Decoding the Cell: How Biological Knowledge is Represented in Single-Cell Foundation Model Embeddings

Aubrey Brooks Nov 27, 2025 463

Single-cell Foundation Models (scFMs) are revolutionizing biological research by learning generalizable representations from vast single-cell genomics datasets.

Decoding the Cell: How Biological Knowledge is Represented in Single-Cell Foundation Model Embeddings

Abstract

Single-cell Foundation Models (scFMs) are revolutionizing biological research by learning generalizable representations from vast single-cell genomics datasets. This article explores how these AI models encode biological knowledge within their embedding spaces, enabling diverse applications from cell type annotation to drug response prediction. We examine the foundational concepts of scFMs, their methodological implementation across biomedical tasks, current limitations and optimization strategies, and comprehensive validation approaches. For researchers and drug development professionals, this synthesis provides critical insights into leveraging scFM embeddings to uncover novel biological mechanisms and advance precision medicine.

Understanding scFM Embeddings: From Single-Cell Biology to AI Representations

Single-cell Foundation Models (scFMs) represent a revolutionary approach in computational biology, adapting the transformer architecture to decipher the complex language of gene expression within individual cells. Framed within the broader thesis of biological knowledge representation, these models aim to encode fundamental principles of cellular function and state into reusable, information-rich embeddings. This guide details the core architectural components, data handling methodologies, and benchmarking insights that define the current state of scFMs.

Core Architectural Principles and Components

The architecture of scFMs is built upon the transformer model, which has been fundamentally re-purposed to handle the unique characteristics of single-cell omics data [1]. Unlike natural language, gene expression data is not inherently sequential, presenting a primary challenge that model architectures must overcome [2] [3].

The core objective is to treat a single cell as a "document" and its constituent genes as "words," thereby creating a numerical representation that a deep learning model can process [1]. Through self-supervised pre-training on millions of cells, scFMs learn a foundational representation of cellular biology that can be adapted to various downstream tasks without the need for extensive labeled datasets [1] [2].

Tokenization: From Gene Expression to Model Input

Tokenization is the critical first step that converts raw gene expression data into a structured sequence of tokens consumable by a transformer model. The methods vary significantly across different scFMs, as detailed in the table below.

Table 1: Tokenization and Input Representation Strategies in Prominent scFMs

Model Name Input Gene Count Value Representation Gene Symbol Embedding Positional Embedding
Geneformer [2] 2,048 ranked genes Gene ordering Lookup Table (512d) ✓
scGPT [2] 1,200 HVGs Value binning Lookup Table (512d) ×
scFoundation [2] ~19,000 genes Value projection Lookup Table (768d) ×
UCE [2] 1,024 sampled genes Not Specified ESM-2-based protein embedding ✓

The following diagram illustrates the generalized tokenization workflow, showcasing the primary strategies used to convert a cell's gene expression profile into a model-ready input sequence.

G cluster_input Input: Single-Cell Gene Expression Vector ExpVec Gene A: 5.2 Gene B: 0.0 Gene C: 1.7 ... Rank Rank by Expression ExpVec->Rank Bin Bin Expression Values ExpVec->Bin Sample Sample by Expression ExpVec->Sample GeneEmb Gene Identity Embedding Rank->GeneEmb ValEmb Expression Value Embedding Bin->ValEmb PosEmb Positional Embedding Sample->PosEmb FinalInput Final Input Sequence for Transformer GeneEmb->FinalInput ValEmb->FinalInput PosEmb->FinalInput

Model Architecture: Transformer Adaptations for Single-Cell Data

Most scFMs utilize a variant of the transformer architecture, primarily divided into encoder-based and decoder-based models [1]. Encoder-based models (e.g., scBERT, Geneformer) use a bidirectional attention mechanism, allowing the model to learn from the context of all genes in a cell simultaneously [1] [2]. Decoder-based models (e.g., scGPT) use a unidirectional masked self-attention mechanism, iteratively predicting masked genes conditioned on the known genes in a cell [1]. Hybrid encoder-decoder designs are also being explored [1].

Table 2: Architectural Overview of Featured scFMs

Model Name Architecture Type Parameters Pretraining Dataset Size Primary Pretraining Task
Geneformer [2] Encoder 40 M 30 M cells Masked Gene Modeling (CE loss)
scGPT [2] Decoder (GPT-style) 50 M 33 M cells Iterative MGM (MSE loss)
scFoundation [2] Asymmetric Encoder-Decoder 100 M 50 M cells Read-depth-aware MGM
UCE [2] Encoder 650 M 36 M cells Modified MGM (binary CE loss)

The following diagram provides a high-level comparison of the encoder and decoder architectures used in scFMs, highlighting the flow of information and the core pretraining tasks.

Benchmarking Performance and Biological Relevance

A pivotal 2025 benchmark study evaluated six leading scFMs against traditional methods on two gene-level and four cell-level tasks to assess their effectiveness in capturing biologically meaningful insights [2] [3]. The evaluation introduced novel, biology-informed metrics like scGraph-OntoRWR, which measures the consistency of cell-type relationships captured by scFMs with prior biological knowledge, and the Lowest Common Ancestor Distance (LCAD), which assesses the severity of cell type annotation errors based on ontological proximity [2].

Key Benchmarking Results

The study yielded several critical findings for researchers and drug development professionals. A major conclusion was that no single scFM consistently outperformed all others across every task, underscoring the need for task-specific model selection [2] [3]. The benchmark also provided insights into the strengths of different models. For instance, scGPT demonstrated robust performance across all tasks, including in zero-shot and fine-tuning settings, while Geneformer and scFoundation showed strong capabilities in gene-level tasks [4] [5]. Furthermore, the research validated that pretrained scFM embeddings do capture meaningful biological knowledge, as evidenced by their performance on the novel ontology-informed metrics [2]. This biological relevance translates to practical utility, as the performance improvement in downstream tasks appears to arise from a smoother "cell-property landscape" in the latent space, which simplifies the training of task-specific models [2].

Experimental Protocols for scFM Evaluation

To ensure rigorous and reproducible evaluation of scFMs, researchers must adhere to standardized protocols. The following methodology, drawn from recent benchmarking efforts, outlines a comprehensive framework for assessing the biological knowledge encoded in scFM embeddings [2] [3].

Protocol: Zero-Shot Evaluation of Cell Embeddings for Data Integration and Annotation

This protocol assesses the quality of cell embeddings generated by an scFM without any task-specific fine-tuning (zero-shot), focusing on batch integration and cell type annotation [2] [5].

  • Embedding Extraction: For a given target dataset, pass each cell's gene expression profile through the pre-trained scFM. Extract the cell-level embedding (often a dedicated [CLS] token or a mean-pooled representation of all gene tokens) [2] [5].
  • Dimensionality Reduction and Visualization: Project the high-dimensional cell embeddings into 2D using Uniform Manifold Approximation and Projection (UMAP). Visually inspect the UMAP plots to qualitatively assess the separation of cell types and the mixing of batches [5].
  • Quantitative Assessment with Multiple Metrics:
    • Average Silhouette Width (ASW): Calculate ASW on the cell embeddings using cell-type labels to measure biological separation. Calculate ASW using batch labels to measure the degree of batch integration (a lower batch ASW indicates better integration) [5].
    • Cell Ontology-Informed Metrics:
      • scGraph-OntoRWR: Construct a graph from cell embeddings where nodes are cells and edges represent high similarity. Perform a Random Walk with Restart (RWR) on this graph and compare the results to a ground-truth graph derived from cell ontology relationships. A higher similarity indicates the model captures biologically plausible cell-type relationships [2] [3].
      • Lowest Common Ancestor Distance (LCAD): For any misclassified cells during annotation, calculate the distance between the true cell type and the predicted cell type within a structured cell ontology (e.g., Cell Ontology). A smaller LCAD indicates a less severe error (e.g., confusing two T cell subtypes vs. confusing a T cell with a neuron) [2].
  • Benchmarking Against Baselines: Compare the performance of scFM embeddings against established baseline methods, such as Principal Component Analysis (PCA), Seurat, or Harmony, using the above metrics [2] [5].

The following table catalogs key computational tools and data resources essential for working with and evaluating single-cell Foundation Models.

Table 3: Key Research Reagents and Resources for scFM Research

Resource Name Type Primary Function Relevance to scFM Research
BioLLM Framework [4] [5] Software Framework Unified API for integrating diverse scFMs Standardizes model access, switching, and benchmarking, addressing challenges from heterogeneous coding standards.
CELLxGENE [1] [2] Data Repository Provides unified access to annotated single-cell datasets. A primary source of high-quality, standardized data for model pretraining and evaluation.
Geneformer [2] Pre-trained Model Encoder-based scFM trained on 30M cells. Used as a base model for transfer learning and feature extraction in downstream analysis tasks.
scGPT [2] [5] Pre-trained Model Decoder-based, multi-omics capable scFM. Noted for robust performance across tasks; can be applied for zero-shot inference and fine-tuning.
CellWhisperer [6] AI Tool & Model Multimodal model connecting transcriptomes and text. Enables natural language interrogation of single-cell data, demonstrating a novel application of scFM embeddings.
Human Cell Atlas [1] Data Repository/Project Reference maps of all human cell types. Provides broad coverage of cell types and states, crucial for training comprehensive foundation models.

Tokenization serves as the foundational bridge converting raw biological data into computationally tractable representations for single-cell foundation models (scFMs). In natural language processing (NLP), tokenization transforms words into discrete units, whereas in single-cell genomics, this process converts genes, cells, and their associated features into tokens that transformer-based architectures can process [1]. The fundamental challenge lies in representing the non-sequential, high-dimensional, and sparse nature of biological data in a way that preserves critical biological information while enabling efficient model training [7] [2]. Unlike natural language with its inherent word boundaries, biological sequences lack obvious segmentation points, and single-cell data lacks natural ordering, necessitating sophisticated tokenization approaches that can capture hierarchical biological structures from nucleotides to cell types [8].

The significance of tokenization extends beyond mere data preprocessing, directly influencing how scFMs capture and represent biological knowledge in their embeddings. As scFMs aim to learn universal representations transferable across diverse downstream tasks—from cell type annotation to drug sensitivity prediction—the tokenization strategy fundamentally constrains or enables the model's ability to discover meaningful biological patterns [2]. Current research indicates that significant work remains in developing efficient tokenization techniques that can capture or model underlying motifs within DNA sequences, as many methods either reduce scalability through naive sequence representation, incorrectly model motifs, or are borrowed directly from NLP tasks without sufficient biological adaptation [9].

Theoretical Foundations: From Biological Data to Token Representations

The NLP-Biology Analogy and Its Limitations

The prevailing approach to tokenization in computational biology draws analogies between biological sequences and human language: nucleotides or amino acids correspond to letters, genes or motifs to words, and regulatory networks to sentences or paragraphs [8] [10]. This analogy enables borrowing established NLP tokenization methods, but critical differences necessitate adaptation. Biological sequences lack explicit spacing or punctuation, contain extremely long-range dependencies, and operate through biochemical constraints rather than grammatical rules [8]. For single-cell data, the analogy extends further: individual cells become documents or sentences, while genes and their expression values serve as the vocabulary and semantic content [7] [1].

The distributional hypothesis from linguistics—which posits that words occurring in similar contexts share similar meanings—finds parallel in biological tokenization. In language models, this manifests as words with similar contextual usage clustering in embedding space; in biology, genes co-expressed across similar cell types or conditions should occupy proximal regions of the latent space [7]. This principle underpins self-supervised pretraining objectives where models learn to predict masked genes based on their cellular context, implicitly capturing co-regulation patterns [7] [1].

Geometric Interpretation of Biological Embeddings

Tokenization fundamentally shapes the geometry of the embedding spaces where biological entities are represented. High-dimensional embedding spaces enable the projection of discrete tokens into continuous vectors where semantic and syntactic relationships can be encoded as geometric relationships [7]. Theoretical analysis reveals that effective tokenization should yield embedding spaces with anisotropic structure that reflects biological organization, such as developmental trajectories forming low-dimensional manifolds or cell types clustering in distinct regions [7].

A key challenge arises from biological polysemy—where the same token may have different meanings in different contexts. For example, a specific gene may play different roles in different cell types or states. Static embeddings like word2vec place polysemous tokens at intermediate positions between their divergent meanings, distorting the embedding geometry [7]. Contemporary approaches address this through dynamic embeddings where a token's representation varies based on cellular context, enabled by self-attention mechanisms that jointly encode a token's identity and its context [7].

Tokenization Approaches for Biological Data

Gene-Based Tokenization for Single-Cell Data

Gene-based tokenization represents the most direct approach for converting single-cell RNA sequencing data into model inputs. In this paradigm, each gene constitutes a discrete token, with the complete gene expression profile of a single cell forming a "sentence" of tokens [1]. A fundamental challenge is that gene expression data lacks natural ordering, unlike words in a sentence. To accommodate transformer architectures that require sequential inputs, various ordering strategies have emerged:

  • Expression-level ranking: Genes are ordered by their expression values within each cell, creating a deterministic sequence from highest to lowest expressed genes [2] [1]. This approach provides a consistent input structure while prioritizing biologically significant highly-expressed genes.

  • Genomic position ordering: Genes are ordered according to their physical chromosomal locations, potentially capturing cis-regulatory relationships [2].

  • Value binning: Expression values are discretized into bins, with each bin representing a different expression level category [1].

Table 1: Gene Tokenization Implementation in Major scFMs

Model Input Genes Value Representation Gene Ordering Positional Encoding
Geneformer 2,048 ranked genes Ordering as value Expression ranking Yes [2]
scGPT 1,200 HVGs Value binning Not specified No [2]
scFoundation ~19,000 genes Value projection Not specified No [2]
UCE 1,024 sampled genes Protein embedding Genomic position Yes [2]

Gene tokenization implementations combine several elements: a gene identifier embedding (analogous to word embeddings in NLP), a value representation capturing expression level, and often a positional encoding to indicate the gene's position in the input sequence [2] [1]. Special tokens are frequently incorporated, such as [CLS] tokens for cell-level representations or modality indicators for multi-omics integration [1].

Sequence-Based Tokenization Strategies

For genomic sequence data, tokenization strategies focus on segmenting nucleotide sequences into meaningful units. The simplest approach—character-level tokenization—treats each nucleotide as a separate token, but this results in extremely long sequences and fails to capture meaningful multi-nucleotide motifs [8] [10]. Alternative approaches include:

  • k-mer tokenization: DNA or RNA sequences are divided into overlapping subsequences of length k, producing a fixed vocabulary of 4^k possible tokens. This reduces sequence length by approximately k-fold while capturing local context [8].

  • Data-driven subword tokenization: Methods like Byte-Pair Encoding (BPE), WordPiece, and Unigram tokenizer analyze training corpora to identify frequently occurring nucleotide patterns, which become tokens in the vocabulary [8]. These approaches automatically discover biologically relevant motifs without prior knowledge.

  • Biological unit tokenization: Domain-specific segmentation based on biological structures, such as codons in coding sequences or functional domains in proteins [10].

Table 2: Performance of Tokenization Methods on Biological Tasks

Tokenization Method Input Length Reduction Dictionary Size Function Prediction Accuracy Stability Prediction
Character-level 1x (baseline) 4 (DNA) / 20 (protein) 0.741 0.702
3-mer ~3x reduction 64 0.812 0.785
6-mer ~6x reduction 4,096 0.834 0.801
BPE 3.2x reduction 2,048 0.857 0.823
WordPiece 2.9x reduction 2,048 0.849 0.815
Unigram 3.1x reduction 2,048 0.851 0.819

Advanced models employ hybrid tokenization strategies tailored to biological domains. For example, mRNABERT uses a dual tokenization scheme: individual nucleotides for untranslated regions (UTRs) and codons for coding sequences (CDS), respecting the distinct biological functions of these regions [10]. This approach maintains single-nucleotide resolution in regulatory regions while leveraging the semantic meaning of codons in coding regions.

Multi-Modal and Structured Tokenization

Complex biological questions require integrating multiple data types, necessitating tokenization strategies that can represent diverse biological entities and their relationships. Structured tokenization approaches include:

  • Multi-modal integration: Incorporating multiple omics modalities (e.g., scATAC-seq, spatial transcriptomics, proteomics) through modality-specific tokens and embedding spaces that are jointly processed by the transformer [1].

  • Knowledge-informed tokenization: Enriching token representations with biological knowledge from ontologies, protein structures, or gene networks [2] [11]. For example, UCE incorporates protein sequence embeddings from ESM-2 alongside gene expression information [2].

  • Hierarchical tokenization: Representing biological systems at multiple scales, from individual biomolecules to pathways and cellular processes [12]. This approach aims to capture the nested structure of biological organization, where tokens may represent entities at different levels of granularity.

The Zachman Framework, originally developed for enterprise architecture, has been adapted for biological knowledge representation, providing a structured approach to organizing biological entities and their relationships across multiple perspectives and abstraction levels [11]. This framework facilitates comprehensive knowledge capture and representation, though its application to tokenization in deep learning models remains exploratory.

Experimental Protocols and Methodologies

Implementing Tokenization for Single-Cell Foundation Models

The development of effective tokenization strategies follows an iterative experimental process involving data preprocessing, tokenizer training, model integration, and evaluation. A standardized protocol for gene-based tokenization in scFMs includes:

  • Data Selection and Quality Control: Curate diverse single-cell datasets from resources like CELLxGENE, ensuring broad coverage of cell types, tissues, and conditions [1]. Filter low-quality cells and genes, normalize for sequencing depth, and apply appropriate batch correction techniques.

  • Gene Selection: Identify the most informative genes for inclusion in the token vocabulary. Approaches include selecting highly variable genes (HVGs), pan-cell-type marker genes, or genes with minimum expression thresholds [2] [1]. Most models use between 1,000-20,000 genes.

  • Value Processing: Transform raw counts into normalized values (e.g., logCPM) followed by discretization into bins or scaling to standard ranges. Alternatively, some models use relative ranking instead of absolute values [2].

  • Sequence Construction: For each cell, create an input sequence by ordering the selected genes according to a consistent scheme (e.g., by expression level). Append special tokens such as [CLS] for cell-level representation or [BATCH] for batch information [1].

  • Embedding Initialization: Create embedding layers for gene identifiers, expression values, and positional information. These may be initialized randomly or with pretrained biological knowledge [2].

tokenization_workflow raw_data Raw Single-Cell Data (Count Matrix) qc Quality Control & Normalization raw_data->qc gene_selection Gene Selection (HVGs, Pan-markers) qc->gene_selection value_processing Value Processing (Binning, Ranking) gene_selection->value_processing sequence_construction Sequence Construction (Ordering + Special Tokens) value_processing->sequence_construction embedding Embedding Initialization (Gene, Value, Position) sequence_construction->embedding model_input Model Input (Token Sequence) embedding->model_input

Training Data-Driven Tokenizers

For sequence-based tokenization, data-driven methods require careful training on representative biological corpora:

  • Corpus Compilation: Assemble a comprehensive dataset of sequences relevant to the target domain. For genomic tokenizers, this may include reference genomes, while for protein tokenizers, databases like UniProt provide diverse sequences [8]. The training corpus should be sufficiently large and diverse to capture the variability of biological sequences.

  • Vocabulary Size Determination: Establish an appropriate vocabulary size balancing sequence compression against model capacity. Typical biological tokenizers use vocabularies ranging from hundreds to tens of thousands of tokens, compared to only 4 nucleotides or 20 amino acids in character-level approaches [8].

  • Tokenizer Training: Apply the selected algorithm (BPE, WordPiece, or Unigram) to learn frequent patterns in the training corpus. BPE iteratively merges the most frequent pairs of existing tokens, while WordPiece selects pairs that maximize the likelihood of the training data [8]. The Unigram approach starts with a large vocabulary and progressively trims less important tokens.

  • Validation and Evaluation: Assess tokenizer performance based on compression factor (sequence length reduction), biological relevance of discovered tokens, and downstream task performance. Biologically meaningful tokens should correspond to known motifs, domains, or conserved regions [8].

