limitations
Cell Type Annotation Large scFMs (e.g., CellFM) [55] Struggles with rare/novel cell types
Perturbation Effect Prediction Simple baselines competitive with scFMs [61] Performance degrades with distribution shift
Gene Function Prediction Large scFMs (e.g., CellFM) [55] Limited by training data quality and coverage
Gene-Gene Relationship Capture Value projection models [55] Sensitive to technical artifacts in data

This performance variability directly illustrates the NFL theorem's central premise: elevated performance on one class of problems (e.g., cell annotation) is exactly paid for in performance on other problem classes (e.g., perturbation prediction) [56]. The architectural choices and training objectives that enable a model to excel at recognizing established cell types may simultaneously limit its flexibility for predicting novel cellular responses to genetic or chemical perturbations.

Experimental Protocols for scFM Benchmarking

Standardized Evaluation Framework

Robust benchmarking of scFMs requires carefully designed experimental protocols that control for confounding factors and enable fair comparisons across models. The SimBench framework, originally developed for evaluating scRNA-seq simulation methods, provides a template for comprehensive assessment [62]. Adapted for foundation model evaluation, this approach involves:

  • Dataset Curation: Collecting diverse scRNA-seq datasets representing various sequencing technologies, tissue types, and experimental conditions [62].
  • Data Preprocessing: Applying standardized quality control, normalization, and batch correction to ensure consistent input data [62].
  • Task-Specific Splitting: Partitioning data into training, validation, and test sets using appropriate strategies for each analytical task (e.g., stratified splitting by cell type for annotation tasks).
  • Performance Quantification: Employing multiple metrics tailored to each task type, with statistical tests to determine significance of observed differences.

For perturbation prediction specifically, PertEval-scFM implements a standardized pipeline where models are evaluated in zero-shot settings—predicting effects of unseen perturbations without task-specific fine-tuning [61]. This approach directly tests the generalizable biological knowledge encoded in the models' representations.

Critical Assessment Metrics

Different analytical tasks require specialized evaluation metrics to comprehensively assess model performance:

  • Cell Annotation: Accuracy, F1-score, balanced accuracy for imbalanced cell types
  • Perturbation Prediction: Mean squared error for continuous outcomes, area under ROC curve for binary outcomes, statistical significance of predicted effects
  • Gene Function Prediction: Enrichment in known functional pathways, precision-recall for gene set recovery
  • Batch Effect Correction: kBET index, graph connectivity, conservation of biological variance

The diagram below illustrates the comprehensive benchmarking workflow necessary for proper scFM evaluation:

architecture Diverse Datasets Diverse Datasets Data Preprocessing Data Preprocessing Diverse Datasets->Data Preprocessing Model Inference Model Inference Data Preprocessing->Model Inference Task Evaluation Task Evaluation Model Inference->Task Evaluation Performance Metrics Performance Metrics Task Evaluation->Performance Metrics NFL Conclusion NFL Conclusion Performance Metrics->NFL Conclusion

Benchmarking Workflow for scFM Evaluation

Computational Frameworks and Platforms

Implementing and evaluating scFMs requires specialized computational infrastructure and software frameworks. The leading models leverage diverse platforms and architectures:

  • MindSpore: Huawei's AI framework used for training CellFM, optimized for Ascend processors [55]
  • PyTorch/TensorFlow: Standard deep learning frameworks used for most other scFMs
  • HPC Clusters: Distributed computing systems with multiple NPUs/GPUs (e.g., Atlas800 servers with Ascend910 NPUs for CellFM training) [55]
  • RetNet Architecture: Modified transformer framework with linear complexity used in CellFM to enable training on massive cell populations [55]

High-quality training data is essential for performant scFMs. Key resources include:

  • Public Repositories: NCBI GEO, European Nucleotide Archive, Genome Sequence Archive, ImmPort [55]
  • Data Standardization Tools: SynEcoSys single-cell database for quality control, gene name standardization, and format unification [55]
  • Benchmark Datasets: Curated collections for specific tasks (e.g., perturbation datasets in PertEval-scFM) [61]

Table 3: Essential Research Reagents and Computational Tools

Resource Type Specific Examples Primary Function
Training Data 100M human cells (CellFM) [55] Model pre-training and foundation knowledge
Benchmark Data PertEval-scFM datasets [61] Standardized model evaluation and comparison
AI Framework MindSpore, PyTorch, TensorFlow [55] Model implementation and training infrastructure
Architecture Modified RetNet, Transformer variants [55] Neural network backbone for processing scRNA-seq data
Evaluation Metrics KDE statistic, accuracy, MSE [61] [62] Quantifying model performance across tasks

The No-Free-Lunch theorem provides a crucial theoretical framework for understanding the current landscape of single-cell foundation models. Rather than indicating a failure of scFM approaches, the performance variability observed across different analytical tasks reflects a fundamental mathematical truth: no single model can excel at all possible problems. This recognition is liberating rather than limiting—it encourages the development of specialized models tailored to specific biological questions and data contexts.

For researchers and drug development professionals, these insights suggest a pragmatic approach to scFM utilization:

  • Task-Aligned Model Selection: Choose foundation models based on their demonstrated strengths for specific analytical needs rather than assuming general superiority.
  • Specialized Fine-Tuning: Leverage pre-trained models as starting points for task-specific adaptation rather than expecting universal solutions.
  • Ensemble Approaches: Combine multiple specialized models to address diverse analytical needs within complex research pipelines.
  • Rigorous Validation: Implement comprehensive benchmarking using domain-relevant metrics before deploying scFMs in critical applications.

The future of single-cell foundation models lies not in pursuit of a mythical universal model, but in developing a diverse ecosystem of specialized tools, each optimized for particular biological contexts and analytical challenges. By embracing this nuanced understanding, the research community can more effectively harness the power of foundation models to advance our understanding of cellular biology and accelerate therapeutic development.

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, leveraging large-scale deep learning to interpret complex single-cell genomics data. Trained on millions of single-cell transcriptomes, these models learn universal biological patterns that can be adapted to various downstream tasks such as cell type annotation, perturbation analysis, and drug response prediction [63]. The "pre-train then fine-tune" paradigm allows scFMs to transfer knowledge from vast, diverse datasets to specific biological questions with minimal task-specific labeling [3] [63]. However, with an increasing diversity of available scFMs, researchers face significant challenges in selecting the most appropriate model for their specific research context, particularly when balancing performance requirements against computational constraints.

This guide objectively compares scFM performance through the critical lenses of dataset size, task complexity, and computational resources, synthesizing insights from recent comprehensive benchmarking studies. The evaluation reveals that no single scFM consistently outperforms all others across every scenario [3]. Instead, the optimal model selection depends on a careful consideration of these three interconnected factors, with simpler machine learning approaches sometimes providing more efficient solutions for specific, resource-constrained applications [3] [17].

Performance Comparison Across Key Factors

Benchmarking studies have systematically evaluated scFMs against traditional methods across diverse tasks. The table below summarizes key findings from these comprehensive evaluations, illustrating how model performance varies with task requirements and dataset characteristics.

Table 1: Performance Comparison of Single-Cell Foundation Models vs. Baseline Methods

Task Category Representative Tasks Top-Performing scFMs Competitive Baseline Methods Key Performance Insights
Cell-level Tasks Cell type annotation, Batch integration scGPT, Geneformer Seurat, Harmony, scVI scFMs show robust performance on novel cell types and complex batch effects [3]
Gene-level Tasks Gene function prediction, Tissue specificity scGPT, scFoundation Functional Representation of Gene Signatures (FRoGS) scFM gene embeddings capture biological relationships beyond corresponding RNA counts [3]
Perturbation Analysis Drug response, Genetic perturbation scVI PCA Traditional methods can outperform scFMs on certain perturbation tasks [17]
Clinical Prediction Cancer cell identification, Drug sensitivity scGPT, Geneformer Random Forest, XGBoost scFMs excel with complex, heterogeneous data; simpler models adapt better to small, focused datasets [3]

The Dataset Size Factor

The scale of available training data significantly influences scFM selection and performance. Benchmarking reveals a clear relationship between dataset size and the advantage of using foundation models versus simpler approaches.

Table 2: Model Selection Guidance by Dataset Size

Dataset Scale Recommended Approach Rationale Representative Models
Large-scale (>1M cells) Foundation Models scFMs leverage pre-training on diverse cellular contexts, capturing universal biological patterns [3] [63] scGPT, Geneformer, scFoundation
Medium-scale (10K-1M cells) scFMs with Fine-tuning Transfer learning from pre-trained scFMs provides performance boost without extensive computational cost [3] scVI, scGPT (with fine-tuning)
Small-scale (<10K cells) Traditional ML Methods Simple models adapt more efficiently to specific datasets with limited samples [3] Seurat, Harmony, PCA, Random Forest

Notably, large-scale pretraining enables scFMs to develop emergent capabilities such as zero-shot learning, where models can make predictions on novel cell types without task-specific training [3]. However, for studies with highly specific, limited data, traditional machine learning methods often provide more practical solutions without the computational overhead of adapting large foundation models [3].

The Task Complexity Dimension

Task complexity represents another critical dimension in model selection, with scFMs demonstrating particular strength in biologically complex scenarios that require integration of diverse knowledge.

Table 3: Task Complexity and Model Performance

Complexity Level Task Examples Optimal Model Type Performance Advantage
High Complexity Novel cell type discovery, Cross-tissue analysis, Rare cell identification Foundation Models Superior generalization and biological insight capture [3]
Medium Complexity Standard cell type annotation, Batch effect correction scFMs or Traditional Methods (context-dependent) scFMs provide robust performance; traditional methods sufficient for standard cases [3]
Low Complexity Well-defined perturbation prediction, Simple classification tasks Traditional Methods Comparable performance with greater efficiency [17]

For biologically intricate tasks like characterizing novel cell types or analyzing cross-tissue homogeneity, scFMs consistently outperform traditional methods. This advantage stems from their ability to capture complex gene-gene interactions and relational structures across diverse cellular contexts learned during large-scale pretraining [3]. Evaluation metrics like scGraph-OntoRWR, which measures consistency with established biological knowledge, confirm that scFMs better capture meaningful biological relationships compared to traditional approaches [3].

Computational Resource Considerations

Computational requirements vary significantly across models, creating practical constraints for researchers with limited resources.

Table 4: Computational Resource Requirements

Resource Aspect High-Resource scFMs Moderate-Resource Options Lightweight Alternatives
Training Cost Extensive pretraining requiring specialized infrastructure (weeks/months) [63] Transfer learning from existing models (days/weeks) Traditional ML methods (hours/days) [3]
Inference Cost Significant GPU memory for large models Moderate requirements for inference Minimal computational requirements
Storage Large model files (GBs) Moderate size Very small footprint
Representative Models scFoundation, Large scGPT variants scVI, Geneformer, Standard scGPT PCA, Seurat, Harmony [17]

The roughness index (ROGI) has been proposed as a practical proxy metric to evaluate model suitability for specific datasets without extensive benchmarking, helping researchers identify appropriate models based on their computational constraints [3]. This approach simplifies the model selection process while accounting for resource limitations.

Experimental Protocols in scFM Benchmarking

Standardized Evaluation Frameworks

Comprehensive benchmarking studies employ rigorous methodologies to ensure fair and informative comparisons between scFMs and baseline methods. The experimental pipeline typically follows a structured approach:

  • Data Curation and Preparation: Benchmarking begins with assembling diverse, high-quality datasets representing various biological conditions, technologies, and tissue types. These datasets are carefully selected to cover realistic research scenarios, including cross-tissue homogeneity and intra-tumor heterogeneity [3]. Standardized preprocessing ensures comparability across models.

  • Feature Extraction: For scFMs, evaluations typically use zero-shot cell and gene embeddings extracted from pre-trained models without additional fine-tuning. This approach tests the intrinsic quality of representations learned during pre-training [3]. Baseline methods employ their standard feature extraction protocols.

  • Task-Specific Evaluation: Models are evaluated across a hierarchy of tasks progressing from fundamental to complex biological questions. This includes:

    • Data Integration: Assessing batch effect removal while preserving biological variation using metrics like Integration Local Inverse Simpson's Index (iLISI) [17].
    • Cell Type Annotation: Evaluating accuracy on both common and novel cell types, with special metrics like Lowest Common Ancestor Distance (LCAD) to measure biological meaningfulness of errors [3].
    • Perturbation Analysis: Testing prediction of cellular responses to genetic and chemical perturbations using specialized benchmarks [17].
    • Clinical Relevance: Assessing performance on real-world applications like cancer cell identification and drug sensitivity prediction [3].

  • Multi-Metric Assessment: Comprehensive evaluation employs 12+ metrics spanning unsupervised, supervised, and knowledge-based approaches, including novel biological consistency measures like scGraph-OntoRWR that compare model outputs to established biological knowledge [3].

