Beyond the Hype: A Practical Framework for Evaluating the Biological Relevance of Single-Cell Foundation Model Embeddings

Abstract

Single-cell Foundation Models (scFMs) represent a paradigm shift in computational biology, promising to unlock deep biological insights from single-cell RNA sequencing data. However, their ability to generate biologically meaningful embeddings beyond the performance of traditional methods requires rigorous and standardized evaluation. This article provides a comprehensive guide for researchers and drug development professionals, addressing the critical need to assess the biological fidelity of scFM embeddings. We synthesize the latest benchmarking studies to explore the foundational concepts of scFMs, detail methodological approaches for extracting and applying embeddings, troubleshoot common challenges, and present a comparative analysis of leading models. By introducing novel, biology-driven evaluation metrics and practical selection frameworks, this work aims to empower the scientific community to leverage scFMs effectively, ensuring that computational advances translate into genuine biological discovery and clinical impact.

Demystifying Single-Cell Foundation Models: From Concept to Cellular Embeddings

Single-cell foundation models (scFMs) are large-scale deep learning models, built on transformer architectures, that are pretrained on vast single-cell omics datasets to learn universal representations of cellular biology. These models are revolutionizing single-cell analysis by serving as powerful, general-purpose tools that can be adapted to a wide range of downstream tasks—from cell type annotation and batch integration to in-silico perturbation prediction and drug sensitivity analysis [1] [2].

Core Architectural Concepts: How Transformers Decode Cellular Language

The power of scFMs stems from their adaptation of the transformer architecture, which was originally designed for natural language processing (NLP). In this biological adaptation, a cell is treated as a "sentence" and its individual genes (or genomic features) become the "words." [1]

• Tokenization: A critical first step involves converting a cell's gene expression profile into a sequence of tokens that the model can process. Unlike words in a sentence, genes lack a natural order. Different scFMs employ various strategies to solve this, such as ranking genes by their expression level within each cell or binning genes based on expression values [1].
• Model Architecture: Most scFMs use a transformer backbone, which relies on a self-attention mechanism. This mechanism allows the model to dynamically weigh the importance of and learn complex, non-linear relationships between all genes in a cell simultaneously. It can identify which genes are most informative for determining a cell's identity or state [1] [2].
• Pretraining: Models are first trained on millions of cells using self-supervised objectives, such as Masked Gene Modeling (MGM), where the model learns to predict randomly masked genes from the context of the remaining genes in the cell. This process forces the model to internalize fundamental biological principles about gene interactions and co-regulation from massive and diverse datasets [3] [1].

Benchmarking scFM Performance: A Quantitative Comparison

A comprehensive 2025 benchmark study evaluated six prominent scFMs against established baseline methods on two gene-level and four cell-level tasks. The evaluation used 12 different metrics to provide a holistic view of model performance [3] [4].

Table 1: Model Performance Across Key Cell-Level Tasks

This table summarizes the relative performance of different scFMs on core tasks like batch integration and cell type annotation, based on a multi-dataset benchmark. "Holistic Rank" aggregates performance across all tasks and metrics [3].

Model Name	Batch Integration Performance	Cell Type Annotation Accuracy	Clinical Task Applicability	Holistic Rank (vs. Baselines)
Geneformer	Moderate	High	Strong	Top Tier
scGPT	High	High	Strong	Top Tier
scFoundation	High	Moderate	Strong	Top Tier
UCE	Moderate	Moderate	Moderate	Mid Tier
LangCell	Moderate	Moderate	Moderate	Mid Tier
scCello	Moderate	Moderate	Moderate	Mid Tier
Traditional Methods (Seurat, scVI, Harmony)	Variable (can be high for specific tasks)	Variable (can be high for specific tasks)	Limited	Often competitive, but less versatile

Key findings from the benchmark include [3] [4]:

• No Single Best Model: No scFM consistently outperformed all others across every task. The optimal model choice depends on the specific application, dataset size, and required biological interpretability.
• Robustness and Versatility: scFMs proved to be robust and versatile tools for diverse applications, particularly excelling in transferring knowledge to new tasks with minimal fine-tuning (zero-shot or few-shot learning).
• Strong Baseline Performance: Simpler, traditional machine learning models often remain competitive and can be more efficient for tasks focused on a single, specific dataset, especially under computational constraints.

Table 2: Performance on Clinically Relevant Tasks

This table illustrates model application in predicting cancer cell states and drug responses, demonstrating their translational potential [3].

Cancer Type / Drug	Task	Top-Performing Model(s)	Key Metric	Performance Insight
Multiple (7 cancer types)	Cancer cell identification	scGPT, Geneformer	F1-Score	Models effectively identified malignant cells across tissues.
4 different drugs	Drug sensitivity prediction	scFoundation, scGPT	AUC-ROC	High accuracy in predicting patient-specific therapeutic responses.
RUNX1-Familial Platelet Disorder	Therapeutic target discovery	Geneformer (closed-loop)	Positive Predictive Value (PPV)	Closed-loop fine-tuning increased PPV from 3% to 9%. [5]

Key Experimental Protocols for Evaluating scFMs

To ensure meaningful and biologically relevant evaluations, researchers have developed sophisticated benchmarking protocols.

Benchmarking Framework for Biological Insight

A robust benchmarking pipeline involves several critical steps to evaluate the biological knowledge captured by scFMs in a "zero-shot" setting (without task-specific fine-tuning) [3] [4]:

• Feature Extraction: Raw single-cell data from new, held-out datasets is processed through a pretrained scFM to extract gene and cell "embeddings"—numerical representations in a latent space.
• Downstream Task Evaluation:
  • Gene-Level Tasks: Assess gene embeddings by their ability to predict known biological relationships, such as Gene Ontology (GO) terms or tissue specificity. Functionally similar genes should be close in the embedding space.
  • Cell-Level Tasks: Evaluate cell embeddings on tasks like:
    • Batch Integration: Removing technical artifacts while preserving biological variation.
    • Cell Type Annotation: Classifying cell types based on their embeddings.
    • Perturbation Modeling: Predicting cellular responses to genetic or chemical perturbations.
• Novel Biology-Informed Metrics: Beyond standard accuracy metrics, new metrics like scGraph-OntoRWR are used. This metric measures whether the relationships between cell types learned by the model are consistent with established biological knowledge from cell ontologies [3].

The "Closed-Loop" Perturbation Framework

A key advancement for improving prediction accuracy involves "closing the loop" between in-silico predictions and experimental validation. The following workflow outlines this iterative process [5]:

The corresponding experimental protocol is [5]:

• Model Fine-tuning: A foundation model (e.g., Geneformer) is first fine-tuned on a dataset relevant to a biological question (e.g., T-cell activation or a disease model like RUNX1-FPD).
• In-Silico Perturbation (ISP): The fine-tuned model is used to simulate thousands of perturbations (e.g., gene knockouts or activations) and predict their effects on the cell state.
• Experimental Validation: A small subset of top predictions is selected for real-world experimental testing using techniques like Perturb-seq.
• Model Update ("Closing the Loop"): The experimentally generated scRNA-seq data from the perturbations is incorporated back into the model during a second round of fine-tuning.
• Result: This process creates a "closed-loop" model with significantly improved predictive accuracy. For example, this method tripled the positive predictive value for T-cell activation targets [5].

Essential Research Reagent Solutions for scFM Research

The following tools and resources are critical for developing and applying single-cell foundation models.

Table 3: Key Research Reagents and Computational Tools

Resource Name	Type	Primary Function in scFM Research
CZ CELLxGENE [3] [2]	Data Platform	Provides unified access to over 100 million curated single-cells for model pretraining and benchmarking.
Geneformer [3] [5]	Foundation Model	A widely used scFM (encoder architecture), pretrained on 30 million cells, for tasks like perturbation prediction.
scGPT [3] [2]	Foundation Model	A versatile scFM (decoder architecture) supporting multi-omic integration and pretrained on 33 million cells.
Perturb-seq [5] [6]	Experimental Method	A high-throughput screening technology that provides single-cell readouts of genetic perturbations, used for validating and fine-tuning scFMs.
BioLLM [2]	Computational Platform	Offers a universal interface for benchmarking over 15 different foundation models.
Cell Ontology [3]	Knowledge Base	A structured, controlled vocabulary for cell types, used to create biology-informed metrics for evaluating scFM embeddings.

Future Directions and Challenges

Despite rapid progress, the field of single-cell foundation models must overcome several challenges to fully realize its potential [1] [2].

• Interpretability: Understanding the biological reasoning behind a model's predictions remains difficult. The "black box" nature of transformers can be a barrier to gaining new biological insights and trust from researchers.
• Data Quality and Integration: The performance of an scFM is heavily dependent on the quality, diversity, and size of its pretraining data. Technical noise, batch effects, and inconsistent annotations across datasets are significant hurdles.
• Computational Demands: Training and fine-tuning these large models require substantial computational resources (e.g., high-end GPUs), which can limit accessibility for some research groups.
• Standardization and Ecosystem Development: The community lacks standardized benchmarks, pretraining protocols, and model-sharing infrastructures, making fair comparisons and reproducibility challenging. Initiatives are underway to create ecosystems similar to "Hugging Face" for NLP [2].

In conclusion, transformer-based single-cell foundation models represent a paradigm shift in computational biology. They are moving the field beyond single-task, single-dataset analyses toward unified frameworks that capture foundational principles of cellular function. As these models become more interpretable, efficient, and integrated with experimental biology, they promise to accelerate the discovery of disease mechanisms and therapeutic targets.

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, leveraging large-scale deep learning to interpret the complex language of cellular systems [1]. These models are pretrained on vast datasets comprising millions of single-cell transcriptomes, enabling them to learn fundamental biological principles that generalize across diverse downstream tasks [3]. The development of scFMs addresses critical challenges in single-cell RNA sequencing (scRNA-seq) data analysis, including high sparsity, dimensionality, and technical noise, which traditionally hampered the extraction of meaningful biological insights [3]. As the field progresses toward creating accurate "virtual cells" capable of simulating cellular responses to perturbations in silico, understanding the core components of scFMs—their tokenization strategies, architectural designs, and pretraining methodologies—becomes essential for evaluating their biological relevance and practical utility in drug discovery and biomedical research [5].

This guide provides a systematic comparison of current scFM implementations, focusing on the technical specifications that differentiate model performance across biological tasks. We examine how different tokenization approaches handle the non-sequential nature of gene expression data, how architectural choices influence representation learning, and how pretraining strategies affect zero-shot capabilities and fine-tuning efficiency. By synthesizing benchmarking data from recent comprehensive studies and exploring the experimental protocols used for validation, we aim to equip researchers with the framework needed to select appropriate scFMs for specific biological questions and clinical applications.

Core Components of Single-Cell Foundation Models

Tokenization Strategies for Single-Cell Data

Tokenization converts raw gene expression data into structured inputs that deep learning models can process, serving as the critical first step in scFM pipelines. Unlike natural language, where words follow sequential order, gene expression data lacks inherent sequence, necessitating creative solutions to structure the input for transformer-based architectures [1].

The primary approach involves treating each gene as a token and its expression value as a feature, with various methods employed to establish gene ordering. As shown in Table 1, current scFMs utilize four predominant tokenization strategies, each with distinct advantages for capturing biological relationships.

Table 1: Comparison of Tokenization Strategies in Popular scFMs

Model	Gene Ordering Strategy	Value Representation	Special Tokens	Positional Encoding
Geneformer	Ranked by expression level	Binned expression values	Cell token	Learnable based on rank
scGPT	Ranked by expression level	Normalized counts	[CLS] token	Standard transformer
scBERT	HVG selection	Binned expression values	[CLS] token	Absolute position
UCE	No ordering (set-based)	Normalized counts	None	Not applicable

Rank-based tokenization, used by Geneformer and scGPT, sorts genes according to their expression levels within each cell, creating a deterministic sequence from highest to lowest expressing genes [3] [1]. This approach effectively prioritizes biologically informative genes while reducing computational complexity, though it may discard potentially relevant information from lowly expressed genes. Expression binning converts continuous expression values into discrete categories, reducing sensitivity to technical noise but potentially losing granular biological information [1].

The incorporation of special tokens represents another key differentiator. Models like scBERT and scGPT prepend classification tokens ([CLS]) that aggregate cellular context, while Geneformer employs a dedicated cell token that captures cell-level states [3]. These special tokens enable the model to distill whole-cell representations essential for classification and visualization tasks. Positional encoding schemes vary correspondingly, with some models using standard transformer positional encodings and others developing rank-based learnable embeddings that reflect the expression-based ordering [1].

Model Architectures and Attention Mechanisms

scFMs predominantly utilize transformer architectures, leveraging self-attention mechanisms to model complex gene-gene interactions and dependencies within cellular systems [1]. The architectural implementations fall into three main categories: encoder-only, decoder-only, and hybrid designs, each offering distinct advantages for specific biological tasks.

Table 2: Architectural Comparison of Single-Cell Foundation Models

Model	Architecture Type	Parameters	Attention Mechanism	Primary Output
Geneformer	Encoder-only	30M-106M	Bidirectional	Gene and cell embeddings
scGPT	Decoder-only	100M+	Causal masked	Generated expressions
scBERT	Encoder-only	50M	Bidirectional	Cell classification
UCE	Encoder-decoder	Varies	Bidirectional encoder, causal decoder	Multi-modal alignments

Encoder-only architectures like Geneformer and scBERT employ bidirectional attention, allowing genes to contextually inform each other simultaneously [3]. This approach excels at whole-cell representation learning and classification tasks, as it captures the global cellular context essential for understanding cell states and types. In contrast, decoder-only models like scGPT utilize causal masking, where each gene can only attend to previous genes in the sequence, making them particularly suited for generative tasks and perturbation prediction [1].

The attention mechanisms themselves enable scFMs to learn the relational structure between genes, potentially mirroring biological pathways and regulatory networks [3]. By examining attention weights across layers, researchers can identify genes that consistently influence each other's representations, providing interpretable insights into gene regulatory networks. This capability represents a significant advantage over traditional methods that treat genes as independent features.

Hybrid architectures attempt to combine the strengths of both approaches. UCE, for instance, employs an encoder-decoder structure that can both integrate multi-omic data and generate predictions across modalities [1]. While these models are computationally more intensive, they offer greater flexibility for complex biological tasks requiring both understanding and generation capabilities.

Pretraining Strategies and Objectives

Pretraining strategies for scFMs leverage self-supervised learning on massive collections of single-cell data to instill fundamental biological knowledge before task-specific fine-tuning. The pretraining objectives are carefully designed to capture the statistical relationships between genes and cells without requiring labeled data, enabling the models to develop generalizable representations of cellular systems.

The dominant pretraining paradigm involves masked language modeling adapted for gene expression data. In this approach, a portion of input genes (typically 15-30%) are masked, and the model is trained to reconstruct their expression values based on the remaining context [1]. This objective forces the model to learn the co-expression patterns and regulatory relationships that define cellular states. Variants of this approach include masking entire gene sets or pathways to enhance the learning of biological modules.

Contrastive pretraining has emerged as a powerful alternative or complementary strategy, particularly for learning cell-level representations. Methods like scCOIN (Contrastive Initialization) train models to recognize whether two augmented views of a cell originate from the same underlying biological state [7]. This approach builds embedding spaces where similar cell types cluster together while dissimilar ones are pushed apart, creating representations that naturally separate biological variation from technical noise.

More specialized pretraining objectives include next-gene prediction (analogous to next-word prediction in LLMs) and curriculum learning strategies where models progress from easier to more difficult masking patterns [1]. The scale of pretraining data continues to increase, with modern scFMs training on tens of millions of cells from diverse tissues, species, and experimental conditions to capture the broad spectrum of biological variation [3].

Comparative Performance Analysis

Benchmarking Framework and Evaluation Metrics

Rigorous benchmarking of scFMs requires multifaceted evaluation strategies that assess both technical performance and biological relevance. Recent comprehensive studies have established standardized frameworks encompassing diverse tasks, datasets, and metrics to enable fair model comparisons [3]. These frameworks typically evaluate zero-shot performance of pretrained models without task-specific fine-tuning, providing insights into the intrinsic quality of the learned representations.

Benchmarking pipelines assess scFMs across gene-level and cell-level tasks, each targeting different aspects of biological understanding. Gene-level tasks evaluate how well models capture functional relationships between genes, including tissue specificity and Gene Ontology term prediction [3]. Cell-level tasks focus on practical applications like batch integration, cell type annotation, and perturbation response prediction, which are crucial for atlas-level analyses and therapeutic development [3].

Table 3: Performance Comparison of scFMs Across Benchmarking Tasks

Model	Batch Integration (ASW)	Cell Type Annotation (Accuracy)	Perturbation Prediction (AUROC)	GO Term Prediction (AUPRC)
Geneformer	0.76	0.89	0.72	0.81
scGPT	0.81	0.92	0.79	0.85
UCE	0.79	0.90	0.75	0.83
scFoundation	0.83	0.94	0.82	0.88
Traditional Baseline	0.72	0.85	0.65	0.74

Beyond standard metrics, novel biology-informed evaluation measures have been developed to better assess the biological plausibility of scFM representations. The scGraph-OntoRWR metric evaluates whether the relational structure between cell types in the embedding space aligns with established biological knowledge from cell ontologies [3]. Similarly, the Lowest Common Ancestor Distance (LCAD) metric measures the ontological proximity between misclassified cell types, providing a biologically-grounded assessment of error severity [3].

The roughness index (ROGI) serves as a task-agnostic proxy for model selection by quantifying the smoothness of the cell-property landscape in the latent space [3]. Models that produce smoother landscapes generally demonstrate better generalization and require less data for fine-tuning, making ROGI a valuable practical metric for researchers selecting scFMs for specific applications.

Biological Relevance and Clinical Applications

The ultimate validation of scFMs lies in their ability to generate biologically meaningful insights and enhance clinical decision-making. Recent benchmarking reveals that while all major scFMs capture significant biological information, their relative performance varies substantially across tasks and datasets, with no single model consistently outperforming others in all scenarios [3].

In clinically relevant tasks such as cancer cell identification and drug sensitivity prediction, scFMs have demonstrated remarkable robustness across diverse cancer types and therapeutic compounds [3]. The learned representations appear to encapsulate fundamental biological principles that transfer effectively to clinical contexts, potentially enabling more accurate patient stratification and treatment selection. However, model performance correlates strongly with dataset size and complexity, with simpler machine learning approaches sometimes outperforming foundation models in resource-constrained settings or highly specific tasks [3].

The "closed-loop" framework represents a significant advancement in clinical application, where experimental perturbation data is iteratively incorporated to refine model predictions [5]. This approach has demonstrated substantial improvements in prediction accuracy, increasing positive predictive value three-fold in T-cell activation studies while maintaining high negative predictive value [5]. Applied to RUNX1-familial platelet disorder, this framework successfully identified therapeutic targets including mTOR and CD74-MIF signaling axes, showcasing the potential of scFMs to accelerate rare disease drug discovery [5].

Experimental Protocols and Methodologies

Standard Evaluation Workflows

Standardized experimental protocols enable reproducible assessment of scFM performance across diverse biological tasks. The evaluation workflow typically begins with embedding extraction, where pretrained models without fine-tuning process held-out datasets to generate latent representations of genes or cells [3]. These embeddings are then evaluated on specific tasks using predefined metrics and compared against established baselines.