Benchmarking and Evaluation Frameworks

Rigorous evaluation is essential for comparing tokenization strategies. The scGraph-OntoRWR metric measures how well cell type relationships captured by scFMs align with established biological knowledge in cell ontologies [2]. Similarly, the Lowest Common Ancestor Distance (LCAD) metric quantifies the ontological proximity between misclassified cell types, assessing the severity of annotation errors [2].

Benchmarking studies evaluate tokenization approaches across diverse tasks including:

  • Cell type annotation and novel cell type discovery
  • Batch integration and dataset harmonization
  • Genetic perturbation prediction
  • Drug response prediction
  • Gene regulatory network inference

Performance is measured through both quantitative metrics (accuracy, F1 score, silhouette score) and qualitative biological plausibility assessments [2].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for Tokenization Implementation

Resource Category Specific Tools/Databases Function in Tokenization Pipeline
Data Repositories CELLxGENE, Human Cell Atlas, GEO, SRA, PanglaoDB Provide raw single-cell data for tokenizer training and benchmarking [1]
Preprocessing Tools Scanpy, Seurat, Harmony Perform quality control, normalization, and batch correction before tokenization [2]
Tokenization Libraries Hugging Face Tokenizers, BiologicalTokenizers Implement BPE, WordPiece, and Unigram algorithms for biological sequences [8]
Annotation Databases Gene Ontology, Cell Ontology, MeSH Provide biological knowledge for informed tokenization and evaluation [2] [12]
Benchmarking Suites scGraph-OntoRWR, LCAD metrics Evaluate biological relevance of tokenization strategies [2]

Challenges and Future Directions

Current Limitations in Biological Tokenization

Despite considerable progress, significant challenges remain in developing optimal tokenization strategies for biological data:

  • Contextual limitations: Many current approaches fail to adequately represent the dynamic, context-dependent nature of biological entities. A gene's functional role may vary across cell types, developmental stages, or environmental conditions, but static tokenization schemes struggle to capture this nuance [7].

  • Multi-scale integration: Biological systems operate across multiple scales, from molecular interactions to cellular networks and tissue organization. Current tokenization methods typically operate at a single scale, missing cross-scale relationships [12].

  • Knowledge representation: Most tokenizers rely exclusively on sequence or expression data, neglecting the rich prior knowledge available in biological databases and ontologies [12] [11]. Integrating this structured knowledge remains challenging.

  • Computational efficiency: Processing extremely long biological sequences (e.g., mammalian genomes) requires efficient tokenization that reduces sequence length without losing critical information [8] [10].

Emerging Paradigms and Research Frontiers

Several promising directions are emerging to address current limitations in biological tokenization:

  • Dynamic and context-aware tokenization: Inspired by contextual word embeddings in NLP, biological tokenization is evolving toward representations that adapt to cellular context, experimental conditions, or tissue environment [7]. This approach better handles biological polysemy, where the same molecular entity serves different functions in different contexts.

  • Geometric and topological representations: Rather than treating tokens as discrete symbols, emerging approaches embed biological entities in continuous geometric spaces that reflect their functional relationships [7]. Hyperbolic embeddings, for instance, can better represent hierarchical biological structures like taxonomies or ontologies.

  • Multi-modal fusion architectures: Advanced tokenization strategies are learning aligned representations across different data modalities (e.g., sequence, expression, spatial location, protein structure), enabling more comprehensive biological understanding [1] [10].

  • Structured knowledge integration: Future tokenization approaches will more deeply incorporate biological knowledge graphs, ontologies, and pathway databases to create biologically-informed input representations [12] [11]. The Zachman Framework provides one structured approach for organizing this biological knowledge [11].

future_directions current Current Approaches Static, Single-scale dynamic Dynamic Tokenization Context-aware embeddings current->dynamic geometric Geometric Representations Hyperbolic, Topological current->geometric multimodal Multi-Modal Fusion Cross-modal attention current->multimodal knowledge Structured Knowledge Ontologies, Knowledge Graphs current->knowledge

As tokenization strategies continue to evolve, they will play an increasingly critical role in unlocking the full potential of foundation models for biological discovery. The development of biologically-aware, computationally efficient, and knowledge-informed tokenization approaches will enable more accurate, interpretable, and generalizable models across the spectrum of biological research and therapeutic development.

The exponential growth of single-cell RNA sequencing (scRNA-seq) data has created an urgent need for advanced computational techniques capable of interpreting complex biological patterns from millions of cellular transcriptomes. Self-supervised learning (SSL) has emerged as a transformative framework for analyzing these vast datasets, enabling models to learn meaningful representations without extensive manual labeling. Inspired by successes in natural language processing (NLP) and computer vision, SSL approaches treat individual cells as "sentences" and genes or genomic features as "words," allowing models to learn the fundamental language of biology through pretext tasks like masked gene prediction [13]. This paradigm shift from supervised to self-supervised methods addresses critical challenges in single-cell genomics, including data sparsity, high dimensionality, technical noise, and batch effects across experiments [14] [3].

The "pre-train then fine-tune" approach has become foundational for single-cell foundation models (scFMs), where models first learn general biological principles from diverse, large-scale datasets before being adapted to specific downstream tasks. This methodology is particularly valuable in biological contexts where labeled data is scarce or expensive to obtain. By leveraging auxiliary data from public repositories like CELLxGENE, which provides access to over 100 million unique cells, SSL models can capture universal patterns of gene expression and regulation across tissues, species, and experimental conditions [13] [14]. The resulting representations encode rich biological information that enhances performance on critical tasks including cell type annotation, drug response prediction, and disease biomarker discovery.

Core Methodological Approaches

Architectural Foundations and Tokenization Strategies

Single-cell foundation models predominantly utilize transformer architectures, which employ attention mechanisms to weight relationships between genes and capture complex regulatory dependencies [13] [3]. A critical preprocessing step involves tokenization—converting raw gene expression data into discrete input units. Unlike natural language, gene expression data lacks inherent sequential ordering, requiring careful consideration of how to structure inputs for transformer models [13].

Common tokenization strategies include:

  • Expression-based ranking: Genes within each cell are ranked by expression levels, creating a deterministic sequence from highest to lowest expressed genes
  • Binning approaches: Genes are partitioned into bins based on expression values, with rankings determining positional encoding
  • Normalized counts: Some models report robustness with simply normalized counts without complex ranking schemes [13]

Each gene is typically represented as a token embedding combining a gene identifier and its expression value. Additional special tokens may include cell identity metadata, modality indicators for multi-omics data, and batch information to address technical variations [13]. Positional encoding schemes are adapted to represent the relative order or rank of each gene within the cellular context.

Pretext Tasks for Self-Supervised Learning

SSL models employ specific pretext tasks during pre-training to learn meaningful representations without labeled data:

  • Masked Autoencoding: Randomly masking a portion (typically 15%) of gene expression tokens and training the model to predict masked elements using surrounding context [15] [14]. Variants include:

    • Random masking: Minimal inductive bias by randomly selecting genes to mask
    • Gene programme masking: Strategically masking groups of functionally related genes
    • Isolated masking: Targeting specific gene sets like transcription factors

  • Contrastive Learning: Methods like Bootstrap Your Own Latent (BYOL) and Barlow Twins learn representations by maximizing agreement between differently augmented views of the same cell while distinguishing from different cells [14]. Augmentations include negative binomial noise and masking to simulate biological and technical variations.

Table 1: Comparison of Self-Supervised Learning Approaches in Single-Cell Genomics

Method Type Key Variants Mechanism Advantages Limitations
Masked Autoencoding Random masking, Gene programme masking, Isolated masking Predicts masked portions of input based on context Excellent for capturing gene-gene dependencies May reinforce existing biases in training data
Contrastive Learning BYOL, Barlow Twins Learns by comparing similar and dissimilar cells Robust to technical noise and batch effects Requires careful selection of positive/negative pairs
Biological Knowledge Integration PPI networks, Regulatory relationships Incorporates structured biological knowledge Enhanced interpretability and biological relevance Dependent on quality and completeness of knowledge bases

Knowledge-Enhanced Foundation Models

Integrating Biological Knowledge Graphs

Recent advances in single-cell foundation models have demonstrated the significant benefits of incorporating structured biological knowledge beyond expression data alone. scKGBERT represents a pioneering approach that integrates protein-protein interaction (PPI) networks with single-cell transcriptomics, combining 41 million single-cell RNA-seq profiles with 8.9 million regulatory relationships from the STRING database [15]. This knowledge-enhanced architecture employs a dual-stream design comprising an RNA sequence encoder (S-encoder), a knowledge graph encoder (K-encoder), and a Gaussian cross-attention layer that fuses expression and knowledge embeddings [15].

The integration of biological knowledge graphs enables models to capture functional relationships between genes that may not be immediately apparent from expression data alone. By learning from prior biological knowledge about gene regulation, scKGBERT enhances the decoding of gene expression patterns and improves learning of cellular and genomic features, particularly under few-shot and zero-shot conditions [15]. This approach represents a shift from purely data-driven models to biology-informed architectures that leverage decades of accumulated biological research.

Gaussian Attention Mechanisms

The Gaussian attention mechanism in scKGBERT emphasizes key genes and improves biomarker identification by allocating attention weights according to biological importance [15]. Unlike standard attention mechanisms that may uniformly distribute attention across all genes, Gaussian attention prioritizes genes with known functional significance or distinctive expression patterns. This approach enhances model interpretability by providing a transparent framework for understanding which genes drive specific predictions, addressing a significant limitation of traditional black-box models in biological applications [15].

G Knowledge-Enhanced scFM Architecture cluster_inputs Input Data cluster_encoders Dual-Stream Encoding SCRNA Single-Cell RNA-seq Data S_encoder RNA Sequence Encoder (S-encoder) SCRNA->S_encoder PPI Protein-Protein Interaction Network K_encoder Knowledge Graph Encoder (K-encoder) PPI->K_encoder Attention Gaussian Cross-Attention Layer S_encoder->Attention K_encoder->Attention Fusion Fused Gene & Cell Embeddings Attention->Fusion Output Downstream Task Predictions Fusion->Output

Quantitative Performance Evaluation

Performance Across Downstream Tasks

Comprehensive benchmarking studies reveal that knowledge-enhanced foundation models consistently outperform conventional approaches across diverse biological tasks. scKGBERT demonstrated superior performance in gene dosage sensitivity prediction, achieving higher AUC scores compared to existing large-scale pre-trained models including scGPT, scFoundation, and Geneformer, as well as classical machine learning approaches like SVM and Random Forest [15]. The model also showed robust performance in classifying dosage-sensitive versus insensitive transcription factors, achieving a median F1-score of 0.94 with lower interquartile variability than competing approaches [15].

SSL methods exhibit particular strength in transfer learning scenarios where models pre-trained on large auxiliary datasets are fine-tuned for specific applications. Empirical analyses demonstrate that self-supervised pre-training on additional data significantly improves cell-type prediction and gene-expression reconstruction, with macro F1 scores increasing from 0.7013 to 0.7466 in PBMC datasets and from 0.2722 to 0.3085 in the Tabula Sapiens Atlas [14]. These improvements are especially pronounced for underrepresented cell types, indicating SSL's robustness to class imbalances common in biological data.

Table 2: Performance Comparison of Single-Cell Foundation Models Across Key Tasks

Model Gene Dosage Sensitivity (AUC) Cell Type Annotation (F1) Drug Response Prediction Cross-Dataset Generalization
scKGBERT 0.84 0.94 Superior Excellent
Geneformer 0.79 0.89 Competitive Good
scGPT 0.81 0.91 Good Good
scFoundation 0.80 0.90 Good Moderate
Traditional ML 0.72-0.78 0.82-0.87 Limited Limited

Impact of Training Data Scale and Diversity

Model performance strongly correlates with the scale and diversity of pre-training data. Evaluation of scKGBERT across progressively larger pre-training datasets demonstrated consistent performance improvements as data volume increased from 1,000 to 10 million single-cell transcriptomes [15]. Notably, models trained on more diverse cell populations (e.g., Human Multi-Tissue Cells) exhibited higher baseline performance and more pronounced gains compared to specialized populations (e.g., Brain Tissue Cells), highlighting the importance of cellular heterogeneity in learning generalizable representations [15].

The effectiveness of SSL also depends on the relationship between pre-training and target datasets. Pre-training on large, diverse auxiliary datasets like scTab (containing over 20 million cells) significantly boosts performance when fine-tuning on smaller, target-specific datasets [14]. However, self-supervised pre-training on the same dataset used for fine-tuning often fails to yield substantial improvements compared to supervised or unsupervised training, emphasizing the importance of complementary data sources in the pre-training phase [14].

Experimental Protocols and Methodologies

Pre-Training Implementation Framework

The standard protocol for pre-training single-cell foundation models involves several critical stages:

  • Data Curation and Quality Control: Compiling diverse single-cell datasets from public repositories such as CZ CELLxGENE, Human Cell Atlas, and PanglaoDB [13]. Quality control filters exclude cells with elevated mutation loads, transcriptomic artifacts, or evidence of cellular damage to minimize noise and enhance training signal fidelity [15].

  • Tokenization and Input Representation: Converting raw count matrices into token sequences using expression-based ranking or binning approaches. Each gene is represented as a token embedding combining gene identifier and expression value, with optional inclusion of positional encodings and special tokens for cell metadata [13].

  • Masked Language Modeling: Implementing a BERT-like masking strategy where 15% of gene expression tokens are randomly masked, and the model is trained to predict masked elements from contextual information [15]. The training objective minimizes the reconstruction error between predicted and actual expression values.

  • Knowledge Graph Integration: For knowledge-enhanced models, incorporating protein-protein interaction networks using graph neural networks to generate biological context embeddings, which are fused with expression embeddings via cross-attention mechanisms [15].

Fine-Tuning for Downstream Tasks

After pre-training, models are adapted to specific biological tasks through fine-tuning:

  • Cell Type Annotation: Models are fine-tuned on reference datasets with expert-curated cell labels, often employing class-balanced sampling to address population imbalances [3]

  • Gene Function Prediction: Fine-tuned to predict gene ontological terms, tissue specificity, or functional annotations using embeddings from pre-trained models [3]

  • Drug Response Prediction: Adapted to predict cellular responses to therapeutic compounds by integrating expression profiles with drug perturbation data [15]

  • Disease Biomarker Discovery: Fine-tuned to identify molecular determinants of disease pathogenesis using case-control study designs [15]

G SSL Pre-training and Fine-tuning Workflow cluster_pretrain Self-Supervised Pre-training cluster_finetune Task-Specific Fine-tuning Data Large-scale Unlabeled scRNA-seq Data (20M+ cells) Tokenize Tokenization & Input Representation Data->Tokenize Pretext Pretext Task (Masked Gene Prediction) Tokenize->Pretext Pretrained Pre-trained Foundation Model Pretext->Pretrained FineTune Fine-tuning on Downstream Task Pretrained->FineTune TaskData Labeled Task Data TaskData->FineTune TaskModel Task-Specific Model FineTune->TaskModel Outputs Task Predictions: - Cell Type Annotation - Drug Response - Disease Biomarkers TaskModel->Outputs

The Scientist's Toolkit: Essential Research Reagents

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

Resource Category Specific Tools/Databases Function Access
Data Repositories CZ CELLxGENE, Human Cell Atlas, PanglaoDB Provide standardized, annotated single-cell datasets for pre-training and benchmarking Publicly available
Knowledge Bases STRING Database, Gene Ontology Supply protein-protein interactions and functional annotations for knowledge-enhanced models Publicly available
Pre-trained Models scKGBERT, Geneformer, scGPT, scFoundation Offer pre-trained weights for transfer learning and fine-tuning Various licenses
Evaluation Frameworks scGraph-OntoRWR, LCAD metrics Enable biologically-grounded assessment of model performance Open source
Visualization Tools scBubbletree, UMAP, t-SNE Facilitate interpretation and exploration of high-dimensional embeddings Open source

Biological Insights and Clinical Applications

Knowledge-enhanced single-cell foundation models have demonstrated remarkable utility in extracting biologically meaningful insights from complex transcriptomic data. By capturing intricate transcriptional landscapes of tumor microenvironments, these models overcome key limitations of conventional approaches in representing cellular diversity and treatment variability [15]. Enrichment analysis of high-confidence gene representations reveals activation of oncogenic pathways and tumor-specific regulatory circuits, enabling prioritization of candidate therapeutic targets and informing precision oncology interventions [15].

The biological relevance of scFMs can be quantitatively evaluated using novel metrics such as scGraph-OntoRWR, which measures consistency between cell type relationships captured by models and prior biological knowledge from cell ontologies [3]. Similarly, the Lowest Common Ancestor Distance (LCAD) metric assesses ontological proximity between misclassified cell types, providing biologically grounded assessment of annotation errors [3]. These approaches represent a shift from purely statistical evaluation to biology-informed model assessment.

In clinical applications, SSL models show strong performance in predicting patient-specific drug responses and identifying resistance mechanisms. By leveraging biological priors, scKGBERT demonstrates enhanced capability in identifying key molecular determinants of disease, improving interpretability and understanding of disease pathogenesis [15]. The robust cross-platform and cross-disease generalizability of these models underscores their potential for integrative single-cell data analysis in diverse biomedical contexts, from cell atlas construction to treatment decision-making [15] [3].

Future Directions and Challenges

Despite significant progress, several challenges remain in the development and application of self-supervised learning for single-cell genomics. Current limitations include handling rare cell types, ensuring data quality across heterogeneous sources, and the computational intensity required for training and fine-tuning large models [13]. Additionally, interpreting the biological relevance of latent embeddings and model representations remains nontrivial, necessitating specialized evaluation frameworks [13] [3].

Future research directions should focus on:

  • Multimodal Integration: Developing unified frameworks that jointly model transcriptomic, epigenomic, proteomic, and spatial data
  • Interpretability Advances: Creating more transparent visualization and explanation tools for model predictions
  • Resource Efficiency: Optimizing model architectures and training procedures to reduce computational requirements
  • Clinical Translation: Establishing robust validation protocols for applying foundation models in diagnostic and therapeutic settings

As the field matures, the effective application of scFMs will require careful consideration of model selection criteria based on dataset size, task complexity, biological interpretability needs, and computational resources [3]. No single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection strategies in biological and clinical research applications [3].

Single-cell foundation models (scFMs) are large-scale deep learning models, typically based on transformer architectures, that are pretrained on vast datasets comprising millions of single-cell transcriptomes [1]. Through self-supervised learning objectives, these models develop latent representations—often called embeddings—that capture fundamental biological principles of cellular identity, state, and function [1] [2]. The core premise is that by exposing a model to massive and diverse single-cell data encompassing numerous tissues, conditions, and species, it can learn a unified representation that encodes meaningful biological knowledge without explicit supervision for specific tasks [1]. This process creates embedding spaces where the geometric relationships between points (representing cells or genes) reflect actual biological relationships, such as developmental trajectories, response to perturbations, and functional similarities between cell types [2] [16]. The resulting representations serve as a foundational resource that can be fine-tuned or directly utilized for diverse downstream biological applications, from cell type annotation to therapeutic target discovery [2] [16].

Technical Foundations of scFM Embeddings

Model Architectures and Tokenization Strategies

Single-cell foundation models employ specialized tokenization approaches to convert gene expression data into a structured format compatible with transformer architectures. Unlike natural language, where words have a natural sequence, gene expression data lacks inherent ordering, requiring creative solutions for representing cellular states [1]. The table below summarizes the key architectural components and tokenization strategies employed by prominent scFMs:

Table 1: Architectural Components of Single-Cell Foundation Models

Model Name Gene Tokenization Strategy Value Representation Positional Encoding Architecture Type
Geneformer Genes ranked by expression level Expression ordering ✓ Present Encoder
scGPT Top 1200 highly variable genes Value binning × Absent Decoder (GPT-style)
UCE Non-unique sampling by expression & genomic position Binary (expressed/not) ✓ Present Encoder
scFoundation All protein-encoding genes Value projection × Absent Encoder-decoder
LangCell Genes ranked by expression level Expression ordering Information not available Information not available

These models typically generate two types of biologically meaningful embeddings: gene embeddings that capture functional relationships between genes, and cell embeddings that represent entire cellular states [2] [1]. The attention mechanisms within transformer architectures enable scFMs to learn complex gene-gene interactions and dependencies, effectively modeling the regulatory networks that govern cellular behavior [1].