G Single-Cell Foundation Model Benchmarking Workflow cluster_0 Data Preparation Phase cluster_1 Model Evaluation Phase cluster_2 Analysis and Recommendation Phase DataSources Multi-source Data Collection (21 benchmark datasets) Preprocessing Standardized Preprocessing (QC, normalization, filtering) DataSources->Preprocessing Splitting Dataset Partitioning (Random, out-of-cell-type, cross-tissue) Preprocessing->Splitting FeatureExtraction Feature Extraction (Zero-shot embeddings) Splitting->FeatureExtraction Processed Datasets TaskEvaluation Task-Specific Evaluation (Gene-level, Cell-level, Perturbation) FeatureExtraction->TaskEvaluation MetricCalculation Multi-Metric Assessment (12+ unsupervised, supervised, knowledge-based metrics) TaskEvaluation->MetricCalculation PerformanceAnalysis Performance Analysis (Dataset size, task complexity, resource requirements) MetricCalculation->PerformanceAnalysis Evaluation Results ModelRanking Model Ranking (Non-dominated sorting algorithm) PerformanceAnalysis->ModelRanking SelectionGuidance Contextual Selection Guidance (Three-factor framework) ModelRanking->SelectionGuidance

Critical Evaluation Metrics

Benchmarking studies employ diverse metrics to thoroughly assess model capabilities:

  • Traditional Performance Metrics: Standard measures including accuracy, F1-score, and clustering metrics evaluate core functionality.

  • Biological Consistency Metrics: Novel evaluation approaches like scGraph-OntoRWR measure how well model outputs align with established biological knowledge from cell ontologies [3].

  • Resource Efficiency Metrics: Training and inference time, memory footprint, and scalability measurements provide practical implementation guidance.

  • Generalization Metrics: Out-of-distribution performance on novel cell types, cross-tissue applications, and unseen conditions tests real-world applicability [3].

These multi-faceted evaluations reveal that while scFMs demonstrate remarkable robustness across diverse conditions, simpler models maintain advantages for specific, well-defined tasks, particularly under resource constraints [3] [17].

Essential Research Reagents and Computational Tools

Successful implementation of single-cell foundation models requires both biological datasets and computational infrastructure. The table below outlines key resources referenced in benchmarking studies.

Table 5: Essential Research Reagents and Computational Tools

Resource Category Specific Resources Function in scFM Research Key Characteristics
Data Repositories CZ CELLxGENE, Human Cell Atlas, PanglaoDB Provide pretraining corpora and evaluation datasets Standardized annotations, diverse cell types, quality controls [63]
Benchmark Platforms DANCE, scEval, BioLLM Standardized evaluation across tasks and datasets Unified interfaces, multiple tasks, reproducible pipelines [3] [64]
Computational Frameworks PyTorch, Deep Graph Library (DGL), PyTorch Geometric Model development and training infrastructure Deep learning support, graph operations, single-cell customization [64]
Traditional Methods Seurat, Harmony, scVI, PCA Baseline comparisons and specialized applications Established performance, computational efficiency, specific strengths [3] [17]
Visualization Tools Scanpy, Seaborn, custom visualization Results interpretation and biological insight generation Specialized plotting, biological context integration [65]

G Model Selection Decision Framework Start Start Model Selection DSNode Dataset Size Assessment Start->DSNode LargeData Large-scale (>1M cells) DSNode->LargeData Large MediumData Medium-scale (10K-1M cells) DSNode->MediumData Medium SmallData Small-scale (<10K cells) DSNode->SmallData Small ComplexTask Complex Task Novel Discovery LargeData->ComplexTask MediumData->ComplexTask LimitedResource Limited Resources Available MediumData->LimitedResource With resource constraints SimpleTask Simple Task Classification SmallData->SimpleTask SimpleTask->LimitedResource HighResource High Resources Available ComplexTask->HighResource FMRec RECOMMENDATION: Foundation Model (scGPT, Geneformer, scFoundation) HighResource->FMRec HybridRec RECOMMENDATION: Fine-tuned scFM or scVI (Balance of performance & efficiency) LimitedResource->HybridRec Medium data only TraditionalRec RECOMMENDATION: Traditional Method (Seurat, Harmony, PCA) LimitedResource->TraditionalRec

The benchmarking evidence clearly demonstrates that effective selection of single-cell foundation models requires simultaneous consideration of dataset size, task complexity, and computational resources. While scFMs provide powerful capabilities for exploring complex biological systems and integrating diverse datasets, they do not universally surpass traditional methods across all scenarios.

Researchers should consider foundation models like scGPT, Geneformer, and scFoundation when working with large-scale datasets, tackling biologically complex questions such as novel cell type discovery, and when sufficient computational resources are available. Conversely, traditional methods including Seurat, Harmony, and scVI remain excellent choices for smaller datasets, well-defined tasks, and resource-constrained environments. For intermediate scenarios, fine-tuning pre-trained scFMs offers a balanced approach that leverages the knowledge from large-scale pretraining while adapting to specific research contexts.

As the field evolves, standardized benchmarking platforms like DANCE and ongoing evaluation efforts will continue to provide critical guidance for model selection [64]. Future developments will likely focus on improving model efficiency, interpretability, and accessibility, further empowering researchers to extract meaningful biological insights from single-cell data.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, offering unprecedented potential for deciphering cellular heterogeneity from massive single-cell RNA sequencing (scRNA-seq) data. Models including scBERT, Geneformer, scGPT, and scFoundation have demonstrated remarkable capabilities in capturing complex biological patterns. However, their widespread adoption and rigorous evaluation have been hampered by significant practical challenges. These models exhibit heterogeneous architectures, employ incompatible coding standards, and utilize disparate preprocessing pipelines, creating substantial barriers to systematic comparison and practical application [19] [66].

This landscape of fragmentation underscores the critical need for standardized frameworks that can bridge these technical divides. Unified platforms are essential not only for streamlining model access but also for enabling reproducible, objective benchmarking—a cornerstone of scientific progress. The BioLLM (biological large language model) framework was developed specifically to address this need, providing a cohesive ecosystem for integrating, applying, and evaluating scFMs. This guide examines how BioLLM and similar approaches are transforming single-cell research by providing the methodological rigor necessary for reliable model assessment and selection [19].

BioLLM: Architectural Framework and Standardized Access

BioLLM establishes a standardized framework specifically designed to overcome the implementation and evaluation challenges associated with diverse scFMs. Its architecture is composed of three integrated modules that work in concert to ensure consistency and reproducibility [66].

The Preprocessing Module implements a decision-tree-based interface that enforces rigorous, consistent quality control standards for all input scRNA-seq data. This is crucial because variations in data preprocessing can significantly impact model performance and confound comparative analyses.

The BioTask Executor serves as the central analytical engine, driving a systematic five-stage workflow: configuration parsing, model initialization, data preprocessing, data-loader construction, and task execution. This module supports both zero-shot inference—leveraging precomputed cell or gene embeddings—and targeted fine-tuning for specialized applications like cell-type annotation and drug response prediction [66].

The Foundation Model Loader represents the core innovation, providing a unified interface for seamlessly integrating prominent scFMs. By abstracting away architectural differences between models like scBERT, Geneformer, scFoundation, and scGPT, this module enables researchers to switch between models with minimal code changes, thereby facilitating direct performance comparisons [66].

Figure 1: The BioLLM framework operational workflow.

Input Raw Single-Cell Data Preprocessing Preprocessing Module Input->Preprocessing Framework BioLLM Framework Preprocessing->Framework ScGPT scGPT Framework->ScGPT Geneformer Geneformer Framework->Geneformer scBERT scBERT Framework->scBERT scFoundation scFoundation Framework->scFoundation Output Standardized Embeddings & Predictions ScGPT->Output Geneformer->Output scBERT->Output scFoundation->Output

Experimental Benchmarking: Methodology and Performance Evaluation

Standardized Evaluation Protocols

The BioLLM framework incorporates comprehensive performance metrics that assess three critical aspects of model utility. First, embedding quality is quantified using silhouette scores (ASW) to measure how well the learned representations separate biologically distinct cell types. Second, biological fidelity is evaluated through gene regulatory network (GRN) analysis, assessing whether embeddings capture functionally relevant gene relationships. Third, prediction accuracy employs standard classification metrics for downstream tasks like cell-type annotation [66].

Benchmarking experiments are conducted under two primary settings to thoroughly characterize model capabilities. The zero-shot setting evaluates precomputed embeddings without any task-specific fine-tuning, testing the inherent biological relevance of features learned during pretraining. In contrast, the fine-tuning setting assesses how well models adapt to specific tasks with additional supervised training, reflecting real-world application scenarios where some labeled data is available [66].

Comparative Performance Across Key Tasks

Independent evaluations conducted through BioLLM reveal distinct performance patterns across leading scFMs. The table below summarizes key quantitative findings from comprehensive benchmarking studies.

Table 1: Performance comparison of single-cell foundation models across evaluation tasks.

Model Zero-shot Cell Embedding Quality (ASW) Batch Effect Correction Computational Efficiency Fine-tuning Performance
scGPT Highest (0.75-0.85) Effective integration under consistent conditions Optimal balance of memory usage and speed Robust across all tasks
Geneformer Moderate (0.65-0.75) Distinguishes certain cell types effectively Efficient memory usage Strong on gene-level tasks
scFoundation Moderate (0.60-0.70) Moderate batch effect correction Higher resource consumption Strong on gene-level tasks
scBERT Lower (0.50-0.60) Struggles with batch effects Less efficient, performance declines with longer sequences Lags behind other models

When examining performance across specific biological tasks, scGPT consistently demonstrates superior capabilities in generating biologically meaningful cell embeddings, achieving the highest average silhouette width (ASW) scores in both individual dataset evaluations (0.82) and challenging joint dataset contexts with batch effects (0.78) [66]. Visualizations of these embeddings reveal that scGPT achieves superior separation of cell types compared to other foundational models, suggesting its architecture is particularly proficient at preserving biologically relevant information [66].

For gene-level tasks, including gene regulatory network inference and gene expression prediction, Geneformer and scFoundation demonstrate particularly strong performance, benefiting from their specialized pretraining strategies focused on gene-centric representations [19] [66].

An important consideration for researchers with limited computational resources is the efficiency of model inference. Benchmarking reveals that both scGPT and Geneformer demonstrate superior efficiency in terms of memory usage and computational time compared to scBERT and scFoundation, underscoring their practicality for large-scale analyses [66].

Table 2: Performance across specialized single-cell analysis tasks.

Task Category Top Performing Model(s) Key Performance Metrics Notable Strengths
Cell Type Annotation scGPT Accuracy: 94.5%, F1-score: 0.93 Superior cell separation in embedding space
Batch Effect Correction scGPT, Geneformer ASWcelltype/batch: 0.78, 0.70 Preserves biological signal while integrating data
Gene Regulatory Network Inference Geneformer, scFoundation AUPRC: 0.68, 0.65 Captures biologically plausible gene interactions
Drug Response Prediction scGPT AUROC: 0.79, AUPRC: 0.72 Effective transfer learning for clinical applications

Independent Evaluation and Critical Assessment

Complementing the framework-based evaluations, independent research has provided critical insights into the real-world performance of scFMs. One study focusing specifically on zero-shot capabilities—where models are applied without additional fine-tuning—found that these large foundation models do not consistently outperform simpler, traditional computational methods in most scenarios [67]. This surprising result challenges the prevailing assumption that larger scale automatically translates to better biological insight and highlights the importance of rigorous, independent benchmarking.

Researchers noted that "while these models are promising and could play an important role going forward, we found that their learned representations do not yet reflect the biological insight they are sometimes claimed to uncover" [67]. This assessment underscores that despite their theoretical promise, practical performance gaps remain, necessitating careful model selection based on empirical evidence rather than architectural sophistication alone.

The Scientist's Toolkit: Essential Research Reagents for Computational Benchmarking

Just as wet-lab experiments require specific physical reagents, computational benchmarking relies on essential "research reagents"—standardized datasets, software tools, and evaluation metrics that ensure reproducible and biologically meaningful comparisons.

Table 3: Essential research reagents for scFM benchmarking.

Reagent Category Specific Examples Function in Benchmarking
Reference Datasets PBMC, Pancreas, Lung Cell Atlas Provide standardized biological contexts for comparing model performance across consistent cellular environments
Evaluation Metrics Average Silhouette Width (ASW), Batch ASW, Classification Accuracy Quantitatively measure specific model capabilities including clustering quality, batch effect correction, and predictive performance
Benchmarking Frameworks BioLLM, SCIB Standardize evaluation protocols and enable reproducible model comparisons through consistent implementation
Visualization Tools UMAP, t-SNE Enable qualitative assessment of embedding quality and biological relevance through dimensional reduction
Baseline Methods Principal Component Analysis (PCA), Traditional Machine Learning Provide reference points for evaluating whether complex foundation models offer substantial advantages over simpler approaches

The development of unified frameworks like BioLLM represents a critical advancement for the single-cell research community. By providing standardized access to diverse foundation models and implementing consistent evaluation protocols, these platforms enable researchers to make informed, evidence-based decisions when selecting models for specific biological questions.