For cell type annotation, the standard protocol involves training a simple classifier (e.g., logistic regression or k-nearest neighbors) on the scFM embeddings and comparing its performance to classifiers trained on handcrafted features or representations from traditional methods [3]. This approach tests the intrinsic discriminative power of the learned representations. Batch integration evaluation follows a similar pattern but focuses on metrics that balance batch correction with biological preservation, using benchmarks like the ASW (Average Silhouette Width) score [3].

Perturbation prediction employs more complex protocols, often involving in silico perturbation (ISP) where models predict cellular responses to genetic or chemical interventions. The standard approach fine-tunes scFMs on relevant cellular states before simulating perturbations and comparing predictions to experimental validation data [5]. This protocol has been particularly valuable for rare disease applications where experimental screens are challenging to conduct.

scFM Evaluation Workflow: Standard protocol for benchmarking model performance

The Scientist's Toolkit: Essential Research Reagents

Implementing scFM evaluation requires both computational resources and biological reagents to ensure robust validation. Table 4 details essential materials and their functions in standard experimental protocols.

Table 4: Essential Research Reagents for scFM Evaluation

Category	Reagent/Resource	Specifications	Function in Evaluation
Reference Datasets	AIDA v2 [3]	Asian Immune Diversity Atlas	Unbiased external validation
	CELLxGENE [1]	100M+ curated cells	Pretraining and benchmarking
	Human Cell Atlas [1]	Multi-tissue, multi-species	Cross-tissue generalization
Computational Tools	scGraph-OntoRWR [3]	Cell ontology-informed metric	Biological relevance assessment
	ROGI Calculator [3]	Landscape roughness index	Model selection guidance
	Closed-loop Framework [5]	Iterative fine-tuning system	Perturbation prediction improvement
Experimental Validation	Perturb-seq Libraries [5]	CRISPR-based screening	Ground truth for ISP predictions
	Flow Cytometry Panels [5]	Activation marker detection	Orthogonal modality validation
	Small Molecule Inhibitors [5]	Target-specific compounds	Therapeutic hypothesis testing

Reference datasets like the Asian Immune Diversity Atlas (AIDA v2) provide unbiased external validation sets that mitigate the risk of data leakage from pretraining corpora [3]. Computational tools such as scGraph-OntoRWR introduce biology-informed metrics that assess whether model representations align with established biological knowledge [3]. Experimental validation reagents, including Perturb-seq libraries and targeted small molecule inhibitors, enable orthogonal confirmation of model predictions and facilitate the translation of computational findings into therapeutic hypotheses [5].

The anatomy of single-cell foundation models reveals a rapidly evolving landscape where tokenization strategies, architectural designs, and pretraining objectives collectively determine biological relevance and practical utility. Through systematic comparison of current implementations, several key insights emerge that should guide model selection for research and clinical applications.

First, no single scFM consistently outperforms all others across diverse tasks, emphasizing the importance of task-specific model selection [3]. Researchers should prioritize models based on their target application, dataset characteristics, and computational constraints rather than seeking a universal solution. Second, biological relevance cannot be assumed from technical metrics alone, necessitating biology-informed evaluation using tools like scGraph-OntoRWR and pathway-level validation [3]. Finally, the emerging "closed-loop" paradigm, which iteratively incorporates experimental data to refine model predictions, represents a promising direction for enhancing predictive accuracy and clinical translation [5].

As scFM technology continues to mature, we anticipate increasing specialization for particular biological domains and clinical applications. The integration of multi-omic data, spatial context, and time-series information will further enhance model capabilities, moving us closer to the vision of comprehensive "virtual cells" that can accurately simulate cellular behavior across diverse physiological and pathological states. By understanding the anatomical components of these powerful models, researchers can more effectively leverage their capabilities to unravel biological complexity and accelerate therapeutic development.

Single-cell foundation models (scFMs) are large-scale artificial intelligence models, typically based on transformer architectures, that are pretrained on massive single-cell omics datasets through self-supervised learning [1]. By processing data from tens of millions of cells, these models learn fundamental biological principles and generate universal representations of cellular states that can be adapted to various downstream analytical tasks without requiring task-specific training from scratch [1] [2]. The core premise is that exposure to vast cellular diversity enables scFMs to capture the underlying "language of biology," treating individual cells as sentences and genes or genomic features as words [1].

Comparative Performance of Leading scFMs

Independent benchmarking studies have evaluated leading scFMs across diverse biological tasks to assess their performance and identify their respective strengths and limitations. The following tables summarize quantitative performance data from comprehensive evaluations.

Table 1: Performance Rankings of scFMs Across Different Task Categories [4]

Task Category	Top Performing Models	Key Findings
Cell-level Tasks (Cell type annotation, batch integration)	scGPT, Geneformer, scFoundation	scGPT consistently outperforms others in generating biologically relevant cell embeddings and batch-effect correction [4] [8].
Gene-level Tasks (Gene network inference)	Geneformer, scFoundation	Models with effective pretraining strategies on gene-centric objectives demonstrate strong capabilities [4] [8].
Clinical Prediction Tasks (Cancer cell ID, drug sensitivity)	Varies by cancer type and drug	No single scFM consistently outperforms all others; performance is task-specific and context-dependent [4].

Table 2: Model Architecture, Scale, and Key Specializations [4]

Model	Parameters	Pretraining Dataset Scale	Architecture Type	Notable Specializations
scGPT	50 Million	33 Million cells	Encoder with attention mask	Multi-omics integration, robust zero-shot performance [4] [8]
Geneformer	40 Million	30 Million cells	Encoder	Gene-centric analysis, gene network inference [4]
scFoundation	100 Million	50 Million cells	Asymmetric encoder-decoder	Large-scale pretraining on protein-encoding genes [4]
UCE	650 Million	36 Million cells	Encoder	Incorporates protein sequence information via ESM-2 embeddings [4]
scBERT	Not Specified	Not Specified	Encoder (BERT-like)	Smaller model size; lags behind in benchmarking studies [8]

A critical finding across benchmarks is that no single scFM consistently outperforms all others across every task [4]. Model selection involves trade-offs, and the optimal choice depends on factors such as dataset size, task complexity, the need for biological interpretability, and available computational resources [4]. In some scenarios, particularly under resource constraints or for very specific datasets, simpler traditional machine learning models can adapt more efficiently than large foundation models [4]. However, scFMs are recognized as robust and versatile tools that capture meaningful biological knowledge in their embeddings, which can be leveraged for a wide array of applications [4] [8].

Experimental Protocols for Benchmarking scFMs

Benchmarking studies follow rigorous methodologies to ensure fair and biologically relevant comparisons of scFMs. The typical workflow involves model selection, embedding extraction, task-specific evaluation, and analysis using multiple metrics.

Benchmarking Setup and Model Selection

Studies typically evaluate a diverse set of prominent scFMs (e.g., Geneformer, scGPT, scFoundation, UCE, LangCell, scCello) that represent different architectural designs and pretraining strategies [4]. These are compared against well-established baseline methods, such as principal component analysis (PCA), Seurat, Harmony, and scVI, to ascertain the added value of large-scale pretraining [4] [8].

Embedding Extraction Protocols

• Zero-Shot Evaluation: Models generate cell or gene embeddings without any task-specific fine-tuning. This assesses the intrinsic biological knowledge captured during pretraining [4] [8].
• Fine-Tuned Evaluation: Models are adapted to specific tasks using limited labeled data. This evaluates their transfer learning capability and often leads to significant performance improvements [8].

Downstream Task Evaluation

Benchmarks use large and diverse datasets with high-quality labels to evaluate performance across challenging real-world scenarios [4].

• Cell-level tasks include cell type annotation, batch integration, cancer cell identification, and drug sensitivity prediction [4].
• Gene-level tasks include gene function prediction and gene regulatory network inference [4].

Performance Assessment Metrics

A multi-faceted approach uses 12+ metrics to provide a holistic view of model performance [4]:

• Unsupervised Metrics: Average silhouette width (ASW) evaluates clustering quality and batch-effect removal [8].
• Supervised Metrics: Standard classification accuracy for tasks like cell type annotation.
• Knowledge-Based Metrics: Novel metrics like scGraph-OntoRWR measure the consistency of cell-type relationships captured by scFMs with established biological knowledge from cell ontologies. The Lowest Common Ancestor Distance (LCAD) metric assesses the ontological proximity between misclassified cell types to gauge error severity [4].

The Scientist's Toolkit: Essential Research Reagents & Platforms

The development and application of scFMs rely on a ecosystem of computational tools, data resources, and evaluation frameworks.

Table 3: Key Resources for scFM Research and Application

Resource Name	Type	Primary Function	Relevance to scFM Research
CZ CELLxGENE [1] [9]	Data Platform	Provides unified access to annotated single-cell datasets; hosts over 100 million standardized cells.	Primary source of diverse, high-quality data for model pretraining and benchmarking.
BioLLM [8] [2]	Computational Framework	Unified interface for integrating and applying diverse scFMs. Standardizes APIs for model switching and evaluation.	Enables seamless comparative analysis and benchmarking of different scFMs on custom tasks.
PertEval-scFM [10]	Benchmarking Framework	Standardized framework for evaluating perturbation effect prediction.	Specialized tool for assessing a critical application of scFMs in predicting cellular responses to stimuli.
CellWhisperer [9]	AI Tool & Model	Multimodal AI that connects transcriptomes with text descriptions, enabling chat-based data exploration.	Demonstrates the integration of scFMs with LLMs for intuitive biological discovery and interpretation.
Human Cell Atlas [1] [2]	Reference Atlas	A global collaborative project to create comprehensive reference maps of all human cells.	Provides biological context, ground truth, and a vision for the application of scFMs in mapping cellular biology.

Discussion and Future Directions

The evaluation of scFMs reveals a rapidly evolving field where these models demonstrate significant promise in learning universal representations from vast cell atlases. Their key advantage lies in capturing fundamental biological relationships, which provides a powerful foundation for diverse downstream tasks through transfer learning [4] [8]. However, challenges remain, including the need for more interpretable models, better handling of multimodal data, and improved generalization to novel biological contexts, particularly in clinical applications like drug sensitivity prediction [4] [2]. Future progress will likely depend on standardized benchmarking frameworks like BioLLM [8], the development of more biologically grounded evaluation metrics [4], and continued collaboration to build the data infrastructure and model architectures that will push the boundaries of computational cell biology.

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by providing an unprecedented, granular view of transcriptomics at the resolution of individual cells [3] [1]. The exponential growth of single-cell transcriptomics data has created both an opportunity and a challenge: how can researchers effectively harness this vast, complex information to unlock deeper biological insights? Single-cell foundation models (scFMs) have emerged as a promising solution [3] [1] [4].

Inspired by the success of foundation models in natural language processing, scFMs are large-scale deep learning models pretrained on massive, diverse single-cell datasets using self-supervised learning [1]. These models aim to learn universal biological knowledge during pretraining, which can then be transferred to various downstream tasks through fine-tuning or zero-shot learning [3] [4]. The core output of these models are embeddings - numerical representations that capture semantic meaning about genes and cells [11]. These embeddings serve as the critical bridge between raw model outputs and actionable biological insight, transforming high-dimensional, sparse transcriptomic data into dense, meaningful representations that preserve biological relationships [3] [11].

This guide provides an objective comparison of current scFMs, evaluating their performance across key biological tasks and examining the evidence for their ability to generate biologically relevant embeddings.

How Single-Cell Foundation Models Work: From Cells to Embeddings

Architectural Foundations and Tokenization

scFMs typically use transformer architectures, which process input data through attention mechanisms that learn to weight relationships between different elements of the data [1]. A crucial preprocessing step is tokenization - converting raw gene expression data into discrete units (tokens) that the model can process:

• Gene tokens: Individual genes are treated as fundamental tokens, analogous to words in a sentence [1]
• Value representation: Expression levels are incorporated through value embeddings, value binning, or expression-level ordering [3] [4]
• Positional encoding: Since genes lack natural ordering, models use various strategies including expression-level ranking or genomic position [1]
• Special tokens: Cell identity tokens, modality indicators, and batch information tokens may be added to enrich context [1]

The model processes these tokens through multiple transformer layers, ultimately producing latent embeddings for both genes and cells that capture their functional relationships and biological characteristics [1].

The Embedding Generation Process

The following diagram illustrates the workflow through which scFMs transform raw single-cell data into biologically meaningful embeddings:

Comparative Performance Analysis of Major scFMs

Recent comprehensive benchmarking studies have evaluated six prominent scFMs against well-established baseline methods under realistic conditions [3] [4]. The evaluated models represent the current state-of-the-art in the field:

Table 1: Key Single-Cell Foundation Models in Current Benchmarking Studies

Model Name	Omics Modalities	Model Parameters	Pretraining Dataset Size	Key Architectural Features
Geneformer [3] [4]	scRNA-seq	40 M	30 M cells	Encoder architecture, gene ranking by expression
scGPT [3] [4]	scRNA-seq, scATAC-seq, CITE-seq, spatial	50 M	33 M cells	Encoder with attention mask, multi-modal capability
UCE [4]	scRNA-seq	650 M	36 M cells	Protein-based gene embeddings, genomic position encoding
scFoundation [4]	scRNA-seq	100 M	50 M cells	Asymmetric encoder-decoder, read-depth awareness
LangCell [4]	scRNA-seq	40 M	27.5 M cell-text pairs	Incorporates textual cell descriptions
scCello [4]	scRNA-seq	Not specified	Not specified	Specialized for cell type annotation

Quantitative Performance Across Biological Tasks

A comprehensive benchmark evaluated these models across two gene-level and four cell-level tasks using 12 different metrics, including novel biology-informed metrics like scGraph-OntoRWR and Lowest Common Ancestor Distance (LCAD) [3] [4].

Table 2: Performance Comparison Across Key Biological Tasks

Task Category	Specific Task	Top Performing Models	Key Findings	Performance vs. Baselines
Gene-Level Tasks [3]	Tissue specificity prediction	Geneformer, scGPT	Functionally similar genes embedded closer in latent space	Mixed: scFMs capture biological relationships but don't always outperform simpler methods
	Gene Ontology term prediction	UCE, scFoundation	Protein-informed embeddings (UCE) show advantages for certain functional annotations	Varies by specific task and dataset size
Cell-Level Tasks [3] [4]	Batch integration	scGPT, Harmony (baseline)	scFMs robust to technical variations but not always superior to specialized methods	scFMs competitive but simpler methods often adequate for specific datasets
	Cell type annotation	scBERT, scCello	Specialized models excel at dedicated tasks	Domain-specific models outperform general scFMs on their target tasks
	Cancer cell identification	Multiple scFMs	Strong performance on clinically relevant tasks	scFMs show promise for clinical translation
	Drug sensitivity prediction	Multiple scFMs	Captures relevant biological pathways	Potential for drug development applications
Perturbation Prediction [10]	Zero-shot perturbation effect	Various scFMs	Limited performance for strong or atypical perturbations	Do not consistently outperform simpler baseline models

Experimental Protocols for Evaluating Biological Relevance

Gene-Level Evaluation Protocol

To assess whether scFM embeddings capture biologically meaningful gene relationships, researchers implement the following experimental protocol [3]:

• Gene Embedding Extraction: Extract gene embeddings from the input layers of scFMs
• Similarity Calculation: Compute cosine similarity between gene embedding vectors
• Functional Validation: Evaluate whether functionally similar genes (based on Gene Ontology annotations or pathway membership) cluster together in embedding space
• Benchmark Comparison: Compare against specialized methods like Functional Representation of Gene Signatures (FRoGS) that learn gene embeddings via random walks on biological hypergraphs

Cell-Level Evaluation Protocol

For evaluating cell embedding quality, researchers employ both standard metrics and novel biology-informed approaches [3] [4]:

• Batch Integration Assessment:

  • Dataset: Five high-quality datasets with manual annotations covering diverse biological conditions
  • Metrics: Both traditional integration metrics and novel biological conservation metrics
  • Challenge: Removing batch effects while preserving biological variation

• Cell Type Annotation Evaluation:

  • Introduction of cell ontology-informed metrics (scGraph-OntoRWR)
  • Lowest Common Ancestor Distance (LCAD) to measure severity of misclassification
  • Assessment of whether model-captured cell type relationships align with established biological knowledge

• Clinically Relevant Task Validation:

  • Evaluation across seven cancer types and four drugs
  • Focus on challenging real-world scenarios like novel cell types and intra-tumor heterogeneity

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Computational Tools and Resources for scFM Research

Tool/Resource	Type	Primary Function	Relevance to Embedding Analysis
CELLxGENE Census [9] [1]	Data Resource	Standardized access to annotated single-cell datasets	Provides diverse, high-quality data for model training and validation
Gene Expression Omnibus (GEO) [9] [1]	Data Repository	Public repository of functional genomics data	Source of diverse transcriptional profiles for multimodal learning
Seurat [3] [4]	Analysis Toolkit	Single-cell data analysis	Established baseline for comparison of integration performance
Harmony [3] [4]	Integration Algorithm	Batch effect correction	High-performing baseline for data integration tasks
scVI [3] [4]	Generative Model	Probabilistic modeling of scRNA-seq data	Baseline for evaluating scFM performance against specialized models
Cell Ontology [3] [4]	Knowledge Base	Structured controlled vocabulary for cell types	Provides biological ground truth for evaluating embedding quality

Interpreting Embedding Quality: Key Metrics and Visualization

Novel Metrics for Biological Relevance

Beyond traditional performance metrics, recent research has introduced innovative approaches specifically designed to evaluate the biological relevance of scFM embeddings [3] [4]:

• scGraph-OntoRWR: Measures consistency between cell type relationships captured by scFMs and established biological knowledge in cell ontologies
• Lowest Common Ancestor Distance (LCAD): Evaluates the severity of cell type misclassification by measuring ontological proximity between predicted and actual cell types
• Roughness Index (ROGI): Quantifies the smoothness of the cell-property landscape in the latent space, which correlates with model performance on downstream tasks

Visualization and Interpretation Framework

The following diagram illustrates the comprehensive evaluation framework for assessing the biological relevance of scFM embeddings:

Practical Guidelines for Model Selection

Based on comprehensive benchmarking results, researchers can follow these evidence-based recommendations for selecting and applying scFMs [3] [4]:

• Consider Dataset Size and Resources:

  • For small datasets or limited computational resources, simpler machine learning models may be more efficient and effective
  • For large, diverse datasets, scFMs leverage their pretraining to provide robust performance

• Match Model to Task Complexity:

  • For specialized tasks like cell type annotation, domain-specific models (scBERT, scCello) may outperform general scFMs
  • For multifaceted analysis requiring a unified framework, scFMs provide versatility

• Evaluate Need for Biological Interpretability:

  • When biological insight is the primary goal, prioritize models with strong performance on biology-informed metrics
  • For preprocessing and integration tasks, traditional metrics may suffice

• Assess Computational Constraints:

  • Consider the computational intensity required for training and fine-tuning
  • Evaluate whether zero-shot embeddings provide sufficient performance without additional fine-tuning

The benchmarking evidence clearly indicates that no single scFM consistently outperforms all others across diverse tasks and datasets [3] [4]. Therefore, researchers should select models based on their specific task requirements, dataset characteristics, and available resources rather than seeking a universally superior option.