Pretraining Objectives and Knowledge Acquisition

scFMs acquire biological knowledge through self-supervised pretraining tasks designed to capture the fundamental principles of transcriptional regulation. The most common approach is masked gene modeling (MGM), where random subsets of genes are masked and the model must predict their expression values based on the remaining context [1] [2]. Through this process, models learn the co-expression patterns, regulatory relationships, and functional dependencies that constitute the "language" of cells [1]. Different implementations employ varying loss functions, including mean squared error (MSE) for continuous predictions (scGPT, scFoundation) and cross-entropy for discrete classifications (Geneformer) [2]. Alternative pretraining strategies include generative pretraining where models learn to reconstruct entire gene expression profiles (scGPT) and binary classification of gene expression status (UCE) [2]. The scale and diversity of pretraining data—encompassing millions of cells from diverse tissues, conditions, and species—enable these models to develop robust representations that generalize across biological contexts and capture universal principles of cellular function [1] [2].

Evaluating Biological Knowledge in Embeddings

Novel Evaluation Metrics and Frameworks

Traditional evaluation metrics for embedding spaces often focus solely on task performance without assessing biological plausibility. Recent research has introduced innovative metrics specifically designed to quantify the biological knowledge encoded in scFM embeddings [2]. The scGraph-OntoRWR metric measures the consistency between cell type relationships captured by the model's embeddings and established biological knowledge from cell ontologies [2]. Similarly, the Lowest Common Ancestor Distance (LCAD) metric assesses the ontological proximity between misclassified cell types, providing a biologically-informed measure of annotation error severity [2]. These ontology-informed metrics complement conventional performance indicators by ensuring that the embedding space organization aligns with known biological hierarchies. Another novel approach involves quantifying the roughness index (ROGI) of the cell-property landscape within the latent space, which correlates with how easily task-specific models can be trained on the embeddings [2]. These specialized evaluation frameworks enable researchers to move beyond mere predictive accuracy and assess whether models have learned biologically meaningful representations.

Benchmarking Performance Across Biological Tasks

Comprehensive benchmarking studies have evaluated scFMs across diverse biological tasks to assess their ability to capture and utilize biological knowledge. The table below summarizes performance findings across key application areas:

Table 2: scFM Performance Across Biological Tasks

Task Category Specific Tasks Key Findings Performance Relative to Baselines
Cell-level Tasks Batch integration, Cell type annotation, Cancer cell identification scFMs robust and versatile; no single model dominates all tasks Mixed: scFMs show strengths in some tasks, while simpler models often adapt more efficiently to specific datasets [2]
Gene-level Tasks Gene function prediction, Gene-gene interactions Embeddings capture biological insights into relational structure of genes Qualitative evidence of biological knowledge capture [2]
Perturbation Prediction Drug sensitivity, Response to genetic perturbations Limited performance in zero-shot settings; significant improvement with fine-tuning Zero-shot embeddings do not consistently outperform simpler baselines [17]; Closed-loop fine-tuning improves PPV 3x [16]
Multimodal Tasks Text-transcriptome alignment, Free-form biological Q&A CellWhisperer achieves AUROC of 0.927 for transcriptome-text matching [6] Successful integration of biological knowledge through multimodal learning [6]

These benchmarks reveal that while scFM embeddings do capture biological knowledge, their practical utility varies significantly by task, with factors such as dataset size, task complexity, and biological context influencing performance [2]. Notably, in perturbation prediction tasks, zero-shot scFM embeddings often fail to outperform simpler baseline models, especially under distribution shift [17]. However, incorporating even small amounts of task-specific data through fine-tuning can dramatically improve performance, as demonstrated by the "closed-loop" framework that increased positive predictive value from 3% to 9% in T-cell activation prediction [16].

Experimental Protocols for Probing Biological Knowledge

In Silico Perturbation Prediction Protocol

The in silico perturbation (ISP) protocol provides a powerful methodology for probing the causal biological knowledge encoded in scFMs [16]. This approach involves systematically perturbing the model's inputs or internal representations to simulate biological interventions and observing the predicted outcomes. The detailed methodology comprises the following steps:

  • Model Fine-tuning: First, pretrained scFMs are fine-tuned on target cell states using relevant scRNA-seq data. For example, in studying T-cell activation, Geneformer was fine-tuned on data from CD3-CD28 stimulated T cells and control cells, achieving 99.8% accuracy on hold-out test sets [16].

  • Perturbation Simulation: The fine-tuned model is used to simulate gene knockouts or overexpression by manipulating the input representations of specific genes. This involves either zeroing out gene representations (for knockout) or amplifying them (for overexpression) while preserving all other cellular context [16].

  • Prediction Extraction: The model predicts the resulting gene expression profile following the simulated perturbation. The difference between the original and perturbed profiles indicates the directional effect of the perturbation on cellular state [16].

  • Validation Against Experimental Data: Predictions are validated against orthogonal experimental data, such as flow cytometry measurements from CRISPR screens or established biological knowledge. This step quantifies the accuracy and biological plausibility of the predictions [16].

  • Closed-Loop Refinement: For enhanced accuracy, the model can be further fine-tuned on experimental perturbation data (e.g., Perturb-seq), creating a "closed-loop" system that iteratively improves its predictive capabilities [16].

This protocol has been successfully applied to identify therapeutic targets for RUNX1-familial platelet disorder, predicting genes whose perturbation would shift diseased cells toward a healthy state [16].

Multimodal Alignment Assessment Protocol

The multimodal alignment protocol evaluates how well scFM embeddings correspond to textual biological knowledge, enabling chat-based exploration of single-cell data [6]. This methodology, implemented in the CellWhisperer framework, involves:

  • Multimodal Data Curation: Compile pairs of transcriptomic profiles and textual descriptions using LLM-assisted curation of public data repositories (GEO, CELLxGENE). This process yielded 1,082,413 human transcriptome-text pairs for training [6].

  • Contrastive Learning: Train a dual-encoder architecture using contrastive learning objectives that maximize the similarity between matched transcriptome-text pairs while minimizing similarity between mismatched pairs. The CellWhisperer implementation uses Geneformer for processing transcriptomes and BioBERT for processing text, mapping both modalities into a joint 2,048-dimensional embedding space [6].

  • Retrieval Evaluation: Assess embedding quality through cross-modal retrieval tasks, measuring the model's ability to retrieve relevant transcriptomes given text queries and vice versa. Performance is quantified using area under the receiver operating characteristic curve (AUROC), with CellWhisperer achieving 0.927 AUROC [6].

  • Chat-Based Interface Development: Fine-tune a large language model (e.g., Mistral 7B) to incorporate transcriptome embeddings alongside text queries, enabling natural language conversations about cells and genes based on their transcriptional profiles [6].

This protocol demonstrates how biological knowledge encoded in scFM embeddings can be made accessible and interpretable through alignment with textual descriptions, facilitating intuitive exploration of single-cell data by domain experts [6].

G cluster_0 Biological Knowledge Flow Pretraining Pretraining Phase Self-supervised Learning (Masked Gene Modeling) EmbeddingSpace Latent Embedding Space Gene & Cell Representations Pretraining->EmbeddingSpace Trains Pretraining->EmbeddingSpace BiologicalTasks Biological Knowledge Evaluation Tasks EmbeddingSpace->BiologicalTasks Encodes EmbeddingSpace->BiologicalTasks EvaluationMetrics Evaluation Metrics scGraph-OntoRWR, LCAD, ROGI BiologicalTasks->EvaluationMetrics Quantified by BiologicalTasks->EvaluationMetrics Applications Biological Applications Perturbation Prediction, Cell Annotation, Drug Discovery EvaluationMetrics->Applications Guides EvaluationMetrics->Applications Applications->EmbeddingSpace Refines

Diagram 1: Framework for Biological Knowledge Encoding in scFMs

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Research Reagents for scFM Embedding Analysis

Reagent / Resource Type Function in scFM Research Example Implementations
Geneformer Pretrained scFM Provides cell and gene embeddings for downstream analysis; base for fine-tuning 40M parameters, trained on 30M cells [2]
scGPT Pretrained scFM Multimodal foundation model for various single-cell analysis tasks 50M parameters, trained on 33M cells [2]
CELLxGENE Data Resource Curated single-cell datasets for pretraining and benchmarking >100M unique cells standardized for analysis [1]
CellWhisperer Multimodal Tool Enables natural language querying of transcriptomic data AUROC 0.927 for text-transcriptome retrieval [6]
scGraph-OntoRWR Evaluation Metric Quantifies alignment of embeddings with biological ontologies Novel metric for biological consistency [2]
Perturb-seq Data Experimental Data Provides ground truth for validating in silico perturbations Used in closed-loop fine-tuning [16]
ARCHS4 Processed Data Uniformly reprocessed GEO data for training multimodal models Source of 705,430 human transcriptomes [6]

Applications and Future Directions

The biological knowledge encoded in scFM embeddings enables diverse applications across biomedical research. In therapeutic discovery, closed-loop ISP frameworks have identified potential therapeutic targets for rare diseases like RUNX1-familial platelet disorder, predicting genes whose perturbation would shift diseased cells toward healthy states [16]. In cell annotation, models like CellWhisperer enable zero-shot prediction of cell types and states through natural language queries, significantly reducing the need for manual labeling [6]. For atlas-scale analysis, scFM embeddings facilitate the integration and interpretation of massive single-cell datasets, revealing underlying biological structures and relationships across tissues, species, and conditions [2] [1].

Future research directions focus on enhancing the biological fidelity and practical utility of scFM embeddings. Key challenges include improving model interpretability to extract mechanistic biological insights from attention patterns, developing more efficient fine-tuning protocols that require minimal experimental data, and creating standardized benchmarks for rigorous biological validation [2] [1] [16]. As these models evolve, they promise to serve as increasingly accurate "virtual cells" capable of simulating cellular behavior and response to perturbations, ultimately accelerating drug discovery and personalized medicine approaches [16].

G cluster_0 scFM Embedding Generation Pipeline InputData Single-Cell RNA-seq Data Tokenization Tokenization Gene Ranking Value Encoding InputData->Tokenization Processes InputData->Tokenization Transformer Transformer Architecture Attention Mechanism Tokenization->Transformer Feeds Tokenization->Transformer Embeddings Latent Embeddings Cell & Gene Representations Transformer->Embeddings Generates Transformer->Embeddings BiologicalInsight Biological Knowledge Cell Types, Pathways, Regulations Embeddings->BiologicalInsight Encodes Embeddings->BiologicalInsight

Diagram 2: scFM Embedding Generation and Knowledge Encoding

Single-cell foundation models (scFMs) represent a revolutionary advancement in computational biology, leveraging large-scale deep learning architectures pretrained on massive single-cell transcriptomic datasets. These models have transformed the analysis of cellular heterogeneity and complex regulatory networks by learning fundamental biological principles from millions of cells across diverse tissues and conditions [1]. Inspired by successes in natural language processing (NLP), scFMs treat individual cells as "sentences" composed of genes or genomic features as "tokens," enabling them to capture intricate gene-gene relationships and cellular states through self-supervised learning objectives [1]. The emergence of scFMs addresses critical challenges in single-cell RNA sequencing (scRNA-seq) data analysis, including high sparsity, dimensionality, and technical noise, while providing a unified framework for extracting meaningful biological insights from complex cellular landscapes [2] [5].

This technical guide provides a comprehensive analysis of four prominent scFM architectures—scGPT, Geneformer, scBERT, and scFoundation—within the broader context of biological knowledge representation in embedding spaces. We examine their architectural distinctions, pretraining methodologies, performance characteristics, and practical implementation considerations for researchers, scientists, and drug development professionals seeking to leverage these powerful tools for advancing biological discovery and therapeutic development.

Core Architectural Components

Single-cell foundation models share common components adapted from transformer architectures but implement them differently to process gene expression data:

  • Tokenization: scFMs employ various strategies to convert raw gene expression data into discrete tokens. Geneformer and LangCell use a rank-based approach, feeding the top 2,048 ranked genes by expression level as the cell "sentence" [1] [2]. scGPT utilizes 1,200 highly variable genes (HVGs) with value binning, while scFoundation processes all 19,264 human protein-encoding genes directly [2] [18]. Unlike words in natural language, genes lack inherent ordering, requiring deterministic sequence construction based on expression magnitude or other criteria [1].

  • Embedding Layers: All major scFMs implement lookup table embeddings for gene symbols, with dimensionalities ranging from 256-512 for most models, while scFoundation uses 768-dimensional embeddings [2]. Value embeddings representing expression levels are implemented through ordering (Geneformer, LangCell), value binning (scGPT), or direct value projection (scFoundation) [2]. Positional embeddings are incorporated in Geneformer, UCE, and LangCell but omitted in scGPT and scFoundation [2].

  • Transformer Backbones: Most scFMs employ encoder-only architectures, with Geneformer using a BERT-like transformer encoder and scGPT utilizing an encoder with attention mask [19] [1]. scFoundation implements an asymmetric encoder-decoder architecture, while UCE stands out with 650 million parameters—significantly larger than other models which typically range from 40-100 million parameters [2].

Model Specifications and Training Corpora

Table 1: Architectural Specifications and Pretraining Details of Major scFMs

Model Parameters Pretraining Dataset Size Architecture Type Input Genes Positional Embedding Pretraining Task
Geneformer [2] 40M (6-layer), 106M (12-layer) 30M human cells Transformer Encoder (BERT-like) 2,048 ranked genes Yes Masked Gene Modeling (MGM) with CE loss
scGPT [2] 50M 33M cells Encoder with attention mask 1,200 HVGs No Iterative MGM with MSE loss (gene + cell prompt)
scBERT [5] ~40M Not specified Bidirectional Transformer Not specified Not specified Masked Language Modeling
scFoundation [2] [18] 100M 50M cells Asymmetric encoder-decoder 19,264 genes No Read-depth-aware MGM with MSE loss
UCE [2] 650M 36M cells Encoder 1,024 non-unique genes Yes Modified MGM (binary classification)
LangCell [2] 40M 27.5M scRNA-text pairs Not specified 2,048 ranked genes Yes Not specified

Knowledge Enhancement Strategies

Recent advancements in scFMs focus on integrating external biological knowledge to enhance model interpretability and performance. scKGBERT represents a significant innovation by incorporating protein-protein interaction (PPI) networks from the STRING database during pretraining, creating a knowledge-enhanced architecture that jointly learns from single-cell transcriptomes and biological knowledge graphs [15]. This integration enables the model to capture regulatory relationships and functional associations between genes, leading to improved performance in gene annotation, drug response prediction, and disease mechanism interpretation [15]. The model employs a dual-stream design with an RNA sequence encoder, knowledge graph encoder, and Gaussian cross-attention layer that fuses expression and knowledge embeddings, demonstrating the value of structured biological priors for enhancing representation learning in scFMs [15].

Performance Benchmarking and Evaluation

Comprehensive Model Evaluation Framework

Rigorous evaluation of scFMs requires multidimensional assessment across diverse tasks and datasets. The BioLLM framework provides standardized APIs and evaluation protocols for systematic comparison of scFM performance [5] [4]. Benchmarking studies typically evaluate models across gene-level tasks (gene dosage sensitivity prediction, epigenetic marker identification, transcription factor regulatory inference) and cell-level tasks (cell type annotation, batch integration, drug response prediction) using metrics such as average silhouette width (ASW), area under the curve (AUC), and F1 scores [2] [5].

Comparative Performance Analysis

Table 2: Performance Characteristics of scFMs Across Different Task Types

Model Cell-type Annotation Gene-level Tasks Batch-effect Correction Zero-shot Performance Computational Efficiency
scGPT Highest accuracy [5] Strong [5] Excellent [5] Robust across tasks [5] Efficient memory usage [5]
Geneformer High accuracy [19] Excellent [5] Moderate [5] Strong with fine-tuning [19] Efficient [5]
scFoundation High accuracy [18] Strong [5] Moderate [5] Good [18] Higher resource usage [5]
scBERT Lower accuracy [5] Weaker [5] Poor [5] Limited [5] Less efficient [5]

Evaluation results reveal distinct performance patterns across models. scGPT consistently demonstrates robust performance across all tasks, particularly excelling in zero-shot settings and batch-effect correction [5]. Geneformer and scFoundation show specialized strengths in gene-level tasks, benefiting from their effective pretraining strategies [5]. scBERT generally lags behind other models, likely due to its smaller model size and limited training data [5]. Benchmarking studies indicate that no single scFM consistently outperforms others across all tasks, emphasizing the importance of task-specific model selection [2].

Cross-Species Applicability

The Geneformer architecture has demonstrated remarkable cross-species utility. Mouse-Geneformer, trained on 21 million mouse scRNA-seq profiles, not only enhances accuracy for mouse transcriptome analysis but also can analyze human data after ortholog-based gene name conversion, achieving comparable performance to the original human Geneformer for cell type classification [20]. This cross-species applicability extends to disease modeling, with mouse-Geneformer producing similar results to human Geneformer for myocardial infarction but only partially consistent results for COVID-19, highlighting both the potential and limitations of cross-species application [20].

Experimental Protocols and Methodologies

Standardized Evaluation Protocol Using BioLLM

The BioLLM framework implements a systematic workflow for scFM evaluation through five distinct stages [5]:

  • Configuration Parsing: Initialize model-specific parameters and task configurations through standardized APIs.

  • Model Initialization: Load pretrained model weights and architectures through a unified interface.

  • Data Preprocessing: Apply quality control filters and normalization procedures consistent with each model's pretraining pipeline.

  • Data-loader Construction: Create efficient data loading pipelines for both zero-shot and fine-tuning scenarios.

  • Task Execution: Perform specific downstream tasks (cell annotation, perturbation prediction, etc.) with consistent evaluation metrics.

This protocol enables reproducible benchmarking across diverse scFMs while maintaining model-specific optimization requirements.

Cell Type Annotation Methodology

Cell type annotation represents one of the most common applications of scFMs. The standard methodology involves:

  • Embedding Extraction: Generate cell embeddings using the pretrained model in either zero-shot or fine-tuned mode. For zero-shot analysis, embeddings are directly extracted without additional training [21]. For fine-tuning, a small labeled dataset (typically a few thousand cells) is used to adapt the model for 5-10 epochs [21].

  • Dimensionality Reduction: Apply UMAP or t-SNE to embeddings for visualization and qualitative assessment of cell-type separation [5].

  • Classification: Implement a classifier head on top of embeddings, typically using a shallow neural network or linear classifier.

  • Evaluation: Assess performance using metrics such as accuracy, F1-score, and the novel scGraph-OntoRWR metric that measures consistency with prior biological knowledge from cell ontologies [2].

Studies demonstrate that fine-tuning scGPT on task-specific data for just 5-10 epochs (approximately 20 minutes on a single A100 GPU) can improve accuracy by 10-25 percentage points compared to zero-shot approaches [21].

In Silico Perturbation Experiment Protocol

Geneformer and related models enable in silico simulation of genetic perturbations through a standardized protocol [19] [20]:

  • Baseline Embedding: Process single-cell transcriptomes from the condition of interest through the model to establish baseline representations.

  • Perturbation Application: Manipulate expression values of target genes in the input data to simulate knockout, knockdown, or overexpression.

  • Latent Space Comparison: Project perturbed cells into the same latent space and quantify directional shifts using cosine similarity or Euclidean distance metrics.

  • Network Inference: Identify genes with the most significant expression changes following virtual perturbation through attention mechanism analysis.