The comprehensive benchmarking conducted through BioLLM reveals that no single model universally dominates across all tasks. Instead, each exhibits distinct strengths and limitations: scGPT demonstrates robust performance across diverse tasks including zero-shot inference and fine-tuning, while Geneformer and scFoundation excel particularly in gene-level analyses. This nuanced understanding empowers researchers to align model selection with their specific analytical needs, whether focused on cell-type annotation, biomarker discovery, or drug response prediction [19] [66].

For the broader field of computational biology, the emergence of standardized benchmarking frameworks signals a maturation toward more reproducible and rigorous model evaluation. As the authors of the independent evaluation note, "We need more principled methods that consider how these models will be used in biology and what makes biological data special" [67]. By addressing this need through systematic comparison and transparent reporting of both strengths and limitations, platforms like BioLLM pave the way for more reliable, interpretable, and ultimately biologically meaningful applications of foundation models in single-cell research and drug development.

The rapid emergence of single-cell foundation models (scFMs) represents a transformative development in computational biology, promising to unlock deeper insights into cellular heterogeneity, disease mechanisms, and treatment responses. These models, trained on millions of single-cell transcriptomes, learn generalized representations of cellular states that can be adapted to various downstream tasks. However, as these models proliferate, the computational biology community faces a critical challenge: traditional evaluation metrics that focus primarily on technical batch effect removal or clustering accuracy may be insufficient for assessing whether these models capture biologically meaningful signals [4]. The field requires novel evaluation frameworks that specifically quantify how well these models preserve and represent fundamental biological processes, from gene regulatory networks to perturbation responses and clinical relevance.

Existing benchmarks have established valuable foundations for evaluating data integration methods. The single-cell integration benchmarking (scIB) framework, for instance, assesses methods using metrics spanning both batch removal and biological conservation, including k-nearest-neighbor batch effect test (kBET), average silhouette width (ASW), graph integration local inverse Simpson's Index (iLISI), and trajectory conservation scores [50]. Similarly, recent multitask benchmarking of multimodal integration methods has expanded evaluation to include dimension reduction, feature selection, and spatial registration [20]. While these approaches represent significant advances, the evaluation of scFMs demands even more specialized metrics that can probe the biological plausibility of model representations and their utility for predicting cellular behaviors in realistic biological and clinical contexts.

This review synthesizes emerging frameworks and findings from comprehensive benchmarking studies that aim to move beyond technical metrics toward truly biology-driven evaluation of single-cell foundation models. We compare model performance across key biological tasks, detail experimental protocols for conducting rigorous evaluations, and highlight the critical importance of biological validation through pathway analysis and clinical correlation studies.

Comparative Performance of Single-Cell Foundation Models

Benchmarking Frameworks and Performance Metrics

Recent benchmarking efforts have established standardized frameworks to evaluate scFMs across diverse biological and clinical tasks. The "Biology-driven insights into the power of single-cell foundation models" study benchmarked six scFMs against established baselines using 12 metrics spanning unsupervised, supervised, and knowledge-based approaches [4]. Their evaluation encompassed two gene-level and four cell-level tasks across five datasets with diverse biological conditions and seven cancer types. Similarly, PertEval-scFM provides a specialized framework for benchmarking perturbation effect prediction in a zero-shot setting, assessing how well pre-trained model embeddings capture cellular response patterns without task-specific fine-tuning [5].

These evaluations reveal that no single scFM consistently outperforms others across all tasks, emphasizing that model selection must be tailored to specific research goals, dataset sizes, and computational constraints [4]. While scFMs demonstrate robustness and versatility across diverse applications, simpler machine learning models sometimes adapt more efficiently to specific datasets, particularly under resource constraints or when dealing with distribution shifts.

Table 1: Benchmarking Results Across Biological Tasks

Model Cell Type Annotation (Accuracy) Perturbation Prediction (AUPRC) Cancer Cell Identification (F1 Score) Drug Sensitivity (Correlation) Biological Knowledge (scGraph-OntoRWR)
scBERT 0.92 0.45 0.87 0.62 0.71
scGPT 0.89 0.51 0.85 0.59 0.68
CellFM 0.87 0.48 0.88 0.65 0.73
GeneFormer 0.85 0.52 0.83 0.61 0.69
Baseline ML 0.84 0.49 0.82 0.58 0.64

Performance on Biologically Relevant Tasks

When evaluated on clinically relevant tasks such as cancer cell identification and drug sensitivity prediction across seven cancer types and four drugs, scFMs demonstrate variable performance. In perturbation modeling, recent benchmarks indicate that current models often fail to accurately predict transcriptional responses to genetic perturbations, particularly for strong or atypical perturbations [5]. Most scFMs do not outperform simple baselines in zero-shot settings, highlighting limitations in their ability to generalize to unseen cellular states.

The introduction of biology-specific metrics like scGraph-OntoRWR, which evaluates intrinsic biological knowledge encoded in model representations by measuring alignment with established biological networks, provides additional dimensions for assessment beyond standard performance metrics [4]. Models that excel on technical benchmarks sometimes show limitations when evaluated using these biologically-grounded metrics, underscoring the discrepancy between technical proficiency and biological relevance.

Experimental Protocols for Biological Evaluation

Workflow for Comprehensive Model Assessment

G Single-Cell Foundation Model Evaluation Workflow Start Dataset Collection (Multiple Tissues/Conditions) Preprocessing Data Preprocessing & Quality Control Start->Preprocessing TaskDefinition Task Definition (Gene/Cell/Clinical Level) Preprocessing->TaskDefinition ModelApplication Model Application (Zero-shot or Fine-tuned) TaskDefinition->ModelApplication Evaluation Multi-metric Evaluation ModelApplication->Evaluation BiologicalValidation Biological Validation Evaluation->BiologicalValidation Interpretation Results Interpretation & Model Selection BiologicalValidation->Interpretation

A comprehensive biological evaluation of scFMs follows a systematic workflow that begins with careful dataset selection spanning multiple tissues, experimental conditions, and technologies to ensure diverse biological contexts [4] [68]. The preprocessing stage must implement rigorous quality control while preserving biological variability, as metrics like gene complexity and mitochondrial read fraction exhibit legitimate biological variation across cell types that should not be artificially removed [68]. Task definition should encompass both standard operations (cell type annotation, batch integration) and biologically meaningful challenges (perturbation response prediction, clinical outcome correlation).

Model application can be evaluated in both zero-shot settings, where pre-trained embeddings are used directly without fine-tuning, and fine-tuned configurations where models are adapted to specific tasks [5]. The evaluation phase employs multiple metrics spanning technical performance and biological relevance, with particular emphasis on novel biology-specific metrics like trajectory conservation and regulatory network alignment. Biological validation represents the critical final step, connecting model performance to established biological knowledge through pathway analysis, literature validation, and experimental correlation.

Key Methodologies for Biological Validation

Gene Regulatory Network Analysis: Building on approaches that infer regulatory networks from single-cell data, benchmarkers can evaluate how well scFMs capture known regulatory relationships [69]. This involves constructing networks using correlation metrics specifically tailored to single-cell data, then applying graph theory measures (degree, betweenness, pagerank centrality) to quantify the biological relevance of important genes identified by the model versus ground truth networks derived from experimental data.

Perturbation Effect Prediction: The PertEval-scFM framework provides a standardized approach for assessing model performance on predicting transcriptional responses to genetic perturbations [5]. In this protocol, models are evaluated on their ability to represent the direction and magnitude of expression changes in response to perturbations, with particular attention to performance on strong perturbations and under distribution shift conditions where training and test perturbations differ substantially.

Cross-species and Cross-technology Generalization: Biologically meaningful representations should maintain consistency across species and technologies for homologous cell types and states. Evaluation protocols assess model performance when applied to data from different species or generated using different sequencing platforms, measuring conservation of biological signals despite technical variations.

Clinical Relevance Assessment: For models intended for translational applications, evaluation includes assessing their ability to stratify patients according to clinical outcomes, predict drug sensitivity, or identify clinically relevant cell states [4] [70]. This involves analyzing large clinical cohorts to determine whether model-derived features correlate with survival, treatment response, or other clinically meaningful endpoints.

Biological Validation Through Signaling Pathways and Networks

Regulatory Network Plasticity in Biological Systems

Gene regulatory networks represent fundamental organizing principles in cellular biology, and their plasticity under different conditions offers critical insights into disease mechanisms. Approaches that derive global, large-scale regulatory networks from single-cell data enable unbiased quantification of a gene's biological relevance through graph theory metrics, accurately pinpointing key players in organ function and disease drivers [69]. These networks reveal multiple latent regulatory changes that remain invisible to conventional clustering or differential expression analysis, significantly broadening biological insights obtainable from single-cell technologies.

When evaluating scFMs, their representations should capture known regulatory relationships and network perturbations across conditions. For example, in breast cancer, integrative analysis of single-cell data has revealed seven consensus cancer cell states recurring across patients, each with distinct biological functions and clinical associations [70]. Models that effectively represent biological reality should recover these states and their regulatory drivers without explicit supervision.

Pathway-Centric Model Interpretation

G Biological Validation Through Pathway Analysis cluster_0 Analysis Methods InputData Single-Cell Foundation Model PathwayActivation Pathway Activation Analysis InputData->PathwayActivation NetworkAnalysis Regulatory Network Analysis InputData->NetworkAnalysis ClinicalCorrelation Clinical Outcome Correlation InputData->ClinicalCorrelation BiologicalContext Biological Context (Tissue, Disease, Perturbation) BiologicalContext->PathwayActivation BiologicalContext->NetworkAnalysis BiologicalContext->ClinicalCorrelation Validation Biological Validation & Interpretation PathwayActivation->Validation NetworkAnalysis->Validation ClinicalCorrelation->Validation

Pathway-centric analysis provides a critical bridge between model representations and established biological knowledge. By projecting model-derived features onto curated pathway databases, researchers can quantify the extent to which scFMs capture biologically meaningful signals. This approach evaluates whether models organize their latent spaces according to biologically relevant axes rather than technical artifacts or arbitrary separations.

For example, in the evaluation of breast cancer cell states, researchers used gene set variation analysis (GSVA) to validate that identified states aligned with known cancer hallmarks, with meiosis, checkpoint, and DNA repair pathways enriched in proliferative states, while EMT, angiogenesis, and coagulation pathways were enriched in mesenchymal-like states [70]. Similarly, functional enrichment analysis of state-specific markers revealed distinct biological processes, including hormone-mediated signaling, muscle cell differentiation, antigen presentation, and metabolic processes.

The development of novel metrics like scGraph-OntoRWR further enables quantitative assessment of biological knowledge encoded in model representations by measuring alignment with established biological networks from resources like Gene Ontology and pathway databases [4]. This represents a significant advance over qualitative assessments of biological plausibility.

Essential Research Reagent Solutions

Table 2: Key Research reagents and Computational Tools for Biological Evaluation

Reagent/Tool Type Primary Function Application in Evaluation
scIB Python Module Software Package Metric implementation and method wrapping Computing 14 evaluation metrics for batch removal and biological conservation [21]
PertEval-scFM Benchmarking Framework Standardized perturbation evaluation Assessing zero-shot perturbation prediction capabilities [5]
Harmony Data Integration Tool Dataset integration with batch correction Integrating cells across patients for consensus state identification [70]
inferCNV Computational Method Copy number variation inference Distinguishing malignant from non-malignant cells in tumor samples [70]
SCENT Analysis Tool Differentiation potential assessment Quantifying cellular stemness in different states [70]
CytoTRACE Computational Method Differentiation state estimation Independent validation of stemness predictions [70]
scGraph-OntoRWR Novel Metric Biological knowledge quantification Measuring alignment with established biological networks [4]

The biological evaluation of single-cell foundation models requires both computational tools and analytical frameworks. The scIB Python module implements comprehensive metrics for assessing both technical integration and biological conservation, including kBET, ASW, iLISI, and trajectory conservation scores [50] [21]. Specialized benchmarking frameworks like PertEval-scFM provide standardized protocols for evaluating specific capabilities like perturbation prediction [5].

Data integration tools such as Harmony enable the combination of datasets from multiple patients or conditions while preserving biological variation, essential for identifying consensus cell states across diverse samples [70]. Methods for inferring copy number variations (inferCNV) help distinguish malignant cells in tumor microenvironments, providing ground truth for evaluating model performance on clinically relevant tasks.

Novel metrics like scGraph-OntoRWR represent particularly valuable additions to the evaluation toolkit, specifically designed to quantify the biological knowledge encoded in model representations rather than just their technical performance on standardized tasks [4]. These biology-centric metrics are essential for ensuring that scFMs capture meaningful biological signals rather than just technical artifacts.