A Methodological Toolkit for Extracting and Evaluating scFM Embeddings

Single-cell Foundation Models (scFMs) are revolutionizing biological research by learning universal representations from vast single-cell transcriptomics datasets. A critical decision in their application lies in the method used to extract these representations: using the model's outputs directly without any further training (zero-shot), or adapting the model to a specific dataset with additional training (fine-tuning). This guide provides an objective comparison of these two approaches, drawing on the latest benchmarking studies to help researchers and drug development professionals select the optimal strategy for extracting biologically relevant cell and gene embeddings.

Understanding scFM Embeddings and Extraction Approaches

What Are Cell and Gene Embeddings?

In single-cell RNA sequencing (scRNA-seq) data, scFMs learn to represent the complex, high-dimensional gene expression profile of a cell in a lower-dimensional, information-rich latent space [1]. Gene embeddings are vector representations that capture functional similarities and relationships between genes, while cell embeddings represent the overall state, type, or function of a cell [3]. These embeddings serve as foundational features for diverse downstream analyses, from cell type annotation to perturbation prediction.

Zero-Shot Embedding Extraction

The zero-shot approach uses the pre-trained model's internal representations directly without any further training on the target data. This method is particularly valuable in exploratory contexts where labeled data is unavailable or when computational resources for fine-tuning are limited [12].

Fine-Tuned Embedding Extraction

Fine-tuning involves further training the pre-trained scFM on a specific dataset or task, allowing the model to adapt its knowledge and generate task-specific embeddings. This approach is essential when the target data distribution differs significantly from the pre-training corpus [13].

Comparative Performance Analysis

Benchmarking studies have systematically evaluated the performance of both approaches across fundamental single-cell analysis tasks. The table below summarizes key findings from recent large-scale evaluations.

Table 1: Performance Comparison of Zero-Shot vs. Fine-Tuned Embeddings Across Key Tasks

Task Category	Specific Task	Zero-Shot Performance	Fine-Tuned Performance	Key Insights
Cell-level Tasks	Cell Type Annotation	Mixed results; sometimes outperformed by simpler methods like HVG selection [12]	Superior for dataset-specific adaptation; enables accurate novel cell type discovery [3]	Fine-tuning is preferred when labeled training data is available
	Batch Integration	Inconsistent across models; struggles with technical variation between datasets [12]	Better preservation of biological variance while removing technical artifacts [3]	Task-specific adaptation improves integration of diverse datasets
Gene-level Tasks	Gene Function Prediction	Captures basic functional relationships and tissue specificity [3]	Enhanced precision in predicting novel gene functions and interactions [14]	Both approaches benefit from large-scale pretraining
Perturbation Analysis	Drug Response Prediction	Limited improvement over simple baselines, especially under distribution shift [15] [10]	Enables zero-shot generalization to unseen cell lines when using efficient adapters [13]	Fine-tuning with conditional adapters enables prediction for novel contexts

Experimental Protocols for Evaluation

Benchmarking Framework Design

Recent comprehensive benchmarks have established rigorous protocols for evaluating embedding quality. These frameworks typically assess multiple scFMs (including Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello) against established baseline methods like Highly Variable Genes (HVG) selection, Seurat, Harmony, and scVI [3] [4]. Evaluations are conducted under realistic conditions across both gene-level and cell-level tasks, using diverse datasets with high-quality labels to ensure biological relevance.

Evaluation Metrics for Biological Relevance

To move beyond technical metrics and assess true biological insight, researchers have developed novel evaluation strategies:

• scGraph-OntoRWR: Measures consistency between cell type relationships captured by scFMs and established biological knowledge in cell ontologies [3] [4]
• Lowest Common Ancestor Distance (LCAD): Assesses the severity of cell type annotation errors by measuring ontological proximity between misclassified types [3]
• Gene Ontology (GO) Term Prediction: Evaluates whether functionally similar genes are embedded in close proximity in the latent space [3]
• Perturbation Effect Prediction: Tests model ability to predict transcriptional responses to genetic and chemical perturbations [15] [13]

Table 2: Essential Metrics for Evaluating Embedding Biological Relevance

Metric Category	Specific Metrics	Application	Interpretation
Cell-level Evaluation	Average BIO (AvgBIO), Average Silhouette Width (ASW), scGraph-OntoRWR, LCAD	Assessing cell type separation, batch integration, and annotation accuracy	Higher values indicate better cell type separation and biological consistency
Gene-level Evaluation	GO term prediction accuracy, Tissue specificity prediction	Evaluating functional gene relationships captured in embeddings	Higher accuracy indicates better preservation of biological gene functions
Perturbation Evaluation	Mean Squared Error (MSE) of predicted vs. actual expression	Testing predictive power for novel drug responses	Lower values indicate better generalization to unseen perturbations

Visualization of Evaluation Workflow

Visualization of the comprehensive evaluation workflow used to compare zero-shot and fine-tuned embedding extraction approaches across multiple task categories and evaluation metrics.

The Scientist's Toolkit: Essential Research Reagents

Implementing robust evaluation of scFM embeddings requires specific computational tools and resources. The table below details essential components of the experimental pipeline.

Table 3: Essential Research Reagents and Computational Tools for scFM Evaluation

Tool Category	Specific Tools/Datasets	Function in Evaluation	Key Features
Benchmarking Datasets	Tabula Sapiens, Pancreas datasets, PBMC (12k), Asian Immune Diversity Atlas (AIDA) v2 [3] [12]	Provide standardized biological contexts for evaluating embedding quality	Diverse tissues, multiple batch effects, high-quality annotations
Baseline Methods	HVG selection, Seurat, Harmony, scVI [3] [12]	Establish performance benchmarks for comparison	Represent traditional single-cell analysis approaches
Evaluation Frameworks	PertEval-scFM [15] [10], AnnDictionary [16]	Standardize assessment protocols across studies	Provider-agnostic LLM integration, multithreaded processing
Biological Knowledge Bases	Gene Ontology (GO), Cell Ontology [3]	Provide ground truth for biological relevance assessment	Curated functional and structural relationships
Novel Evaluation Metrics	scGraph-OntoRWR, LCAD [3] [4]	Quantify biological consistency beyond technical metrics	Measure alignment with established biological knowledge

Decision Framework and Recommendations

When to Prefer Zero-Shot Embedding Extraction

• Exploratory analysis where cell composition or labels are unknown [12]
• Resource-constrained environments where computational costs of fine-tuning are prohibitive
• Rapid prototyping and initial dataset assessment
• Large-scale studies where batch effects across many samples make fine-tuning impractical

When to Prefer Fine-Tuned Embedding Extraction

• Clinically relevant tasks requiring high precision, such as cancer cell identification or drug sensitivity prediction [3]
• Cross-modal predictions where the target modality (e.g., chemical structures) wasn't seen during pre-training [13]
• Specialized applications with distribution shifts from pre-training data
• Scenarios with sufficient labeled data for effective model adaptation

Visualization of Decision Workflow

Decision workflow for selecting between zero-shot and fine-tuned embedding extraction approaches based on research goals, resources, and task requirements.

The choice between zero-shot and fine-tuned approaches for extracting cell and gene embeddings depends critically on the specific biological question, available resources, and required precision. Zero-shot methods offer speed and simplicity for exploratory analysis but may lack task-specific optimization. Fine-tuned approaches deliver superior performance for specialized applications, particularly with the advent of parameter-efficient methods like adapters that preserve pre-trained knowledge while enabling customization [13].

Current evidence suggests that no single scFM consistently outperforms all others across every task or dataset [3]. Successful application requires thoughtful model selection based on dataset size, task complexity, and computational constraints. As the field evolves, emerging evaluation metrics that directly assess biological relevance—such as scGraph-OntoRWR and LCAD—provide crucial tools for moving beyond technical benchmarks to genuine biological insight, ultimately accelerating drug discovery and therapeutic development.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity, enabling the discovery of novel cell types and providing unprecedented insights into developmental biology and disease mechanisms [1] [17]. However, the characteristic high dimensionality, sparsity, and technical noise of scRNA-seq data present significant analytical challenges [4]. Inspired by breakthroughs in natural language processing (NLP), researchers have developed single-cell foundation models (scFMs) to address these challenges [1]. These models are large-scale deep learning architectures pretrained on vast datasets through self-supervised objectives, designed to learn universal representations of cellular biology that can be adapted to various downstream tasks [1] [4].

scFMs typically employ transformer-based architectures, treating individual cells as "sentences" and genes or genomic features as "words" or "tokens" [1]. By training on millions of cells encompassing diverse tissues, species, and experimental conditions, these models aim to capture fundamental biological principles governing cellular identity and function [1]. The promise of scFMs lies in their potential to integrate heterogeneous datasets and extract biological insights beyond the capabilities of traditional computational methods [4]. This review provides a comprehensive comparison of leading scFMs across three fundamental evaluation tasks: cell type annotation, batch integration, and gene function prediction, offering researchers evidence-based guidance for model selection.

Comparative Performance of scFMs Across Core Tasks

Table 1: Overall performance of scFMs across core evaluation tasks. Performance ratings are based on comprehensive benchmarking studies [4] [18].

Model	Cell Type Annotation	Batch Integration	Gene Function Prediction	Overall Strengths
scGPT	Excellent	Excellent	Good	Robust performance across all tasks; handles multiple omics modalities [18]
Geneformer	Good	Good	Excellent	Strong gene-level tasks; effective pretraining [18]
scFoundation	Good	Fair	Excellent	Strong gene-level tasks; large parameter count [4] [18]
UCE	Fair	Good	Fair	Protein-based gene embeddings [4]
LangCell	Good	Fair	Fair	Incorporates text-cell pairs [4]
scCello	Fair	Fair	Fair	Specialized architecture [4]
scBERT	Limited	Limited	Limited	Smaller model size; limited training data [18]

Cell Type Annotation

Cell type annotation represents a critical bottleneck in scRNA-seq analysis, traditionally requiring extensive manual curation by domain experts [16] [17]. Computational approaches have evolved from marker-based methods to correlation-based matching and supervised learning [17]. scFMs offer the potential to automate this process by learning discriminative features that distinguish cell types across diverse biological contexts.

Experimental Protocol for Annotation Benchmarking

Benchmarking studies typically evaluate annotation performance using the following protocol [16] [4]:

• Data Preparation: High-quality reference datasets with validated cell type labels (e.g., Tabula Sapiens) are preprocessed through standard pipelines including normalization, highly variable gene selection, and clustering.
• Feature Extraction: Model-generated cell embeddings are obtained in zero-shot or fine-tuned settings.
• Cluster Annotation: Differentially expressed genes for each cluster serve as input for annotation, with models predicting cell type labels.
• Evaluation: Predictions are compared against manual annotations using metrics including accuracy, Cohen's kappa, and ontology-aware metrics like Lowest Common Ancestor Distance (LCAD) to assess biological plausibility of errors [4].

Performance Comparison

Table 2: Cell type annotation performance across scFMs and alternative approaches.

Method	Accuracy Range	Strengths	Limitations
scGPT	High (80-90% on major types) [16] [18]	Robust across tissues	Computational demands
Geneformer	Medium-High [4]	Strong on developmental data	Limited to ranked gene inputs
LLM-based (Claude 3.5)	High (80-90% agreement) [16]	Natural language interface	Requires API access
Reference-based Methods	Medium-High [17]	Established reliability	Reference dependency
Marker-based Methods	Variable [17]	Interpretable	Limited to known markers

Independent benchmarking reveals that no single scFM consistently outperforms all others across every dataset and tissue type [4]. Performance varies based on factors including dataset size, tissue complexity, and the presence of rare cell types. scGPT demonstrates particularly robust performance, while specialized LLMs like Claude 3.5 Sonnet achieve high agreement with manual annotations [16]. The emerging finding that foundation models capture biologically meaningful relationships between cell types is validated by novel metrics like scGraph-OntoRWR, which measures consistency with established biological ontologies [4].

Batch Integration

Batch integration represents a fundamental challenge in single-cell genomics, where technical variations between experiments can obscure biological signals [19]. Effective integration is crucial for constructing comprehensive cell atlases and enabling cross-study comparisons, particularly when datasets originate from different biological systems or sequencing technologies [19].

Experimental Protocol for Integration Benchmarking

Standardized evaluation of batch integration methods typically involves [19] [4]:

• Dataset Selection: Challenging integration scenarios including cross-species, organoid-tissue comparisons, and different sequencing technologies.
• Integration Methods: Application of various algorithms including cVAE-based methods (e.g., scVI), transformer-based scFMs, and traditional approaches.
• Dual Evaluation:
  • Batch correction: Quantified using metrics like graph integration local inverse Simpson's Index (iLISI) to measure batch mixing.
  • Biological preservation: Assessed via metrics like normalized mutual information (NMI) to ensure cell type distinction is maintained.
• Downstream Analysis: Evaluation of integrated embeddings for trajectory inference and differential expression.

Performance Comparison

Table 3: Batch integration performance across methodological approaches.

Method	Batch Correction	Biological Preservation	Best Use Cases
scGPT	Excellent [18]	Excellent [18]	Multi-omic integration; complex batch effects
sysVI (VAMP + CYC)	High [19]	High [19]	Substantial batch effects; cross-system integration
scVI	Medium [19] [4]	Medium [19]	Standard batch effects within similar systems
Harmony	Medium [4]	Medium [4]	Small-scale integration; linear batch effects
Adversarial Methods	High [19]	Low (may remove biological signal) [19]	Limited recommended use cases

Benchmarking reveals that traditional integration methods struggle with substantial batch effects arising from different biological systems or technologies [19]. While scFMs like scGPT demonstrate robust integration capabilities, specialized methods like sysVI—which combines VampPrior with cycle-consistency constraints—show particular promise for challenging integration scenarios by effectively separating technical artifacts from biological variation [19]. Simple increases in KL regularization strength in cVAE models prove ineffective as they non-specifically remove both biological and technical variation, while adversarial approaches may incorrectly mix biologically distinct cell types [19].

Gene Function Prediction

Predicting gene function from scRNA-seq data represents a fundamental task for elucidating biological mechanisms. scFMs approach this task by learning contextual representations of genes across diverse cellular environments, capturing functional relationships beyond co-expression patterns.

Experimental Protocol for Gene Function Prediction

Gene-level evaluation typically follows this protocol [4]:

• Embedding Extraction: Gene embeddings are obtained from the model's input layers (gene token embeddings) or attention patterns.
• Functional Annotation: Models are tasked with predicting Gene Ontology terms or annotating gene sets with biological processes.
• Validation: Predictions are compared against established functional annotations from databases like GO, KEGG, or expert curation.
• Knowledge Transfer: Assessed through zero-shot performance on novel gene sets or functions.

Performance Comparison

Table 4: Gene function prediction capabilities across scFMs.

Model	Functional Annotation Accuracy	Key Innovations	Limitations
Geneformer	High [4] [18]	Contextualized gene embeddings	Limited gene set size
scFoundation	High [4] [18]	Large vocabulary; full gene set	Computational demands
UCE	Medium [4]	Protein language model integration	Complex architecture
LLM-based (Claude 3.5)	High (>80% recovery) [16]	Natural language reasoning	Not scRNA-seq native

Geneformer and scFoundation demonstrate particularly strong performance in gene-level tasks, benefiting from their specialized pretraining strategies [18]. Notably, general-purpose LLMs like Claude 3.5 Sonnet show remarkable capability in functional annotation of gene sets, recovering matching annotations in over 80% of test cases [16]. This suggests that biological knowledge encoded in general language models can effectively complement domain-specific scFMs for functional prediction tasks.

Integrated Workflow for scFM Evaluation

The evaluation of scFMs across diverse tasks requires a systematic approach that accounts for both technical performance and biological relevance. The following diagram illustrates a comprehensive workflow for assessing scFM embeddings:

Diagram 1: Comprehensive scFM evaluation workflow covering major tasks and metrics.

Essential Research Reagents and Computational Tools

Key Research Solutions Table

Table 5: Essential computational tools and resources for scFM evaluation research.

Category	Tool/Resource	Primary Function	Application in Evaluation
Framework	BioLLM [18]	Unified scFM interface	Standardized model comparison
Framework	AnnDictionary [16]	LLM provider abstraction	Flexible backend for annotation
Data	CZ CELLxGENE [1]	Curated single-cell data	Pretraining and benchmarking
Data	Tabula Sapiens [16]	Reference atlas	Ground truth for annotation
Integration	sysVI [19]	cVAE with VampPrior + cycle-consistency	Challenging batch integration
Integration	Harmony [4]	PCA-based integration	Traditional baseline comparison
Evaluation	scGraph-OntoRWR [4]	Ontology-informed metric	Biological relevance assessment
Evaluation	TAES [20]	Trajectory-aware metric	Developmental biology focus

Comprehensive benchmarking reveals distinct performance trade-offs among current scFMs across the core evaluation tasks of cell type annotation, batch integration, and gene function prediction. While scFMs demonstrate remarkable versatility and robust performance across diverse applications, no single model consistently outperforms all others in every task or dataset [4]. Model selection must therefore be guided by specific research needs, considering factors such as dataset size, task complexity, need for biological interpretability, and computational resources [4].

Notably, simpler machine learning models can outperform sophisticated foundation models in specific tasks, particularly under resource constraints or when working with well-characterized biological systems [4]. However, scFMs provide superior capabilities for integrating heterogeneous datasets and extracting novel biological insights, especially through their zero-shot embeddings that capture fundamental biological relationships [4].

Future developments in scFMs will likely address current limitations in interpretability, computational efficiency, and ability to handle the continuous emergence of novel cell types and biological states [1] [4]. As these models evolve, standardized evaluation frameworks like those discussed here will be crucial for guiding researchers toward the most appropriate analytical tools for their specific biological questions.

The emergence of single-cell foundation models (scFMs) has revolutionized the analysis of single-cell RNA sequencing (scRNA-seq) data, offering powerful tools for integrating heterogeneous datasets and exploring biological systems. However, a critical question has remained: how can we effectively evaluate whether these complex models are capturing meaningful biological insights rather than just optimizing for standard computational metrics? Traditional evaluation metrics often fail to assess the biological relevance of the learned representations, creating a gap between computational performance and biological utility.

To address this challenge, the field has introduced novel biology-informed evaluation metrics, primarily scGraph-OntoRWR and the Lowest Common Ancestor Distance (LCAD). These metrics leverage formal biological ontologies—structured, computable knowledge systems that define biological concepts and their relationships—to ground model evaluation in established biological knowledge [21]. This guide provides a comprehensive comparison of how these biology-informed metrics are redefining the evaluation landscape for scFMs, offering researchers robust frameworks for assessing model performance against biologically meaningful benchmarks.

Understanding the Biological Evaluation Framework

The Role of Biological Ontologies

Biological ontologies serve as the foundational backbone for the advanced evaluation metrics discussed in this guide. Unlike simple dictionaries, ontologies are formal, explicit specifications of shared conceptualizations within the biological domain [21]. They create rich networks of relationships between biological concepts, enabling both humans and computers to reason about biological entities in sophisticated ways. For example, while a dictionary might define a "heart" as a "muscular organ that pumps blood," an ontology would specify that a heart is part of the circulatory system, has components like chambers and valves, is located in the thoracic cavity, and participates in blood circulation processes [21].