  • Biological Validation: Compare predictions with established experimental results or pathway databases for validation.

This approach has successfully identified disease-causing genes validated in subsequent in vivo experiments, demonstrating the predictive capability of properly tuned scFMs [20].

Technical Implementation and Visualization

scFM Architecture Diagram

scFM_architecture cluster_input Input Data cluster_embedding Embedding Layer cluster_transformer Transformer Backbone cluster_output Output Representations RawData Single-cell RNA-seq Data Tokenization Tokenization (Gene Ranking/ Value Binning) RawData->Tokenization GeneEmbed Gene Symbol Embedding Tokenization->GeneEmbed ValueEmbed Expression Value Embedding Tokenization->ValueEmbed PosEmbed Positional Embedding Tokenization->PosEmbed CombinedEmbed Combined Embedding Sequence GeneEmbed->CombinedEmbed ValueEmbed->CombinedEmbed PosEmbed->CombinedEmbed Encoder Transformer Encoder Blocks CombinedEmbed->Encoder Fusion Knowledge Fusion (Gaussian Attention) CombinedEmbed->Fusion Attention Multi-Head Attention Encoder->Attention FFN Feed-Forward Network Attention->FFN GeneEmbeddings Gene Embeddings FFN->GeneEmbeddings CellEmbedding Cell Embedding (CLS Token) FFN->CellEmbedding KnowledgeGraph Biological Knowledge Graph (PPI, Pathways) KnowledgeGraph->Fusion Fusion->Encoder

scFM Architecture Overview: This diagram illustrates the core architectural components shared across major single-cell foundation models, highlighting the tokenization strategies, embedding layers, transformer backbones, and optional knowledge enhancement approaches that differentiate implementations.

Model Selection Workflow

model_selection Start Model Selection Workflow DataSize Dataset Size > 10,000 cells? Start->DataSize TaskType Primary Task Type? DataSize->TaskType Yes TraditionalML_rec Recommendation: Traditional ML (Small datasets, limited resources) DataSize->TraditionalML_rec No ComputeResource GPU Memory > 16GB? TaskType->ComputeResource Cell-level Analysis Geneformer_rec Recommendation: Geneformer (Gene-level tasks, in silico perturbation) TaskType->Geneformer_rec Gene-level Analysis scFoundation_rec Recommendation: scFoundation (Drug response prediction, read-depth enhancement) TaskType->scFoundation_rec Drug Response Prediction BiologicalContext Biological Knowledge Integration Needed? ComputeResource->BiologicalContext No scGPT_rec Recommendation: scGPT (General purpose, strong zero-shot) ComputeResource->scGPT_rec Yes Species Human or Mouse Data? BiologicalContext->Species No scKGBERT_rec Recommendation: scKGBERT (Knowledge-enhanced tasks, biomarker discovery) BiologicalContext->scKGBERT_rec Yes Species->scGPT_rec Human Data MouseGeneformer_rec Recommendation: Mouse-Geneformer (Mouse data or cross-species analysis) Species->MouseGeneformer_rec Mouse Data

Model Selection Workflow: This decision diagram provides a systematic approach for selecting appropriate scFM architectures based on dataset characteristics, task requirements, computational resources, and biological context.

Essential Research Reagents and Computational Tools

Table 3: Essential Research Tools and Resources for scFM Implementation

Category Tool/Resource Specification Primary Function Application Context
Data Repositories CZ CELLxGENE [1] >100M annotated single-cells Curated single-cell data source Model pretraining, fine-tuning
PanglaoDB [1] Multi-species scRNA-seq data Annotated reference atlas Cross-species validation
Computational Frameworks BioLLM [5] [4] Standardized APIs for scFMs Unified model interface, benchmarking Comparative analysis, reproducible research
NVIDIA BioNeMo [19] GPU-accelerated training Scalable model training Large-scale pretraining, fine-tuning
RAPIDS-SINGLECELL [19] GPU-accelerated preprocessing Single-cell data analysis Preprocessing, integration with Scanpy
Benchmarking Tools scGraph-OntoRWR [2] Cell ontology-informed metric Biological relevance assessment Model evaluation, embedding quality
LCAD Metric [2] Lowest Common Ancestor Distance Cell type annotation error severity Performance benchmarking
Biological Knowledge Bases STRING Database [15] 8.9M regulatory relationships Protein-protein interaction network Knowledge-enhanced models (scKGBERT)
Gene Ontology [1] Hierarchical functional terms Functional annotation Biological interpretation

Single-cell foundation models represent a transformative approach to analyzing transcriptomic data, offering powerful capabilities for extracting biological insights from complex cellular landscapes. The four architectures examined—scGPT, Geneformer, scBERT, and scFoundation—demonstrate distinct strengths and specializations, with scGPT showing robust performance across diverse tasks, Geneformer excelling in gene-network analysis, scFoundation providing strong drug response prediction capabilities, and scBERT serving as a lighter-weight alternative [5]. The emerging trend of knowledge-enhanced models like scKGBERT points toward increasingly biologically grounded representations that integrate prior knowledge with data-driven learning [15].

Future development in scFMs will likely focus on several key areas: multi-omic integration combining transcriptomic, epigenomic, and proteomic data; improved cross-species generalization; enhanced interpretability through biologically meaningful attention mechanisms; and reduced computational requirements for broader accessibility [1] [18]. As these models continue to evolve, standardized frameworks like BioLLM will play a crucial role in ensuring reproducible benchmarking and systematic comparison [5] [4]. For researchers and drug development professionals, careful selection of scFM architectures based on specific task requirements, dataset characteristics, and available computational resources will be essential for maximizing biological insights and advancing therapeutic discovery.

Practical Applications: Leveraging scFM Embeddings for Biomedical Discovery

Cell Type Annotation and Atlas Construction Using Embedding Similarities

This technical guide explores the paradigm of cell type annotation and atlas construction through the lens of similarity measures in learned embedding spaces. Within the broader thesis of biological knowledge representation in single-cell foundation model (scFM) research, we demonstrate how latent embeddings encode fundamental biological principles, enabling accurate cell identity assignment and the integration of multimodality and multitissue data into unified atlases. We provide a comprehensive benchmarking of current methodologies, detail experimental protocols for evaluating embedding quality, and present a scalable framework for constructing biologically consistent cellular maps. The findings indicate that while scFMs provide robust and versatile foundations for these tasks, model selection is highly dependent on specific dataset characteristics and task requirements, with no single solution universally outperforming others across all scenarios [2].

The exponential growth of single-cell RNA sequencing (scRNA-seq) data has provided an unprecedented opportunity to decode cellular heterogeneity. Single-cell foundation models (scFMs), pretrained on millions of cells, have emerged as powerful tools for this purpose [1]. These models learn to project high-dimensional, sparse gene expression profiles into dense, informative latent embeddings that capture underlying biological states [2]. The core thesis of this research is that these embeddings serve as effective representations of biological knowledge, where the geometric relationships and similarities between data points in the latent space reflect genuine biological relationships between cells [2] [1].

Cell type annotation and atlas construction are two fundamental downstream tasks that directly leverage these embedding similarities. Annotation involves classifying individual cells into known types based on their proximity to reference populations in the embedding space. Atlas construction involves integrating multiple datasets into a unified structure that preserves biological variation while removing technical artifacts [22]. The success of these tasks hinges on the model's ability to learn a latent space where the distance between embeddings accurately mirrors cellular function and identity, a property that can be quantitatively evaluated using novel, biology-informed metrics [2].

Core Methodologies and Experimental Protocols

scFMs are typically built on transformer architectures and are pretrained on vast corpora of single-cell data using self-supervised objectives, often inspired by language modeling tasks [1]. A critical preprocessing step is tokenization, where genes—along with their expression values—are converted into discrete input tokens. Strategies for ordering these non-sequential gene tokens include ranking by expression level or binning by expression value [2] [1]. The model learns during pretraining to predict masked genes or other features, thereby internalizing complex gene-gene relationships and co-expression patterns. As summarized in Table 1, leading scFMs vary in their input gene handling, embedding dimensions, and architectural details [2].

Protocol for Cell Type Annotation via Embedding Similarity

The following protocol outlines a standard workflow for supervised cell type annotation using scFM embeddings.

  • Feature Extraction (Zero-Shot): Input the target scRNA-seq dataset into a pretrained scFM without fine-tuning to obtain zero-shot cell embeddings. These embeddings serve as the feature representation for each cell [2].
  • Reference Mapping:
    • Obtain a reference dataset with pre-validated, ground-truth cell type labels.
    • Project the reference data into the same embedding space, either by passing it through the same scFM or by using a pre-embedded reference atlas.
  • Similarity Calculation:
    • For each target cell, calculate its similarity (e.g., cosine similarity, Euclidean distance) to all reference cells or to the centroids of reference cell type clusters in the embedding space.
  • Label Transfer:
    • Assign the target cell the label of its k-nearest neighbors (k-NN) in the reference set or the label of the reference cluster centroid to which it is most similar.
  • Validation and Error Assessment:
    • Lowest Common Ancestor Distance (LCAD): For misclassified cells, calculate the LCAD metric. This measures the ontological proximity in the Cell Ontology between the predicted and true cell type, providing a biologically-informed severity score for annotation errors [2].
    • Confidence Scoring: Generate a confidence score for each annotation based on the similarity distance to the assigned reference group.
Protocol for Atlas Construction via Graph-Based Integration

The GIANT (Gene-based data integration and analysis technique) methodology offers a robust protocol for atlas-scale integration by focusing on gene-level embeddings [22].

  • Input Data Processing:
    • Collect multiple single-cell datasets from different tissues, modalities (e.g., scRNA-seq, scATAC-seq, spatial transcriptomics), and platforms.
    • For each dataset and each cell cluster identified within it, construct a gene graph. In these graphs, nodes represent genes, and edges represent correlations in expression or epigenetic accessibility [22].
  • Recursive Graph Embedding:
    • Connect all gene graphs from different data sources in a dendrogram based on cluster similarity.
    • Apply a recursive projection algorithm that embeds genes from all graphs into a unified latent space. This method uses the dendrogram to enforce similarity constraints, ensuring that graphs nearby in the tree structure are embedded closely together in the latent space [22].
  • Atlas Query and Analysis:
    • The resulting unified gene-embedding space allows for joint functional analysis. Genes performing similar functions across tissues and modalities will be co-located in this space.
    • Cell types and states can be characterized by the collective embedding positions of their most informative genes.
Workflow Visualization

The following diagram illustrates the core logical workflow for cell type annotation and the gene-centric approach to atlas construction.

cluster_annotation A. Cell Type Annotation Workflow cluster_atlas B. Atlas Construction (GIANT Method) Data scRNA-seq Data scFM Single-Cell Foundation Model (scFM) Data->scFM Embedding Cell Embeddings scFM->Embedding Similarity Similarity Calculation (k-NN, Cosine) Embedding->Similarity Annotation Cell Type Annotation Similarity->Annotation Reference Reference Data (Known Cell Types) Reference->Similarity Validation Validation (LCAD Metric) Annotation->Validation MultiData Multi-tissue/ Modality Data GeneGraphs Construct Gene Graphs MultiData->GeneGraphs Dendrogram Cluster Dendrogram GeneGraphs->Dendrogram RecursiveEmbed Recursive Graph Embedding Dendrogram->RecursiveEmbed UnifiedSpace Unified Gene Embedding Space RecursiveEmbed->UnifiedSpace Analysis Joint Functional Analysis UnifiedSpace->Analysis

Quantitative Benchmarking of scFMs and Traditional Tools

A comprehensive benchmark evaluating six scFMs against established baselines reveals a nuanced landscape of performance trade-offs [2]. The evaluation, conducted across gene-level and cell-level tasks using 12 distinct metrics, provides critical insights for model selection.

Table 1: Performance Benchmark of Single-Cell Analysis Tools and Models [2]

Model / Tool Primary Function Key Strengths Notable Limitations
scGPT [2] General-purpose scFM Versatile across tasks; supports multi-modal data. Performance varies with task complexity.
Geneformer [2] General-purpose scFM Robust gene-level representations. Uses ranked genes, not raw counts.
scFoundation [2] General-purpose scFM Trained on a large number of genes. Computationally intensive.
GIANT [22] Gene-based Atlas Integration Excellent cross-modality/tissue integration; discovers gene functions. Not a cell-level annotation tool.
Harmony [23] Batch Correction Efficient batch effect removal; preserves biology. Cell-based, not gene-based.
Seurat [23] scRNA-seq Analysis Toolkit Mature, versatile; robust data integration and label transfer. Traditional, non-foundation model approach.
scVI [2] [23] Generative Modeling Superior batch correction and imputation. Requires dataset-specific training.

The benchmark concluded that no single scFM consistently outperforms all others across every task. scFMs are robust and versatile, but simpler machine learning models can be more efficient and effective for specific datasets, particularly under resource constraints [2]. The decision to use a complex scFM versus a simpler alternative should be guided by factors such as dataset size, task complexity, need for biological interpretability, and available computational resources [2].

Successful implementation of the described protocols relies on a suite of computational tools and data resources. The table below catalogs the essential "research reagents" for this field.

Table 2: Key Research Reagents and Resources for Embedding-Based Analysis [2] [22] [23]

Category Name Function / Purpose
Foundation Models Geneformer, scGPT, scFoundation Provide pretrained models for generating zero-shot cell and gene embeddings [2].
Analysis Ecosystems Seurat (R), Scanpy (Python) Provide comprehensive environments for preprocessing, clustering, and visualization of single-cell data [23].
Integration Tools Harmony, GIANT Correct batch effects and integrate datasets (cell-based and gene-based, respectively) [22] [23].
Data Resources CZ CELLxGENE, Human Cell Atlas Provide curated, large-scale single-cell datasets for model pretraining and as reference atlases [1].
Evaluation Metrics scGraph-OntoRWR, LCAD Novel biology-informed metrics to evaluate the consistency of model outputs with prior knowledge and the severity of annotation errors [2].

Evaluation and Validation of Embedding Quality

Beyond standard clustering metrics, validating the biological relevance of an embedding space is paramount. Two innovative metrics designed for this purpose are:

  • scGraph-OntoRWR: This metric evaluates whether the relational structure of cell types captured in the scFM embedding space is consistent with established biological knowledge from the Cell Ontology. It measures the agreement between the graph of cell type relationships learned by the model and the canonical ontology graph, thus uncovering the intrinsic biological knowledge encoded by scFMs [2].
  • Roughness Index (ROGI): ROGI quantitatively estimates the "smoothness" of the cell-property landscape in the pretrained latent space. A smoother landscape, indicated by a lower ROGI, is easier for task-specific models to learn from and is correlated with improved downstream task performance. ROGI can serve as a proxy to recommend an appropriate model in a dataset-dependent manner without requiring extensive benchmarking [2].

Experimental results using these metrics confirm that pretrained scFM embeddings do capture meaningful biological insights into the relational structure of genes and cells, which directly benefits downstream tasks like annotation and atlas construction [2].

The use of embedding similarities for cell type annotation and atlas construction represents a significant advancement in single-cell genomics. By framing cells and genes within a learned latent space, scFMs provide a powerful framework for representing and interrogating biological knowledge. The benchmarks and protocols outlined in this guide offer a roadmap for researchers to apply these methods effectively.

Future progress in this field hinges on several key developments: enhancing the interpretability of latent embeddings to directly link model representations to biological mechanisms, improving model scalability to accommodate the ever-growing volume of single-cell data, and creating more standardized and comprehensive benchmark tasks that reflect real-world clinical and research applications [2] [1]. As these models mature, they are poised to become indispensable tools in advancing our understanding of cellular function, disease mechanisms, and therapeutic development.

Batch Effect Correction and Data Integration in Multi-Study Analyses

The expansion of large-scale biological datasets, particularly in single-cell genomics, has created an urgent need for unified computational frameworks capable of integrating and analyzing data across multiple studies. Single-cell foundation models (scFMs) represent a transformative approach to this challenge, treating individual cells as sentences and genes as words to learn fundamental biological principles from millions of cells across diverse tissues and conditions [13]. However, a significant obstacle persists: batch effects. These technical artifacts arising from run-to-run variation in reagents, equipment, protocols, or personnel systematically bias data and can obscure true biological signals, ultimately hampering the utility of scFM embeddings for downstream analyses [24] [25].

In the context of scFM research, batch effect correction is not merely a preprocessing step but a foundational requirement for constructing biologically meaningful knowledge representations. When scFMs are trained on data afflicted by batch effects, the resulting embeddings may capture technical variances rather than genuine biological relationships, compromising their utility for tasks such as cell type annotation, perturbation prediction, and gene function analysis [26] [13]. This technical guide provides an in-depth examination of batch effect correction methodologies, with particular emphasis on their critical role in enabling robust biological knowledge representation within scFM embeddings.

Batch Effect Correction Methodologies: Principles and Applications

Algorithmic Foundations and Technical Approaches

Batch effect correction methods employ diverse mathematical frameworks to disentangle technical artifacts from biological signals. The established methods can be broadly categorized based on their underlying correction principles and the data objects they modify.

Table 1: Core Batch Effect Correction Methods and Technical Specifications

Method Input Data Type Correction Object Algorithmic Approach Handles Incomplete Data
ComBat [27] [28] [25] Normalized count matrix Count matrix Empirical Bayes with linear correction No
limma [27] [25] Normalized count matrix Count matrix Linear models with empirical Bayes moderation No
Harmony [28] Normalized count matrix Embedding Soft k-means with linear correction within clusters No
BERT [27] Incomplete omic profiles Count matrix Binary tree decomposition using ComBat/limma Yes
ComBat-seq [28] Raw count matrix Count matrix Negative binomial regression model No
Percentile Normalization [25] Relative abundance data Feature distributions Non-parametric percentile transformation Case-control required

The ComBat algorithm utilizes an empirical Bayes framework to estimate location and scale parameters for each feature within a batch, effectively shrinking these parameters toward the overall mean to correct systematic biases [25]. This approach is particularly effective when batch effects are not confounded with biological effects of interest [25]. The limma method employs similar linear modeling techniques with empirical Bayes moderation of the variances, making it particularly powerful for datasets with small sample sizes [27] [25].

More recently, Batch-Effect Reduction Trees (BERT) represents a significant advancement for handling incomplete omic profiles, which are common in large-scale integrative analyses. BERT decomposes the data integration task into a binary tree of batch-effect correction steps, applying ComBat or limma to features with sufficient data while propagating other features without alteration. This approach retains up to five orders of magnitude more numeric values compared to previous methods and leverages parallel computing for up to 11× runtime improvement [27].

For single-cell RNA sequencing data specifically, Harmony has demonstrated particularly robust performance by integrating cells across datasets without introducing detectable artifacts. Harmony operates by computing a low-dimensional principal component analysis (PCA) embedding and applying soft k-means with linear correction within small clusters in the embedded space [28].

Non-parametric approaches like percentile normalization offer model-free alternatives that convert case abundance distributions to percentiles of equivalent control features within each study before pooling data across studies. This method effectively controls for diffuse batch effects that are common in microbiome datasets [25].

Quantitative Performance Comparison

Rigorous benchmarking studies provide critical insights into the practical performance characteristics of different batch correction methods under various experimental conditions.

Table 2: Performance Benchmarking of Batch Correction Methods

Method Data Retention Runtime Efficiency ASW Batch Score ASW Biological Score Artifact Introduction
BERT Retains all numeric values [27] 11× improvement vs. HarmonizR [27] 2× improvement for imbalanced conditions [27] Preserved through covariate integration [27] Minimal (well-calibrated)
Harmony Preserves original matrix [28] Moderate Effective removal [28] Preserves biological variation [28] Lowest in independent tests [28]
ComBat Modifies count matrix [28] Fast Effective removal [28] Can over-correct if confounded [25] Detectable in some tests [28]
HarmonizR Significant data loss (up to 88%) [27] Baseline Effective removal [27] Preserved on complete data [27] Not reported
LIGER/MNN Preserves original matrix [28] Variable Effective removal [28] Can remove biological variation [28] High [28]

In simulation studies with 6000 features across 20 batches (10 samples each) with up to 50% missing values, BERT demonstrated complete retention of all numeric values, while HarmonizR with blocking of 4 batches exhibited up to 88% data loss. The sequential execution time of BERT decreased with increasing numbers of missing values, and the limma-based implementation showed an average 13% runtime improvement over ComBat [27].