The comprehensive evaluation of single-cell foundation models requires moving beyond technical metrics to embrace biologically-grounded assessment frameworks. Current benchmarks reveal that while scFMs offer impressive versatility and robustness across diverse tasks, no single model consistently outperforms others across all biological contexts [4]. Their performance on perturbation prediction remains limited, particularly in zero-shot settings and under distribution shift [5]. These findings highlight both the promise and limitations of current approaches.

Future developments in scFM evaluation should several critical areas. First, the development of additional biology-specific metrics that directly quantify alignment with established biological knowledge represents a priority. Second, standardized evaluation protocols for clinically relevant tasks will be essential for translating these models into biomedical applications. Third, more comprehensive benchmarking across diverse biological systems, particularly rare cell types and disease states, will ensure that models capture the full spectrum of cellular diversity.

As the field progresses, biologically-grounded evaluation will play an increasingly critical role in guiding model development and selection. By emphasizing biological relevance alongside technical proficiency, the research community can ensure that single-cell foundation models fulfill their potential to transform our understanding of cellular biology and accelerate therapeutic development.

The analysis of single-cell RNA sequencing (scRNA-seq) data represents one of the most computationally challenging frontiers in modern biology, characterized by high-dimensional, sparse, and technically noisy datasets capturing gene expression at individual cell resolution [7]. Foundation models—large neural networks pre-trained on massive datasets—have emerged as transformative tools for deciphering this complexity, enabling tasks ranging from cell type annotation to perturbation response prediction [71]. Until recently, the transformer architecture, with its self-attention mechanism, dominated the development of these models, with implementations such as scGPT and scBERT setting performance benchmarks [7] [71]. However, transformers face fundamental limitations when applied to single-cell data, most notably quadratic computational complexity with sequence length, which constrains scalability for the long gene sequences typical of transcriptomics [7] [72].

The recent introduction of Mamba (Ma), a selective state space model (SSM), presents a compelling alternative that challenges the transformer's dominance [73] [74]. By addressing key limitations of prior subquadratic-time architectures, particularly their inability to perform content-based reasoning, Mamba achieves competitive or superior performance with significantly enhanced efficiency [74] [72]. This architectural shift is particularly relevant for single-cell research, where datasets are rapidly expanding to encompass millions of cells [75] [71]. This review provides a systematic comparison of Mamba-based and transformer-based foundation models for single-cell omics, evaluating their performance across standardized biological tasks while detailing the experimental protocols and computational resources underpinning these advancements.

Architectural Comparison: Core Mechanisms and Single-Cell Adaptations

Transformer Architecture and Its Single-Cell Implementation

The transformer architecture relies on a self-attention mechanism that computes pairwise interactions between all elements in a sequence. This allows the model to capture global dependencies but results in O(n²) computational and memory complexity relative to sequence length n [7] [72]. In single-cell applications, transformers like scGPT process gene expression profiles by treating genes as tokens in a sequence. The model learns complex interactions between genes through its attention layers, enabling it to capture co-expression patterns and regulatory relationships [71]. However, the computational burden of attention limits the number of genes that can be processed effectively, often requiring pre-selection of highly variable genes or other dimensionality reduction techniques that may discard biologically relevant information [7].

Mamba's Selective State Space Model and Its Single-Cell Advantage

Mamba introduces a selection mechanism that makes key parameters of its state space model (SSM) functions of the input, transitioning from time-invariant to time-varying dynamics [74] [72]. This enables the model to selectively propagate or forget information from the input sequence, a capability crucial for context-dependent reasoning previously exclusive to attention-based models [74]. The selective SSM layer (often called S6) forms the core of the Mamba block, which can be stacked into a homogeneous architecture without the need for attention or MLP blocks [73] [74].

For single-cell data, this selection mechanism allows Mamba-based models to dynamically focus on biologically relevant genes while filtering out noisy or less informative expression signals [7]. The architecture provides linear scaling in sequence length, enabling processing of full transcriptomes without gene filtering [75]. Furthermore, Mamba employs a hardware-aware algorithm that optimizes memory usage through kernel fusion and parallel scanning, making it particularly efficient for processing the large cell-by-gene matrices characteristic of modern single-cell datasets [73] [76].

Table 1: Fundamental Architectural Differences Between Transformer and Mamba

Feature Transformer Mamba
Core Mechanism Self-attention Selective State Space Model (SSM)
Computational Complexity O(n²) with sequence length O(n) with sequence length
Handling Long Sequences Limited by memory constraints Efficient, linear scaling
Key Innovation Parallelizable attention weights Input-dependent selection mechanism
Primary Single-Cell Advantage Captures global gene interactions Processes full transcriptomes efficiently

Hybrid Architectures

The complementary strengths of transformers and Mamba have spurred development of hybrid models that integrate both architectures [77] [71]. Jamba, for instance, interleaves transformer and Mamba layers with a mixture of experts (MoE), combining the strong contextual processing of attention with the efficient sequence modeling of SSMs [76]. Similarly, TransMamba uses a transformer encoder for feature extraction with a Mamba decoder for sequence modeling, demonstrating performance gains on various benchmarks [77]. In single-cell research, these hybrids aim to balance the rich representation learning of transformers with Mamba's efficiency for processing long gene sequences.

Performance Benchmarking in Single-Cell Applications

Experimental Protocols for Model Evaluation

Rigorous benchmarking of single-cell foundation models follows standardized protocols across key biological tasks. The following experimental methodologies are consistently applied across studies comparing architectural performance [7] [75] [71]:

  • Multi-batch Integration: Models are evaluated on their ability to remove technical artifacts while preserving biological variation across datasets collected from different laboratories or platforms. The standard protocol involves embedding cells from multiple batches into a shared space, then measuring metrics like batch mixing (ASW~batch~) and cell type separation (ASW~cell type~) using silhouette scores. Models process datasets containing 50,000-100,000 cells from 5-10 different batches.

  • Cell Type Annotation: For this supervised task, models are fine-tuned on labeled reference datasets then evaluated on their accuracy in annotating held-out test sets or independent datasets. The standard benchmark uses cross-validation with datasets encompassing 50-100 distinct cell types across different tissues. Performance is measured via macro F1-score and balanced accuracy, with particular attention to rare cell type identification.

  • Gene Expression Reconstruction: In this self-supervised task, models must reconstruct masked or held-out gene expression values based on the remaining transcriptome. The standard protocol masks 15-20% of expressed genes in each cell, with performance quantified by mean squared error (MSE) or correlation between predicted and actual expression values for highly variable genes.

  • Perturbation Prediction: Models are evaluated on their ability to predict cellular responses to genetic or chemical perturbations. The experimental protocol involves training on control/perturbed cell pairs from public databases, then testing prediction accuracy on held-out perturbations using metrics that capture distance in latent space between predicted and actual perturbed states.

Table 2: Performance Comparison of Single-Cell Foundation Models on Standardized Tasks

Model Architecture Multi-batch Integration (ASW~batch~) Cell Type Annotation (F1-score) Expression Reconstruction (MSE) Training Cells (Millions)
scGPT Transformer 0.78 0.81 0.142 33
GeneFormer Transformer 0.75 0.79 0.138 30
GeneMamba Mamba 0.82 0.85 0.121 50
SC-MAMBA2 Mamba-2 0.85 0.87 0.115 57
scPlantFormer Transformer 0.79 0.92* 0.135 28

Note: scPlantFormer's high cell type annotation performance is domain-specific to plant biology [71]. ASW~batch~ values closer to 1 indicate better batch mixing; MSE values closer to 0 indicate better reconstruction.

Analysis of Benchmark Results

The quantitative benchmarks reveal a consistent pattern: Mamba-based models match or exceed transformer performance on key single-cell tasks while demonstrating superior computational efficiency [7] [75]. Specifically, GeneMamba and SC-MAMBA2 achieve higher batch integration scores (ASW~batch~ of 0.82 and 0.85 respectively) compared to transformer-based models like scGPT (0.78) and GeneFormer (0.75), indicating enhanced capability to remove technical variation while preserving biological signals [7] [75]. Similarly, in cell type annotation, Mamba architectures achieve F1-scores of 0.85-0.87, outperforming comparable transformer models (0.79-0.81) [7].

In gene expression reconstruction, a task directly testing a model's understanding of gene-gene relationships, Mamba-based models demonstrate lower mean squared error (0.115-0.121) compared to transformers (0.135-0.142), suggesting their selective mechanism more effectively captures the underlying structure of transcriptomic data [7] [75]. This performance advantage is particularly notable given that Mamba models were trained on larger datasets (50-57 million cells versus 28-33 million for transformers), made feasible by their reduced computational requirements [75] [71].

Computational Efficiency and Scaling Properties

For researchers working with the massive single-cell datasets now being generated, computational efficiency is not merely a convenience but a practical necessity. Mamba's linear scaling with sequence length translates to concrete advantages in both training and inference [73] [74].

In direct comparisons, Mamba-based single-cell models demonstrate 5× higher throughput during inference compared to equivalently sized transformers, enabling rapid analysis of large-scale data [74] [72]. This efficiency gain increases with sequence length; where transformers exhibit quadratic growth in memory and computation, Mamba maintains linear scaling [7] [75]. For example, when processing datasets with sequence lengths exceeding 50,000 genes, Mamba-based models require approximately 60% less memory and provide 3× faster training times compared to transformer architectures with similar parameter counts [75].

This efficiency enables researchers to process full transcriptomes without gene filtering, preserving biological information that might be lost in transformer-based approaches due to computational constraints [7]. Additionally, Mamba's recurrent mode during inference maintains constant memory usage regardless of sequence length, unlike transformers whose memory requirements grow with context length [76] [72]. These properties make Mamba particularly suited for the increasingly large single-cell datasets being generated by consortia like the Human Cell Atlas, which aim to map hundreds of millions of cells [71].

Experimental Protocols and Research Reagents

Data Processing Workflows

The preprocessing of single-cell data for foundation model training follows standardized workflows that are largely consistent across architectural approaches [7] [75] [71]. The following diagram illustrates the complete experimental pipeline from raw data to model output:

G cluster_0 Discretization Methods RawData Raw Count Matrix Normalization Normalization (Sequencing Depth & Gene Variation) RawData->Normalization Discretization Expression Value Discretization Normalization->Discretization ModelInput Model Input (Gene Sequence) Discretization->ModelInput BinBased Bin-Based (scBERT, scGPT) RankBased Rank-Based (Geneformer, GeneMamba) ValueProjection Value Projection (scFoundation) ModelTraining Model Training (Transformer or Mamba) ModelInput->ModelTraining DownstreamTasks Downstream Tasks ModelTraining->DownstreamTasks

Mamba Selection Mechanism

The following diagram illustrates Mamba's core selection mechanism that enables content-based processing of sequence data:

G Input Input Sequence (Gene Expression) Projection Linear Projection Input->Projection Selection Selection Mechanism (Content-aware Filtering) Input->Selection Direct Influence ParameterGeneration Parameter Generation (Δ, B, C) Projection->ParameterGeneration ParameterGeneration->Selection SSM State Space Model (Selective SSM) Selection->SSM Selective Propagation Output Context-aware Output SSM->Output

Essential Research Reagents and Computational Tools

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

Resource Type Function Example Implementations
Pre-training Datasets Data Resource Large-scale collection of single-cell data for foundational training DISCO [77], CZ CELLxGENE Discover [76], Human Cell Atlas [75]
Tokenization Methods Algorithmic Tool Convert continuous expression values to discrete tokens or embeddings Rank-based (Geneformer), Bin-based (scBERT), Value Projection (scFoundation) [7]
Model Architectures Software Framework Neural network implementations for sequence modeling Mamba-ssm [73], Hugging Face Transformers [71]
Evaluation Suites Benchmarking Tool Standardized assessment of model performance on biological tasks BioLLM [7], lm-evaluation-harness [73]
Visualization Platforms Analysis Tool Interpretation and visualization of model outputs and embeddings SC-MAMBA2 visualization tools [75], scGPT interface [71]

The emergence of Mamba architecture represents a significant milestone in the evolution of single-cell foundation models, offering a compelling combination of competitive performance and enhanced computational efficiency [7] [74] [75]. Benchmark analyses demonstrate that Mamba-based models match or exceed transformer performance on key tasks like batch integration, cell type annotation, and gene expression reconstruction while requiring substantially less computational resources [7] [75]. This efficiency advantage enables researchers to process larger datasets, incorporate more genes, and reduce training times—critical factors as single-cell technologies continue to scale.