The Open Biological and Biomedical Ontology (OBO) Foundry represents a major community effort to coordinate ontology development across biological sciences, providing standardized relationships such as is_a, part_of, and participates_in with clearly defined logical properties [21]. This ontological framework enables the creation of evaluation metrics that can measure how well computational models capture these established biological relationships, moving beyond purely statistical measures of model performance.

Limitations of Traditional Evaluation Metrics

Traditional metrics for evaluating scFMs have primarily focused on computational efficiency and statistical performance, including measures like clustering accuracy, batch integration scores, and reconstruction error. However, these approaches suffer from significant limitations:

• Disconnection from Biological Reality: High scores on statistical metrics do not guarantee that the model has learned biologically meaningful representations [4].
• Inability to Capture Hierarchical Relationships: Traditional metrics cannot assess whether misclassifications are biologically reasonable (e.g., confusing a T-cell with a B-cell rather than with a neuron) [4] [21].
• Lack of Context for Errors: Standard approaches treat all errors equally without considering the biological severity of different types of misclassifications [4].

The introduction of ontology-informed metrics addresses these limitations by embedding biological knowledge directly into the evaluation process, creating a more meaningful assessment framework for biological applications.

Comparative Analysis of Biology-Informed Evaluation Metrics

The table below summarizes the core characteristics, implementation, and applications of the two primary biology-informed evaluation metrics:

Feature	scGraph-OntoRWR	LCAD (Lowest Common Ancestor Distance)
Core Function	Measures consistency of cell type relationships captured by scFMs with prior biological knowledge [4]	Measures ontological proximity between misclassified cell types in annotation tasks [4] [21]
Methodological Approach	Random walk with restart algorithm on ontology graphs combined with model embeddings [4]	Calculation of distance to common ancestor in cell type ontology hierarchies [4] [21]
Evaluation Perspective	Assesses biological plausibility of entire relationship networks learned by models [4]	Evaluates biological reasonableness of individual classification errors [4]
Output Type	Consistency score between model-derived and ontology-derived cell relationships [4]	Distance metric quantifying severity of misclassification errors [4]
Key Advantage	Reveals whether models capture biologically meaningful cell type hierarchies	Differentiates severe errors (distantly related cells) from minor errors (closely related cells) [4]

Performance Comparison Across scFMs

Experimental results from a comprehensive 2025 benchmark study evaluating six prominent scFMs reveal how these biology-informed metrics provide unique insights into model performance:

Table: Model Performance Rankings Across Evaluation Paradigms

Model	Traditional Metrics Ranking	scGraph-OntoRWR Ranking	LCAD Performance	Overall Biology-Informed Ranking
Geneformer	2	2	Strong	2 [4]
scGPT	3	3	Moderate	3 [4] [18]
UCE	4	4	Moderate	4 [4]
scFoundation	1	1	Strong	1 [4]
LangCell	5	5	Weak	5 [4]
scCello	6	6	Weak	6 [4]

Table: Task-Specific Performance with Biology-Informed Metrics

Model	Batch Integration	Cell Type Annotation	Cancer Cell Identification	Drug Sensitivity Prediction
Geneformer	2	3	1	2 [4]
scGPT	3	2	3	3 [4] [18]
scFoundation	4	1	2	1 [4]
Traditional ML	5	5	5	5 [4]

The benchmark study demonstrated that no single scFM consistently outperformed others across all tasks, emphasizing that model selection must be tailored to specific applications and data characteristics [4]. scGraph-OntoRWR and LCAD provided crucial insights that traditional computational metrics missed, particularly in assessing the biological relevance of the learned representations.

Experimental Protocols and Methodologies

Implementation of scGraph-OntoRWR

The scGraph-OntoRWR metric operates by comparing the relational structure between cell types learned by scFMs against the established relationships in biological ontologies. The implementation involves these key steps:

• Ontology Graph Construction: Extract the cell-type hierarchy from relevant biological ontologies such as the Cell Ontology, representing relationships as a directed graph with cell types as nodes and "isa" or "partof" relationships as edges [21].

• Model Embedding Extraction: Generate cell embeddings using the scFM in zero-shot mode (without task-specific fine-tuning) to capture the intrinsic knowledge learned during pre-training [4].

• Similarity Graph Construction: Calculate pairwise similarities between all cell types based on their embeddings in the model's latent space, typically using cosine similarity or correlation measures.

• Random Walk with Restart Execution: Perform RWR algorithm on both the ontology-derived graph and model-derived similarity graph to capture global relationship structures [4].

• Consistency Calculation: Compute the alignment between the steady-state distributions of the random walks on the ontology graph and model-derived graph, yielding the final scGraph-OntoRWR consistency score [4].

The following workflow diagram illustrates the scGraph-OntoRWR methodology:

Implementation of LCAD Metric

The LCAD metric operates on cell type annotation tasks and evaluates the biological reasonableness of misclassifications by leveraging the hierarchical structure of cell ontologies:

• Reference Ontology Establishment: Load a comprehensive cell-type ontology with established "is_a" relationships defining the hierarchy of cell types [21].

• Cell Type Annotation: Perform cell type annotation using the scFM embeddings and a chosen classification approach, recording all misclassified cells.

• LCA Identification: For each misclassification pair (true label vs. predicted label), identify the lowest common ancestor in the ontology hierarchy.

• Distance Calculation: Compute the ontological distance between the true cell type and the LCA, and between the predicted cell type and the LCA.

• LCAD Score Computation: Calculate the final LCAD score, which represents the average ontological distance of errors, with lower scores indicating biologically reasonable errors (confusion between closely related cell types) [4].

The diagram below illustrates the LCAD calculation process:

The Scientist's Toolkit: Essential Research Reagents

Implementing biology-informed evaluation requires specific computational reagents and resources. The table below details essential components for researchers seeking to apply these metrics:

Reagent/Resource	Function	Biological Significance
Gene Embeddings	Numerical representations of genes in latent space	Capture functional similarities between genes based on co-expression patterns across diverse cellular contexts [21]
Cell Ontologies	Structured vocabularies defining cell types and relationships	Provide ground truth for evaluating biological relevance of model outputs [4] [21]
Attention Mechanisms	Model components that identify important relationships between inputs	Reveal gene-gene interactions and regulatory relationships learned from data [4]
Benchmark Datasets	Curated single-cell data with high-quality annotations	Enable standardized evaluation and comparison of different modeling approaches [4]
GO Term Annotations	Gene Ontology functional classifications	Serve as biological prior knowledge for validating gene embeddings [21]

Implications for Single-Cell Research and Drug Development

The adoption of biology-informed evaluation metrics has significant implications for both basic research and applied drug development:

Enhanced Model Selection and Development

scGraph-OntoRWR and LCAD enable researchers to select models based on biological performance rather than just computational efficiency. The benchmark studies revealed that while simpler machine learning models sometimes adapted more efficiently to specific datasets under resource constraints, scFMs demonstrated superior performance in capturing biologically meaningful patterns when evaluated with these ontology-informed metrics [4]. This guides researchers to make more informed decisions about when the complexity of scFMs is justified by their biological insights.

Clinical and Therapeutic Applications

In drug development and clinical applications, these biology-informed metrics offer crucial advantages:

• Target Identification: Models validated with scGraph-OntoRWR are more likely to identify biologically relevant drug targets by capturing authentic cellular relationships [4].
• Toxicity Prediction: LCAD helps ensure that cellular response predictions reflect biologically plausible mechanisms, reducing false leads in drug screening [4].
• Biomarker Discovery: Models evaluated with these metrics produce more reliable biomarkers grounded in established biological knowledge rather than computational artifacts.

The integration of foundation models with formal ontological frameworks represents a promising direction for future research, particularly for clinical applications where model interpretability and biological relevance are paramount [21].

The introduction of biology-informed evaluation metrics represents a paradigm shift in how we assess computational models in single-cell biology. scGraph-OntoRWR and LCAD move beyond standard statistical metrics to ground model evaluation in established biological knowledge, providing crucial insights that traditional approaches miss. As the field continues to evolve, these metrics will play an increasingly important role in ensuring that our computational tools generate biologically meaningful insights rather than just computational optimizations. For researchers and drug development professionals, adopting these evaluation frameworks enables more informed model selection and ultimately accelerates the translation of computational discoveries into biological insights and therapeutic advances.

Single-cell foundation models (scFMs) represent a transformative advancement in computational biology, leveraging large-scale deep learning trained on vast single-cell transcriptomics datasets to interpret cellular heterogeneity. These models, often built on transformer architectures, learn universal biological knowledge during pretraining, which enables them to be adapted for various downstream tasks through fine-tuning or zero-shot learning [1]. The granular view provided by single-cell RNA sequencing (scRNA-seq) has revolutionized research paradigms in biology and drug development, offering unprecedented resolution to observe cellular states and their responses to perturbations [4] [3]. This review focuses on two critical application areas—drug sensitivity prediction and cancer cell identification—to objectively evaluate the current capabilities and limitations of scFMs against traditional methods, providing researchers with evidence-based guidance for model selection in biomedical research.

Drug Sensitivity Prediction: Evaluating scFM Performance

Performance Benchmarks Against Traditional Methods

Drug response prediction represents a cornerstone of personalized medicine, aiming to tailor treatments based on an individual's genetic profile. While scFMs theoretically offer advantages through their contextualized representations of cellular states, empirical evidence suggests their performance remains comparable to, but not consistently superior than, simpler machine learning approaches.

A comprehensive benchmark study evaluating six scFMs against established baselines across seven cancer types and four drugs revealed that no single scFM consistently outperformed others across all tasks. The study incorporated 12 evaluation metrics spanning unsupervised, supervised, and knowledge-based approaches, providing a holistic assessment of model capabilities [4] [3]. Similarly, the PertEval-scFM framework, specifically designed for evaluating perturbation effect prediction, found that zero-shot scFM embeddings offered limited improvement over simple baseline models, particularly under distribution shift conditions where training and test data come from different experimental conditions [22] [23].

Table 1: Performance Comparison of Drug Response Prediction Approaches

Method Category	Representative Examples	Key Strengths	Key Limitations
Single-cell Foundation Models	scGPT, Geneformer, scFoundation	Robust and versatile across diverse applications; capture biological insights in embeddings	Do not consistently outperform simpler models; computationally intensive; struggle with strong/atypical perturbations
Traditional ML with Feature Reduction	Ridge Regression with TF activities, SVR with LINCS L1000 genes	High performance with reduced features; computationally efficient; more interpretable	Performance depends on appropriate feature selection; may miss novel biological patterns
Deep Learning Models	TGSA, MMLP	Can model complex non-linear relationships	Often fail to exceed baseline performance; less interpretable

Notably, research on cell line data has demonstrated that simpler regression algorithms like Support Vector Regression (SVR) combined with biologically-informed feature selection methods can achieve competitive performance. One study found that using the LINCS L1000 dataset for feature selection (approximately 1,000 major genes) yielded strong results, while integration of mutation and copy number variation information provided minimal predictive improvement [24]. Furthermore, a systematic evaluation of feature reduction methods revealed that transcription factor activities outperformed other approaches in predicting drug responses for 7 of 20 drugs evaluated, effectively distinguishing between sensitive and resistant tumors [25].

Experimental Protocols for scFM Evaluation in Drug Sensitivity

The PertEval-scFM framework provides a standardized methodology for evaluating scFM performance in predicting cellular responses to perturbations [22] [23]. The protocol begins with data preparation using Perturb-seq data, which combines gene expression information from both perturbed and unperturbed cells. The process involves selecting highly variable genes to focus on the most informative aspects of cellular responses. Subsequently, scFM models generate embeddings—numerical representations of cells based on their gene expression profiles. These embeddings enable sophisticated comparisons between perturbed cells and their control counterparts.

The evaluation phase employs a zero-shot learning protocol, where models predict perturbation effects without task-specific fine-tuning. Performance is assessed by measuring how well the embeddings predict known drug responses compared to baseline models using raw gene expression data. The framework specifically tests model robustness under distribution shifts, where training and testing conditions vary, mimicking real-world scenarios where model generalization is essential [22].

Research Reagent Solutions for Drug Response Studies

Table 2: Essential Research Reagents and Resources for Drug Response Prediction

Resource Name	Type	Primary Function	Key Features
PRISM Dataset	Drug screening database	Provides drug response data for model training	Broad coverage of cancer/non-cancer drugs; extensive cell line collection
GDSC (Genomics of Drug Sensitivity in Cancer)	Pharmacogenetic dataset	Drug sensitivity benchmarking	969 cancer cell lines; 297 compounds; 243,466 IC50 values
LINCS L1000	Gene signature database	Feature selection for dimensionality reduction	~1,000 informative genes; captures majority of transcriptomic information
Perturb-seq	Experimental data	Measures transcriptional responses to perturbations	Combines gene expression from perturbed/unperturbed cells
CCLE (Cancer Cell Line Encyclopedia)	Molecular profile database	Provides multi-omics data for cell lines	Gene expression, mutation, and CNV profiles for 734 cell lines

Cancer Cell Identification: Traditional Approaches vs. scFM Methods

Comparative Performance in Malignant Cell Detection

Accurately identifying malignant cells within complex tumor ecosystems represents a fundamental challenge in single-cell transcriptomics analysis. Traditional computational approaches have primarily relied on detecting copy number variations (CNAs) through algorithms like InferCNV, CopyKAT, and SCEVAN, which compare target cells to reference normal cells to infer large-scale chromosomal alterations [26] [27]. While these methods have proven valuable, they face limitations including dependency on reference cells, inability to detect cancer cells without CNAs, and confusion from CNAs in normal cells [27].

Emerging deep learning approaches like CanCellCap demonstrate the potential of multi-domain learning frameworks, achieving 0.977 average accuracy in cancer cell identification across 13 tissue types, 23 cancer types, and 7 sequencing platforms [27]. This model integrates domain adversarial learning and Mixture of Experts (MoE) to simultaneously extract common and tissue-specific gene expression patterns while mitigating sequencing platform effects through a masking-reconstruction strategy. CanCellCap significantly outperformed five state-of-the-art methods across 33 benchmark datasets and maintained high performance on unseen cancer types, tissue types, and even across species [27].

Table 3: Performance Comparison of Cancer Cell Identification Methods

Method	Underlying Principle	Accuracy Range	Strengths	Weaknesses
InferCNV	Copy number variation inference	Varies by dataset	Well-established; widely used	Requires reference cells; misses cancers without CNAs
CopyKAT	Copy number variation with Gaussian mixture model	Varies by dataset	Can identify confident normal cells; works without paired normal samples	Struggles with low tumor purity; performance depends on cell quality
SCEVAN	Copy number variation with segmentation	Varies by dataset	Joint segmentation algorithm; identifies breakpoints	Requires confident normal cells for baseline
CanCellCap	Multi-domain deep learning	Up to 0.977 (average)	High accuracy across tissues/platforms; works without references	Complex architecture; computational demands for training
scFMs (Zero-shot)	Latent representation learning	Under evaluation	No need for predefined features; transfer learning potential	Limited benchmarking data available

The biological relevance of scFM embeddings for cancer cell identification is increasingly validated through innovative evaluation metrics. The scGraph-OntoRWR metric measures the consistency of cell type relationships captured by scFMs with prior biological knowledge, while the Lowest Common Ancestor Distance (LCAD) metric assesses the ontological proximity between misclassified cell types, providing a biologically-grounded perspective on error severity [4] [3]. These approaches demonstrate that pretrained scFM embeddings do capture meaningful biological insights into the relational structure of genes and cells, which benefits downstream classification tasks.

Experimental Workflow for Cancer Cell Identification

The standard methodology for evaluating cancer cell identification methods involves several key stages, beginning with comprehensive data collection from resources like the Tumor Immune Single-cell Hub (TISCH), which provides annotated single-cell datasets across diverse tissues, cancer types, and sequencing platforms [27]. Following data acquisition, preprocessing steps filter low-quality cells and genes, normalize expression values, and integrate metadata including tissue origin, cancer type, and sequencing platform.

For traditional CNA-based methods, the workflow typically involves selecting appropriate reference cells (usually immune cells or normal cells from the same lineage), running CNA inference algorithms, and then classifying cells as malignant or normal based on their CNA profiles. In contrast, deep learning approaches like CanCellCap employ a multi-domain learning framework that disentangles tissue-common cancer patterns, tissue-specific expression, and sequencing platform effects through domain adversarial learning and mixture of experts architectures [27].

Validation typically involves benchmarking against ground truth labels curated from original study annotations, with performance assessed using metrics including accuracy, F1 score, recall, precision, and AUROC. Rigorous testing across unseen cancer types, tissue types, and sequencing platforms provides critical insights into model generalizability and robustness—essential characteristics for real-world clinical applications [27].

Table 4: Key Resources for Cancer Cell Identification Studies

Resource Name	Type	Primary Application	Notable Characteristics
TISCH (Tumor Immune Single-cell Hub)	Curated database	Provides annotated tumor scRNA-seq data	Multiple cancer types; tissue origins; sequencing platforms
CellxGene	Single-cell data platform	Data source for model training/validation	>100 million unique cells; standardized annotations
InferCNV	Computational algorithm	CNA-based cancer cell identification	Compares to reference cells; hidden Markov model
CopyKAT	Computational algorithm	CNA-based cancer cell identification	Gaussian mixture model; identifies confident normal cells
CanCellCap	Deep learning model	Multi-domain cancer cell identification	0.977 average accuracy; works across tissues/platforms

Integrated Analysis: Biological Relevance and Practical Considerations

Guidelines for Model Selection in Biomedical Research

The evaluation of scFMs across drug sensitivity prediction and cancer cell identification reveals several consistent patterns that can guide researcher decision-making. First, dataset characteristics significantly influence model performance. For drug response prediction, simpler machine learning models with appropriate feature selection often outperform complex foundation models, particularly under resource constraints or when dealing with specific, well-characterized drug classes [25] [24]. Conversely, for cancer cell identification across diverse tissue types and experimental conditions, specialized deep learning models like CanCellCap demonstrate superior performance and generalization compared to traditional CNA-based methods [27].

Second, task complexity should dictate model choice. While scFMs offer remarkable versatility across multiple applications, their performance gains are most evident in complex, heterogeneous tasks requiring integration of diverse biological knowledge. For more focused applications, traditional methods and simpler ML approaches provide efficient and interpretable solutions [4]. Notably, no single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on specific research objectives, dataset size, and computational resources [4] [3].

Third, biological interpretability remains crucial for biomedical applications. The introduction of ontology-informed evaluation metrics like scGraph-OntoRWR and LCAD represents significant progress in quantifying the biological relevance of model embeddings [4] [3]. These metrics validate that scFMs can capture meaningful biological relationships, providing confidence that model predictions reflect underlying biology rather than technical artifacts.

Future Directions and Development Needs

Despite their promise, current-generation scFMs face several challenges requiring attention. For drug response prediction, models struggle with predicting strong or atypical perturbation effects, likely because training data predominantly includes mild perturbations [22] [23]. Improving prediction accuracy will require higher-quality datasets capturing a broader range of cellular states and perturbation intensities. Additionally, current benchmarks indicate that scFM embeddings do not provide consistent improvements over baseline models, particularly under distribution shift, highlighting the need for more robust representation learning approaches [22] [23].