Independent evaluations of single-cell RNA sequencing batch correction methods have revealed significant differences in calibration. Methods including MNN, SCVI, and LIGER performed poorly in rigorous testing, often altering the data considerably. ComBat, ComBat-seq, BBKNN, and Seurat introduced artifacts that could be detected in controlled setups, while Harmony was the only method that consistently performed well across all evaluations [28].

Integration with Single-Cell Foundation Models

The scFM Architecture and Training Pipeline

Single-cell foundation models represent a paradigm shift in computational biology, leveraging transformer-based architectures pretrained on massive datasets to learn generalizable representations of cellular states [13]. The standard scFM pipeline incorporates multiple stages where batch effect correction plays a critical role in ensuring robust biological knowledge representation.

scFM_pipeline cluster_legend Batch-Critical Steps DataCollection Data Collection (100M+ cells) QualityControl Quality Control & Filtering DataCollection->QualityControl BatchCorrection Batch Effect Correction QualityControl->BatchCorrection Tokenization Tokenization & Embedding BatchCorrection->Tokenization ModelTraining Transformer Model Training Tokenization->ModelTraining DownstreamTasks Downstream Tasks ModelTraining->DownstreamTasks

ScFM architecture typically begins with the compilation of large and diverse datasets from public repositories such as CZ CELLxGENE, NCBI GEO, and the Human Cell Atlas [13]. For example, CellFM—a state-of-the-art scFM with 800 million parameters—was trained on approximately 100 million human cells meticulously curated from diverse organs and sequencing technologies [26]. Following data collection, quality control procedures filter cells and genes to establish a high-quality training corpus [26] [13].

The critical batch correction step occurs prior to tokenization, ensuring that technical variances do not propagate through the model training process. Tokenization then converts the normalized gene expression data into discrete tokens, with common strategies including ranking genes by expression levels or binning expression values [13]. These tokens are processed through transformer layers with attention mechanisms that learn relationships between genes, ultimately producing latent embeddings for each cell and gene that form the basis for downstream analytical tasks [26] [13].

Strategic Integration Points for Batch Correction

The optimal integration of batch correction within the scFM pipeline depends on the specific model architecture and research objectives. Two primary paradigms have emerged:

Pre-training integration involves applying batch correction methods directly to the count matrices or normalized expressions before model training. This approach ensures that the foundational representations learned by the scFM are free from technical artifacts. For value-projection-based models like CellFM and scFoundation, which aim to predict precise gene expression values, pre-training integration is particularly crucial as it preserves the full resolution of the data [26] [13].

Post-training adaptation incorporates batch information during fine-tuning or through specialized architectural components. Some models report robustness to technical biases without explicit batch correction, while others incorporate batch information as special tokens during training [13]. The Low-Rank Adaptive (LoRA) mechanism in CellFM enables efficient fine-tuning with reduced trainable parameters, potentially allowing for dataset-specific batch effect adjustment during task adaptation [26].

Experimental Protocols for Batch Effect Correction

Protocol 1: BERT for Large-Scale Incomplete Omic Data

The BERT framework provides a robust protocol for integrating large-scale datasets with substantial missing values, as commonly encountered in multi-study analyses.

Step 1: Data Preprocessing - Remove singular numerical values from individual batches (typically affecting <1% of available values) to satisfy the requirement that each batch exhibits at least two numerical values per feature [27].

Step 2: Tree Construction - Decompose the data integration task into a binary tree where pairs of batches are selected at each level for batch-effect correction [27].

Step 3: Parallel Processing - Process independent sub-trees using a user-defined number of BERT processes (parameter P), with iterative reduction of processes (parameter R) until reaching a specified number of intermediate batches (parameter S) for sequential integration [27].

Step 4: Covariate Integration - Specify categorical covariates (e.g., biological conditions) that are passed to ComBat/limma at each tree level to preserve biological variation while removing batch effects [27].

Step 5: Quality Assessment - Compute quality control metrics including average silhouette width (ASW) for both batch of origin and biological condition to evaluate correction effectiveness [27].

Protocol 2: Evaluation Framework for scFM Embeddings

Rigorous evaluation of batch effect correction effectiveness is essential for ensuring biologically meaningful scFM embeddings.

Step 1: Null Simulation - Generate pseudobatches by randomly assigning cells to batch labels A or B within a public scRNA-seq dataset. Well-calibrated methods should not significantly alter the data in this null scenario [28].

Step 2: k-NN Graph Preservation - Evaluate changes in the k-nearest neighbor graph structure before and after correction, comparing to the established ground truth [28].

Step 3: Cluster Integrity Assessment - Examine effects on clustering and cell type identification, measuring the preservation of known biological groupings while removing technical batch clusters [28].

Step 4: Differential Expression Validation - Perform differential expression analysis between established clusters after correction, verifying that known biological signatures persist while batch-associated false positives are eliminated [28].

Step 5: Embedding Space Analysis - Calculate ASW scores with respect to both batch labels and biological conditions, with effective correction demonstrating low ASW batch and high ASW biological scores [27].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Computational Tools for Batch Effect Correction in scFM Research

Tool/Resource Function Application Context
BERT [27] High-performance data integration for incomplete omic profiles Large-scale multi-study analyses with significant missing data
Harmony [28] Efficient batch correction via integrated clustering scRNA-seq data integration with minimal artifact introduction
ComBat/limma [27] [25] Empirical Bayes batch effect adjustment Bulk RNA-seq and standardized omic datasets
CellFM [26] Single-cell foundation model with 800M parameters Cell annotation, perturbation prediction, gene function analysis
Pluto Bio [24] Multi-omics data harmonization platform Visualization and validation without coding requirements
Percentile Normalization [25] Non-parametric case-control normalization Microbiome studies with diffuse batch effects

Effective batch effect correction represents a foundational requirement for constructing biologically meaningful knowledge representations in single-cell foundation models. As scFMs continue to evolve in scale and complexity—exemplified by models like CellFM with 800 million parameters trained on 100 million human cells [26]—the critical importance of robust data integration methodologies cannot be overstated. The strategic implementation of batch correction protocols ensures that technical artifacts do not confound the latent embeddings that form the core of scFM knowledge representation, ultimately enabling more accurate predictions in cell annotation, perturbation response, and gene function analysis. Future advancements in batch effect correction will likely focus on increasingly scalable algorithms capable of handling the growing volume of single-cell data while preserving subtle biological signals essential for translational research applications.

Predicting Cellular Responses to Genetic Perturbations and Drug Treatments

Predicting how a cell will respond to a genetic or drug perturbation represents one of the most significant challenges in biological science and therapeutic development. The ability to accurately simulate cellular behavior in silico would dramatically accelerate our understanding of disease mechanisms and revolutionize drug discovery pipelines. Recent advances in artificial intelligence have enabled the development of single-cell foundation models (scFMs)—deep learning models pre-trained on vast amounts of single-cell data that can be fine-tuned for specific prediction tasks. These models represent a crucial step toward creating "virtual cells" that can simulate cellular responses to diverse perturbations without requiring exhaustive experimental validation [16] [1]. The development of these computational approaches is particularly valuable for rare diseases, where patient samples are scarce and experimental screening is challenging [16]. This technical guide explores the current state of scFMs for perturbation prediction, with a specific focus on how biological knowledge is represented within model embeddings and leveraged for accurate forecasting of cellular behavior.

Foundations of Single-Cell Foundation Models (scFMs)

Core Architectures and Pretraining Strategies

Single-cell foundation models typically leverage transformer architectures, originally developed for natural language processing, to learn meaningful representations of cellular states from gene expression data. In these models, individual cells are treated analogously to sentences, while genes or other genomic features along with their expression values serve as tokens or words [1]. The transformer's attention mechanism allows the model to learn and weight relationships between any pair of input tokens, enabling it to determine which genes in a cell are most informative of the cell's identity or state, and how they co-vary across cellular contexts [1].

Most scFMs employ one of several architectural variants: (1) BERT-like encoder architectures with bidirectional attention mechanisms that learn from the context of all genes in a cell simultaneously; (2) GPT-inspired decoder architectures with unidirectional masked self-attention that iteratively predict masked genes conditioned on known genes; or (3) hybrid encoder-decoder combinations [1]. These models are pre-trained on massive single-cell datasets—often incorporating tens of millions of cells from diverse tissues and conditions—using self-supervised objectives, typically through masked gene modeling where the model learns to predict randomly masked portions of the gene expression profile [2] [1].

Tokenization Strategies for Biological Data

A critical challenge in applying transformer architectures to single-cell data is the non-sequential nature of gene expression data. Unlike words in a sentence, genes in a cell have no inherent ordering. To address this, different scFMs employ various tokenization strategies:

  • Expression-based ranking: Genes are ranked within each cell by expression levels, and the ordered list of top genes is treated as the "sentence" [1]
  • Value binning: Expression values are partitioned into bins, with rankings determined by these binned values [1]
  • Genomic position ordering: Some models order genes by their genomic positions [2]
  • Normalized counts: Several models report no clear advantages for complex ranking strategies and simply use normalized counts [1]

Each gene is typically represented as a token embedding that combines a gene identifier and its expression value in the given cell. Special tokens may be added to represent cell identity, metadata, or experimental conditions, enriching the biological context available to the model [1].

Knowledge Representation in Model Embeddings

The fundamental premise of scFMs is that by exposing a model to millions of cells encompassing diverse tissues and conditions, the model can learn the fundamental principles of cellular biology that generalize to new datasets or prediction tasks [1]. The embeddings learned by these models potentially capture complex biological relationships, including:

  • Gene-gene regulatory interactions
  • Pathway associations
  • Cell type and state relationships
  • Functional gene modules

This rich biological knowledge embedded within the model parameters enables the adaptation of scFMs to various downstream tasks with relatively few additional labeled examples, mirroring the transfer learning capabilities of foundation models in other domains [2] [1].

Experimental Frameworks for Perturbation Prediction

In Silico Perturbation (ISP) Prediction

The Geneformer model provides a representative framework for in silico perturbation prediction. The typical workflow involves:

  • Model Fine-tuning: The pre-trained foundation model is fine-tuned on a specific biological context (e.g., T-cell activation or disease states) using relevant single-cell RNA sequencing data [16]
  • Perturbation Simulation: The fine-tuned model performs in silico perturbations by systematically altering the expression of target genes and predicting the resulting cellular state
  • Response Quantification: The model outputs the predicted direction and magnitude of cellular state changes, typically measured as shifts in embedding space or classification probabilities [16]

In a benchmark study of open-loop ISP predictions using Geneformer, researchers fine-tuned the model to predict T-cell activation status using data from multiple studies where T cells were stimulated via CD3-CD28 beads or phorbol myristate acetate/ionomycin [16]. While the pre-trained model embeddings clustered by study rather than activation status, the fine-tuned model successfully classified cells by activation status with 99.8% accuracy on a hold-out test set [16].

Closed-Loop Framework for Improved Accuracy

A significant limitation of standard ISP approaches is their inability to incorporate experimental validation results to improve future predictions. To address this, researchers have developed a "closed-loop" framework that extends scFMs by incorporating actual perturbation data during model fine-tuning [16].

The closed-loop approach follows this methodology:

  • Initial Prediction: Perform open-loop ISP to identify candidate genes whose perturbation may shift cellular states in desired directions
  • Experimental Validation: Conduct targeted perturbation experiments (e.g., CRISPR screens) on top candidates
  • Model Refinement: Incorporate the experimental perturbation data (e.g., Perturb-seq data) alongside original training data to fine-tune the foundation model
  • Iterative Prediction: Use the refined model to generate improved ISP predictions [16]

This framework demonstrated substantial improvements in prediction accuracy. In the T-cell activation setting, closed-loop ISP increased positive predictive value three-fold—from 3% to 9%—with concurrent improvements in negative predictive value (99%), sensitivity (76%), and specificity (81%) [16]. The area under the receiver operator characteristic curve (AUROC) significantly increased from 0.63 (95% CI: 0.58-0.68) for standard ISP to 0.86 (95% CI: 0.83-0.89) for closed-loop ISP [16].

Table 1: Performance Comparison of Open-Loop vs. Closed-Loop ISP for T-cell Activation Prediction

Metric Open-Loop ISP Closed-Loop ISP Improvement
Positive Predictive Value (PPV) 3% 9% 3-fold increase
Negative Predictive Value (NPV) 98% 99% 1% increase
Sensitivity 48% 76% 28% increase
Specificity 60% 81% 21% increase
AUROC 0.63 0.86 0.23 increase

Notably, performance improvements saturated at approximately 20 perturbation examples, suggesting that even modest experimental validation can substantially enhance prediction accuracy [16].

Multi-Modal Prediction: From Transcriptome to Morphology

Beyond transcriptomic responses, researchers have developed models that predict morphological changes resulting from perturbations. MorphDiff represents an innovative approach that uses a transcriptome-guided latent diffusion model to simulate high-fidelity cell morphological responses to perturbations [29].

The MorphDiff framework operates through two primary components:

  • Morphology Variational Autoencoder (MVAE): Compresses high-dimensional cell morphology images (from Cell Painting assays) into low-dimensional latent representations
  • Latent Diffusion Model (LDM): Generates cell morphology latent representations conditioned on L1000 gene expression profiles [29]

The model can operate in two modes:

  • MorphDiff(G2I): Takes L1000 gene expression as condition and generates corresponding cell morphology images from random noise
  • MorphDiff(I2I): Transforms unperturbed cell morphology images to predicted perturbed morphology using gene expression as guidance [29]

In evaluations across three large-scale datasets (covering 1028 drug perturbations and 130 genetic perturbations), MorphDiff accurately predicted cell morphological changes under unseen perturbations and enhanced mechanism of action (MOA) retrieval, achieving accuracy comparable to ground-truth morphology and outperforming baseline methods by 16.9% and 8.0%, respectively [29].

Case Study: Application to RUNX1-Familial Platelet Disorder

Disease Context and Experimental Design

RUNX1-familial platelet disorder (RUNX1-FPD) is a rare pediatric-onset hematologic disease affecting approximately 20,000 people in the United States. The disorder is caused by loss-of-function mutations in RUNX1, affecting hematopoietic stem cells (HSCs) and characterized by thrombocytopenia, impaired platelet function, immune dysregulation, and increased risk of early-onset myeloid neoplasms. Currently, no interventions exist to prevent progression to myeloid neoplasms [16].

To demonstrate the utility of closed-loop virtual cell models for rare diseases, researchers applied the framework to RUNX1-FPD. Since patient samples are scarce, the team leveraged human HSCs engineered to have RUNX1 loss-of-function mutations that model RUNX1-FPD. The experimental approach included:

  • Model System Validation: Comparison of RUNX1-engineered HSCs with scRNA-seq data from RUNX1-FPD patients to validate the disease model
  • Model Fine-tuning: Fine-tuning Geneformer to classify HSCs between RUNX1-engineered HSCs and control HSCs
  • ISP Application: Performing open-loop ISP to identify genes that, when deleted, would shift RUNX1-knockout HSCs toward a control-like state [16]
Target Identification and Therapeutic Insights

Comparison of differential expression and ISP results identified 14 genes predicted by both methods to significantly shift RUNX1-knockout cells toward control cells [16]. From these targets, researchers selected eight genes with available specific small molecule inhibitors for experimental validation: PRKCB, UBB, and others mentioned in the preprint [16].

The application of the closed-loop model to RUNX1-FPD identified two therapeutic targets (mTOR and CD74-MIF signaling axis) and two novel pathways (protein kinase C and phosphoinositide 3-kinase) [16]. This demonstrates the potential of scFMs to accelerate rare disease drug discovery by prioritizing therapeutic targets for experimental validation.

Performance Benchmarking of scFMs

Comparative Evaluation Across Tasks

A comprehensive benchmark study of six scFMs against well-established baselines provides insights into their relative performance across different task types [2]. The evaluation encompassed:

  • Gene-level tasks: Gene network inference and gene function prediction
  • Cell-level tasks: Batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [2]

The benchmarking revealed 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, biological interpretability, and computational resources [2].

Table 2: Benchmarking Results of scFMs Across Different Task Types

Task Category Best Performing Approaches Key Findings
Batch Integration scGPT, Geneformer scFMs robust to technical variations but sensitive to data quality
Cell Type Annotation scBERT, scGPT Performance depends on cell type complexity and training data diversity
Cancer Cell Identification Multiple scFMs with similar performance High accuracy in distinguishing malignant from normal cells
Drug Sensitivity Prediction Varies by cancer type and drug Performance depends on concordance between training and target domains
Perturbation Prediction Closed-loop frameworks Significant improvement over standard differential expression
Biological Relevance of Model Embeddings

To assess the biological relevance of scFM embeddings, researchers developed novel evaluation metrics including:

  • scGraph-OntoRWR: Measures consistency of cell type relationships captured by scFMs with prior biological knowledge
  • Lowest Common Ancestor Distance (LCAD): Measures ontological proximity between misclassified cell types to assess severity of annotation errors [2]

These metrics confirmed that pretrained zero-shot scFM embeddings indeed capture meaningful biological insights into the relational structure of genes and cells, which benefits downstream tasks [2]. Additionally, researchers found that performance improvements arise from a smoother cell-property landscape in the pretrained latent space, which reduces the difficulty of training task-specific models [2].

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

Resource Category Specific Examples Function and Application
Foundation Models Geneformer, scGPT, scFoundation Pre-trained models for transfer learning to specific biological contexts
Perturbation Screening Data CRISPRi/CRISPRa screens, Perturb-seq Experimental data for model training and validation
Reference Datasets CELLxGENE, Human Cell Atlas, PanglaoDB Curated single-cell data for model pretraining and benchmarking
Computational Frameworks MorphDiff, Closed-loop ISP Specialized tools for perturbation prediction and analysis
Analysis Platforms CellProfiler, DeepProfiler Feature extraction from cellular morphology images
Experimental Validation Tools Flow cytometry, scRNA-seq Orthogonal validation of computational predictions

Signaling Pathways Identified Through scFM Approaches

runx1_pathway RUNX1_mutation RUNX1 Loss-of-Function Mutation Hematopoietic_defects Hematopoietic Stem Cell Defects RUNX1_mutation->Hematopoietic_defects mTOR_signaling mTOR Signaling Dysregulation Hematopoietic_defects->mTOR_signaling CD74_MIF_axis CD74-MIF Signaling Axis Alteration Hematopoietic_defects->CD74_MIF_axis PKC_pathway Protein Kinase C Pathway Changes Hematopoietic_defects->PKC_pathway PI3K_pathway Phosphoinositide 3-Kinase Pathway Modulation Hematopoietic_defects->PI3K_pathway Therapeutic_targets Potential Therapeutic Targets mTOR_signaling->Therapeutic_targets CD74_MIF_axis->Therapeutic_targets PKC_pathway->Therapeutic_targets PI3K_pathway->Therapeutic_targets

Diagram 1: RUNX1-FPD Signaling Pathways Identified via scFM

Experimental Workflow for Closed-Loop Perturbation Prediction

workflow Pretrained_model Pretrained scFM (Geneformer) Task_fine_tuning Task-Specific Fine-Tuning Pretrained_model->Task_fine_tuning Open_loop_ISP Open-Loop ISP Prediction Task_fine_tuning->Open_loop_ISP Experimental_validation Experimental Validation (Perturb-seq) Open_loop_ISP->Experimental_validation Closed_loop_tuning Closed-Loop Model Fine-Tuning Experimental_validation->Closed_loop_tuning Refined_predictions Refined ISP Predictions Closed_loop_tuning->Refined_predictions Therapeutic_targets Identified Therapeutic Targets Refined_predictions->Therapeutic_targets

Diagram 2: Closed-Loop scFM Experimental Workflow

Single-cell foundation models represent a transformative approach for predicting cellular responses to genetic and drug perturbations. The integration of biological knowledge within model embeddings enables these systems to serve as effective virtual cell models, particularly when enhanced through closed-loop frameworks that incorporate experimental feedback. Current applications demonstrate significant promise for accelerating therapeutic discovery, especially for rare diseases where traditional screening approaches are impractical.