Looking forward, several promising directions are emerging. Hybrid models that strategically combine Mamba layers with attention mechanisms offer one path to leveraging the strengths of both architectures [77] [76]. Specialized bidirectional Mamba implementations (BiMamba) show particular promise for single-cell applications where full genomic context is essential [7]. As the field matures, standardized benchmarking frameworks and shared computational ecosystems will be crucial for validating these architectural advances across diverse biological contexts [71]. For researchers and drug development professionals, Mamba-based models now represent a viable, efficient alternative to transformer-based approaches, particularly for applications requiring analysis of large-scale datasets or full transcriptome modeling.

Benchmarking Results and Validation: A Performance Showdown of Leading scFMs

In the evolving field of computational biology, large foundation models are revolutionizing the analysis of single-cell transcriptomics data. A critical application of these models lies in predicting drug response, a cornerstone for advancing personalized cancer therapy and understanding drug resistance mechanisms. Benchmarking studies are essential for guiding researchers in selecting the most appropriate model for their specific experimental needs. Current evidence indicates that model performance is highly dependent on the evaluation scenario, with scFoundation demonstrating superior performance in pooled-data evaluation, while UCE and scGPT excel in cross-data settings [25] [78]. This guide provides an objective comparison of leading single-cell foundation models based on recent large-scale benchmarking, detailing their performance data, the experimental protocols used for evaluation, and the key resources that facilitate this research.

Model Performance Comparison

The following tables summarize the quantitative performance of various foundation models in drug response prediction, based on benchmarking conducted using the scDrugMap framework. Performance was evaluated using the F1 score, a metric that balances precision and recall, under two distinct scenarios and training strategies [25].

Table 1: Model Performance in Pooled-Data Evaluation on Primary Collection

Model Training Strategy Mean F1 Score Notes
scFoundation Layer Freezing 0.971 Best overall performance in this setting [25]
scFoundation Fine-Tuning (LoRA) 0.947 Best performance with fine-tuning [25]
LLaMa3-8B Layer Freezing ~0.94 (in specific cancers) Comparable to scFoundation in some cancer types [25]
scBERT Layer Freezing 0.630 Lowest performing model in this setting [25]

Table 2: Model Performance in Cross-Data Evaluation

Model Context Mean F1 Score Notes
UCE After fine-tuning on tumor tissue 0.774 Highest performance post fine-tuning [25]
scGPT Zero-shot learning setting 0.858 Superior performance without task-specific training [25]

Key Experimental Protocols

The performance data presented above were derived from rigorous and standardized benchmarking experiments. The primary framework for this evaluation is scDrugMap, an integrated tool designed for flexible assessment of foundation models on single-cell data [25].

Evaluation Scenarios

Benchmarking was conducted under two main scenarios to test model generalizability [25]:

  • Pooled-Data Evaluation: In this scenario, data from multiple studies are aggregated into a single, large dataset. Models are then trained and tested on this pooled dataset. This approach tests a model's ability to learn from a large and diverse set of samples.
  • Cross-Data Evaluation: This scenario tests a model's ability to generalize to entirely new data. Models are trained on data from one set of studies and then tested on held-out datasets from different studies. This is a more challenging and realistic assessment of how a model might perform in practice.

Model Training Strategies

For each evaluation scenario, two common strategies were employed to adapt the pre-trained foundation models to the specific task of drug response prediction [25]:

  • Layer Freezing: The pre-trained layers of the foundation model are kept frozen (their parameters are not updated). Only the task-specific prediction head (a few final layers) is trained on the new data. This is a parameter-efficient method.
  • Fine-Tuning with LoRA: Instead of fully fine-tuning all model parameters, Low-Rank Adaptation (LoRA) is used. LoRA injects trainable rank-decomposition matrices into the model's layers, allowing for efficient and effective adaptation to the downstream task with significantly fewer trainable parameters.

Underlying Data Collections

The benchmarking relied on two manually curated data collections [25]:

  • Primary Collection: Comprised 326,751 single cells from 36 datasets across 23 studies, covering 11 cancer types and therapies including targeted therapy, chemotherapy, and immunotherapy.
  • Validation Collection: Comprised 18,856 single cells from 17 datasets across 6 independent studies, used for external validation.

The following diagram illustrates the core experimental workflow implemented by scDrugMap for benchmarking these models.

Start Start: scDrugMap Benchmarking Data Curated Data Collection Start->Data SubData1 Primary Collection (326,751 cells) Data->SubData1 SubData2 Validation Collection (18,856 cells) Data->SubData2 EvalScenario Evaluation Scenario SubData1->EvalScenario SubData2->EvalScenario Scenario1 Pooled-Data Evaluation EvalScenario->Scenario1 Scenario2 Cross-Data Evaluation EvalScenario->Scenario2 ModelStrategy Model Training Strategy Scenario1->ModelStrategy Scenario2->ModelStrategy Strategy1 Layer Freezing ModelStrategy->Strategy1 Strategy2 Fine-Tuning (LoRA) ModelStrategy->Strategy2 Output Output: Performance Metrics (F1 Score, etc.) Strategy1->Output Strategy2->Output

The Scientist's Toolkit

To conduct benchmarking experiments in single-cell drug response prediction or to apply these foundation models in research, several key resources and tools are essential. The following table lists critical solutions and their functions.

Table 3: Essential Research Reagents & Solutions

Research Reagent / Tool Function Key Features / Notes
scDrugMap [25] Integrated framework for drug response prediction Provides both a Python command-line tool and an interactive web server; supports evaluation of multiple foundation models.
BioLLM [78] Unified framework for integrating and benchmarking scFMs Standardized APIs for seamless model switching and consistent evaluation; supports zero-shot and fine-tuning tasks.
Low-Rank Adaptation (LoRA) [25] Parameter-efficient fine-tuning strategy Reduces the number of trainable parameters when adapting large pre-trained models to new tasks.
Primary Data Collection [25] Curated benchmark dataset 326,751 cells from 36 datasets; used for primary model training and evaluation.
Validation Data Collection [25] External benchmark dataset 18,856 cells from 17 datasets; used for independent model validation and testing generalizability.

The benchmarking of single-cell foundation models for drug response prediction reveals a landscape where no single model dominates all scenarios. The choice between scFoundation, UCE, and scGPT should be guided by the specific research context and data structure. For analyses involving large, aggregated datasets, scFoundation is the current best choice. For tasks requiring generalization to new, unseen studies—such as predicting response in a novel cancer type or drug—UCE (with fine-tuning) or scGPT (in a zero-shot setting) are more suitable. As the field progresses, standardized frameworks like scDrugMap and BioLLM will be crucial for ensuring fair and reproducible evaluations, ultimately accelerating the application of these powerful models in translational research and drug discovery.

Zero-shot learning (ZSL) represents a paradigm shift in machine learning, enabling models to recognize and classify data they have never encountered during training. This capability is particularly valuable in biological domains like single-cell genomics, where obtaining labeled data for every cell type or condition is impractical. Within the context of single-cell foundation model (scFM) benchmarking research, ZSL offers a powerful method for assessing model generalization without task-specific fine-tuning. This guide objectively compares the zero-shot capabilities of scFMs against traditional and alternative machine learning approaches, providing researchers and drug development professionals with experimental data and methodologies to evaluate model performance in realistic, data-scarce scenarios.

Zero-shot learning is a machine learning technique where a model can classify data it has never seen before without requiring training examples for those specific categories [79]. Instead of relying on direct training data for each possible class, ZSL uses semantic information, attributes, or prior knowledge about the categories to make predictions [79] [80]. This approach mimics human capability to identify new objects by understanding their characteristics and relationships to known concepts [79].

In the context of single-cell genomics, ZSL enables foundation models to generalize to unseen cell types, conditions, or perturbation effects by leveraging learned biological principles rather than explicit examples [8] [4]. The core mechanism involves mapping inputs to a semantic embedding space where relationships between known and unknown classes can be established through shared attributes or functional characteristics [79] [81].

Core Principles and Methodologies

Fundamental Mechanisms

Zero-shot learning operates through several key mechanisms that enable generalization to unseen categories:

  • Semantic Embeddings: ZSL models use vector space representations of words, objects, or tasks to establish relationships between known and unknown classes [81]. In single-cell biology, these embeddings might capture gene functional annotations, pathway associations, or cellular characteristics.

  • Attribute-Based Reasoning: Models learn to associate visual or data features with semantic attributes, allowing them to infer properties of unseen classes [79] [81]. For example, a model might learn that certain gene expression patterns correlate with specific cellular functions.

  • Mapping Functions: ZSL systems acquire transformations between different representations (e.g., visual, textual, or conceptual) to bridge known and unknown domains [81].

Comparative Learning Paradigms

It is essential to distinguish zero-shot learning from related approaches:

Table 1: Comparison of Limited-Data Learning Paradigms

Aspect Zero-Shot Learning (ZSL) One-Shot Learning (OSL) Few-Shot Learning (FSL)
Training Examples for New Classes No examples Exactly one example per class Few examples (typically 2-100) per class [79] [81]
Primary Approach Semantic descriptions, attributes, and embeddings Similarity metrics and metric learning Meta-learning techniques [79]
Key Methodologies Semantic embedding models, attribute-based methods Siamese Networks, Prototypical Networks Model-Agnostic Meta-Learning (MAML), prototypical networks [79] [80]
Ideal Applications When examples for new classes are impractical to obtain Scenarios with only one example available When a few examples can be collected [79]

Experimental Benchmarking in Single-Cell Biology

Benchmarking Frameworks for scFMs

Recent research has established standardized frameworks for evaluating zero-shot capabilities in single-cell foundation models:

  • PertEval-scFM: A specialized benchmark for evaluating perturbation effect prediction in zero-shot settings [5]. This framework tests whether embeddings produced by scFMs contain meaningful information for predicting how cells change after genetic perturbations.

  • Comprehensive Multi-Task Benchmarks: Holistic evaluations encompassing gene-level and cell-level tasks across diverse biological conditions and cancer types [4]. These benchmarks assess models under realistic conditions using multiple metrics spanning unsupervised, supervised, and knowledge-based approaches.

Key Performance Metrics

Researchers employ diverse metrics to quantify zero-shot performance:

  • Unseen-Class Evaluation: Accuracy on entirely unknown categories not seen during training [81]
  • Semantic Grounding: Measurement of semantic similarity between predictions and ground truth [81]
  • Embedding Distance Validation: Cosine similarity between predicted and ground-truth embeddings [81]
  • Cluster Coherence: Assessment of how well unseen classes form coherent groups in embedding space [81]
  • scGraph-OntoRWR: A novel metric designed specifically to uncover intrinsic knowledge encoded by scFMs [4]

Performance Comparison: Zero-Shot Capabilities

Model Performance Across Tasks

Experimental evaluations reveal varying zero-shot capabilities across different scFMs and tasks:

Table 2: Zero-Shot Performance of Single-Cell Foundation Models Across Biological Tasks

Model/Task Cell Type Annotation Accuracy Perturbation Effect Prediction Drug Sensitivity Prediction Batch Integration Quality
scBERT 85-92% [4] Not Reported Not Reported Not Reported
scGPT 82-90% [4] Limited improvement over baselines [5] Moderate performance High
CellFM 80-88% [4] Not Reported Not Reported Not Reported
Simple Baselines 75-85% [4] Competitive performance [5] Variable Moderate
Traditional ML 70-82% [4] Strong performance on calibrated metrics [5] Moderate to high Low to moderate

Comparison with Alternative Approaches

When compared with other learning paradigms and traditional methods, zero-shot approaches show distinct advantages and limitations:

Table 3: Zero-Shot Learning vs. Alternative Approaches in Single-Cell Analysis

Approach Data Efficiency Generalization to Novel Classes Computational Cost Interpretability
Zero-Shot Learning High (no new examples needed) High in theory, variable in practice [4] [5] Low at inference Moderate to low
Fine-Tuned Models Low (requires substantial data) Limited to training distribution High during training Moderate
Few-Shot Learning Moderate (needs few examples) Good with relevant examples [79] Moderate Moderate
Traditional ML Low to moderate Poor without retraining Variable Often high

Experimental Protocols for Zero-Shot Evaluation

Unseen-Class Evaluation Protocol

Proper assessment of true zero-shot capability requires rigorous experimental design:

  • Data Partitioning: Completely separate classes used for training and evaluation, ensuring no overlap in cell types, conditions, or perturbations [81]

  • Semantic Attribute Definition: Establish clear attribute spaces or class relationships that enable knowledge transfer from seen to unseen classes [79] [81]

  • Evaluation Metrics: Employ comprehensive assessment including accuracy, semantic similarity, and embedding coherence [4] [81]

  • Statistical Validation: Use multiple random splits and cross-validation to ensure result reliability [4]

Perturbation Effect Prediction Methodology

The PertEval-scFM benchmark employs this standardized protocol for evaluating perturbation prediction:

  • Embedding Extraction: Generate model embeddings for paired perturbed and unperturbed cells [5]

  • Similarity Assessment: Measure the distance between embeddings of matched pairs [5]

  • Baseline Comparison: Compare against simple linear baselines and established methods [5]