For both application areas, developing standardized benchmarking frameworks and biologically meaningful evaluation metrics remains essential. Initiatives like PertEval-scFM for perturbation prediction and comprehensive cross-method comparisons for cancer cell identification provide valuable foundations for objective performance assessment [22] [27]. Future work should focus on enhancing model interpretability, improving generalization to rare cancer types or novel drugs, and increasing computational efficiency to enable broader adoption in research and clinical settings.

Navigating the Challenges: Data, Computational, and Interpretation Hurdles in scFM Analysis

Confronting Data Quality and Batch Effects in Pretraining Corpora

The development of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, promising to unlock deep insights into cellular function and disease mechanisms from vast single-cell genomics datasets [1]. These models, built on transformer architectures and pretrained on millions of single-cell transcriptomes, aim to learn unified representations of cellular states that can be adapted to diverse downstream tasks such as cell type annotation, batch effect correction, and perturbation effect prediction [1] [8]. However, their performance is fundamentally constrained by two interconnected challenges: data quality and batch effects in pretraining corpora. The accumulation of single-cell data from diverse sources, technologies, and experimental conditions has created a "Tower of Babel" in pretraining datasets, where inconsistent quality and technical artifacts systematically distort the biological signals these models are designed to capture [1] [8]. This review provides an objective comparison of how leading scFMs confront these challenges, evaluating their performance across standardized benchmarks to offer practical guidance for researchers and drug development professionals.

Comparative Performance Analysis of Leading scFMs

Zero-Shot Cell Representation Quality and Batch Effect Correction

The ability of scFMs to generate biologically meaningful cell embeddings without task-specific fine-tuning is a crucial test of their pretraining efficacy. Standardized evaluations through frameworks like BioLLM have revealed significant performance variations across models when assessing embedding quality using metrics like Average Silhouette Width (ASW), which measures how well embeddings separate biologically distinct cell types [8].

Table 1: Zero-Shot Cell Embedding Performance Across Single-Cell Foundation Models

Model	Architecture Type	Cell Type Separation (ASW)	Batch Effect Correction	Input Length Sensitivity	Computational Efficiency
scGPT	GPT-based decoder	Consistently superior	Best performer	Improves with longer sequences	High (memory & time efficient)
Geneformer	BERT-based encoder	Strong capabilities	Moderate	Slight negative correlation	High (memory & time efficient)
scFoundation	Not specified	Strong capabilities	Moderate	Slight negative correlation	Moderate resource usage
scBERT	BERT-based encoder	Lagged behind	Poor performance	Declines with longer sequences	Moderate resource usage

As evidenced in Table 1, scGPT consistently outperforms other models in generating biologically relevant cell embeddings, achieving superior separation of cell types in UMAP visualizations and demonstrating the most effective batch-effect-removal capabilities in zero-shot settings [8]. This advantage is attributed to scGPT's capacity to capture complex cellular features and its architectural proficiency in preserving biologically relevant information. Notably, scGPT's embedding quality improves with longer input gene sequences, suggesting its ability to leverage richer information, whereas scBERT's performance declines with increased sequence length, indicating potential difficulties in learning meaningful cell features from extended contexts [8].

Perturbation Effect Prediction Capabilities

Predicting cellular responses to genetic perturbations represents one of the most valuable but challenging applications of scFMs. Recent benchmarking studies have yielded surprising results, with simple baselines often outperforming sophisticated foundation models on this critical task [28].

Table 2: Perturbation Effect Prediction Performance Comparison

Model	Double Perturbation Prediction (L2 Distance)	Unseen Perturbation Prediction	Genetic Interaction Identification	Performance vs. Simple Baselines
scGPT	Higher error than additive baseline	Did not consistently outperform mean prediction or linear models	Rarely predicted synergistic interactions correctly	Underperformed versus additive and linear baselines
scFoundation	Higher error than additive baseline	Not included in full benchmark due to gene matching requirements	Mostly predicted buffering interactions	Underperformed versus additive baseline
GEARS	Higher error than additive baseline	Did not consistently outperform mean prediction or linear models	Mostly predicted buffering interactions	Underperformed versus additive and linear baselines
Simple Additive Model	Lowest error	N/A	By definition cannot predict interactions	Served as performance baseline
Linear Model with Pretrained Embeddings	N/A	Outperformed foundation models	N/A	Superior to foundation models

As Table 2 illustrates, multiple foundation models—including scGPT, scFoundation, and GEARS—demonstrated higher prediction error (L2 distance) compared to a simple additive baseline that sums individual logarithmic fold changes for double perturbations [28]. In predicting unseen perturbations, none of the deep learning models consistently outperformed a deliberately simple baseline that always predicts the overall average expression, nor a linear model using embeddings from the training data [28]. Furthermore, when extracting gene embeddings from scFoundation and scGPT and using them in a simple linear model, the performance matched or exceeded that of the foundation models with their native decoders, suggesting that the learned representations contain valuable information but the complex architectural components may not be optimally leveraging them for this specific task [28].

Experimental Methodologies for scFM Evaluation

Standardized Benchmarking Frameworks and Protocols

The development of standardized evaluation frameworks has been crucial for objective comparison of scFM capabilities. Two prominent approaches have emerged: the BioLLM framework for comprehensive model assessment [8] and the PertEval-scFM framework specifically designed for perturbation prediction tasks [10] [28].

BioLLM Evaluation Protocol [8]:

• Data Preprocessing: Implements a decision-tree-based preprocessing interface with rigorous quality control standards for input data
• Model Initialization: Loads foundation models through a unified interface regardless of architectural differences
• Task Execution: Supports both zero-shot inference via cell/gene embeddings and targeted fine-tuning
• Performance Assessment: Evaluates three crucial aspects:
  • Embedding quality through silhouette scores
  • Biological fidelity through gene regulatory network analysis
  • Prediction accuracy through standard classification metrics

Perturbation Prediction Benchmarking Protocol [28]:

• Data Partitioning: Fine-tuning models on all single perturbations and half of double perturbations, then assessing prediction error on held-out double perturbations
• Robustness Validation: Running analyses multiple times with different random partitions
• Baseline Comparison: Including simple baselines like "no change" and "additive" models
• Multiple Metric Assessment: Examining L2 distances for highly expressed genes, Pearson delta measures, and genetic interaction predictions

The experimental workflow below illustrates the standardized benchmarking process for evaluating scFMs on perturbation prediction tasks:

Research Reagent Solutions for scFM Evaluation

Table 3: Essential Research Reagents and Computational Tools for scFM Benchmarking

Reagent/Tool	Type	Primary Function	Application in Evaluation
BioLLM Framework	Software framework	Unified interface for diverse scFMs	Standardizes model access, switching, and benchmarking across architectures [8]
PertEval-scFM	Benchmarking framework	Specialized evaluation of perturbation predictions	Provides standardized tasks for assessing perturbation effect prediction [10]
CZ CELLxGENE	Data repository	Provides unified access to annotated single-cell datasets	Source of diverse, standardized training and evaluation data [1]
Gene Ontology Annotations	Biological database	Functional gene classifications	Enables biological fidelity assessment and functional interpretation of results [28]
UMAP	Visualization tool	Dimensionality reduction for high-dimensional data	Visualizes cell embeddings and assesses cluster separation [8]
Average Silhouette Width (ASW)	Evaluation metric	Quantifies cluster separation quality	Measures biological relevance of cell embeddings [8]

Discussion: Implications for Model Selection and Development

Interpreting Performance Discrepancies Across Tasks

The contrasting performance of scFMs across different tasks reveals important insights into their current capabilities and limitations. While scGPT demonstrates superior performance in generating biologically meaningful cell embeddings and correcting batch effects [8], its underperformance compared to simple baselines in perturbation prediction highlights a significant gap between representation learning and predictive accuracy [28]. This discrepancy suggests that current scFMs may be effectively learning structural patterns in single-cell data but struggling with causal reasoning about how perturbations alter cellular states.

The superior performance of simple linear models equipped with pretrained embeddings from scFMs [28] indicates that the learned representations do capture biologically relevant information, but the complex architectural components may not be optimally leveraging these representations for specific prediction tasks. This finding has important implications for resource allocation in model development, suggesting that investment in higher-quality, more diverse training data may yield greater returns than further architectural complexity.

Data Quality as a Performance Determinant

Recent research has established formal scaling laws that quantify how data quality directly influences model performance, introducing a dimensionless quality parameter (Q) that captures the usable information in a corpus [29]. This quality-aware scaling law predicts loss as a joint function of model size, data volume, and data quality, demonstrating that higher-quality data can substantially reduce required model size and compute requirements [29]. These findings are particularly relevant for scFMs, given the extensive documentation of data quality challenges in single-cell genomics, including batch effects, technical noise, and inconsistent processing across datasets [1].

The asymmetric principle for optimal data allocation—where pretraining benefits most from broad diversity in patterns while fine-tuning is more sensitive to data quality [30]—provides a strategic framework for scFM development. This suggests that scFM pretraining should prioritize assembling diverse corpora spanning multiple cell types, tissues, and experimental conditions, while fine-tuning for specific tasks like perturbation prediction should focus on smaller but higher-quality datasets.

The systematic comparison of single-cell foundation models reveals a complex landscape where no single model dominates across all tasks. scGPT emerges as the leader for cell representation and batch correction tasks [8], while simpler approaches remain competitive—and sometimes superior—for perturbation prediction [28]. These findings highlight the critical importance of task-specific model selection rather than assuming general superiority of foundation models across all applications.

For researchers and drug development professionals, practical recommendations include:

• For cell type annotation and batch correction: scGPT currently provides the most robust performance, particularly in zero-shot settings [8]
• For perturbation effect prediction: Simple linear baselines and additive models provide strong benchmarks that current foundation models struggle to consistently outperform [28]
• For embedding extraction: Foundation models learn biologically meaningful representations that can be effectively utilized in simpler downstream models [28]

The confrontation with data quality and batch effects in pretraining corpora remains an ongoing challenge, but standardized frameworks like BioLLM [8] and PertEval-scFM [10] now provide the necessary tools for objective evaluation. As the field matures, the strategic integration of diverse, high-quality data following formal scaling principles [29] promises to unlock the full potential of single-cell foundation models in biological discovery and therapeutic development.

Single-cell foundation models (scFMs) represent a transformative advance in computational biology, leveraging large-scale deep learning trained on millions of single-cell transcriptomes to create versatile tools for biological discovery [1]. These models, typically built on transformer architectures, approach single-cell biology by treating cells as "sentences" and genes or genomic features as "words" or "tokens" [1]. The fundamental promise of scFMs lies in their pre-training on massive, diverse datasets—often encompassing tens of millions of cells from platforms like CELLxGENE—which enables them to learn universal biological patterns that can be adapted to various downstream tasks with minimal fine-tuning [4] [1]. However, this power comes with significant computational costs and practical constraints that researchers must navigate. A comprehensive 2025 benchmark study reveals that despite high expectations, no single scFM consistently outperforms others across all tasks, and simpler machine learning models often prove more efficient for specific datasets, particularly under resource constraints [4]. This guide provides an objective comparison of scFM performance against alternatives, supported by experimental data, to inform strategic model selection in biological research and drug development.

Understanding the scFM Landscape: Architectures and Pretraining Strategies

Model Architectures and Input Representations

scFMs employ varied approaches to overcome the fundamental challenge that gene expression data lacks natural sequential ordering. Most models use transformer architectures but differ significantly in their tokenization strategies and input representations [4] [1]. Common approaches include ranking genes by expression levels within each cell, binning genes by expression values, or using normalized counts without complex ranking schemes [1]. The table below summarizes key architectural differences among prominent scFMs:

Table: Architectural Variations in Single-Cell Foundation Models

Model Name	Model Parameters	Pretraining Dataset Size	Input Genes	Value Embedding	Positional Embedding	Architecture
Geneformer	40M	30M cells	2048 ranked genes	Ordering	✓	Encoder
scGPT	50M	33M cells	1200 HVGs	Value binning	×	Encoder with attention mask
UCE	650M	36M cells	1024 non-unique genes sampled by expression	/	✓	Encoder
scFoundation	100M	50M cells	~19,264 human protein-encoding genes	Value projection	×	Asymmetric encoder-decoder
LangCell	40M	27.5M scRNA-text pairs	2048 ranked genes	Ordering	✓	Encoder

Pretraining Strategies and Biological Knowledge Capture

These models employ different self-supervised pretraining tasks, primarily based on masked gene modeling (MGM) where the model learns to predict masked portions of the gene expression profile [4] [1]. This process allows scFMs to capture biological relationships between genes and cell states, encoding knowledge about regulatory networks and cellular functions [4]. The pretraining phase is computationally intensive, requiring substantial resources, but aims to create a foundational understanding of cellular biology that can be efficiently transferred to various downstream applications [1].

Experimental Benchmarking: scFMs Versus Simpler Alternatives

Comprehensive Benchmarking Methodology

Recent benchmarking studies have employed rigorous methodologies to evaluate scFM performance against traditional approaches. The 2025 benchmark by PMC evaluated six scFMs against established baselines under realistic conditions across multiple task types [4]. The evaluation encompassed:

• Two gene-level tasks assessing functional relationships and gene-gene interactions
• Four cell-level tasks including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction
• Diverse datasets spanning five biologically varied conditions for preclinical tasks and seven cancer types with four drugs for clinical applications
• Twelve evaluation metrics combining unsupervised, supervised, and novel knowledge-based approaches like scGraph-OntoRWR, which measures consistency of captured cell type relationships with established biological knowledge [4]

The PerturBench framework further specialized in perturbation prediction, evaluating models on covariate transfer and combinatorial prediction tasks across six published datasets with diverse perturbation modalities [31].

Quantitative Performance Comparisons

Experimental results reveal a nuanced performance landscape where scFMs excel in some domains while simpler models remain competitive in others:

Table: Performance Comparison Across Biological Tasks

Task Category	Top Performing Models	Key Findings	Performance Advantage
Cell Type Annotation	scFMs with ontology-informed metrics	scFMs capture biological relationships between cell types consistent with prior knowledge [4]	scFMs show superior biological insight capture
Batch Integration	scFMs and traditional methods (Seurat, Harmony)	scFMs robust to technical biases; traditional methods competitive [4] [1]	Context-dependent; scFMs better for complex batch effects
Perturbation Effect Prediction	Simple baselines (kNN, random forest) vs. scFMs	scFM embeddings do not provide consistent improvements, especially under distribution shift [10] [31]	Simpler models often outperform or match scFMs
Drug Sensitivity Prediction	Mixed: scFMs and simpler ML	No single scFM dominates; task-specific performance variations [4]	Dataset and task-dependent
Unseen Perturbation Prediction	scFMs show promise	scFMs leverage pretrained knowledge of gene interactions [31]	scFMs have emergent potential for novel predictions

A critical finding across multiple studies is that while scFMs demonstrate robust performance across diverse applications, simpler machine learning models frequently match or exceed scFM performance for specific tasks, particularly under resource constraints or when dealing with dataset-specific characteristics [4] [10]. The PertEval-scFM benchmark specifically concluded that zero-shot scFM embeddings do not consistently outperform simpler baseline models for perturbation effect prediction [10].

Decision Framework: When to Choose an scFM

Key Decision Factors and Recommendations

Based on comprehensive benchmarking data, researchers should consider these factors when deciding between scFMs and simpler alternatives:

Table: Model Selection Decision Framework

Decision Factor	Foundation Model Recommended	Simpler Model Recommended	Rationale
Dataset Size	Large, diverse datasets (>100,000 cells)	Smaller, focused datasets	scFMs require substantial data to demonstrate advantage [4]
Task Complexity	Novel cell type identification, cross-tissue analysis	Standard cell type annotation, well-established classifications	scFMs excel at capturing subtle biological relationships [4]
Computational Resources	Ample resources for fine-tuning	Limited computational budget	scFM training/fine-tuning is resource-intensive [1]
Need for Interpretation	Biological insight discovery, gene relationship mapping	Predictive accuracy without interpretation needs	scFMs offer better biological interpretability [4]
Domain Specificity	Generalizable across tissues/conditions	Single tissue type or condition	scFMs leverage cross-domain knowledge [4] [1]
Perturbation Prediction	Unseen perturbation prediction	Covariate transfer with seen perturbations	scFMs show promise for novelty; simpler models excel with known space [31]

Visual Decision Workflow

The following diagram illustrates the decision pathway for selecting between foundation models and simpler alternatives:

Experimental Protocols for scFM Evaluation

Benchmarking Methodology for Biological Relevance

To evaluate scFMs in real-world scenarios, researchers have developed sophisticated experimental protocols that assess both performance and biological relevance:

• Zero-Shot Embedding Evaluation: Pre-trained embeddings are directly applied to downstream tasks without fine-tuning to assess inherent biological knowledge [4] [10]

• Cell Ontology-Informed Metrics: Novel metrics like scGraph-OntoRWR measure consistency between model-captured cell type relationships and established biological ontologies [4]

• Lowest Common Ancestor Distance (LCAD): Quantifies ontological proximity between misclassified cell types, assessing severity of annotation errors [4]

• Roughness Index (ROGI) Analysis: Measures landscape roughness in latent space, correlating smoother representations with better task performance [4]

• Perturbation Prediction Under Distribution Shift: Tests model robustness by evaluating performance on out-of-distribution samples and unseen perturbation types [10] [31]

Essential Research Reagents and Computational Tools

Table: Key Research Reagents and Computational Tools for scFM Evaluation

Resource Category	Specific Tools/Datasets	Function in Evaluation	Access Information
Benchmarking Frameworks	PerturBench [31], PertEval-scFM [10]	Standardized model evaluation platforms	Publicly available GitHub repositories
Data Resources	CZ CELLxGENE [1], Asian Immune Diversity Atlas (AIDA) v2 [4]	High-quality, diverse single-cell datasets for testing	Public data portals
Baseline Methods	Seurat [4], Harmony [4], scVI [4]	Established traditional methods for performance comparison	Open-source packages
Evaluation Metrics	scGraph-OntoRWR [4], LCAD [4], ROGI [4]	Specialized metrics for biological relevance assessment	Custom implementations in benchmarking code
Perturbation Datasets	Norman19, Srivatsan20, Frangieh21, OP3 [31]	Curated perturbation response data for specific task evaluation	Publicly available with standardized preprocessing

The decision to use single-cell foundation models represents a trade-off between computational cost and potential biological insight. Current evidence suggests that scFMs serve as powerful tools for exploring complex biological systems and extracting novel insights, particularly for tasks requiring generalization across diverse conditions or discovery of new biological relationships [4]. However, for well-established tasks with sufficient training data, simpler machine learning approaches often provide comparable performance with significantly lower computational requirements [4] [10] [31].

Researchers should select foundation models when working with large, diverse datasets; when task complexity requires capturing subtle biological relationships; when computational resources permit; and when biological interpretability is a primary goal. Conversely, simpler models remain competitive for standardized tasks, smaller datasets, and resource-constrained environments. As the field evolves, the development of more efficient scFMs and better understanding of their capabilities will further refine these guidelines, but current evidence emphasizes the importance of task- and dataset-specific model selection rather than defaulting to the most complex available approach.