Future developments in this field will likely focus on several key areas: (1) improving model interpretability to extract biologically meaningful insights from embedding spaces; (2) developing multi-modal foundation models that integrate transcriptomic, proteomic, and morphological data; (3) enhancing scalability to accommodate ever-growing single-cell datasets; and (4) establishing standardized benchmarking frameworks to guide model selection for specific biological questions [2] [1]. As these models continue to evolve, they will increasingly serve as indispensable tools for biological discovery and therapeutic development, bringing us closer to the vision of comprehensive virtual cell models that can accurately simulate cellular behavior across diverse contexts and perturbation types.

The advent of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, revolutionizing our ability to decipher the complex language of gene regulation at cellular resolution. These large-scale deep learning models, pretrained on vast datasets comprising millions of single-cell transcriptomes, learn context-aware representations of genes that capture rich biological relationships beyond what traditional methods can achieve [1]. The core premise of scFMs lies in their capacity to transform high-dimensional, sparse single-cell RNA sequencing (scRNA-seq) data into meaningful latent embeddings—vector representations that encode fundamental biological properties of genes and cells [1] [2].

Gene regulatory network (GRN) inference has long been a central challenge in systems biology, with conventional methods often struggling with the high dimensionality, technical noise, and complex dependencies inherent in single-cell data [30] [31]. The emergence of scFMs offers a transformative approach by providing gene embeddings that serve as sophisticated starting points for network inference. These embeddings, learned through self-supervised objectives on massive datasets, capture nuanced gene-gene relationships, functional similarities, and co-regulatory patterns that form the foundation for reconstructing accurate regulatory networks [32].

This technical guide examines the theoretical foundations, methodological frameworks, and practical implementations for leveraging scFM-derived gene embeddings to infer GRNs. By treating genes as contextual entities within a biological "language" and their expression patterns as semantic relationships, we can extract regulatory principles that drive cellular identity and function, ultimately advancing drug discovery and therapeutic development.

Theoretical Foundations: From Embeddings to Regulatory Principles

The Biological Knowledge Representation Hypothesis

Single-cell foundation models learn representations of genes that implicitly encode biological knowledge through their training on massive, diverse single-cell datasets. The fundamental hypothesis is that by processing millions of cellular "sentences" composed of gene "words," these models internalize the grammatical rules of gene regulation—how genes co-express, coordinate, and influence each other across different cellular contexts and states [1]. This learned knowledge manifests in the geometric relationships within the embedding space, where genes with similar regulatory roles or functional relationships occupy proximate regions [2].

The embedding space produced by scFMs exhibits specific structural properties that make it particularly suitable for GRN inference. First, it demonstrates functional coherence, wherein genes participating in common biological processes or pathways cluster together in the latent space. Second, it captures regulatory hierarchies, with transcription factors and their potential targets displaying predictable spatial relationships. Third, it maintains context awareness, where the same gene may occupy different positions depending on cellular state, tissue type, or experimental condition [32]. These properties enable researchers to move beyond simple correlation-based network inference toward causal regulatory relationship identification.

Architectures and Training Paradigms

Current scFMs predominantly utilize transformer architectures, which employ attention mechanisms to model complex dependencies between genes within individual cells [1]. The self-supervised pretraining typically employs masked gene modeling, where the model learns to predict randomly masked gene expressions based on their context—other genes within the same cell [32]. This process forces the model to learn the underlying regulatory principles that connect gene expressions.

Table 1: Key Single-Cell Foundation Models for Gene Embedding Extraction

Model Name Architecture Parameters Pretraining Dataset Size Key Features Gene Embedding Dimension
Geneformer Transformer Encoder 40M 30 million cells Rank-based gene ordering 256-512
scGPT Transformer 50M 33 million cells Multi-modal capability 512
scBERT Transformer Encoder Not specified Not specified Gene2vec initialization 200
scFoundation Asymmetric Encoder-Decoder 100M 50 million cells Read-depth-aware training 512
UCE Transformer Encoder 650M 36 million cells Protein sequence integration 1280

Methodological Framework: From Embeddings to Networks

Gene Embedding Extraction Strategies

The process of extracting gene embeddings from scFMs varies based on model architecture and training methodology. In encoder-based models like scBERT, gene embeddings are typically extracted from the final transformer layer after processing a representative set of cells [32]. For models employing asymmetric architectures like scFoundation, gene representations are often derived from the decoder layers, which reconstruct gene expressions from latent representations [32].

A critical consideration in embedding extraction is context specification—whether to generate context-independent embeddings averaged across all cells or context-dependent embeddings specific to particular cell types, states, or conditions. For GRN inference, context-dependent approaches generally yield superior results, as they capture the dynamic nature of gene regulation across different cellular environments [2]. Practical implementations often extract embeddings from multiple cellular contexts and aggregate them using attention mechanisms or other weighting schemes to preserve regulatory specificity while maintaining generalizable relationships.

Network Inference Algorithms

Once gene embeddings are obtained, multiple algorithmic approaches can transform these representations into regulatory networks. The core principle underlying most methods is that regulatory relationships manifest as predictable geometric patterns in the embedding space.

Similarity-based methods represent the most straightforward approach, where regulatory potential between genes is quantified using distance metrics in the embedding space. However, simple cosine similarity or Euclidean distance often fails to capture the directional nature of regulatory relationships [33].

Graph neural network (GNN) approaches have demonstrated superior performance for GRN inference from embeddings. Methods like scRegNet combine scFM-derived embeddings with graph-based learning, where the embeddings provide initial node features that are then refined through message passing in a graph structure [32]. This hybrid approach leverages both the biological knowledge encoded in the pretrained embeddings and the topological constraints inherent in regulatory networks.

Regularization-based methods incorporate prior biological knowledge to guide network inference. For example, LINGER uses motif information as manifold regularization, encouraging connected genes in the network to share regulatory features [31]. This approach aligns with the biological reality that transcription factors regulate target genes through specific binding motifs.

Table 2: Comparison of Network Inference Methods Using scFM Embeddings

Method Algorithm Type Embedding Utilization Prior Knowledge Integration Reported Performance (AUROC)
scRegNet GNN-based Initial node features Limited 0.81-0.89
LINGER Neural network + regularization Feature input TF motifs, external bulk data 4-7x improvement over baselines
Gene2role Role-based embedding Direct similarity computation Multi-hop topology Not specified
GENIE3 Ensemble tree-based Not applicable None 0.30-0.35
Traditional correlation Similarity-based Not applicable None 0.50-0.65

Experimental Workflow for GRN Inference

The following diagram illustrates the complete workflow for inferring gene regulatory networks from single-cell data using foundation model embeddings:

G scData Single-Cell RNA-seq Data preprocessing Data Preprocessing & Normalization scData->preprocessing scFM Single-Cell Foundation Model (eg. Geneformer, scGPT) preprocessing->scFM geneEmbeddings Gene Embedding Extraction scFM->geneEmbeddings networkInference Network Inference Algorithm (eg. GNN, Similarity) geneEmbeddings->networkInference grn Inferred Gene Regulatory Network networkInference->grn validation Experimental Validation (ChIP-seq, Perturbation) validation->scData Iterative Refinement grn->validation

Practical Implementation and Protocols

Step-by-Step Protocol: scRegNet Framework

The scRegNet framework exemplifies a modern approach to GRN inference that combines scFM embeddings with graph neural networks. Below is a detailed protocol for implementation:

Step 1: Data Preprocessing and Normalization

  • Input: Raw single-cell RNA-seq count matrix (cells × genes)
  • Filter genes expressed in <1% of cells and cells with <200 detected genes
  • Normalize using log(TPM+1) transformation with feature scaling
  • Select highly variable genes (2000-3000 genes) for downstream analysis
  • Batch correction if multiple datasets are integrated

Step 2: Gene Embedding Extraction

  • Load pretrained scFM (scBERT, Geneformer, or scFoundation)
  • For each cell type of interest, sample 1000 representative cells
  • Pass cells through model and extract gene embeddings from final layer
  • Average embeddings across cells to obtain context-specific gene representations
  • Dimension: 200-512 dimensions per gene depending on model

Step 3: Graph Construction and Network Inference

  • Construct initial k-nearest neighbor graph (k=20) based on embedding similarities
  • Initialize GNN with gene embeddings as node features
  • Train using link prediction task with experimentally validated TF-target pairs
  • Apply gradient descent with early stopping to prevent overfitting
  • Output: Regulatory probability scores for all possible gene pairs

Step 4: Network Refinement and Thresholding

  • Apply precision-recall analysis to determine optimal probability threshold
  • Filter edges below confidence threshold (typically P < 0.7)
  • Incorporate motif information to validate TF-target relationships
  • Integrate protein-protein interaction data to support complex formation

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents and Computational Tools for scFM-Based GRN Inference

Resource Type Function Application in GRN Inference
CZ CELLxGENE Data Platform Unified access to annotated single-cell data Pretraining and benchmark datasets
BEELINE Benchmarking Suite Standardized evaluation of GRN methods Performance validation on gold-standard networks
scGPT Software Library Pre-trained foundation model Gene embedding extraction and perturbation modeling
Geneformer Software Library Pre-trained foundation model Context-aware gene representation learning
CellOracle GRN Tool Network inference from multi-omics data Baseline comparison and validation
ENCODE ChIP-seq Experimental Data TF binding sites from ChIP-seq Ground truth for regulatory relationship validation
GTEx eQTL Experimental Data Expression quantitative trait loci Cis-regulatory validation
DeepTFni Software Tool TF-target prediction from scATAC-seq Multi-modal integration for improved accuracy

Validation and Interpretation Frameworks

Experimental Validation Strategies

Validating inferred regulatory networks requires multiple complementary approaches to assess different aspects of network accuracy. Direct validation utilizes experimentally determined TF-DNA interactions from ChIP-seq data as ground truth, measuring performance using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPR) [31]. For example, LINGER demonstrated a fourfold to sevenfold relative increase in AUPR ratio compared to existing methods when validated against ChIP-seq data from blood cells [31].

Functional validation employs genetic perturbations to test predicted regulatory relationships. By comparing predicted versus observed expression changes after TF knockout or overexpression, researchers can quantify the causal accuracy of inferred edges. However, recent benchmarks show that even advanced foundation models struggle to outperform simple additive baselines in predicting perturbation effects, highlighting the need for improved validation frameworks [34].

Biological consistency validation examines whether inferred networks recapitulate known biological principles. This includes enrichment of co-regulated genes in specific pathways, appropriate hierarchical organization with master regulators at the top, and consistency with known temporal expression patterns during differentiation or cell cycle.

Biological Interpretation of Embedding-Driven Networks

The transition from network structure to biological insight requires specialized interpretation techniques. Regulator hierarchy analysis identifies master regulator TFs through centrality metrics (betweenness, eigenvector centrality) within the inferred network [35]. Studies in Synechococcus elongatus demonstrated that despite moderate accuracy in predicting individual TF-gene interactions, network-level topological analysis successfully revealed organizational principles of circadian regulation [35].

Module detection applies community detection algorithms to identify densely connected gene groups that likely represent functional units or co-regulated programs. These modules can be characterized through gene ontology enrichment to determine their biological functions and through expression correlation analysis to validate co-regulation.

Dynamic network analysis tracks how regulatory relationships change across conditions, timepoints, or cell states. By comparing context-specific networks inferred from corresponding embeddings, researchers can identify regulatory switches that drive cell fate decisions or disease transitions.

Current Limitations and Future Directions

Methodological Challenges and Limitations

Despite their promise, current approaches for GRN inference from scFM embeddings face several significant challenges. The accuracy ceiling remains a concern, with even state-of-the-art methods like LINGER achieving AUPR values substantially below 0.5 on certain validation sets [31]. Benchmark studies consistently show that simple baseline models often compete with or outperform complex foundation models on specific prediction tasks, particularly for perturbation effect prediction [34].

The interpretation gap presents another major challenge. While scFMs generate powerful embeddings, understanding how these representations encode specific regulatory relationships remains difficult. Attention mechanisms provide some interpretability, but mapping attention patterns to biologically meaningful regulatory logic is still an open research problem [1].

Technical artifacts including batch effects, sampling biases, and platform-specific signals can confound embedding relationships and lead to spurious regulatory inferences. Methods that explicitly model these technical factors during both pretraining and inference are needed to improve robustness.

Emerging Solutions and Future Developments

Future progress in embedding-driven GRN inference will likely come from several promising directions. Multi-modal integration combines transcriptomic embeddings with epigenetic, proteomic, and spatial information to create more comprehensive regulatory models. For example, LINGER's integration of scATAC-seq data with transcriptomics significantly improved cis-regulatory inference [31].

Lifelong learning approaches that continuously incorporate new experimental data as it becomes available will address the limited generalization of current models. The LINGER framework demonstrates how external bulk data can be leveraged to enhance inference from single-cell multiome data through elastic weight consolidation [31].

Causal representation learning aims to move beyond correlational relationships to model the directional causal influences between genes. By incorporating perturbation data directly into the pretraining objective, future scFMs could learn embeddings that explicitly encode regulatory directionality rather than mere association.

The field is also moving toward tissue- and cell-type-specific foundation models that capture regulatory principles unique to particular biological contexts, addressing the current limitation of one-size-fits-all models that may miss context-specific regulatory mechanisms.

Gene-level analysis through single-cell foundation model embeddings represents a powerful framework for inferring gene regulatory networks that transcends the limitations of traditional correlation-based methods. By leveraging the rich biological knowledge encoded in these embeddings through sophisticated graph-based learning algorithms, researchers can uncover regulatory principles that drive cellular identity and function. While challenges remain in validation, interpretation, and causal inference, the rapid advancement of both experimental and computational methods promises increasingly accurate and biologically meaningful network models that will accelerate therapeutic development and deepen our understanding of cellular regulation.

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling unprecedented analysis of cellular heterogeneity and function. These models, trained on millions of single-cell transcriptomes, learn fundamental biological principles that can be generalized across diverse downstream tasks [1] [13]. However, the rapid proliferation of scFMs has created a significant implementation challenge: heterogeneous architectures, pretraining protocols, and coding standards have severely hindered their practical adoption and comparative evaluation [4] [36]. This fragmentation necessitates a standardized framework to unlock the potential of biological knowledge representation embedded within scFM embeddings.

BioLLM (Biological Large Language Model) addresses this critical need as a unified software framework for integrating and applying diverse scFMs to single-cell RNA sequencing analysis [4] [36]. By providing standardized APIs and comprehensive documentation, BioLLM eliminates architectural and coding inconsistencies, enabling researchers to execute streamlined model access and consistent benchmarking. This white paper examines the technical architecture of BioLLM, its experimental validation, and practical implementation guidelines for researchers and drug development professionals seeking to leverage standardized scFM frameworks for enhanced biological insight.

BioLLM Architectural Framework and Core Components

Unified Interface Design and Integration Philosophy

BioLLM employs a modular architecture that abstracts away the implementation specifics of individual scFMs while exposing a consistent interface for model interaction. This design philosophy allows researchers to switch between different foundation models without modifying their core analysis pipelines, significantly accelerating methodological comparisons and reducing technical debt [4]. The framework's integration layer handles model-specific peculiarities including tokenization strategies, input normalization procedures, and output formatting, ensuring consistent data flow regardless of the underlying model architecture.

The framework supports both zero-shot inference and fine-tuning workflows, accommodating diverse research scenarios from rapid exploratory analysis to targeted model optimization [36]. This flexibility is particularly valuable for drug development professionals who require both quick validation of biological hypotheses and specialized model adaptation for specific disease contexts or compound screening applications.

Supported Model Ecosystem and Capabilities

BioLLM integrates several prominent scFMs, each with distinct architectural features and performance characteristics. The table below summarizes key models supported within the ecosystem and their specialized capabilities:

Table 1: Single-Cell Foundation Models Integrated within BioLLM

Model Name Architecture Type Parameters Pretraining Dataset Size Specialized Capabilities
scGPT Transformer Decoder 50 million 33 million cells Robust performance across all tasks; multi-omic integration [4] [2]
Geneformer Transformer Encoder 40 million 30 million cells Strong gene-level tasks; network inference [4] [2]
scFoundation Asymmetric Encoder-Decoder 100 million 50 million cells Gene-level tasks; large-scale pretraining [4] [2]
UCE Protein-Enhanced Encoder 650 million 36 million cells Cross-modal integration; protein context [2]
scBERT Transformer Encoder Smaller architecture Limited training data Cell type annotation [4]

Computational Workflow and Data Processing

The end-to-end workflow within BioLLM standardizes the entire analytical process from raw data input to biological interpretation. The following diagram illustrates the core data processing pipeline:

BioLLM_Workflow BioLLM Standardized Analysis Workflow cluster_0 BioLLM Unified Interface A Raw Single-Cell Data (Expression Matrix) B Quality Control & Normalization A->B C Model Selection & Configuration B->C B->C D Embedding Generation (Zero-shot or Fine-tuned) C->D C->D E Downstream Analysis D->E D->E F Biological Interpretation E->F P1 Pretrained Model Weights P1->C P2 Task-Specific Fine-tuning Parameters P2->D

Experimental Protocols and Benchmarking Methodologies

Standardized Evaluation Framework

BioLLM implements a comprehensive benchmarking framework that assesses scFM performance across multiple biological tasks and datasets. The evaluation encompasses both gene-level and cell-level tasks, including batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [2]. This multi-faceted approach ensures that models are tested against biologically meaningful challenges rather than abstract computational metrics.

The framework employs 12 distinct metrics spanning unsupervised, supervised, and knowledge-based approaches, including novel biological relevance metrics such as scGraph-OntoRWR, which measures the consistency of cell type relationships captured by scFMs with prior biological knowledge [2]. Additionally, the Lowest Common Ancestor Distance (LCAD) metric assesses the ontological proximity between misclassified cell types, providing a biologically-informed measure of annotation error severity.

Performance Benchmarking Results

Comprehensive evaluation through BioLLM has revealed distinct performance trade-offs across leading scFM architectures. The table below summarizes key benchmarking results across critical biological tasks:

Table 2: BioLLM Benchmarking Results Across scFM Architectures

Model Cell Type Annotation (Accuracy) Batch Integration (ASW) Gene Function Prediction (AUPRC) Drug Sensitivity (Pearson R) Zero-shot Transfer capability
scGPT 0.94 0.88 0.82 0.79 Strong [4] [36]
Geneformer 0.89 0.82 0.85 0.71 Moderate [4] [2]
scFoundation 0.91 0.85 0.87 0.74 Moderate [4]
UCE 0.87 0.84 0.84 0.68 Limited [2]
scBERT 0.78 0.75 0.72 0.62 Limited [4] [36]

Benchmarking results consistently highlight scGPT's robust performance across all tasks, particularly in zero-shot and fine-tuning scenarios [4] [36]. Geneformer and scFoundation demonstrate specialized strengths in gene-level tasks, benefiting from effective pretraining strategies that capture gene-gene relationships [2]. In contrast, scBERT lags in performance, likely due to its smaller model size and limited training data [4] [36].

Experimental Protocol for Cell Type Annotation

For researchers implementing cell type annotation using BioLLM, the following standardized protocol ensures reproducible results:

  • Data Preprocessing: Begin with quality-controlled single-cell data containing 10,000-50,000 cells across diverse cell types. Apply library size normalization and log transformation using BioLLM's built-in functions.

  • Model Selection: Choose an appropriate scFM based on dataset size and complexity. For novel cell types, select models with strong zero-shot performance (e.g., scGPT). For well-established cell types, models with precise annotation boundaries (e.g., Geneformer) may be preferable.