  • Cross-Distribution Evaluation: Test performance under distribution shift, including strong or atypical perturbations [5]

G Zero-Shot Perturbation Prediction Evaluation Workflow Input Input Data (Perturbed & Control Cells) EmbeddingExtraction Embedding Extraction via scFM Input->EmbeddingExtraction SimilarityCalculation Similarity Calculation Between Pairs EmbeddingExtraction->SimilarityCalculation BaselineComparison Baseline Model Comparison SimilarityCalculation->BaselineComparison CrossDistributionTest Cross-Distribution Evaluation BaselineComparison->CrossDistributionTest PerformanceMetrics Performance Metrics & Statistical Validation CrossDistributionTest->PerformanceMetrics

The Scientist's Toolkit: Essential Research Reagents

For researchers implementing zero-shot learning evaluation in single-cell biology, these tools and resources are essential:

Table 4: Essential Research Reagents for Zero-Shot Learning Evaluation

Resource Category Specific Examples Function in Zero-Shot Evaluation
Benchmark Datasets PertEval-scFM, specialized single-cell atlases [4] [5] Provide standardized evaluation frameworks and datasets for comparable assessments
Evaluation Metrics scGraph-OntoRWR, embedding coherence, semantic similarity [4] [81] Quantify model performance beyond simple accuracy, capturing biological relevance
Baseline Models Simple linear models, traditional ML approaches [4] [5] Establish performance floor and validate benchmark meaningfulness
Visualization Tools Embedding projection methods, cluster validation tools Enable qualitative assessment of model capabilities and failure modes
Attribute Ontologies Gene ontology, cell type hierarchies, pathway databases [81] Provide semantic structure for knowledge transfer from known to unknown classes

Zero-shot learning represents a promising approach for assessing the generalization capabilities of single-cell foundation models without task-specific fine-tuning. Current benchmarking research reveals that while scFMs show robust performance on standard tasks like cell type annotation, their zero-shot capabilities for complex tasks like perturbation prediction remain limited, often failing to outperform simple baselines [4] [5]. This highlights both the potential of ZSL for biological discovery and the need for continued methodological advancement. For researchers and drug development professionals, zero-shot evaluation provides a rigorous framework for assessing model generalization, with performance strongly dependent on task complexity, dataset size, and the quality of semantic information available for knowledge transfer [4]. As scFMs continue to evolve, zero-shot benchmarking will remain essential for validating their utility in real-world biological and clinical applications.

G Zero-Shot Knowledge Transfer Logic KnownClasses Known Classes (Seen during training) Model Foundation Model (Mapping Function) KnownClasses->Model Training SemanticSpace Semantic Space (Attributes, Ontologies, Relationships) SemanticSpace->Model Leverages UnknownClasses Unknown Classes (Not seen during training) UnknownClasses->Model Inference Prediction Zero-Shot Prediction for Unknown Classes Model->Prediction

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by allowing scientists to probe transcriptomic profiles at the resolution of individual cells. The emergence of single-cell foundation models (scFMs) pretrained on massive datasets promises to transform how we analyze this complex data, offering tools that can integrate heterogeneous datasets and explore biological systems with unprecedented power [3]. These models, inspired by breakthroughs in natural language processing, learn universal biological knowledge during pretraining in a self-supervised manner, potentially equipping them with emergent capabilities for zero-shot learning and efficient adaptation to various downstream tasks [3]. However, with numerous competing scFMs now available, each with different architectures, pretraining strategies, and intended applications, a critical question remains: how do these models actually perform on essential cell-level tasks like annotation, integration, and cancer identification under realistic research conditions?

This comparison guide synthesizes findings from a comprehensive benchmark study of six prominent scFMs evaluated against well-established baselines to address this pressing question. The evaluation encompassed two gene-level and four cell-level tasks under realistic conditions, with pre-clinical batch integration and cell type annotation assessed across five datasets featuring diverse biological conditions [3] [4]. Clinically relevant tasks, including cancer cell identification and drug sensitivity prediction, were evaluated across seven cancer types and four drugs, providing a rigorous assessment of practical utility [3]. Performance was measured using 12 metrics spanning unsupervised, supervised, and knowledge-based approaches, including novel biological relevance metrics like scGraph-OntoRWR, specifically designed to uncover intrinsic knowledge encoded by scFMs [3]. This guide presents the objective results of these benchmarking efforts to empower researchers, scientists, and drug development professionals in selecting optimal scFMs for their specific research needs.

Experimental Design and Methodologies

Benchmarking Framework and Model Selection

The benchmarking framework was designed to evaluate zero-shot gene embeddings and cell embeddings learned from large-scale pretraining [3]. This approach tests the fundamental biological knowledge acquired during pretraining without task-specific fine-tuning. The study evaluated six prominent scFMs—Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello—representing the current state-of-the-art with diverse architectural approaches and pretraining strategies [3]. These models were compared against well-established baseline methods including highly variable genes (HVGs) selection, the anchor-based Seurat, the clustering-based Harmony, and the generative model scVI [3]. This comprehensive selection ensures meaningful comparisons across different computational paradigms.

The evaluation was conducted under realistic conditions that reflect common research scenarios, with careful attention to mitigating data leakage risks. To validate conclusions rigorously, researchers introduced an independent and unbiased dataset: the Asian Immune Diversity Atlas (AIDA) v2 from CellxGene [3]. The benchmark was explicitly application- and biology-oriented, focusing on challenging scenarios often neglected in previous benchmarking efforts, such as novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [3].

Evaluation Metrics and Tasks

Model performance was assessed using a comprehensive set of 12 metrics spanning unsupervised, supervised, and knowledge-based approaches [3]. Two novel cell ontology-informed metrics were introduced to provide biologically grounded perspectives:

  • scGraph-OntoRWR: Measures the consistency of cell type relationships captured by scFMs with prior biological knowledge [3]
  • Lowest Common Ancestor Distance (LCAD): Assesses the ontological proximity between misclassified cell types to evaluate the severity of errors in cell type annotation [3]

The evaluation encompassed both gene-level and cell-level tasks:

Gene-level tasks focused on predicting known biological relationships, including tissue specificity and Gene Ontology (GO) terms, by comparing gene embeddings from scFMs against established approaches like Functional Representation of Gene Signatures (FRoGS) [3].

Cell-level tasks assessed performance on core single-cell data analysis challenges:

  • Dataset integration: Evaluating the creation of a unified cell embedding space that removes batch effects while preserving biological variation [3]
  • Cell type annotation: Assessing accurate labeling of cell types across diverse biological conditions [3]
  • Cancer cell identification: Testing clinically relevant discrimination of malignant cells across seven cancer types [3]
  • Drug sensitivity prediction: Evaluating prediction of therapeutic responses across four drugs [3]

Table 1: Key Evaluation Metrics in scFM Benchmarking

Metric Category Specific Metrics Purpose
Batch Effect Removal kBET, kNN graph connectivity, ASW across batches, graph iLISI, PCA regression Quantify technical artifact removal while preserving biological variation
Biological Conservation ARI, NMI, cell-type ASW, isolated label scores Assess preservation of biological signal and cell identity
Label-Free Conservation Cell-cycle variance conservation, HVG overlap, trajectory conservation Evaluate preservation of biological structure beyond annotations
Knowledge-Based scGraph-OntoRWR, LCAD Measure alignment with established biological knowledge

Experimental Workflow

The following diagram illustrates the comprehensive benchmarking workflow used to evaluate scFMs across diverse tasks and datasets:

G Start Benchmarking Start Models Six scFMs (Geneformer, scGPT, etc.) Start->Models Baselines Traditional Methods (Seurat, Harmony, scVI) Start->Baselines Tasks Evaluation Tasks Models->Tasks Baselines->Tasks GeneTasks Gene-Level Tasks: • Tissue Specificity • GO Term Prediction Tasks->GeneTasks CellTasks Cell-Level Tasks: • Batch Integration • Cell Type Annotation • Cancer ID • Drug Sensitivity Tasks->CellTasks Metrics Evaluation Metrics (12 total) GeneTasks->Metrics CellTasks->Metrics BatchMetrics Batch Effect Metrics (kBET, iLISI, etc.) Metrics->BatchMetrics BioMetrics Biological Metrics (ARI, NMI, ASW, etc.) Metrics->BioMetrics NovelMetrics Novel Knowledge Metrics (scGraph-OntoRWR, LCAD) Metrics->NovelMetrics Output Performance Rankings & Model Selection Guide BatchMetrics->Output BioMetrics->Output NovelMetrics->Output

Diagram Title: scFM Benchmarking Workflow

Comparative Performance Analysis

Cell Type Annotation Results

Cell type annotation represents a fundamental task in single-cell analysis where accurate performance is critical for downstream biological interpretations. Benchmarking results revealed that no single scFM consistently outperformed all others across all annotation tasks and datasets [3] [4]. This task-dependent performance pattern underscores the importance of matching model strengths to specific annotation challenges.

The introduction of ontology-informed metrics provided novel insights into annotation quality. The Lowest Common Ancestor Distance (LCAD) metric, which measures the ontological proximity between misclassified cell types, demonstrated that some scFMs produce errors that are biologically less severe—misclassifying within related cell lineages rather than across distant cell types [3]. This nuanced evaluation moves beyond simple accuracy metrics to assess the biological reasonableness of errors.

In zero-shot settings, scGPT demonstrated robust performance across multiple annotation tasks, particularly when leveraging its generative capabilities [78]. Geneformer and scFoundation also showed strong annotation capabilities, benefiting from their effective pretraining strategies [78]. The specialized model scBERT, despite being specifically designed for cell-type annotation, lagged behind other scFMs, likely due to its smaller model size and limited training data [78].

Table 2: Cell Type Annotation Performance Comparison

Model Overall Accuracy Rare Cell Detection Cross-Tissue Consistency Biological Plausibility of Errors
scGPT High Medium-High High High (low LCAD scores)
Geneformer Medium-High Medium Medium-High Medium-High
scFoundation Medium-High Medium High Medium-High
UCE Medium Medium-Low Medium Medium
LangCell Medium Low-Medium Medium Medium
scCello Medium Medium Medium-Low Medium
scBERT Low-Medium Low Low-Medium Low-Medium

Batch Integration Performance

Batch integration—removing technical artifacts while preserving biological variation—is essential for constructing unified cell atlases from multiple datasets. Benchmarking results indicated that scFMs generally provide robust and versatile integration across diverse batch effect types, including inter-patient, inter-platform, and inter-tissue variations [3].

Quantitative analysis revealed that the performance improvement of scFMs often arises from creating a smoother cell-property landscape in the pretrained latent space, which reduces the difficulty of training task-specific models [3]. This landscape smoothing effect was quantitatively estimated using the roughness index (ROGI), which served as a proxy for dataset-specific model recommendation [3].

In comparative assessments, scGPT again demonstrated strong performance in batch integration tasks, effectively handling complex batch effect structures [78]. The specialized integration method Scanorama also performed well in specific scenarios, particularly when handling simpler batch effect structures [50]. For complex integration tasks with nested batch effects, scVI and scANVI consistently ranked among top performers, effectively balancing batch removal with biological conservation [50].

A critical finding across multiple benchmarking studies was that highly variable gene selection consistently improves the performance of data integration methods, whereas scaling operations can push methods to prioritize batch removal over conservation of biological variation [50]. This highlights the importance of preprocessing decisions alongside model selection.

Cancer Identification and Clinical Applications

Cancer cell identification represents a particularly challenging task for scFMs due to the high heterogeneity within and between tumors and the subtle distinctions between malignant and non-malignant cells. Benchmarking across seven cancer types revealed varying performance levels, with some scFMs demonstrating better generalization across cancer types than others [3].

The evaluation of drug sensitivity prediction across four drugs showed that scFMs can provide reasonable zero-shot predictions, but their performance did not consistently outperform simpler machine learning models adapted to specific datasets, particularly under resource constraints [3]. This finding underscores the importance of task-specific model selection, especially in clinical applications where predictive accuracy directly impacts translational potential.

Notably, the benchmarking study introduced more challenging clinical scenarios often absent from earlier evaluations, including novel cell type identification, cross-tissue homogeneity assessment, and intra-tumor heterogeneity characterization [3]. These rigorous testing conditions provide better indicators of real-world clinical utility.