Single-cell foundation models (scFMs) are revolutionizing biological research by transforming high-dimensional, sparse single-cell RNA sequencing (scRNA-seq) data into meaningful representations of cellular state [3]. For researchers and drug development professionals, selecting the right model and configuring it optimally is crucial for extracting biologically relevant insights. Two of the most critical configuration parameters are input gene length—the number of genes used as model input—and model scaling—the size of the model in terms of its parameters and pretraining data. This guide provides an objective comparison of leading scFMs, examining how these factors impact performance across key biological tasks to inform model selection and application.

Comparative Performance Analysis of scFMs

The performance of single-cell foundation models varies significantly across different tasks, with no single model dominating all others. The table below summarizes the comparative strengths and weaknesses of leading scFMs based on comprehensive benchmarking studies.

Table 1: Comparative Overview of Leading Single-Cell Foundation Models

Model Name	Model Parameters	Pretraining Dataset Scale	Key Strengths	Key Limitations
scGPT [18] [8]	50 M	33 M cells	Robust performance across all tasks (zero-shot & fine-tuning); Embedding quality improves with longer input sequences; Effective batch-effect correction	-
Geneformer [3] [18]	40 M	30 M cells	Strong gene-level task performance; Computationally efficient	Limited by fixed input gene ranking
scFoundation [3] [18]	100 M	50 M cells	Strong gene-level task performance; Handles full gene set	High computational resource requirements
scBERT [18] [8]	-	-	-	Smaller model size; Limited training data; Performance declines with longer inputs

The Critical Role of Input Gene Length

Input gene length profoundly influences model performance, with varying effects across different model architectures. Experimental evidence reveals that models respond differently to increasing input lengths:

• Performance Improvements with Longer Inputs: scGPT demonstrates a positive correlation between input sequence length and embedding quality, where "longer input sequences enable scGPT to capture richer information, resulting in more accurate cell representations" [8].
• Performance Degradation with Longer Inputs: In contrast, scBERT's "performance declined as input sequence length increased across most datasets," suggesting difficulties in learning meaningful features from extended inputs [8].
• Minimal Impact: Geneformer and scFoundation showed only slight correlations with input length, with minimal overall performance changes [8].

These differences stem from fundamental architectural variations. Models like scGPT using value embeddings can flexibly process different numbers of input genes, while Geneformer employs a fixed ranking approach limited to 2,048 genes [3].

Model Scaling and Performance Trade-offs

Model scaling encompasses both parameter count and pretraining data volume, significantly influencing performance:

• Parameter Scaling: Larger models like scFoundation (100M parameters) and scGPT (50M parameters) generally demonstrate stronger performance across diverse tasks compared to smaller models like scBERT [3] [18].
• Data Scaling: The volume of pretraining data correlates with model capability. scFoundation trained on 50 million cells and scGPT on 33 million cells show more robust biological understanding than models with less training data [3].
• Efficiency Trade-offs: While larger models often perform better, they require more computational resources. scGPT and Geneformer demonstrate "superior efficiency in terms of memory usage and computational time compared to scBERT and scFoundation" [8].

Experimental Protocols and Benchmarking Data

Benchmarking Methodologies

Comprehensive benchmarking studies evaluate scFMs using standardized protocols across multiple tasks:

• Gene-Level Tasks: Assess gene embeddings through tissue specificity prediction and Gene Ontology term prediction [3].
• Cell-Level Tasks: Evaluate cell embeddings through batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [3].
• Perturbation Prediction: Test models' ability to predict cellular responses to genetic or chemical perturbations using frameworks like PertEval-scFM [22] [10].

Quantitative Performance Comparison

The following table summarizes key performance metrics across different model configurations, highlighting the impact of input gene length and model scale.

Table 2: Performance Metrics Across Model Configurations and Tasks

Model	Input Gene Length	Cell Type Annotation (Accuracy)	Batch Integration (ASW)	Perturbation Prediction	Computational Efficiency
scGPT	1200 HVGs	High (Superior separation)	0.75 (Best)	Limited improvement over baselines [22]	Efficient memory/time usage
Geneformer	2048 (ranked)	Moderate	0.65	Limited improvement over baselines [22]	Most efficient
scFoundation	19,264 (full set)	Moderate	0.60	Limited improvement over baselines [22]	High resource requirements
scBERT	Variable	Low (Declines with longer inputs)	0.45 (Poorest)	Limited improvement over baselines [22]	Moderate efficiency

Key findings from these benchmarks include:

• No single scFM consistently outperforms others across all tasks, emphasizing the need for task-specific model selection [3]
• scFMs show limited advantage over simpler baselines for perturbation prediction, particularly under distribution shift [22] [10] [23]
• Embedding quality correlates with smoother cell-property landscapes, facilitating easier training of task-specific models [3]

Visualizing Model Selection and Performance Relationships

Diagram 1: scFM Selection Workflow - This diagram outlines the decision process for selecting an appropriate single-cell foundation model based on input gene length requirements, computational resources, and task objectives.

Essential Research Reagent Solutions

Implementing scFMs effectively requires familiarity with key computational tools and resources. The following table outlines essential components for working with these models.

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

Resource Category	Specific Tools/Models	Function and Application
Single-Cell Foundation Models	scGPT, Geneformer, scFoundation, scBERT [3] [18]	Generate cell and gene embeddings for downstream analysis tasks
Evaluation Frameworks	BioLLM, PertEval-scFM [18] [22]	Standardized benchmarking of model performance across diverse tasks
Data Integration Methods	Seurat, Harmony, scVI [3]	Baseline methods for batch effect correction and data integration
Visualization Tools	UMAP, t-SNE	Visualization of high-dimensional embeddings in 2D/3D space
Specialized Metrics	scGraph-OntoRWR, LCAD, ASW [3] [8]	Biologically-informed evaluation of embedding quality and cell type relationships

Optimizing single-cell foundation models for biological relevance requires careful consideration of both input gene length and model scaling. Evidence shows that:

• Input gene length significantly impacts performance, with effects varying by model architecture
• Model scaling involves trade-offs between performance and computational efficiency
• Task-specific selection is crucial, as no single model dominates all applications

For researchers, this means selecting models based on specific experimental needs rather than seeking a universal solution. As the field evolves, improved benchmarking frameworks and specialized models promise to enhance our ability to extract biologically meaningful insights from single-cell data.

Single-cell foundation models (scFMs) are revolutionizing biological research by providing a unified framework for analyzing the immense complexity of single-cell transcriptomics data. Trained on millions of single-cells spanning diverse tissues and conditions, these large-scale artificial intelligence models learn fundamental biological principles that can be adapted to various downstream tasks. The core output of these models is a latent embedding space—a compressed, multidimensional representation where each point corresponds to a cell's state, and the spatial relationships between points reflect biological similarities and differences. However, the very power of these latent spaces presents a significant challenge: interpreting what these learned representations actually mean in biological terms. As these models become increasingly central to biological discovery and therapeutic development, developing robust strategies to decode the biological signals within their latent spaces has emerged as a critical research frontier. This evaluation framework is essential for transitioning scFMs from powerful black boxes to trustworthy tools that can provide genuine biological insights and reliably inform drug development decisions.

Comparative Analysis of Interpretation Strategies and Performance

Researchers have developed multiple innovative strategies to probe the biological relevance of scFM embeddings. The table below summarizes the primary interpretation approaches, their methodologies, and their comparative performance across key biological tasks.

Table 1: Performance Comparison of scFM Interpretation Strategies Across Biological Tasks

Interpretation Strategy	Key Methodological Approach	Biological Task Performance	Strengths	Limitations
Ontology-Informed Metrics [3]	Evaluates embedding space consistency with prior knowledge from cell ontologies using metrics like scGraph-OntoRWR and LCAD.	Cell Type Annotation: High biological plausibilityDataset Integration: Preserves meaningful variation	Direct biological grounding; Quantifies error severity	Dependent on quality and completeness of reference ontologies
Attention Mechanism Analysis [1]	Analyzes attention weights in transformer architectures to identify genes critical for specific predictions.	Gene Regulatory Networks: Identifies key regulatorsPerturbation Prediction: Pinpoints responsive genes	Model-intrinsic; No additional tools needed	Complex to interpret; No direct functional annotation
Biologically-Constrained Architectures [32]	Uses sparse decoders wired with known gene modules (pathways, regulatory networks) forcing latent variables to represent specific biological concepts.	Pathway Activity Inference: High interpretabilityDrug Response: Recapitulates known mechanisms	Built-in interpretability; Direct functional mapping	Constrains model flexibility; Requires prior knowledge
Latent Space Roughness Analysis [3]	Computes the Roughness Index (ROGI) to measure landscape smoothness, correlating it with downstream task performance.	Generalizability: Predicts model transfer successTask Adaptation: Identifies suitable models	Predictive of model performance; Model-agnostic	Indirect biological interpretation

Quantitative benchmarking studies reveal that no single scFM consistently outperforms others across all interpretation tasks. Evaluations of six leading scFMs against established baselines under realistic conditions show that simpler machine learning models can sometimes outperform complex foundation models on specific tasks, particularly when working with limited data or computational resources [3]. In gene-level tasks, models like Geneformer and scFoundation demonstrate strong capabilities, benefiting from their effective pretraining strategies, while scGPT shows robust performance across both zero-shot and fine-tuning scenarios [18]. For cell-type annotation, the introduction of ontology-informed metrics like the Lowest Common Ancestor Distance (LCAD) provides a more biologically nuanced assessment of error severity by measuring the ontological proximity between misclassified cell types [3].

Experimental Protocols for Assessing Biological Relevance

Evaluating Gene Embeddings with Functional Genomics

Objective: To determine whether gene embeddings learned by scFMs capture known biological relationships and functional similarities.

Methodology: Gene embeddings are extracted from the input layers of scFMs and compared against reference embeddings generated from established biological knowledge bases [3]. The Functional Representation of Gene Signatures (FRoGS) approach serves as a benchmark, learning gene embeddings through random walks on a hypergraph with Gene Ontology terms or regulated gene sets as hyperedges [3]. The evaluation involves:

• Extraction: Obtain gene embedding vectors from each scFM's embedding layer.
• Similarity Calculation: Compute cosine similarity between all gene pairs in the embedding space.
• Functional Correlation: Measure the correlation between embedding similarity and functional similarity based on:
  • Gene Ontology (GO) term co-annotation
  • Pathway membership in databases like Reactome and KEGG
  • Protein-protein interaction data
• Classification Performance: Assess how well the embeddings can predict:
  • Tissue-specific expression patterns
  • GO biological process membership

This protocol tests the hypothesis that functionally related genes should cluster together in the latent space, analogous to how semantically similar words cluster in natural language model embeddings.

Cell Ontology-Informed Embedding Validation

Objective: To evaluate whether cell-level embeddings preserve biologically meaningful relationships consistent with established taxonomic knowledge.

Methodology: This approach introduces novel metrics that leverage the hierarchical structure of cell ontologies to assess embedding quality beyond simple clustering metrics [3].

• scGraph-OntoRWR Metric: This metric evaluates how well the relational structure between cell types in the embedding space aligns with prior biological knowledge encoded in cell ontologies [3]. The implementation involves:

  • Constructing a knowledge graph from the Cell Ontology, with nodes representing cell types and edges representing ontological relationships.
  • Building a similarity graph from the scFM embeddings based on k-nearest neighbors.
  • Performing Random Walk with Restart (RWR) on both graphs from the same starting node.
  • Comparing the steady-state distributions using a similarity measure (e.g., Jensen-Shannon divergence) to quantify alignment.

• Lowest Common Ancestor Distance (LCAD): For cell type annotation tasks, LCAD measures the severity of misclassifications by calculating the distance to the most specific common ancestor in the cell ontology hierarchy [3]. This recognizes that misclassifying a "CD4+ T cell" as a "CD8+ T cell" is less severe than misclassifying it as a "neuron," as the former share a more recent common ancestor.

Diagram 1: Cell Ontology Validation Workflow

Biologically-Constrained Model Interpretation

Objective: To directly interpret latent variables by constraining model architectures with prior biological knowledge.

Methodology: The VEGA (VAE Enhanced by Gene Annotations) framework implements a sparse variational autoencoder whose decoder connections mirror user-provided gene modules, forcing latent dimensions to represent specific biological concepts [32]. The experimental protocol includes:

• Gene Module Definition: Curate gene sets representing:
  • Signaling pathways (e.g., Reactome, KEGG)
  • Gene regulatory networks (transcription factor targets)
  • Cell type identity signatures
• Model Architecture: Implement a VAE with a sparse linear decoder where connections are masked according to gene module membership.
• Latent Variable Interpretation: Directly interpret each latent variable as the activity level of its corresponding biological module.
• Differential Activity Testing: Apply Bayesian hypothesis testing to compare module activities across conditions (e.g., treated vs. control), calculating Bayes Factors to quantify significance.

This approach was validated on PBMC datasets stimulated with interferon-β, successfully recapitulating expected pathway activations including interferon-α/β signaling and cell-type-specific tryptophan catabolism [32].

Diagram 2: Biologically-Constrained Architecture

Table 2: Essential Research Reagents and Computational Tools for scFM Interpretability

Resource Category	Specific Examples	Function in Interpretability Research
Data Repositories	CZ CELLxGENE [1], Human Cell Atlas [1], PanglaoDB [1]	Provide standardized, annotated single-cell datasets for model training and benchmarking.
Biological Knowledge Bases	Gene Ontology (GO) [3], Reactome [32], MSigDB [32]	Supply curated gene sets and pathways for grounding latent space interpretations.
Cell Ontologies	Cell Ontology (CL) [3]	Provide hierarchical relationships between cell types for ontology-informed metrics.
Benchmarking Frameworks	BioLLM [18]	Offer standardized APIs for consistent model evaluation and comparison across tasks.
Interpretability Toolkits	Neuronpedia [33]	Enable visualization and exploration of model components and attention mechanisms.

The interpretability of single-cell foundation models is not merely a technical challenge but a fundamental requirement for their meaningful application in biological research and therapeutic development. Our comparative analysis demonstrates that while no single interpretation strategy dominates across all scenarios, the integration of multiple complementary approaches—ontology-informed metrics, attention analysis, biologically-constrained architectures, and landscape assessment—provides a robust framework for validating the biological relevance of scFM embeddings. The field is progressing from treating these models as black boxes toward developing systematic methodologies that explicitly test their alignment with established biological knowledge. As these interpretability techniques mature, they will increasingly enable researchers to not only extract accurate predictions from scFMs but also to discover novel biological insights from the rich patterns encoded in their latent spaces, ultimately accelerating our understanding of cellular mechanisms and therapeutic opportunities.

Benchmarking scFMs: A Comparative Analysis of Model Performance and Robustness

Single-cell RNA sequencing (scRNA-seq) has revolutionized biology by enabling transcriptomic profiling at unprecedented resolution, yet it generates data characterized by high dimensionality, sparsity, and technical noise that complicate analysis. Single-cell foundation models (scFMs) have emerged as transformative tools to address these challenges. These large-scale deep learning models, pretrained on millions of cells, aim to learn universal biological patterns that can be adapted to diverse downstream tasks. The core premise is that by exposing models to vast cellular diversity, they will learn embeddings—numerical representations of genes and cells—that capture biologically meaningful relationships. However, a critical question remains: to what extent do these embeddings genuinely reflect biological reality rather than technical artifacts?

This comparison guide examines four prominent scFMs—scGPT, Geneformer, scFoundation, and scBERT—through the lens of biological relevance. We move beyond mere technical specifications to assess how effectively each model translates complex gene expression patterns into embeddings that reflect known biological relationships, facilitate accurate cell type identification, and predict cellular responses to perturbation. By synthesizing evidence from recent benchmarking studies and performance evaluations, we provide researchers with a structured framework for selecting models whose internal representations most faithfully capture the underlying biology of their systems of interest.

Model Architectures and Pretraining Strategies

The biological relevance of scFM embeddings is fundamentally shaped by their architectural designs and pretraining methodologies. These factors determine how each model processes gene expression data and what patterns it prioritizes during learning.

Table 1: Architectural Specifications and Pretraining Details

Model	Parameters	Pretraining Dataset Size	Tokenization Strategy	Architecture	Pretraining Task
scGPT	~50 million	33 million human cells [34] [4]	Value binning + Attention mask [34]	Transformer Encoder	Iterative masked gene modeling with MSE loss [4]
Geneformer	~40 million	30 million human cells [34] [4]	Gene ranking + Positional encoding [34] [4]	Transformer Encoder	Masked gene modeling with gene ID prediction [4]
scFoundation	~100 million	50 million human cells [34] [4]	Value projection [34]	Asymmetric encoder-decoder	Read-depth-aware masked gene modeling [4]
scBERT	~? (Smaller)	Millions of cells (less than others) [18]	Value binning [34]	Transformer Encoder	Masked gene modeling [34]

Figure 1: The scFM training pipeline transforms raw gene expression data into embeddings through distinct tokenization strategies and pretraining objectives.

Strategic Implications for Biological Representation

The architectural differences between models create distinct inductive biases that influence how they capture biological relationships:

• Gene Ordering vs. Expression Magnitude: Geneformer's rank-based approach prioritizes relative expression patterns within each cell, potentially making it more robust to technical variation in absolute counts. In contrast, scGPT's value binning and scFoundation's value projection directly incorporate expression magnitude, which may preserve finer quantitative differences but be more susceptible to batch effects [34].

• Parameter Scaling and Biological Complexity: scFoundation's larger parameter count (~100 million) suggests greater capacity to model complex gene-gene interactions, while Geneformer's more compact architecture (~40 million parameters) may offer computational efficiency with sufficient representational power for many tasks [34] [4].

• Training Data Diversity: The substantial differences in pretraining dataset sizes—from scBERT's relatively limited collection to scGPT's 33 million cells and scFoundation's 50 million—likely impact each model's exposure to rare cell types and biological contexts [34] [18].

Benchmarking Framework and Experimental Protocols

Rigorous evaluation requires standardized frameworks that assess models across diverse tasks reflective of real-world biological questions. Recent benchmarking initiatives have established comprehensive protocols for this purpose.

Standardized Evaluation Paradigms

The most informative benchmarks examine scFMs across multiple task categories and data conditions:

• Gene-Level Tasks: Evaluate embeddings on gene function prediction, tissue specificity, and Gene Ontology term enrichment to assess whether functionally related genes cluster in embedding space [3] [4].

• Cell-Level Tasks: Test embeddings on cell type annotation, batch integration, and identification of novel cell states to determine how well they preserve biological identity while removing technical artifacts [3] [12].

• Perturbation Response Prediction: Challenge models to predict transcriptional responses to genetic or chemical perturbations, a crucial capability for experimental design and drug discovery [10].

Figure 2: Comprehensive benchmarking evaluates scFMs across multiple task categories using biologically relevant metrics.

Critical Evaluation Metrics for Biological Relevance

Beyond standard performance metrics, specialized measures have been developed to directly quantify biological relevance:

• scGraph-OntoRWR: Measures consistency between cell type relationships in embedding space and established biological ontologies [3] [4].

• Lowest Common Ancestor Distance (LCAD): Assesses the severity of cell type misclassifications by measuring their proximity in ontological hierarchies [3] [4].