  • Embedding Generation: Extract cell embeddings using BioLLM's standardized embedding API. For zero-shot inference, use the get_embeddings() function with default parameters. For fine-tuning, employ the fit() method with labeled reference data.

  • Annotation Transfer: Project query cells into the reference embedding space using BioLLM's annotate_cells() function, which implements k-nearest neighbor classification with optimal k-value determination.

  • Validation: Assess annotation quality using BioLLM's evaluate_annotations() function, which computes both traditional metrics (accuracy, F1-score) and biological consistency metrics (LCAD, scGraph-OntoRWR).

This protocol has been validated across multiple tissue types and species, demonstrating consistent performance when applied to independent datasets such as the Asian Immune Diversity Atlas (AIDA) v2 [2].

Biological Knowledge Representation in scFM Embeddings

Extracting Biologically Meaningful Representations

The latent embeddings generated by scFMs through BioLLM capture rich biological information that extends beyond superficial transcriptional patterns. Experimental evidence confirms that these embeddings encode fundamental aspects of cellular identity, including developmental lineage relationships, functional specialization, and disease-associated alterations [2]. The biological knowledge representation within scFM embeddings manifests through several measurable properties:

  • Hierarchical organization: Embeddings spontaneously arrange cells according to established ontological hierarchies, with closely related cell types clustering in proximity while maintaining appropriate phylogenetic distances [2].

  • Gene relationship modeling: Attention mechanisms within transformer-based scFMs learn gene-gene interaction patterns that reflect biological pathways and co-regulation networks [1] [13].

  • Cross-context generalization: Models pretrained on diverse cellular atlases develop representations that transfer effectively to novel biological contexts, including rare cell types and disease states not encountered during training [2] [37].

Biological Validation Through Knowledge-Guided Metrics

BioLLM introduces novel evaluation approaches that directly assess the biological plausibility of scFM representations rather than merely their computational efficiency. The scGraph-OntoRWR metric implements a random walk with restart algorithm on cell ontology graphs to measure the consistency between embedding-derived cell relationships and established biological knowledge [2]. This approach represents a significant advancement over traditional clustering metrics by directly quantifying biological meaningfulness.

Complementary to this, the Roughness Index (ROGI) serves as a proxy for model selection by quantifying the smoothness of cell-property landscapes in the pretrained latent space [2]. Models that produce smoother landscapes generally yield better performance on downstream tasks, as they reduce the difficulty of training task-specific classifiers and better capture continuous biological processes such as differentiation trajectories.

Successful implementation of standardized scFM analysis requires both computational resources and biological datasets. The following table details essential components of the scFM research toolkit:

Table 3: Essential Research Reagents and Resources for scFM Implementation

Resource Category Specific Examples Function/Purpose Access Method
Reference Datasets CZ CELLxGENE (100M+ cells) [1] [37], Human Cell Atlas [1] [13] Pretraining corpus; reference for annotation transfer Public data portals; standardized .h5ad files
Model Architectures scGPT, Geneformer, scFoundation [4] [2] Core inference engines for embedding generation BioLLM unified APIs; Hugging Face-style repositories
Benchmarking Tools BioLLM evaluation module [4] [36] Performance assessment across multiple metrics Integrated BioLLM functions; custom validation scripts
Biological Ontologies Cell Ontology, Gene Ontology [2] Ground truth for biological consistency metrics OBO format files; ontology lookup services
Specialized Hardware GPU clusters ( NVIDIA A100/H100) Accelerated model training and inference Cloud computing platforms; institutional HPC resources

Implementation Guidelines for Research and Drug Development

Model Selection Framework

Based on comprehensive benchmarking results, researchers should select scFMs according to their specific analytical needs and resource constraints. The following decision workflow illustrates an optimized model selection strategy:

Model_Selection scFM Selection Decision Framework Start Start A Primary Task Type? Start->A B Available Training Data? A->B Cell-level tasks M2 Select Geneformer (Specialized for gene-level analysis) A->M2 Gene-level tasks C Computational Resources? B->C Limited labeled data M3 Select scFoundation (Large-scale pretraining advantages) B->M3 Abundant labeled data D Interpretability Needs? C->D Constrained resources E Multi-omic Integration? C->E Adequate resources M1 Select scGPT (Balanced performance across tasks) D->M1 Standard requirements M4 Select scBERT (Lightweight architecture for limited resources) D->M4 High interpretability required E->M1 Multi-omic data present E->M2 Transcriptomics only

Best Practices for Drug Development Applications

For pharmaceutical researchers implementing scFM analysis in therapeutic contexts, several specialized practices enhance translational relevance:

  • Clinical context integration: Fine-tune models on disease-specific datasets (e.g., tumor microenvironments, inflamed tissues) to improve performance on clinically relevant tasks such as cancer cell identification and drug sensitivity prediction [2].

  • Multi-scale validation: Correlate embedding-derived insights with orthogonal data modalities including histopathology, clinical outcomes, and in vitro assay results to establish biological credibility.

  • Regulatory compliance: Implement version control, data provenance tracking, and reproducible analysis pipelines that meet pharmaceutical industry standards for auditability.

  • Transfer learning optimization: Leverage BioLLM's fine-tuning capabilities to adapt foundation models to specific therapeutic areas, such as oncology, immunology, or neuroscience, using domain-specific data.

BioLLM's standardized implementation directly addresses several key challenges in AI-driven drug discovery, where the acceleration of target identification and compound screening relies on robust, reproducible computational methods [38] [39]. By providing a consistent framework for scFM deployment, BioLLM enables more reliable translation of computational insights into therapeutic development programs.

The standardization enabled by BioLLM represents a critical advancement in the field of single-cell computational biology, transforming scFMs from specialized research tools into robust, accessible resources for biological discovery. The framework's unified interface and comprehensive benchmarking capabilities directly address the fragmentation challenges that have hindered scFM adoption, particularly in method-sensitive applications like drug development.

Future framework development will likely focus on enhanced multimodal integration, incorporating spatial transcriptomics, proteomics, and epigenomic data within unified representation learning paradigms [37]. Additionally, increasing emphasis on model interpretability and biological plausibility will drive the development of more sophisticated evaluation metrics that better capture the complex biological knowledge encoded within scFM embeddings.

For researchers and drug development professionals, adopting standardized frameworks like BioLLM accelerates the transition from descriptive computational analyses to actionable biological insights, ultimately bridging the gap between large-scale single-cell data and mechanistic understanding of disease processes.

Overcoming Challenges: Limitations and Optimization of scFM Biological Representation

Addressing Data Quality Issues and Technical Variability in Pretraining

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, offering the potential to unify the analysis of cellular heterogeneity and complex regulatory networks at scale [1]. These models, typically built on transformer architectures, are pretrained on vast collections of single-cell RNA sequencing (scRNA-seq) data to learn fundamental biological principles that can be transferred to diverse downstream tasks [2] [1]. However, the promise of scFMs is intrinsically tied to a critical challenge: the quality and consistency of their pretraining data. Single-cell genomics data suffers from inherent technical variability including batch effects, differing sequencing depths, technical noise, and varying processing steps across different experiments and platforms [2] [1]. These data quality issues present significant obstacles to building robust, generalizable scFMs that can accurately capture biological signals rather than technical artifacts. This technical guide examines the landscape of data quality challenges in scFM pretraining, provides standardized methodologies for addressing technical variability, and establishes evaluation frameworks for assessing biological knowledge representation in scFM embeddings within the broader context of biological knowledge representation research.

The Data Quality Landscape in Single-Cell Genomics

The pretraining of effective scFMs requires confronting multiple dimensions of technical variability inherent in single-cell genomics data. Major sources of data quality issues include:

  • Batch Effects: Systematic technical differences between experiments conducted at different times, locations, or with different protocols that can overshadow biological signals [2] [1]
  • Variable Sequencing Depth: Differences in the number of reads per cell across experiments, affecting gene detection sensitivity and expression level measurements [1]
  • Platform-Specific Artifacts: Technical noise introduced by different sequencing platforms (10X Genomics, Smart-seq2, etc.) with distinct measurement characteristics [2]
  • Processing Inconsistencies: Variations in data preprocessing steps including normalization, quality control thresholds, and gene filtering across different studies [1]

The complex interplay of these technical factors creates a challenging landscape for scFM development, where models must learn to distinguish biologically meaningful patterns from technical confounders.

Table 1: Common Data Quality Challenges in scFM Pretraining

Challenge Category Specific Issues Impact on scFM Pretraining
Technical Variability Batch effects, platform differences, protocol variations Learns technical artifacts rather than biological signals
Data Sparsity High dimensionality, low signal-to-noise ratio, dropout events Difficulty modeling gene-gene relationships and co-expression
Data Integration Inconsistent gene coverage, annotation differences, normalization methods Reduced generalizability across datasets and tissues
Scale Management Computational intensity, memory constraints with massive datasets Practical limitations on model and dataset sizes
Quantitative Impact of Data Quality on Model Performance

Recent benchmarking studies have quantitatively demonstrated how data quality issues directly impact scFM performance across diverse tasks. A comprehensive 2025 benchmark evaluating six prominent scFMs against established baselines revealed that data quality factors significantly influence model robustness and task performance [2]. The study found that while scFMs show promise as versatile tools for diverse applications, simpler machine learning models can sometimes outperform complex foundation models, particularly under resource constraints or when data quality issues are pronounced [2]. Specific findings include:

  • Performance degradation of 15-30% in cross-dataset generalization tasks when pretraining data exhibits significant batch effects
  • Variation in model robustness across different scFM architectures, with some models showing greater sensitivity to technical variability than others
  • Notable challenges in perturbation effect prediction, where scFM embeddings did not consistently outperform simpler baseline models, especially under distribution shift [17]

These findings underscore the critical importance of addressing data quality issues at the pretraining stage to unlock the full potential of scFMs in biological discovery.

Methodologies for Addressing Data Quality in Pretraining

Data Selection and Curation Protocols

Establishing rigorous data selection criteria forms the foundation for addressing data quality issues in scFM pretraining. Based on recent benchmarking studies and model development practices, the following protocols are recommended:

Dataset Compilation Strategy:

  • Source data from curated repositories including CZ CELLxGENE (containing over 100 million unique standardized cells), PanglaoDB, and the Human Cell Atlas [1]
  • Implement multi-tiered quality control filters: minimum gene detection thresholds (500-1,000 genes/cell), maximum mitochondrial read percentages (typically 10-20%), and doublet detection algorithms
  • Ensure broad biological coverage across tissues, disease states, and experimental conditions to capture comprehensive biological variation [2] [1]

Technical Consistency Measures:

  • Standardize preprocessing pipelines across incorporated datasets when possible
  • Balance dataset compositions to prevent overrepresentation of specific tissues or conditions
  • Implement rigorous cell and gene filtering to remove low-quality measurements while preserving biological diversity [1]

These curation strategies help create a more homogeneous pretraining corpus despite the inherent heterogeneity of source data, providing a stronger foundation for scFM development.

Technical Variability Mitigation Techniques

Multiple technical approaches have been developed specifically to address data quality challenges during scFM pretraining:

Architectural Adaptations:

  • Incorporation of batch-aware attention mechanisms that can learn to downweight technical confounders
  • Value embedding strategies that normalize expression levels across cells and datasets [2]
  • Special tokens representing experimental conditions or batches to explicitly model technical factors [1]

Pretraining Strategy Innovations:

  • Modified masked gene modeling (MGM) objectives with read-depth-awareness to account for sequencing depth variations [2]
  • Multi-task learning incorporating technical artifact prediction to encourage separation of technical and biological signals
  • Transfer learning from models pretrained on higher-quality bulk RNA-seq data to initialize scFMs

Table 2: Technical Variability Mitigation Methods in scFMs

Method Category Specific Techniques Applicable Models
Input Representation Value binning, expression ranking, genomic position ordering scGPT, Geneformer, UCE [2]
Architectural Batch-specific tokens, technical factor attention masking, modality indicators scGPT, scFoundation [2] [1]
Pretraining Objectives Read-depth-aware MGM, iterative MGM with MSE loss, binary expression prediction scFoundation, scGPT, UCE [2]
Embedding Strategies Protein-informed embeddings (ESM-2), gene ontology incorporation UCE, scFoundation [2]

DataQualityPipeline cluster_QC Quality Control Steps cluster_Mitigation Mitigation Strategies DataSources Data Sources (CZ CELLxGENE, GEO, SRA) QualityControl Quality Control & Filtering DataSources->QualityControl TechnicalIntegration Technical Variability Mitigation QualityControl->TechnicalIntegration GeneFilter Gene Filtering (HVGs, minimum expression) QualityControl->GeneFilter ModelPretraining scFM Pretraining TechnicalIntegration->ModelPretraining InputRep Input Representation (ranking, binning) TechnicalIntegration->InputRep EmbeddingEvaluation Embedding Evaluation ModelPretraining->EmbeddingEvaluation CellFilter Cell Filtering (mitochondrial %, doublets) GeneFilter->CellFilter Normalization Normalization & Batch Correction CellFilter->Normalization ArchAdapt Architectural Adaptations (batch tokens, attention) InputRep->ArchAdapt PretrainObj Pretraining Objectives (read-depth awareness) ArchAdapt->PretrainObj

Data Quality Management Pipeline for scFM Pretraining

Standardized Evaluation Protocols for Data Quality Impact

Establishing rigorous evaluation protocols is essential for assessing how effectively scFMs overcome data quality challenges. Based on recent benchmarking frameworks, the following methodologies are recommended:

Cross-Dataset Generalization Testing:

  • Evaluate model performance on held-out datasets with different technical characteristics
  • Test zero-shot transfer to novel biological contexts and experimental conditions
  • Assess performance degradation under increasing technical variability [2] [17]

Controlled Data Quality Experiments:

  • Systematically introduce artificial technical noise or batch effects to measure robustness
  • Conduct ablation studies removing specific data quality interventions
  • Evaluate performance across the data quality spectrum from high to low-quality samples

The PertEval-scFM benchmark provides a standardized framework for evaluating perturbation effect prediction, specifically testing model robustness to distribution shift and data quality variations [17]. Similarly, the comprehensive benchmark by [2] introduces novel ontology-informed metrics like scGraph-OntoRWR that measure biological consistency independent of technical confounders.

Experimental Framework for Assessing Biological Knowledge Representation

Benchmarking Methodology for scFM Embeddings

Comprehensive evaluation of scFM embeddings requires multi-faceted benchmarking across diverse task types and biological contexts. The following experimental framework, derived from recent large-scale benchmarks, provides a standardized approach:

Gene-Level Evaluation Tasks:

  • Gene function prediction using ontology-informed metrics
  • Gene-gene relationship analysis against known biological pathways
  • Protein-protein interaction prediction from co-expression patterns [2]

Cell-Level Evaluation Tasks:

  • Cross-dataset cell type annotation with novel cell type discovery
  • Batch integration quality measuring mixing and biological preservation
  • Perturbation effect prediction under distribution shift [2] [17]
  • Drug sensitivity prediction across multiple cancer types [2]

Evaluation Metrics and Protocols:

  • Standardized use of 12 complementary metrics spanning unsupervised, supervised, and knowledge-based approaches [2]
  • Implementation of novel ontology-informed metrics (scGraph-OntoRWR, LCAD) that compare model-derived relationships with established biological knowledge [2]
  • Rigorous cross-validation strategies with dataset splitting that accounts for technical variability

BenchmarkFramework cluster_GeneTasks Gene-Level Tasks cluster_CellTasks Cell-Level Tasks cluster_Metrics Evaluation Metrics EmbeddingExtraction Embedding Extraction (Zero-shot scFM embeddings) GeneTasks Gene-Level Tasks EmbeddingExtraction->GeneTasks CellTasks Cell-Level Tasks EmbeddingExtraction->CellTasks Evaluation Multi-Metric Evaluation GeneTasks->Evaluation FunctionPred Function Prediction GeneTasks->FunctionPred CellTasks->Evaluation CellAnnotation Cell Type Annotation CellTasks->CellAnnotation BiologicalInterpretation Biological Knowledge Assessment Evaluation->BiologicalInterpretation Unsupervised Unsupervised Metrics (clustering, mixing) Evaluation->Unsupervised RelationshipAnalysis Relationship Analysis PathwayValidation Pathway Validation BatchIntegration Batch Integration PerturbationPred Perturbation Prediction Supervised Supervised Metrics (accuracy, F1-score) KnowledgeBased Knowledge-Based Metrics (scGraph-OntoRWR, LCAD)

scFM Embedding Evaluation Framework

Biological Knowledge Assessment Metrics

Moving beyond traditional performance metrics, assessing the biological relevance of scFM embeddings requires specialized metrics that directly measure alignment with established biological knowledge:

Ontology-Informed Metrics:

  • scGraph-OntoRWR: Measures consistency of cell type relationships captured by scFMs with prior biological knowledge encoded in cell ontologies [2]
  • Lowest Common Ancestor Distance (LCAD): Assesses ontological proximity between misclassified cell types, providing nuanced error analysis beyond simple accuracy [2]

Biological Consistency Measures:

  • Gene set enrichment analysis of embedding neighborhoods to verify functional coherence
  • Pathway preservation metrics evaluating whether known biological pathways remain intact in the latent space
  • Tissue-specific marker gene conservation across embedding dimensions

These specialized metrics directly address the core thesis of biological knowledge representation by quantifying how well scFM embeddings capture established biological relationships rather than merely optimizing task-specific performance.

Research Reagents and Computational Tools

Table 3: Essential Research Reagents for scFM Pretraining and Evaluation

Resource Category Specific Tools/Datasets Function in scFM Research
Data Repositories CZ CELLxGENE, Human Cell Atlas, PanglaoDB, GEO/SRA Provide standardized, annotated single-cell data for pretraining and benchmarking [1]
Benchmarking Frameworks scFM-Bench, PertEval-scFM Standardized evaluation pipelines for comparing scFM performance across diverse tasks [2] [40] [17]
Model Implementations scGPT, Geneformer, scFoundation, UCE Reference implementations of major scFM architectures for reproduction and extension [2] [40]
Evaluation Metrics scGraph-OntoRWR, LCAD, traditional ML metrics Specialized metrics for assessing biological knowledge representation and technical performance [2]
Pretraining Corpora Curated collections from multiple sources (30-50M cells) Large-scale, diverse datasets for foundational model pretraining [2]

Addressing data quality issues and technical variability in scFM pretraining represents a critical frontier in computational biology. The methodologies and frameworks presented in this guide provide a foundation for developing more robust, biologically meaningful foundation models. As the field progresses, several key directions emerge as particularly important for advancing biological knowledge representation in scFMs:

First, the development of more sophisticated data curation protocols that explicitly model and account for technical variability will be essential for scaling scFMs to increasingly diverse and complex datasets. Second, novel architectural innovations that inherently separate technical artifacts from biological signals during pretraining represent a promising avenue for improving model robustness. Finally, the creation of more comprehensive benchmarking frameworks that directly measure biological knowledge representation, such as the ontology-informed metrics discussed herein, will drive the development of scFMs that truly capture the fundamental principles of cellular biology rather than merely excelling at specific computational tasks.

By confronting data quality challenges directly and prioritizing biological relevance in evaluation, the field can unlock the full potential of single-cell foundation models to transform our understanding of cellular function and drive innovations in therapeutic development.

The adoption of foundation models in single-cell biology represents a paradigm shift in how researchers analyze cellular systems. Single-cell foundation models (scFMs) are large-scale deep learning models pretrained on vast single-cell datasets, capable of being adapted to a wide range of downstream tasks through fine-tuning or zero-shot inference [1]. These models typically employ transformer architectures to process single-cell omics data, treating individual cells as analogous to sentences and genes or genomic features as words or tokens [1]. The fundamental premise is that by exposing a model to millions of cells encompassing diverse tissues and conditions, it can learn transferable principles of cellular biology that generalize to new experimental contexts [1].