Model Selection Framework

Task-Specific Recommendations

Based on the comprehensive benchmarking results, the following data-driven recommendations emerge for selecting scFMs based on specific research tasks:

  • For cell type annotation with limited computational resources: scGPT provides the most consistent performance across diverse cell types and tissues, with particularly strong results in zero-shot settings [78].
  • For gene-level tasks and functional predictions: Geneformer and scFoundation demonstrate superior capabilities, leveraging their effective pretraining strategies for capturing gene relationships [78].
  • For complex batch integration tasks: scVI and scANVI handle nested batch effects most effectively, particularly in atlas-level integration tasks [50].
  • For resource-constrained environments: Simpler machine learning models often outperform scFMs when adapted to specific datasets, offering better computational efficiency without substantial performance sacrifices [3] [4].
  • For multimodal data integration: Generic self-supervised learning methods like VICReg and SimCLR sometimes outperform specialized single-cell methods, particularly for cell typing and multimodal integration tasks [82].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Computational Tools for scFM Benchmarking and Application

Tool/Resource Function Application Context
BioLLM Framework Unified interface for diverse scFMs Standardized model access, switching, and evaluation [78]
scIB Python Module Benchmarking pipeline and metrics Comprehensive evaluation of integration methods [50]
Cell Ontologies Structured biological knowledge Biological plausibility assessment (LCAD metric) [3]
AIDA v2 Dataset Independent validation dataset Mitigating data leakage risks in evaluation [3]
HVG Selection Data preprocessing Improving integration performance [50]
ROGI Index Landscape roughness quantification Dataset-specific model recommendation [3]

Decision Framework for Model Selection

The following diagram illustrates a systematic approach for selecting the most appropriate scFM based on research requirements, dataset characteristics, and resource constraints:

G Start Start Model Selection DSize Dataset Size Assessment Start->DSize SmallData Small Dataset (<10,000 cells) DSize->SmallData LargeData Large Dataset (>10,000 cells) DSize->LargeData TaskType Primary Task Identification SmallData->TaskType LargeData->TaskType Annotation Cell Type Annotation TaskType->Annotation Integration Batch Integration TaskType->Integration Clinical Clinical Prediction TaskType->Clinical Resources Computational Resources Annotation->Resources Integration->Resources Clinical->Resources HighResources Adequate Resources Resources->HighResources LowResources Limited Resources Resources->LowResources Rec1 Recommendation: scGPT HighResources->Rec1 Annotation Task Rec2 Recommendation: Geneformer HighResources->Rec2 Gene-Level Task Rec3 Recommendation: scVI/scANVI HighResources->Rec3 Complex Integration Rec4 Recommendation: Simple ML + Fine-tuning LowResources->Rec4

Diagram Title: scFM Selection Framework

The comprehensive benchmarking of single-cell foundation models reveals a rapidly evolving field with significant promise but no universal solutions. The key finding across all studies is that no single scFM consistently outperforms all others across diverse tasks [3] [4]. This underscores the necessity of tailored model selection based on specific factors including dataset size, task complexity, need for biological interpretability, and available computational resources.

The benchmarking efforts highlight that scFMs are robust and versatile tools for diverse applications, but simpler machine learning models can be more adept at efficiently adapting to specific datasets, particularly under resource constraints [3] [4]. This is especially relevant for researchers with limited computational resources or highly specialized analysis needs.

Future developments in scFMs will likely address current limitations in perturbation effect prediction, where zero-shot embeddings from current-generation models show limited improvement over simple baseline models, particularly under distribution shift [5]. Additionally, specialized frameworks for multimodal data integration represent an important direction for future development, as current methods show variable performance in integrating diverse data modalities [82].

As the field progresses, standardized benchmarking frameworks like BioLLM will play an increasingly important role in providing unified interfaces for diverse scFMs, eliminating architectural and coding inconsistencies to enable streamlined model access and evaluation [78]. These efforts, combined with biologically grounded evaluation metrics, will accelerate the maturation of scFMs and their effective application in both basic biological and clinical research.

For researchers embarking on single-cell analysis projects, the evidence-based recommendations provided in this guide offer a starting point for model selection while emphasizing the importance of context-specific validation. As the field continues to evolve at a rapid pace, maintaining awareness of new benchmarking results and updated performance comparisons will remain essential for leveraging the full potential of single-cell foundation models.

In the evolving field of single-cell genomics, foundation models (scFMs) are trained on millions of cells to learn fundamental biological principles. A critical aspect of benchmarking these models involves evaluating their performance on gene-level tasks, which assess how well the models capture functional relationships between genes and their roles in regulatory networks. Unlike cell-level tasks like annotation or batch integration, gene-level tasks probe the model's understanding of the functional genome, testing its ability to predict gene functions and infer causal regulatory interactions [3]. These tasks are biologically paramount because they move beyond descriptive characterization towards a mechanistic understanding of cellular processes, which is essential for applications in drug target identification and understanding disease mechanisms [83].

The evaluation of gene-level tasks is technically challenging due to the high dimensionality, sparsity, and noise inherent to single-cell RNA sequencing (scRNA-seq) data. Furthermore, genes do not follow a sequential order like words in a sentence, requiring models to employ sophisticated tokenization strategies to represent gene expression values effectively for transformer architectures [1]. This article provides a comparative analysis of current scFMs on these pivotal gene-level tasks, summarizing quantitative performance data, detailing experimental protocols, and providing resources to guide researchers in selecting and applying these powerful models.

Experimental Frameworks for Gene-Level Evaluation

Benchmarking studies employ standardized workflows to ensure fair and biologically meaningful comparisons of different scFMs. The following diagram illustrates a typical pipeline for evaluating gene-level tasks.

G cluster_0 Gene Function Prediction cluster_1 Network Inference Start Input: Pre-trained scFM & Gene Embeddings A Task 1: Gene Function Prediction Start->A B Task 2: Network Inference Start->B C Evaluation Metrics A->C B->C D Performance Comparison C->D A1 Input: Gene Embeddings A2 Objective: Predict GO Terms & Tissue Specificity A1->A2 B1 Input: Expression Data & Prior Knowledge B2 Objective: Reconstruct Regulatory Edges (TF-TG) B1->B2

Task 1: Gene Function Prediction

Objective: This task evaluates whether the gene embeddings learned by an scFM encode meaningful biological information by assessing their ability to predict Gene Ontology (GO) terms and tissue specificity [3]. The underlying hypothesis is that functionally similar genes should reside in close proximity within the model's latent embedding space [3].

Protocol:

  • Feature Extraction: Gene embeddings are extracted directly from the input layers of the pre-trained scFMs. These embeddings are fixed-dimensional vectors representing each gene.
  • Baseline Comparison: The performance of scFM embeddings is typically compared against embeddings from specialized methods, such as FRoGS (Functional Representation of Gene Signatures), which learns gene embeddings through random walks on a hypergraph of GO terms or regulated gene sets [3].
  • Classifier Training: A simple supervised classifier (e.g., a linear model or a small neural network) is trained using the gene embeddings as input features to predict known GO term associations or tissue-specific expression patterns.
  • Performance Measurement: Model performance is quantified using standard metrics for classification tasks, such as Average Precision (AUPRC) and Area Under the Receiver Operating Characteristic curve (AUROC), providing a measure of how well the embeddings capture known functional biology.

Task 2: Gene Regulatory Network (GRN) Inference

Objective: This task assesses a model's capability to infer causal regulatory relationships, specifically Transcription Factor - Target Gene (TF-TG) interactions, from single-cell transcriptomics data [83]. Accurate GRN inference is crucial for understanding complex cellular regulation and the effects of perturbations.

Protocol:

  • Data Input: Models are provided with scRNA-seq data from a specific biological context (e.g., a particular cell type or condition).
  • Incorporation of Prior Knowledge: Many advanced methods integrate prior knowledge to enhance inference. This can include:
    • Experimental data from multi-omics assays (e.g., scATAC-seq for chromatin accessibility).
    • Curated databases of known regulatory interactions.
    • Graph structures where prior knowledge is represented as a graph of probable interactions, constraining the solution space for the inference algorithm [83].
  • Network Reconstruction: The model predicts the likelihood of a regulatory edge existing between each TF and TG pair.
  • Benchmarking against Ground Truth: Performance is evaluated against a gold-standard network derived from experimental validation or curated databases. Key metrics include Precision-Recall curves and Mean Average Precision, which measure the accuracy of the ranked list of predicted edges [17] [83].

Performance Comparison of Single-Cell Foundation Models

Quantitative benchmarking reveals that the performance of scFMs can vary significantly across different tasks and datasets. The table below summarizes findings from large-scale studies that evaluate multiple models.

Table 1: Performance of Models on Gene-Level and Perturbation Tasks

Model / Method Primary Architecture Reported Performance on Gene-Level Tasks Key Findings from Benchmarks
scGPT [4] Decoder-only Transformer (GPT) Effective for perturbation effect prediction [4]. Robust and versatile across tasks, but no single scFM consistently outperforms all others [4] [3].
Geneformer [4] [17] Transformer Uses universal gene embeddings for perturbation prediction [17]. Performance is task and dataset-dependent [3].
scVI [17] Variational Autoencoder Considered a gold standard for transcriptomics analysis [17]. Outperformed foundation models in perturbation analysis; identified as better suited for real-world scenarios than many transformer-based scFMs [17].
PCA [17] Linear Dimensionality Reduction Not a foundation model. Competitive or superior performance to scFMs on perturbation tasks, highlighting that simpler methods can be highly effective [17].
Linear Baselines [4] Linear Models Simple linear baselines can be difficult to outperform on gene perturbation effect prediction [4]. Simpler models can be more adept at efficiently adapting to specific datasets, particularly under resource constraints [4].

A key insight from recent benchmarks is that model selection must be tailored to the specific task. A holistic ranking of six scFMs against established baselines found that while scFMs are robust and versatile tools, simpler machine learning models, including PCA and linear baselines, can be more efficient and effective for specific datasets, especially under computational resource constraints [4] [3]. Notably, one benchmarking study concluded that for perturbation analysis, "scVI and PCA are far better suited models for understanding biological perturbations in comparison to existing foundation models" [17]. This underscores the importance of not overlooking established, simpler methods when designing an analysis pipeline.

To conduct rigorous gene-level evaluations, researchers rely on a combination of computational tools, data resources, and benchmarking frameworks. The following table details key components of the experimental toolkit.

Table 2: Key Research Reagents and Resources for scFM Evaluation

Resource Name Type Function in Evaluation
Gene Ontology (GO) [3] Knowledge Base Provides a controlled vocabulary of gene functions used as ground truth for evaluating gene function prediction tasks.
CZ CELLxGENE [1] Data Platform Provides unified access to standardized, annotated single-cell datasets; a primary source for pretraining and benchmarking data (e.g., AIDA v2 dataset) [3].
FRoGS [3] Computational Method Generates functional gene embeddings via random walks on a GO hypergraph; used as a baseline for comparing scFM-derived gene embeddings.
Perturb-Seq Data [17] Experimental Dataset Provides transcriptomic data from genetic perturbations (CRISPR knockouts); crucial for evaluating model performance on causal inference and perturbation prediction.
scGraph-OntoRWR [3] Evaluation Metric A novel ontology-informed metric that measures the consistency of cell type relationships captured by scFMs with prior biological knowledge.
iLISI [17] Evaluation Metric Measures batch effect reduction in integrated datasets, ensuring biological signals are not confounded by technical artifacts.

Integrated Workflow: From Model Input to Biological Insight

The process of evaluating a foundation model on gene-level tasks integrates the previously described components into a cohesive workflow. The following diagram maps the journey from raw data to biological insight, highlighting critical decision points.

G Data Raw scRNA-seq Data Token Tokenization & Embedding Data->Token Model scFM (Transformer) Token->Model Embed Gene & Cell Embeddings Model->Embed Task1 Function Prediction Embed->Task1 Task2 Network Inference Embed->Task2 Insight Biological Insight Task1->Insight Task2->Insight Prior External Prior Knowledge Prior->Task2

The comprehensive benchmarking of single-cell foundation models on gene-level tasks reveals a nuanced landscape. While sophisticated transformer-based models like scGPT and Geneformer demonstrate significant promise and versatility, established methods like scVI and even classical linear models remain fiercely competitive, particularly for perturbation analysis and focused tasks [4] [17]. The critical takeaway for researchers and drug developers is that no single scFM consistently dominates across all tasks and datasets [4] [3]. Therefore, model selection should be guided by a careful consideration of factors such as dataset size, task complexity, the need for biological interpretability, and available computational resources.

Future progress in the field hinges on developing more biologically grounded evaluation metrics, such as the ontology-informed scGraph-OntoRWR, and on improving strategies for integrating diverse prior knowledge to constrain and guide GRN inference [3] [83]. As foundation models continue to scale in size and pretraining datasets become more comprehensive, the community's focus must remain on rigorous, objective benchmarking to ensure these powerful tools deliver meaningful and reliable biological insights, ultimately accelerating discoveries in basic biology and therapeutic development.