• Roughness Index (ROGI): Quantifies the smoothness of cell property landscapes in latent space, with smoother landscapes indicating better generalization [3] [4].

Performance Comparison Across Key Biological Tasks

Synthesizing results from multiple benchmarks reveals distinct performance profiles for each model, with notable trade-offs across different biological applications.

Cell-Type Annotation and Atlas-Level Integration

Table 2: Performance Comparison Across Key Biological Tasks

Model	Cell Type Annotation	Batch Integration	Gene Function Prediction	Perturbation Prediction	Zero-Shot Performance
scGPT	Strong performance across diverse tissues [18]	Robust on complex datasets with biological batch effects [12]	Moderate [18]	Varies significantly across perturbation types [10]	Inconsistent; outperformed by HVG+scVI on some datasets [12]
Geneformer	Effective with fine-tuning [3]	Struggles with technical batch effects [12]	Strong, benefits from effective pretraining [18]	Limited in zero-shot settings [10]	Poor; embeddings often dominated by batch effects [12]
scFoundation	Not specifically reported in benchmarks	Not specifically reported in benchmarks	Strong capabilities [18]	Not specifically reported in benchmarks	Not specifically reported in benchmarks
scBERT	Lags behind larger models [18]	Not specifically reported in benchmarks	Weaker due to smaller size and training data [18]	Not specifically reported in benchmarks	Not specifically reported in benchmarks

Recent benchmarks demonstrate that no single scFM dominates across all applications [3]. In cell type annotation, scGPT consistently ranks among the top performers, particularly when leveraging its fine-tuning capabilities [18]. However, in zero-shot settings—critical for discovery applications where cell identities are unknown—simpler approaches like Highly Variable Gene (HVG) selection combined with established integration methods (Harmony, scVI) sometimes outperform foundation models [12].

For batch integration, scGPT handles biologically complex batch effects (e.g., donor-to-donor variation) more effectively than Geneformer, which struggles with technical batch effects between experimental techniques [12]. Quantitative assessments show that Geneformer's embeddings frequently retain higher proportions of batch-related variance than the original data, indicating inadequate integration [12].

Gene-Level Task Performance and Perturbation Prediction

Gene-level tasks reveal different model strengths. Geneformer and scFoundation demonstrate strong performance in gene function prediction, likely benefiting from their specialized pretraining strategies [18]. scGPT shows more variable results, while scBERT's smaller architecture and limited training data constrain its performance [18].

In the critical area of perturbation prediction, the PertEval-scFM benchmark reveals significant limitations across current models. Zero-shot scFM embeddings do not consistently outperform simpler baseline models, particularly under distribution shift where test conditions differ substantially from training data [10]. All models struggle to predict strong or atypical perturbation effects, highlighting a fundamental challenge in capturing nonlinear cellular responses.

The Scientist's Toolkit: Essential Research Reagents

Implementing scFM evaluation requires specialized computational resources and benchmarking frameworks.

Table 3: Essential Research Reagents for scFM Evaluation

Resource	Type	Function	Relevance to Biological Evaluation
BioLLM Framework	Software framework	Unified interface for diverse scFMs [18]	Standardizes model access and evaluation across architectures
PertEval-scFM	Benchmarking suite	Evaluates perturbation prediction capabilities [10]	Quantifies model performance on crucial experimental design task
Cell Ontology-Informed Metrics	Evaluation metrics	scGraph-OntoRWR, LCAD [3] [4]	Grounds model performance in established biological knowledge
CELLxGENE Datasets	Data resource	Curated single-cell data with unified annotations [35]	Provides standardized biological ground truth for evaluation
AIDA v2 Dataset	Benchmark dataset	Independent, unbiased cell atlas data [3] [4]	Mitigates data leakage risk in evaluation

Based on comprehensive benchmarking evidence, we recommend:

• For versatile application across diverse tasks: scGPT demonstrates the most consistent performance, particularly excelling in cell type annotation and handling complex batch effects [12] [18].

• For gene-centric analyses: Geneformer and scFoundation show particular strength in gene function prediction and capturing gene-gene relationships [18].

• For resource-constrained environments: Simpler approaches like HVG selection with established batch integration methods may provide comparable or superior performance to scFMs in zero-shot settings, particularly for standard cell type identification [12].

• For perturbation modeling: Current scFMs show limited zero-shot capabilities, suggesting continued reliance on specialized perturbation prediction models or extensive fine-tuning [10].

The field continues to evolve rapidly, with promising directions including multi-modal integration, improved zero-shot generalization, and better incorporation of biological prior knowledge. As model architectures advance and training datasets expand, the biological relevance and practical utility of scFM embeddings are likely to improve substantially.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling high-resolution transcriptome profiling at the individual cell level, offering unprecedented insights into cellular heterogeneity and complex biological systems [4] [1]. As the volume of single-cell data has exponentially grown, single-cell foundation models (scFMs) have emerged as powerful computational tools trained on massive datasets to learn universal biological knowledge in a self-supervised manner [4] [1]. These models typically employ transformer architectures to process single-cell data, treating genes as tokens and cells as sentences to decipher the "language of biology" [1]. Despite their promising capabilities, a critical question remains: how well do these models capture biologically meaningful patterns across diverse biological contexts?

Understanding the performance characteristics, strengths, and limitations of scFMs is essential for researchers, scientists, and drug development professionals who rely on these tools for biological discovery and therapeutic development. This comparison guide provides an objective evaluation of leading scFMs based on comprehensive benchmarking studies, focusing specifically on their performance across diverse biological contexts and their ability to generate biologically relevant embeddings. Through systematic analysis of experimental data and performance metrics, we aim to equip researchers with the knowledge needed to select appropriate models for their specific biological questions and experimental contexts.

Experimental Frameworks for scFM Evaluation

Standardized Benchmarking Methodologies

Evaluating scFMs requires carefully designed experimental protocols that assess both technical performance and biological relevance. Major benchmarking studies have converged on several key methodologies. The BioLLM framework implements a standardized approach through three integrated modules: a decision-tree-based preprocessing interface that establishes rigorous quality control standards, a BioTask executor that facilitates both zero-shot inference and model fine-tuning, and comprehensive performance metrics that assess embedding quality, biological fidelity, and prediction accuracy [8].

Benchmarking studies typically evaluate models under two primary settings: zero-shot evaluation using precomputed embeddings without additional training, and fine-tuned evaluation where models are further trained on specific tasks [4] [8]. Performance is assessed across multiple cell-level and gene-level tasks, including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [4]. These evaluations utilize large and diverse benchmarking datasets with high-quality labels, often incorporating independent datasets like the Asian Immune Diversity Atlas (AIDA) v2 to mitigate data leakage risks and validate conclusions [4].

Novel Biological Relevance Metrics

Beyond traditional performance metrics, researchers have developed novel evaluation approaches specifically designed to assess biological relevance. The scGraph-OntoRWR metric measures the consistency of cell type relationships captured by scFMs with prior biological knowledge encoded in cell ontologies [4]. Similarly, the Lowest Common Ancestor Distance (LCAD) metric assesses the ontological proximity between misclassified cell types, providing insight into the biological severity of annotation errors [4].

The roughness index (ROGI) serves as a proxy to evaluate how well a model's latent space organizes cellular states by quantitatively estimating the correlation between model performance and cell-property landscape roughness [4]. These biologically-informed metrics represent significant advances in moving beyond purely technical evaluations toward assessments that better reflect real-world biological applications.

Table 1: Key Evaluation Metrics for scFM Biological Relevance

Metric Category	Specific Metrics	What It Measures	Biological Interpretation
Embedding Quality	Average Silhouette Width (ASW)	Separation of cell types in latent space	Ability to distinguish biologically distinct cell populations
Ontological Consistency	scGraph-OntoRWR	Alignment with established cell ontologies	Capture of known biological relationships between cell types
Annotation Accuracy	Lowest Common Ancestor Distance (LCAD)	Ontological distance of misclassifications	Biological plausibility of cell type prediction errors
Latent Space Organization	Roughness Index (ROGI)	Smoothness of cell property landscape	Organization of continuous biological processes and transitions
Batch Effect Correction	Batch ASW	Removal of technical artifacts while preserving biology	Ability to integrate datasets without obscuring biological signals

Comparative Performance Analysis of Leading scFMs

Several prominent scFMs have been developed with different architectural choices and training strategies. Geneformer utilizes a transformer encoder architecture pretrained on 30 million cells using masked gene modeling with a cross-entropy loss, employing a ranked gene approach where the top 2,048 expressed genes form the input sequence [4]. scGPT also uses a transformer framework but incorporates a more flexible approach supporting multiple omics modalities and employs iterative masked gene modeling with mean squared error loss, typically using 1,200 highly variable genes as input [4] [8].

scFoundation represents a larger-scale model with 100 million parameters trained on 50 million cells using an asymmetric encoder-decoder architecture and read-depth-aware masked gene modeling [4]. UCE takes a unique approach by incorporating protein embeddings from ESM-2 and ordering genes by genomic position rather than expression level [4]. scBERT employs a BERT-like bidirectional architecture trained specifically for cell type annotation with masked language modeling objectives [18] [8].

These architectural differences lead to significant variations in how each model processes and represents biological information, which in turn affects their performance across different tasks and biological contexts.

Performance Across Cell-Level Tasks

Comprehensive benchmarking reveals distinct performance patterns across cell-level tasks such as cell type annotation, batch integration, and embedding quality. In zero-shot cell embedding evaluations, scGPT consistently demonstrates superior performance, achieving higher average silhouette width (ASW) scores and better separation of cell types in visualization analyses [8]. This advantage is particularly evident in individual dataset evaluations where scGPT's embeddings show clearer biological separation compared to other models [8].

For batch effect correction, performance varies significantly across models. scGPT generally outperforms other foundation models in integrating cells of the same type across different experimental conditions, though all models struggle with substantial batch effects across different technologies [8]. Notably, while scGPT effectively mitigates batch effects while preserving biological signals, Geneformer and scFoundation show capabilities in distinguishing certain cell types but with less consistent batch integration [8]. scBERT typically exhibits the poorest performance in batch correction tasks [8].

In cell type annotation tasks, foundation models demonstrate varying capabilities. Models with stronger zero-shot embedding quality generally require less fine-tuning for accurate cell type prediction. The biological plausibility of errors, as measured by LCAD, also varies, with some models producing more biologically reasonable misclassifications (e.g., confusing closely related cell types) than others [4].

Table 2: Performance Comparison of scFMs Across Key Biological Tasks

Model	Zero-Shot Embedding Quality (ASW)	Batch Effect Correction	Cell Type Annotation Accuracy	Computational Efficiency	Key Strengths
scGPT	Consistently high across datasets [8]	Strong performance in technology integration [8]	High accuracy with minimal fine-tuning [18] [8]	Efficient memory usage and computation time [8]	Versatility across tasks, multi-omics capability [8]
Geneformer	Moderate to high quality [8]	Moderate, preserves some cell type distinctions [8]	Strong with adequate fine-tuning [18]	Efficient resource usage [8]	Gene-level analyses, regulatory network inference [4] [18]
scFoundation	Moderate quality [8]	Variable across datasets [8]	Good performance with fine-tuning [18]	Higher computational demands [8]	Large-scale pattern recognition, pan-tissue analyses [4]
UCE	Not comprehensively evaluated	Not comprehensively evaluated	Not comprehensively evaluated	Not comprehensively evaluated	Protein context integration, genomic position awareness [4]
scBERT	Lower quality across evaluations [8]	Poor batch effect correction [8]	Lower accuracy without significant fine-tuning [18] [8]	Less efficient than alternatives [8]	Specialized for cell type annotation tasks [1]

Performance Across Gene-Level Tasks

Gene-level tasks, including gene function prediction, gene-gene interaction inference, and gene regulatory network reconstruction, reveal another dimension of model capabilities. Geneformer and scFoundation demonstrate particularly strong performance in gene-level tasks, benefiting from their effective pretraining strategies that capture meaningful gene relationships [18]. These models show better performance in capturing known biological relationships between genes, as evidenced by their superior performance in gene ontology enrichment analyses and gene regulatory network inference [4].

The ability to model gene-gene interactions varies significantly based on how models handle gene tokenization and attention mechanisms. Models that incorporate gene metadata such as genomic position or protein domains (e.g., UCE) may have advantages in capturing certain types of biological relationships, though comprehensive comparisons in these specific tasks are still emerging [4] [1].

Impact of Input Representations and Scaling Laws

The performance of scFMs is significantly influenced by input representation strategies and model scaling. Studies have systematically investigated the impact of varying gene input lengths on embedding quality, revealing model-specific patterns. scGPT shows improved performance with longer input sequences, suggesting its architecture effectively leverages additional genetic information [8]. In contrast, scBERT's performance typically declines as input sequence length increases, indicating potential limitations in processing larger genetic contexts [8].

Gene ranking strategies also significantly affect model performance. Models that use expression-based ranking (e.g., Geneformer, scGPT) generally outperform those with random or fixed gene orders, confirming that biologically informed input structures enhance model capabilities [4] [1]. The inclusion of value embeddings representing expression levels, alongside gene identity embeddings, consistently proves important for capturing biological meaningful patterns [4].

Regarding scaling laws, larger models pretrained on more diverse datasets (e.g., scFoundation with 100M parameters trained on 50M cells) generally show better generalization across tasks, though with diminishing returns and increased computational costs [4]. However, model size alone doesn't guarantee superior performance, as architectural choices and training strategies significantly influence efficiency and effectiveness [4] [8].

Trade-offs and Practical Considerations for Biological Applications

Task-Specific Model Recommendations

Based on comprehensive benchmarking results, specific scFMs demonstrate particular strengths depending on the biological context and analysis goals. For general-purpose applications requiring robust performance across multiple task types without extensive fine-tuning, scGPT emerges as the most versatile option, demonstrating strong capabilities in both zero-shot and fine-tuned settings [18] [8]. Its consistent performance across embedding quality, batch correction, and cell type annotation makes it particularly suitable for exploratory analyses and researchers seeking a single model for diverse applications.

For gene-centric analyses, including gene function prediction and regulatory network inference, Geneformer and scFoundation show particular strengths, likely due to their effective pretraining strategies that capture rich gene-gene relationships [18]. These models may be preferable for studies focused on understanding gene regulatory mechanisms or identifying novel gene functions.

In resource-constrained environments or for specific focused tasks, simpler machine learning models sometimes outperform complex foundation models, particularly when dealing with small datasets or homogeneous biological contexts [4]. This highlights the importance of matching model complexity to the specific biological question and available data resources.

Computational Efficiency and Practical Deployment

Practical considerations around computational resources significantly impact model selection for different biological applications. Comprehensive evaluations of computational efficiency reveal substantial differences between models. scGPT and Geneformer demonstrate superior efficiency in terms of memory usage and computational time compared to scBERT and scFoundation, making them more practical for large-scale analyses [8].

The trade-off between model performance and resource requirements becomes particularly important when working with large datasets or in environments with limited computational resources. In such cases, the marginal gains from larger models may not justify their substantial computational costs, especially for more focused biological questions [4].

Additionally, the availability of well-documented implementations varies across models, with some like Geneformer and scGPT providing extensive documentation and user-friendly interfaces, while others present greater implementation challenges [18] [8]. These practical considerations significantly impact the real-world usability of different scFMs in biological research settings.

Experimental Protocols and Research Toolkit

Standardized Evaluation Workflow

To ensure reproducible assessment of scFM performance, researchers should follow standardized experimental protocols. The BioLLM framework provides a comprehensive workflow that begins with rigorous quality control and preprocessing, including mitochondrial gene filtering, doublet detection, and normalization [8]. Following preprocessing, models are evaluated in both zero-shot settings—using precomputed embeddings without additional training—and fine-tuned settings where models are adapted to specific tasks with limited labeled data [8].

Evaluation should encompass multiple biological contexts including individual datasets with clear cell type separations, complex datasets with continuous biological processes, and multi-batch datasets with significant technical variation [4]. Performance should be assessed using both standard metrics (e.g., ASW for clustering quality, accuracy for classification) and biology-specific metrics (e.g., scGraph-OntoRWR for ontological consistency, LCAD for biological plausibility of errors) [4].

scFM Evaluation Workflow

Essential Research Reagent Solutions

The following research reagents and computational resources are essential for conducting comprehensive evaluations of scFMs across diverse biological contexts:

Table 3: Essential Research Reagent Solutions for scFM Evaluation

Resource Type	Specific Examples	Function in Evaluation	Key Characteristics
Reference Datasets	CZ CELLxGENE [1], Asian Immune Diversity Atlas (AIDA) v2 [4], Human Cell Atlas [1]	Provide standardized biological contexts for evaluation	Diverse cell types, multiple tissues, high-quality annotations
Benchmarking Frameworks	BioLLM [18] [8], Custom benchmarking pipelines [4]	Standardize model comparison and evaluation	Unified APIs, reproducible metrics, support for multiple models
Evaluation Metrics	scGraph-OntoRWR [4], LCAD [4], ROGI [4], ASW [8]	Quantify biological relevance and technical performance	Biologically informed, computationally tractable, interpretable
Computational Infrastructure	GPU clusters, High-memory nodes, Storage systems	Enable model training and evaluation	Scalable, compatible with deep learning frameworks, adequate storage
Biological Knowledge Bases	Cell Ontology, Gene Ontology, Pathway databases	Provide ground truth for biological relevance assessment	Curated biological knowledge, structured relationships, comprehensive coverage

The comprehensive evaluation of single-cell foundation models across diverse biological contexts reveals a complex landscape with no single model consistently outperforming others across all tasks and contexts [4]. Instead, each model demonstrates distinct strengths and weaknesses, making model selection highly dependent on specific research goals, biological contexts, and computational resources.

scGPT emerges as the most versatile option, demonstrating robust performance across multiple tasks including zero-shot embedding, batch correction, and cell type annotation [18] [8]. Geneformer and scFoundation show particular strengths in gene-level tasks and benefit from effective pretraining strategies [18]. Importantly, simpler machine learning approaches sometimes outperform complex foundation models in specific scenarios, particularly under resource constraints or when dealing with homogeneous datasets [4].

For researchers seeking to leverage scFMs in biological and clinical research, the key recommendation is to align model selection with specific use cases, considering factors such as dataset size, task complexity, need for biological interpretability, and available computational resources [4]. As the field continues to evolve, standardization efforts like the BioLLM framework and the development of biologically meaningful evaluation metrics will be crucial for advancing our understanding of these powerful tools and unlocking their full potential for biological discovery and therapeutic development [18] [8].

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, offering the potential to decipher the complex "language" of cells by treating genes as words and entire cells as sentences [1] [35]. However, the rapid development of diverse models like scGPT, Geneformer, scFoundation, and scBERT has created a significant challenge for researchers and drug development professionals. These models exhibit heterogeneous architectures and coding standards, making their systematic comparison and application difficult [18] [36]. This heterogeneity obscures their relative strengths and weaknesses, complicating the selection of the optimal model for specific biological questions. The BioLLM (Biological Large Language Model) framework was introduced specifically to address this standardization gap. It provides a unified interface and standardized APIs, enabling streamlined model access, consistent benchmarking, and a clearer understanding of the biological relevance captured by different scFM embeddings [18] [36] [3]. This guide provides a comparative analysis of leading scFMs through the lens of the BioLLM framework, detailing the experimental protocols and performance data essential for informed model selection.

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How scFMs Are Evaluated: Frameworks and Metrics

BioLLM Framework Architecture

BioLLM operates by creating a standardized abstraction layer over multiple scFMs. Its architecture is designed to eliminate inconsistencies and facilitate direct comparison, which is vital for assessing the biological relevance of model embeddings.

The following diagram illustrates the standardized evaluation pipeline enabled by the BioLLM framework:

Key Evaluation Metrics and Protocols

Evaluating scFMs extends beyond standard machine learning metrics to include specialized measures that quantify how well the models capture underlying biology. Benchmarking studies, such as the one detailed by [3], typically employ a suite of metrics:

• Traditional Performance Metrics: These include accuracy, F1-score, and Area Under the Curve (AUC) for supervised tasks like cell type annotation, and metrics like Average Silhouette Width (ASW) and batch correction metrics for unsupervised tasks like data integration [3].
• Biology-Informed Metrics: A novel contribution to the field is the development of metrics that leverage prior biological knowledge to assess embedding quality.
  • scGraph-OntoRWR: This metric evaluates the consistency of cell-type relationships discovered by the scFM with the known relationships encoded in cell ontology. A higher score indicates that the model's latent space better reflects established biological knowledge [3].
  • Lowest Common Ancestor Distance (LCAD): Used in cell-type annotation tasks, LCAD measures the ontological distance between a misclassified cell and its correct type. A smaller LCAD indicates a less severe error (e.g., confusing two types of T cells is better than confusing a T cell with a neuron) [3].
  • Mean Average Precision (mAP) for Profiles: As highlighted in [37], mAP is adapted from information retrieval to evaluate profile strength and similarity. It measures a model's ability to rank biologically similar cells or perturbations highly, providing a data-driven way to assess phenotypic activity and consistency.

The evaluation typically encompasses both gene-level and cell-level tasks. Gene-level tasks assess whether functionally related genes are embedded close together in the latent space, often by predicting Gene Ontology (GO) terms or tissue specificity. Cell-level tasks evaluate the model's utility for practical applications like batch integration, cell-type annotation, and drug sensitivity prediction [3].

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Comparative Performance of Single-Cell Foundation Models

The following tables consolidate quantitative performance data from comprehensive benchmark studies, including those synthesized by the BioLLM framework [18] [36] [3]. They provide a clear, comparative view of leading scFMs across critical downstream tasks.

Table 1: Model Performance Across Cell-Level Tasks (Zero-Shot Embeddings)

Model	Architecture Type	Batch Integration (ASW)	Cell Type Annotation (Accuracy)	Novel Cell Type Discovery	Drug Sensitivity Prediction
scGPT	Decoder (GPT-like)	High	High	Strong	High
Geneformer	Encoder (BERT-like)	Medium	Medium	Medium	Medium
scFoundation	Varied	Medium	Medium	Strong	Medium
UCE	Encoder-Decoder	Medium	Medium	Medium	Medium
scBERT	Encoder (BERT-like)	Low	Low	Low	Low

• Performance Key: High, Medium, Low (relative rankings within the benchmarked model set).
• Note: Performance is highly dependent on task and dataset. scGPT consistently ranks highly across diverse tasks, while scBERT's smaller size and training data often result in lower performance [18] [36] [3].

Table 2: Model Strengths, Limitations, and Computational Profile

Model	Key Strengths	Documented Limitations	Pretraining Corpus Scale
scGPT	Versatile; excels in generation & zero-shot tasks [18]	Computationally intensive	Tens of millions of cells [3]
Geneformer	Strong on gene-level tasks & network inference [18]	Less effective on cell-level tasks	~30 million cells [1]
scFoundation	Effective pretraining; good generalizability [36]	--	Hundreds of millions of genes [3]
LangCell	--	--	--
scCello	--	--	--
scBERT	Early pioneering model	Smaller model; limited training data [18]	Millions of cells

A crucial finding from benchmarks is that no single scFM consistently outperforms all others across every task [3]. Model selection must be tailored to the specific biological question and computational constraints.

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A Standardized Workflow for scFM Benchmarking

The benchmarking process for scFMs follows a structured workflow to ensure fairness and reproducibility. The diagram below outlines the key stages, from data preparation to insight generation, as implemented in frameworks like BioLLM.

Detailed Experimental Protocol:

• Data Curation & Preprocessing: High-quality, diverse datasets from sources like CELLxGENE and the Human Cell Atlas are compiled [1] [35]. Rigorous quality control is applied to manage batch effects and technical noise.
• Feature Extraction: Models are evaluated in a zero-shot setting, where their pretrained weights are used to generate cell and gene embeddings without any further task-specific fine-tuning. This tests the general biological knowledge acquired during pretraining [3].
• Downstream Task Execution: The extracted embeddings are used as input features for a range of tasks:
  • Gene-level tasks involve predicting gene-gene interactions or Gene Ontology terms [3].
  • Cell-level tasks include batch integration, cell-type annotation on new datasets, and predicting clinical outcomes like drug sensitivity [3].
• Multi-Metric Evaluation: Model performance is quantified using the suite of traditional and biology-informed metrics described in the previous section [3] [37].
• Biological Insight Analysis: The final step involves interpreting the results to understand why a model performs well, for example, by analyzing attention mechanisms to identify key genes driving a prediction [1] [3].

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Successful evaluation of scFMs relies on a ecosystem of data, software, and computational resources. The following table details key components of the modern computational biologist's toolkit for this purpose.

Table 3: Key Research Reagents & Resources for scFM Evaluation

Item	Type	Function / Application
BioLLM Framework	Software Tool	Provides standardized APIs for integrating and switching between different scFMs for consistent evaluation [18] [36].
copairs Python Package	Software Tool	Enables efficient calculation of the mAP metric for assessing profile strength and similarity [37].
CZ CELLxGENE	Data Resource	A curated corpus of millions of single-cell datasets, often used for pretraining and benchmarking [1] [35].
Human Cell Atlas	Data Resource	A comprehensive reference map of all human cells, providing a benchmark for biological generalizability [1].
Transformer Architecture	Model Backbone	The core neural network architecture (e.g., BERT, GPT) used by most scFMs to process tokenized gene expression data [1] [35].
High-Performance Computing (HPC) / Cloud GPU	Computational Resource	Essential for training large-scale foundation models and running extensive benchmarking studies.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, offering unprecedented potential for analyzing cellular heterogeneity and complex regulatory networks. These large-scale deep learning models, pretrained on vast single-cell genomics datasets, promise to revolutionize data interpretation through self-supervised learning with capacity for various downstream tasks [1]. However, as with all machine learning applications, their real-world utility depends critically on their ability to generalize to truly independent, unbiased datasets—a challenge where proper assessment methodology becomes paramount.

The fundamental promise of scFMs lies in their training on massive and diverse single-cell datasets, capturing universal patterns that can be transferred to various biological analyses [1]. Yet, this very strength introduces substantial risks if model evaluation fails to properly address data leakage and generalization challenges. Data leakage occurs when a model uses information during training that wouldn't be available at the time of prediction, creating overly optimistic performance estimates that collapse when deployed in real-world scenarios [38]. In scientific contexts, this can lead to misguided biological interpretations and compromised research conclusions.

This review examines the current landscape of scFM evaluation methodologies, focusing specifically on how researchers assess model performance while mitigating data leakage risks. We synthesize findings from major benchmarking studies to compare scFM performance against traditional approaches, analyze the experimental protocols designed to ensure rigorous evaluation, and provide practical guidance for researchers seeking to validate scFMs for biological discovery and therapeutic development.

Comparative Performance Analysis: scFMs Versus Traditional Methods

Comprehensive Benchmarking Across Diverse Biological Tasks

Recent comprehensive benchmark studies reveal a nuanced picture of scFM capabilities compared to established methods. A 2025 study evaluating six prominent scFMs (Geneformer, scGPT, UCE, scFoundation, LangCell, and scCello) against well-established baselines under realistic conditions provides particularly insightful data. The evaluation encompassed two gene-level and four cell-level tasks across diverse biological conditions, with performance assessed using 12 metrics spanning unsupervised, supervised, and knowledge-based approaches [4].

Table 1: Performance Comparison of scFMs Versus Traditional Methods Across Task Categories

Task Category	Task Specifics	Top Performing scFM	Traditional Method Performance	Performance Gap
Pre-clinical tasks	Batch integration across 5 datasets	Variable by dataset	Seurat, Harmony, scVI	Context-dependent
Pre-clinical tasks	Cell type annotation across 5 datasets	Variable by dataset	HVG selection + classifiers	Context-dependent
Clinical tasks	Cancer cell identification across 7 cancer types	No consistent leader	Simple ML models	Simpler models sometimes superior
Clinical tasks	Drug sensitivity prediction for 4 drugs	No consistent leader	Simple ML models	Simpler models sometimes superior
Biological insight	Relationship capture (scGraph-OntoRWR)	Specific scFMs	Not applicable	scFMs show advantage
Biological insight	Error severity (LCAD metric)	Specific scFMs	Not applicable	scFMs show advantage

The benchmark results demonstrate that no single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on factors such as dataset size, task complexity, and computational resources [4]. Notably, simpler machine learning models often proved more adept at efficiently adapting to specific datasets, particularly under resource constraints. This finding challenges the assumption that larger, more complex models invariably deliver superior performance for specialized biological applications.

Specialized Task Performance: Perturbation Effect Prediction

The PertEval-scFM benchmark provides additional insights specifically for perturbation effect prediction, a crucial task for understanding cellular processes and disease mechanisms. This standardized evaluation framework assessed zero-shot scFM embeddings against simpler baseline models to determine whether these contextualized representations enhance prediction accuracy [10].

Table 2: Perturbation Effect Prediction Performance

Model Type	Performance Characteristic	Strengths	Limitations
Zero-shot scFM embeddings	No consistent improvement over baselines	Captures some biological relationships	Struggles with distribution shift
All models	Struggle with strong/atypical effects	Reasonable performance on standard perturbations	Limited predictive power for novel effects
Specialized models	Potential advantage for specific perturbations	May capture context-specific patterns	Require targeted development

The PertEval-scFM results demonstrated that scFM embeddings did not provide consistent improvements over baseline models, especially under distribution shift [10]. All models struggled with predicting strong or atypical perturbation effects, highlighting a significant limitation in current approaches and underscoring the need for specialized models and high-quality datasets capturing a broader range of cellular states.

Experimental Protocols for Rigorous scFM Evaluation

Benchmarking Framework Design

The integrity of scFM evaluation hinges on methodological rigor that prevents data leakage and ensures genuine assessment of generalization capability. A comprehensive benchmarking framework introduced in 2025 exemplifies current best practices by incorporating several crucial design elements [4]:

• Zero-shot protocol: The evaluation of zero-shot gene embeddings and cell embeddings learned from large-scale pretraining without task-specific fine-tuning provides a stringent test of inherent model capabilities.

• Diverse benchmarking datasets: Utilization of large and diverse datasets with high-quality labels spanning different biological conditions, including an independent and unbiased dataset (Asian Immune Diversity Atlas v2 from CellxGene) specifically introduced to mitigate data leakage risks and validate conclusions [4].

• Biologically meaningful metrics: Development of novel evaluation perspectives including cell ontology-informed metrics such as scGraph-OntoRWR (measuring consistency of cell type relationships with prior biological knowledge) and Lowest Common Ancestor Distance (LCAD) metric (assessing ontological proximity between misclassified cell types) [4].

• Clinically relevant tasks: Assessment across challenging real-world scenarios often neglected by previous benchmarking efforts, including novel cell types, cross-tissue homogeneity, and intra-tumor heterogeneity [4].

The benchmarking pipeline systematically addresses the three critical issues in practical scFM applications: assessing biological relevance of scFMs, choosing between complex foundation models and simpler alternatives, and providing guidance for model selection across diverse application scenarios [4].

Data Leakage Prevention Strategies

Data leakage represents one of the most insidious threats to valid model evaluation, occurring when information from outside the training dataset influences the model, creating artificially inflated performance metrics [38]. In machine learning generally, leakage manifests primarily through two mechanisms: target leakage (including data that will not be available during real-world predictions) and train-test contamination (improper splitting or preprocessing that mixes training and validation data) [38].

Specific strategies to prevent data leakage in scFM evaluation include:

• Temporal splitting: For time-series data, using chronological splits to prevent future data from entering the training process [38] [39].

• Preprocessing isolation: Applying preprocessing steps such as scaling, normalization, or imputation separately to training and test sets rather than the entire dataset [38] [40].

• Proper data splitting: Implementing careful train/test splits with additional safeguards such as stratified splitting for imbalanced data and maintaining separate validation sets not used during training [39].

• Feature engineering vigilance: Avoiding creating features that introduce future data or unavailable information at prediction time [41].

• Cross-validation caution: Ensuring proper segmentation in k-fold cross-validation, particularly for time-dependent data where data points from the future must not be included in training folds [38].

The profound impact of data leakage is evidenced by studies across multiple scientific fields, where at least 294 scientific papers were found affected by data leakage, leading to overly optimistic performance estimates [38]. In medical imaging applications, models developed with methodological pitfalls like data leakage produced inaccurate predictions despite high apparent performance during internal evaluation [40].

Diagram 1: scFM Evaluation Workflow with Data Leakage Risks. This diagram illustrates the proper workflow for scFM evaluation while highlighting potential data leakage risks (dashed red lines) that can compromise validity.

Computational Frameworks and Benchmarking Tools

Table 3: Essential Research Reagents for scFM Evaluation

Resource Category	Specific Tool/Resource	Function/Purpose	Access Information
Benchmarking frameworks	Custom benchmark from [4]	Holistic evaluation of 6 scFMs across multiple tasks	Supplementary Information of cited paper
Specialized benchmarks	PertEval-scFM [10]	Standardized evaluation for perturbation effect prediction	https://anonymous.4open.science/r/PertEval-C674/
Data repositories	CZ CELLxGENE [1]	Provides unified access to annotated single-cell datasets	https://cellxgene.cziscience.com/
Data repositories	Asian Immune Diversity Atlas (AIDA) v2 [4]	Independent, unbiased dataset for validation	Available via CellxGene
Data repositories	Human Cell Atlas [1]	Broad coverage of cell types and states for training	https://www.humancellatlas.org/
Data repositories	PanglaoDB [1]	Curated compendium of single-cell data	https://panglaodb.se/
Evaluation metrics	scGraph-OntoRWR [4]	Measures consistency of cell type relationships with biological knowledge	Custom implementation
Evaluation metrics	LCAD metric [4]	Assesses ontological proximity between misclassified cell types	Custom implementation
Evaluation metrics	Harmonic Score [42]	Integrates accuracy, privacy, and fairness into single measure	Custom implementation

Single-Cell Foundation Models in Current Use

Table 4: Prominent Single-Cell Foundation Models

Model Name	Omics Modalities	Model Parameters	Pretraining Dataset Size	Key Features
Geneformer [4]	scRNA-seq	40 million	30 million cells	Uses ranked genes; lookup table embedding
scGPT [4] [1]	scRNA-seq, scATAC-seq, CITE-seq, spatial	50 million	33 million cells	Value binning; iterative masked gene modeling
UCE [4]	scRNA-seq	650 million	36 million cells	ESM-2 based protein embedding
scFoundation [4]	scRNA-seq	100 million	50 million cells	Read-depth-aware masked gene modeling
LangCell [4]	scRNA-seq	40 million	27.5 million scRNA-text pairs	Uses cell type labels in training
scBERT [1]	scRNA-seq	Not specified	Millions of single-cell transcriptomes	BERT-like encoder for cell type annotation

Discussion and Future Directions

Interpretation of Current Performance Gaps

The mixed performance of scFMs relative to traditional methods reveals important insights about model development and evaluation. The finding that simpler models sometimes outperform complex foundation models, particularly for specific datasets under resource constraints [4], suggests that scale alone cannot compensate for targeted architectural innovations or dataset-specific optimization. This aligns with broader machine learning principles where appropriate model complexity matched to task requirements typically yields optimal results.

The superior performance of scFMs on biological insight metrics like scGraph-OntoRWR and LCAD [4] indicates that these models do capture meaningful biological relationships despite sometimes underperforming on specific prediction tasks. This suggests that future development should focus on better leveraging these captured relationships for improved practical performance rather than simply scaling model size or training data.

The particular challenge of distribution shift, where scFMs struggle to maintain performance when applied to data different from their training distribution [10], highlights a fundamental limitation in current approaches. This underscores the need for more diverse training datasets and architectural innovations specifically designed to enhance robustness across biological contexts.

Emerging Solutions and Research Directions

Several promising research directions emerge from current limitations in scFM evaluation and performance:

• Unified evaluation metrics: The introduction of multidimensional assessment approaches like the Harmonic Score, which integrates accuracy, privacy, and fairness into a single measure [42], represents an important step toward more comprehensive model evaluation.

• Generalization techniques: Research into techniques like sharpness-aware training (SAT) and its integration with differential privacy (DP-SAT) shows promise for improving the balance between privacy, utility, and fairness [42], though these approaches must be carefully evaluated for potential amplification of model bias.

• Bias mitigation: Studies demonstrating that increased bias in training data leads to reduced accuracy, greater vulnerability to privacy attacks, and higher model bias [42] highlight the critical need for bias detection and mitigation strategies in scFM development.

• Architectural innovations: The development of more biologically plausible model architectures that better capture gene regulatory networks and cellular dynamics represents a promising direction beyond simply scaling existing transformer-based approaches.

Diagram 2: Challenges and Solutions in scFM Research. This diagram maps the relationship between current challenges in scFM development, proposed solutions, and expected outcomes for the field.

Rigorous assessment of single-cell foundation models on independent, unbiased datasets remains essential for advancing their biological relevance and practical utility. Current evidence suggests a nuanced landscape where scFMs demonstrate significant promise for capturing biological relationships but do not consistently outperform simpler methods on specific prediction tasks. The prevention of data leakage through careful experimental design is not merely a technical consideration but a fundamental requirement for valid model evaluation.

As the field progresses, future research should prioritize the development of standardized benchmarking frameworks, biologically informed model architectures, and comprehensive evaluation metrics that collectively enhance model generalizability and real-world utility. Only through such rigorous approach can scFMs truly fulfill their potential to transform our understanding of cellular function and disease mechanisms.

Conclusion

The evaluation of single-cell Foundation Model embeddings confirms their power as robust and versatile tools for capturing biologically relevant patterns, yet no single model is universally superior. The choice between a complex scFM and a simpler alternative must be guided by specific factors: dataset size, task complexity, need for biological interpretability, and computational resources. The emergence of standardized frameworks and biology-driven metrics, such as scGraph-OntoRWR, marks a critical step toward reproducible and insightful analysis. Future progress hinges on developing more interpretable models, creating sustainable ecosystems for model sharing, and validating these tools in challenging clinical scenarios like intra-tumor heterogeneity and treatment decision-making. By adopting a nuanced, task-specific approach to model selection and validation, researchers can fully harness the potential of scFMs to drive groundbreaking discoveries in biomedicine.

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