The field of single-cell transcriptomics is undergoing a seismic shift, driven by the emergence of foundation models trained on datasets of unprecedented scale. The prevailing hypothesis suggests that increasing the volume of training data—from millions to hundreds of millions of cells—correlates directly with enhanced model performance across diverse biological tasks. This comparison guide examines the empirical evidence behind this hypothesis by systematically evaluating models across the scalability spectrum, from those trained on 10 million cells to recently developed models trained on over 100 million cells. For researchers, scientists, and drug development professionals, understanding this scalability frontier is crucial for selecting appropriate models that balance computational demands with biological insight. Recent benchmarking studies reveal that while scale confers significant advantages in certain applications, the relationship between dataset size and performance is more nuanced than previously assumed, with factors such as model architecture, training methodology, and data quality playing pivotal roles in determining ultimate utility for biological discovery and therapeutic development.

Atlas of Scale: Comparative Analysis of scFMs by Training Dataset Size

Table 1: Foundation Models Trained on 10M to 50M Human Cells

Model Name Publication Venue/Year Training Data Scale Parameter Count Core Architectural Approach Key Innovation
Geneformer Nature 2023 30 million cells 86 million Transformer Gene rank prediction
scGPT Nature Methods 2024 33 million cells 100 million Transformer with value categorization Attention mask mechanism
scFoundation Nature Methods 2024 ~50 million cells ~100 million Masked autoencoder (MAE) Direct value projection
Universal Cell Embedding (UCE) Cell 2024 36 million cells 650 million Protein language model integration Cross-species molecular diversity
scBERT Nature Machine Intelligence 2022 Millions of human cells Not specified BERT-style transformer Expression value binning

Table 2: Next-Generation Models Trained on 100M+ Human Cells

Model Name Publication Venue/Year Training Data Scale Parameter Count Core Architectural Approach Key Innovation
CellFM Nature Communications 2025 102 million cells 800 million Modified RetNet (ERetNet) Linear complexity scaling
Tahoe-x1 bioRxiv 2025 100 million+ cells 3 billion Not specified Perturbation-focused training

The dramatic escalation in training data is evidenced by recently released datasets like Tahoe-100M, the world's largest single-cell dataset comprising 100 million cells mapping 60,000 drug-cell interactions across 50 cancer cell lines to 1,200 drug perturbations [84]. Similarly, CellFM was trained on a meticulously curated dataset of approximately 100 million human cells from 19,914 samples across different organs and sequencing technologies, with 46.3 million cells from normal donors and the remainder from diseased donors, including 7.1 million cells from viral infection donors and 3.5 million from lung cancer donors [12]. This represents approximately twice the scale of datasets used for previous state-of-the-art single-species models.

Architectural innovations have been necessary to handle this scale. CellFM employs a modified RetNet framework (ERetNet) with linear complexity to balance efficiency and performance when processing 100 million cells, while incorporating a Low-Rank Adaptation (LoRA) mechanism for efficient fine-tuning [12]. This represents an eightfold parameter increase over previous largest single-species models, enabling more sophisticated pattern recognition while maintaining computational feasibility.

Performance Benchmarks: How Scale Influences Model Utility

Table 3: Performance Comparison Across Biological Tasks

Task Category Specific Metric Models Trained on 10M-50M Cells Models Trained on 100M+ Cells Performance Delta
Cell Annotation Accuracy on novel cell types Moderate (varies by model) CellFM: Significant improvement ++
Perturbation Prediction Zero-shot effect prediction Limited improvement over baselines [5] CellFM: Outperforms existing models +
Gene Function Prediction Identification accuracy Moderate CellFM: Improved accuracy ++
Batch Integration Bio-conservation metrics Competitive (e.g., scGPT, UCE) [85] Not fully benchmarked TBD
Biological Relevance scGraph-OntoRWR metric Variable across models [3] Not fully benchmarked TBD

Comprehensive benchmarking reveals a complex relationship between scale and performance. A landmark 2025 study evaluating six single-cell foundation models (scFMs) against established baselines found that no single scFM consistently outperforms others across all tasks, emphasizing that scale alone does not guarantee superiority [3] [4]. The study introduced novel biology-driven evaluation metrics including scGraph-OntoRWR, which measures consistency of cell type relationships captured by scFMs with prior biological knowledge, and the Lowest Common Ancestor Distance (LCAD) metric, which assesses the severity of errors in cell type annotation [3].

Notably, the benchmark found that scFMs are robust and versatile tools for diverse applications, but simpler machine learning models can be more efficient for specific datasets, particularly under resource constraints [3]. This suggests that while scale provides advantages, the law of diminishing returns may apply, with task-specific requirements sometimes favoring more targeted approaches.

For perturbation prediction, the PertEval-scFM benchmark demonstrated that zero-shot embeddings from current-generation scFMs offer limited improvement over simple baseline models, particularly under distribution shift [5]. However, CellFM reports superior performance in perturbation prediction, suggesting that scale combined with appropriate architecture may overcome limitations observed in smaller models [12].

G 10M-30M Cells 10M-30M Cells Geneformer Geneformer 10M-30M Cells->Geneformer scGPT scGPT 10M-30M Cells->scGPT 50M+ Cells 50M+ Cells scFoundation scFoundation 50M+ Cells->scFoundation UCE UCE 50M+ Cells->UCE 100M+ Cells 100M+ Cells CellFM CellFM 100M+ Cells->CellFM Tahoe-x1 Tahoe-x1 100M+ Cells->Tahoe-x1 Cell Type Annotation Cell Type Annotation Geneformer->Cell Type Annotation Batch Integration Batch Integration scGPT->Batch Integration Gene Function Gene Function scFoundation->Gene Function UCE->Batch Integration Perturbation Prediction Perturbation Prediction CellFM->Perturbation Prediction Tahoe-x1->Perturbation Prediction Architectural Efficiency Architectural Efficiency Architectural Efficiency->CellFM Data Quality & Diversity Data Quality & Diversity Data Quality & Diversity->Tahoe-x1 Task Specificity Task Specificity Task Specificity->Geneformer Computational Resources Computational Resources Computational Resources->scGPT

Diagram 1: Model Scale versus Specialization in scFMs. This visualization illustrates how models of different scales demonstrate strengths across specialized tasks, with architectural efficiency and data diversity becoming increasingly critical at the 100M+ cell scale.

Experimental Frameworks for Benchmarking Scalability

Standardized Evaluation Protocols

Rigorous benchmarking requires standardized experimental protocols to enable fair comparisons across models of different scales. The leading benchmarking studies employ several key methodologies:

Zero-Shot Evaluation Protocol: This approach extracts embeddings from pre-trained models without additional fine-tuning to assess inherent biological knowledge [3]. Embeddings are evaluated on held-out tasks not seen during training, providing insight into the generalizable knowledge captured during pre-training.

Task-Specific Fine-Tuning: After zero-shot evaluation, models are typically fine-tuned on specific downstream tasks with limited labeled data to assess adaptability and data efficiency [3] [12]. Performance is measured against traditional baselines and simpler machine learning approaches.

Biology-Driven Metrics: Beyond technical metrics, novel evaluation frameworks incorporate biological prior knowledge through approaches like scGraph-OntoRWR, which measures the consistency of cell type relationships captured by scFMs with established biological ontologies [3]. The LCAD metric provides biological context to annotation errors by measuring ontological proximity between misclassified cell types.

Perturbation-Specific Benchmarks: The PertEval-scFM framework provides standardized evaluation for perturbation effect prediction, testing models on their ability to predict transcriptional responses to genetic and chemical perturbations in zero-shot settings [5].

The CellxGene Census Benchmarking Initiative

The CellxGene Census provides an independent benchmarking platform evaluating embeddings generated by different large-scale models on consistent data slices [85]. Their framework assesses two primary dimensions:

  • Bio-conservation: Measures how well embeddings preserve biological signal using metrics including Leiden clustering NMI/ARI, silhouette scores with respect to biological labels, and classifier accuracy for biological label prediction.
  • Batch-correction: Evaluates how effectively embeddings remove technical artifacts while preserving biological variation using metrics including batch silhouette scores, neighborhood entropy, and classifier resistance to batch label prediction.

Notably, their benchmarks of embeddings from scVI, fine-tuned Geneformer, scGPT, and UCE on Census data provide comparative insights into how different architectural approaches handle biological conservation versus batch correction [85].

Essential Research Reagents for scFM Development

Table 4: Essential Research Reagents and Computational Resources

Resource Category Specific Solution Function in scFM Development
Data Sources Tahoe-100M Dataset World's largest perturbational single-cell dataset with 100M cells & 60K drug-cell interactions [84]
Data Sources scBaseCount AI-curated repository of 200M cells from public data, standardized for interoperability [84]
Data Sources CellxGene Census Standardized single-cell data with pre-computed embeddings for benchmarking [85]
Computational Frameworks MindSpore (Huawei) AI framework used for training CellFM on Ascend910 NPUs [12]
Computational Frameworks PyTorch/TensorFlow Standard deep learning frameworks for model development
Benchmarking Tools PertEval-scFM Standardized framework for evaluating perturbation prediction [5]
Benchmarking Tools scib-metrics Metrics package for evaluating bio-conservation and batch correction [85]

Implications for Drug Development and Cellular Biology

The scalability frontier in single-cell foundation models presents significant implications for drug development professionals and cellular biologists. Large-scale models like CellFM and Tahoe-x1 demonstrate enhanced capability in predicting cellular responses to chemical and genetic perturbations, potentially accelerating therapeutic discovery [12]. The Tahoe-100M dataset's comprehensive mapping of 60,000 drug-cell interactions across 50 cancer cell lines provides an unprecedented resource for in silico drug screening and mechanism-of-action analysis [84].

For tumor microenvironment studies, the enhanced ability of larger models to capture intra-tumor heterogeneity and identify rare cell populations could uncover novel therapeutic targets and resistance mechanisms [3]. The biological relevance captured through ontology-informed metrics suggests that models trained at sufficient scale better recapitulate known biological relationships, potentially increasing trust in their novel predictions.

However, benchmarking studies consistently emphasize that model selection must be task-specific, with larger models not always outperforming smaller, more targeted approaches, particularly in resource-constrained environments or for specialized applications [3]. The computational resources required for 100M+ cell models are substantial—CellFM was trained on four Huawei Altas800 servers, each equipped with eight Ascend910 NPUs [12]—creating practical constraints for many research groups.

G Research Question Research Question Model Selection Model Selection Research Question->Model Selection Data Availability Data Availability Data Availability->Model Selection Computational Resources Computational Resources Computational Resources->Model Selection Task Requirements Task Requirements Task Requirements->Model Selection 10M-30M Cell Models 10M-30M Cell Models Rapid Prototyping Rapid Prototyping 10M-30M Cell Models->Rapid Prototyping 50M+ Cell Models 50M+ Cell Models Batch Integration Batch Integration 50M+ Cell Models->Batch Integration 100M+ Cell Models 100M+ Cell Models Novel Target Discovery Novel Target Discovery 100M+ Cell Models->Novel Target Discovery Perturbation Screening Perturbation Screening 100M+ Cell Models->Perturbation Screening Traditional ML Traditional ML Model Selection->10M-30M Cell Models Limited Data Model Selection->50M+ Cell Models Balanced Needs Model Selection->100M+ Cell Models Max Performance Model Selection->Traditional ML Constrained Resources

Diagram 2: Decision Framework for Model Selection. This workflow guides researchers in selecting appropriate models based on their specific research questions, available data, computational resources, and task requirements, acknowledging that larger scale does not always equate to better performance for every application.

The scalability frontier in single-cell foundation models represents a dynamic landscape where increasing training data from 10M to 100M+ cells delivers tangible but nuanced benefits. While models like CellFM demonstrate superior performance in specific applications including perturbation prediction and gene function annotation, comprehensive benchmarking reveals that no single model consistently outperforms across all tasks [3]. The relationship between scale and performance is modulated by architectural decisions, data quality and diversity, and task-specific requirements.

For the research community, this suggests a strategic approach to model selection that balances scale with practical constraints and application needs. The emergence of massive curated datasets like Tahoe-100M and standardized benchmarking frameworks like PertEval-scFM provides the foundation for continued progress toward more predictive in silico models of cellular behavior [84] [5]. As the field advances, the integration of multimodal data, more efficient architectures, and biology-driven evaluation metrics will likely further enhance the utility of large-scale foundation models for both basic biological discovery and therapeutic development.

Conclusion

Recent benchmarking efforts conclusively show that single-cell foundation models are powerful, versatile tools that have matured beyond proof-of-concept, delivering robust performance in critical biomedical tasks like drug response prediction and cell type annotation. However, the 'best' model is inherently task-dependent; scFoundation may lead in pooled-data scenarios, while scGPT shows remarkable zero-shot ability, and UCE excels in cross-data fine-tuning. The future of scFM development lies in enhancing biological interpretability, improving scalability through architectures like Mamba, and standardization via community platforms. For researchers, the strategic selection of scFMs based on specific project needs—rather than seeking a universal winner—will be paramount. As these models continue to evolve, they are poised to become indispensable in unlocking deeper insights into cellular mechanisms, accelerating therapeutic discovery, and ultimately paving the way for personalized medicine.

References