However, the architectural framework of these models introduces fundamental theoretical constraints. The predominant approach in scFMs involves representing complex cellular states as single vector embeddings in high-dimensional space [1]. This single-vector paradigm creates an inherent tension between computational efficiency and biological representational capacity. As research pushes toward increasingly sophisticated applications—from perturbation effect prediction to rare cell state identification—these limitations become critically important for researchers interpreting model outputs and designing experimental frameworks based on scFM predictions [17] [41].

Theoretical Foundations of Embedding Limitations

The Dimensionality Constraint in Vector Representations

The theoretical limitations of embedding-based retrieval stem from fundamental constraints in geometric space. Research in communication complexity theory demonstrates that for a given embedding dimension (d), there exists a mathematical upper bound on the number of top-(k) combinations of documents (or cells) that can be returned as relevant for any query [41]. This limitation applies regardless of model architecture or training methodology and presents a fundamental barrier to what single-vector embeddings can represent.

Formally, the number of distinct (k)-subsets of (n) documents that can be represented as nearest neighbors for some query vector is constrained by the embedding dimension (d). This means that as the complexity of biological questions increases—requiring models to connect previously unrelated cell states through logical operators or complex relevance conditions—the representational capacity of fixed-dimensional embeddings becomes exhausted [41]. In practical terms, this dimensional constraint manifests as an inability of scFMs to correctly identify relevant cellular states for complex queries, particularly those involving combinatorial logic or unconventional definitions of similarity.

The Sign-Rank Barrier in Biological Relevance Modeling

A crucial concept for understanding embedding limitations in biological contexts is the sign-rank of the query relevance matrix. The sign-rank places a lower bound on the dimensionality needed to represent all possible relevance relationships between queries (e.g., perturbation conditions) and documents (e.g., cellular states) [41]. For scFMs, this translates to a fundamental limit on how many distinct cellular response patterns can be accurately modeled simultaneously.

When embedding dimensions are insufficient to capture the full sign-rank of the biological relevance matrix, models inevitably sacrifice accuracy on certain query-cell relationships. This theoretical limitation has been empirically observed in scFM benchmarking, where models consistently struggle with predicting strong or atypical perturbation effects [17]. The implication is that as the scope of single-cell atlases expands, the complexity of biological relationships may eventually exceed the representational capacity of current embedding dimensions used in scFMs.

Table 1: Theoretical Limits of Embedding Dimensions for Document Combinations

Embedding Dimension (d) Maximum Representable Top-k Combinations Biological Interpretation
128 ~10⁶ Basic cell type classification
256 ~10¹² Moderate perturbation response
512 ~10¹⁸ Complex multi-optic integration
1024 ~10²⁴ Comprehensive cellular state modeling

scFM Architecture and Its Representational Consequences

Tokenization Strategies for Biological Data

The process of tokenization—converting raw single-cell data into model-compatible tokens—represents both an opportunity and constraint for biological representation. Unlike natural language, gene expression data lacks inherent sequential ordering, creating fundamental challenges for transformer architectures that process sequential tokens [1]. Current scFMs employ various strategies to address this limitation:

  • Expression-level ranking: Genes are ordered by expression magnitude within each cell, creating a deterministic sequence for transformer processing [1]
  • Bin-based partitioning: Genes are partitioned into expression bins, with rankings determining positional encoding [1]
  • Normalized count approaches: Some models report no clear advantage to complex ranking strategies and simply use normalized counts [1]

These tokenization approaches impose an artificial structure on fundamentally non-sequential biological data, potentially creating representational artifacts that limit model performance. The choice of tokenization strategy influences which biological patterns the model can readily identify and which remain obscured by the imposed structure.

Attention Mechanisms in Cellular Representation

Transformer architectures in scFMs employ attention mechanisms to weight relationships between genes or features, theoretically allowing models to learn regulatory relationships and functional connections [1]. However, the theoretical limitations of single-vector embeddings constrain how these learned relationships can be utilized in downstream tasks.

The attention mechanism can identify which genes are most informative about a cell's identity or state, but the final representation must compress this information into a fixed-dimensional vector. This compression necessarily loses information, particularly about rare cell states or subtle expression patterns that constitute important but infrequent biological phenomena [41]. As the diversity of cellular states in training data increases, this representational bottleneck becomes more severe, potentially explaining why scFMs struggle with atypical perturbation effects [17].

Empirical Evidence of scFM Limitations

Benchmarking Results for Perturbation Prediction

The PertEval-scFM benchmark provides critical empirical evidence of scFM limitations in biological applications. This standardized framework evaluates models for perturbation effect prediction through zero-shot inference using pretrained embeddings [17]. The results reveal fundamental gaps in current approaches:

Table 2: scFM Performance on Perturbation Effect Prediction [17]

Model Type Performance on Standard Effects Performance on Strong/Atypical Effects Performance Under Distribution Shift
scFM Embeddings Moderate Poor Poor
Simple Baselines Comparable to scFMs Superior to scFMs Superior to scFMs

Notably, scFM embeddings failed to provide consistent improvements over simpler baseline models, particularly under distribution shift conditions [17]. This suggests that the theoretical limitations of embedding capacity may be manifesting in practical applications, limiting the utility of scFMs for predicting novel perturbation effects or generalizing beyond their training distributions.

Performance Trade-offs Across Architectures

Comprehensive evaluation through frameworks like BioLLM reveals distinct performance trade-offs across scFM architectures [4]. These evaluations demonstrate that:

  • scGPT shows robust performance across diverse tasks, benefiting from effective pretraining strategies [4]
  • Geneformer and scFoundation demonstrate strong capabilities in gene-level tasks [4]
  • scBERT lags behind, likely due to smaller model size and limited training data [4]

These empirical results align with theoretical predictions that model capacity (including embedding dimension) directly influences representational capabilities. The performance hierarchy observed across architectures suggests that current scFMs may not have sufficient parameters or embedding dimensions to capture the full complexity of cellular biology.

G Single-Cell Foundation Model Architecture and Limitations cluster_inputs Input Data Sources cluster_processing Tokenization & Processing cluster_model Model Architecture cluster_limitations Theoretical Limitations RawData Raw Single-Cell Data Tokenization Tokenization Strategies RawData->Tokenization PublicRepos Public Repositories (GEO, SRA, CELLxGENE) PublicRepos->Tokenization CuratedCompendia Curated Compendia (PanglaoDB, Cell Atlas) CuratedCompendia->Tokenization ExpressionRanking Expression-Level Ranking Tokenization->ExpressionRanking BinPartitioning Bin-Based Partitioning Tokenization->BinPartitioning NormalizedCounts Normalized Count Approaches Tokenization->NormalizedCounts Transformer Transformer Architecture ExpressionRanking->Transformer BinPartitioning->Transformer NormalizedCounts->Transformer AttentionMech Attention Mechanism Transformer->AttentionMech Embedding Fixed-Dimensional Embedding AttentionMech->Embedding DimensionalConstraint Dimensionality Constraint on Representational Capacity Embedding->DimensionalConstraint SignRankBarrier Sign-Rank Barrier in Relevance Modeling Embedding->SignRankBarrier InformationLoss Information Loss in Rare Cell States Embedding->InformationLoss

Experimental Protocols for Evaluating scFM Limitations

PertEval-scFM Benchmarking Methodology

The PertEval-scFM framework provides a standardized approach for evaluating scFM limitations in perturbation prediction [17]. The experimental protocol involves:

  • Model Selection and Preparation: Researchers select diverse scFMs with varying architectures (encoder-based, decoder-based, hybrid) and embedding dimensions. Models are prepared for zero-shot inference without additional fine-tuning.

  • Dataset Curation: Evaluation datasets must encompass diverse perturbation types, including:

    • Strong perturbation effects (e.g., complete gene knockouts)
    • Moderate perturbation effects (e.g., partial inhibitions)
    • Atypical effects (e.g., non-canonical pathway activations)
    • Distribution shift scenarios (e.g., novel cell types or conditions)

  • Baseline Establishment: Simple baseline models (e.g., linear models, nearest neighbors) are implemented to provide performance comparison points.

  • Evaluation Metrics: Multiple metrics are employed including:

    • Accuracy in identifying differentially expressed genes
    • Correlation between predicted and actual expression changes
    • Robustness scores under distribution shift
    • Specificity for strong versus weak effects

  • Analysis of Failure Modes: Systematic categorization of error types to identify patterns related to embedding limitations rather than implementation artifacts.

This protocol enables reproducible assessment of scFM limitations and facilitates comparison across different model architectures and biological contexts.

LIMIT Dataset Construction Protocol

The LIMIT dataset provides a methodology for stress-testing embedding models based on theoretical limitations [41]. The construction process involves:

  • Theoretical Foundation: Identify specific dimensional constraints from geometric algebra and communication complexity theory that apply to single-vector embeddings.

  • Task Design: Create simple retrieval tasks where the number of possible relevant document combinations exceeds the theoretical capacity of the embedding dimension.

  • Natural Language Instantiation: Implement tasks using straightforward natural language queries and documents (e.g., "who likes Apples?" with corresponding statements about preferences).

  • Progressive Complexity: Scale task complexity systematically to identify the precise point where embedding dimensions become insufficient.

  • Model Evaluation: Test state-of-the-art embedding models across different dimensions to empirically verify theoretical predictions.

This experimental approach demonstrates how theoretical limitations manifest in practical settings, even with extremely simple queries that lack the complexity of real biological questions.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for scFM Limitation Studies

Reagent/Framework Type Primary Function Application Context
PertEval-scFM Benchmarking Framework Standardized evaluation of perturbation prediction Assessing scFM limitations in biological applications [17]
BioLLM Unified Interface Integration of diverse scFMs with standardized APIs Comparative analysis of architectural trade-offs [4]
LIMIT Dataset Evaluation Dataset Stress-test models based on theoretical constraints Testing fundamental embedding capacity limitations [41]
CZ CELLxGENE Data Resource Unified access to annotated single-cell datasets scFM pretraining and evaluation [1]
PanglaoDB Curated Compendia Collated data from multiple single-cell studies Training data diversity and quality assessment [1]
Human Cell Atlas Reference Data Broad coverage of cell types and states Evaluation under distribution shift [1]

Implications for Biological Research and Drug Development

Consequences for Cellular Representation

The theoretical limitations of scFM embeddings have profound implications for biological research and therapeutic development. In drug discovery, where accurately modeling perturbation responses is crucial, these limitations may lead to:

  • False negative predictions for novel therapeutic mechanisms that create atypical cellular states
  • Reduced generalizability to patient populations with cellular heterogeneity not fully captured in training data
  • Incomplete understanding of combination therapies that create complex, multi-factorial cellular states

The compression of cellular diversity into fixed-dimensional embeddings necessarily simplifies biological complexity, potentially obscuring rare but clinically important cell states or response patterns. This represents a significant challenge for applications requiring high sensitivity to unusual cellular behaviors, such as cancer drug resistance or immune cell activation states.

Alternative Approaches and Future Directions

Addressing the fundamental limitations of single-vector embeddings requires architectural innovations and methodological advances:

  • Multi-vector models that represent cellular states through multiple complementary embeddings
  • Cross-attention mechanisms that enable more flexible query-document interactions without compression to single vectors
  • Hierarchical representations that capture biological organization at multiple scales
  • Specialized architectures designed specifically for the non-sequential nature of omics data

Future research directions should focus on developing models that respect the theoretical constraints of embedding spaces while providing the flexibility needed for complex biological questions. This may involve hybrid approaches that combine the efficiency of single-vector retrieval with the expressivity of more complex interaction models [17] [41].

G Experimental Protocol for scFM Limitation Evaluation cluster_protocol Experimental Protocol for scFM Limitation Evaluation Step1 1. Model Selection Varying architectures & embedding dimensions Step2 2. Dataset Curation Strong/atypical effects Distribution shifts Step1->Step2 Step3 3. Baseline Establishment Simple models for performance comparison Step2->Step3 Step4 4. Evaluation Metrics Accuracy, correlation robustness, specificity Step3->Step4 Step5 5. Failure Mode Analysis Categorize errors related to embedding limitations Step4->Step5

The theoretical limitations of embedding capacity present both challenges and opportunities for single-cell biology research. While current scFMs show promise in many applications, their performance ceilings for complex perturbation prediction and generalization under distribution shift reveal fundamental constraints of the single-vector paradigm [17]. As biological questions increase in complexity—requiring models to connect disparate cellular states and predict novel biological phenomena—these limitations will become increasingly impactful.

Understanding these boundaries enables more informed application of scFMs in biological research and drug development. Researchers can temper expectations for certain use cases, prioritize model selection based on architectural strengths, and direct methodological development toward approaches that overcome these fundamental constraints. The future of biological foundation models lies not in simply scaling existing architectures, but in fundamentally rethinking how we represent cellular complexity in computational systems.

In the burgeoning field of single-cell foundation models (scFMs), the transformation of raw gene expression data into model-interpretable inputs represents a fundamental challenge with profound implications for biological knowledge representation. Single-cell RNA sequencing (scRNA-seq) data possesses unique characteristics that distinguish it from traditional natural language processing (NLP) domains: high dimensionality, extreme sparsity, and the absence of inherent sequential structure among genes [2] [3]. Unlike words in a sentence, genes interact in complex, non-sequential ways, necessitating sophisticated tokenization strategies that can preserve biological meaning while enabling computational efficiency [13] [1]. The optimization of input representations—encompassing both gene selection and value embedding—serves as the critical gateway through which cellular "stories" are translated for artificial intelligence interpretation. This technical guide examines current methodologies, experimental insights, and practical protocols for constructing input representations that maximize the biological fidelity and predictive power of scFM embeddings, positioning this preprocessing step not as mere data preparation but as the foundational layer of biological knowledge representation within the AI architecture.

Tokenization Strategies for Single-Cell Data

Conceptual Framework and Biological Analogies

Tokenization in scFMs refers to the process of converting raw gene expression data into discrete units (tokens) that models can process and learn from, analogous to how words become tokens in natural language processing [13] [1]. In the biological "language" of cells, individual cells are treated as documents or sentences, while genes and their expression values become the words or tokens that collectively describe cellular identity and state [13]. This conceptual framework enables the application of transformer-based architectures to single-cell biology, but requires careful adaptation to address domain-specific challenges. The fundamental challenge in single-cell tokenization stems from the non-sequential nature of genomic data; unlike words in a sentence, genes have no inherent ordering, necessitating the imposition of artificial structures that can capture biological relationships without introducing arbitrary biases [13] [1].

Gene Selection Methodologies

Gene selection constitutes the first critical step in tokenization, determining which genomic features will serve as the vocabulary for cellular representation. Current approaches vary significantly across models, each with distinct implications for biological knowledge preservation.

Table 1: Gene Selection Strategies in Prominent scFMs

Model Name Selection Method # Input Genes Biological Rationale Considerations
Geneformer Ranking by expression 2,048 Captures most biologically informative genes May overlook lowly-expressed regulatory genes
scGPT Highly Variable Genes (HVGs) 1,200 Focuses on genes with high cell-to-cell variation Sensitive to selection parameters; may miss housekeeping genes
UCE Sampling by expression 1,024 Probabilistic representation of expression landscape Non-deterministic; requires careful implementation
scFoundation All protein-encoding genes ~19,000 Comprehensive biological coverage Computationally intensive; includes potentially uninformative genes
LangCell Ranking by expression 2,048 Similar to Geneformer; emphasizes high-expression genes Comparable limitations to ranking approaches

The choice of gene selection strategy represents a trade-off between biological comprehensiveness and computational efficiency. Models employing comprehensive gene sets (e.g., scFoundation) potentially capture more complete biological information but face significant computational burdens, while those using selective approaches (e.g., Geneformer, scGPT) gain efficiency but risk omitting biologically important genes expressed at lower levels [2] [3].

Input Structuring and Sequential Ordering

With genes selected, the non-sequential nature of genomic data necessitates imposing artificial orderings for transformer processing. Several approaches have emerged as dominant paradigms in the field:

  • Expression-based ranking: Genes are ordered by their expression levels within each cell, creating a deterministic sequence where highly expressed genes appear first [13] [1]. This approach, used by Geneformer and LangCell, provides a consistent input structure but may prioritize highly expressed housekeeping genes over biologically informative regulatory genes.

  • Value binning: Expression values are partitioned into discrete bins, with rankings determined by these binned values [13]. This approach, utilized by scGPT, helps normalize technical variations but may lose fine-grained expression information.

  • Genomic position ordering: Some models, like UCE, order genes by their physical chromosomal locations [2]. This strategy incorporates prior biological knowledge about gene proximity but may not reflect functional relationships.

  • No explicit ordering: Surprisingly, some models report no clear advantages for complex ranking strategies and simply use normalized counts without specific ordering [1]. This approach avoids potential biases introduced by arbitrary orderings but may sacrifice structural information that transformers can leverage.

Value Embedding and Representation Techniques

Expression Value Transformation Strategies

Once genes are selected and ordered, their continuous expression values must be transformed into embedding representations that preserve biological meaning while facilitating model learning. Value embedding strategies determine how expression magnitude is encoded within the model's input representation.

Table 2: Value Embedding Approaches Across scFMs

Embedding Type Implementation Examples Technical Approach Advantages Limitations
Ordering as Proxy Geneformer, LangCell Expression magnitude encoded through position in sequence Simplifies architecture; reduces parameters Conflates expression value with positional information
Value Binning scGPT Continuous values discretized into bins Explicit value representation; handles technical noise Loss of resolution; bin boundaries arbitrary
Value Projection scFoundation Direct projection of normalized expression values Preserves continuous nature of expression Requires careful normalization; sensitive to outliers
Binary Representation UCE Focuses on expressed vs. non-expressed status Reduces sparsity issues; emphasizes detection Loses quantitative expression information

The diversity in value embedding approaches reflects ongoing experimentation within the field, with no clear consensus on optimal strategies. Each method embodies different assumptions about what aspects of expression data are most biologically meaningful, with significant implications for downstream knowledge representation [2] [3].

Incorporating Biological Context through Specialized Tokens

Beyond basic gene and value representations, advanced tokenization strategies incorporate additional biological context through specialized tokens:

  • Cell-level context tokens: Several models prepend tokens representing cellular metadata, enabling the model to learn organism, tissue, or experimental context [13] [1].
  • Modality indicators: For multi-omics models, special tokens indicate data type (e.g., scRNA-seq vs. scATAC-seq), allowing the same architecture to process diverse data types [13].
  • Batch information: Some models incorporate batch-specific tokens to explicitly model technical variations, while others report robustness without such additions [13] [1].
  • Biological knowledge integration: Gene ontology terms, chromosomal locations, or protein-protein interaction data can be incorporated to provide rich biological context [13].

These specialized tokens enrich the input representation with structured biological knowledge, potentially enhancing the model's ability to learn biologically meaningful representations.

Experimental Protocols and Benchmarking Insights

Evaluation Frameworks for Input Representation Efficacy

Assessing the effectiveness of different input representation strategies requires comprehensive benchmarking across diverse biological tasks. Recent studies have developed sophisticated evaluation frameworks that move beyond technical metrics to assess biological meaningfulness [2] [3].

The diagram below illustrates a comprehensive benchmarking workflow for evaluating input representation strategies:

G InputRepresentation Input Representation Strategy GeneLevelTasks Gene-Level Tasks InputRepresentation->GeneLevelTasks CellLevelTasks Cell-Level Tasks InputRepresentation->CellLevelTasks GOtermPrediction GOtermPrediction GeneLevelTasks->GOtermPrediction GO Term Prediction TissueSpecificity TissueSpecificity GeneLevelTasks->TissueSpecificity Tissue Specificity BatchIntegration BatchIntegration CellLevelTasks->BatchIntegration Batch Integration CellTypeAnnotation CellTypeAnnotation CellLevelTasks->CellTypeAnnotation Cell Type Annotation CancerIdentification CancerIdentification CellLevelTasks->CancerIdentification Cancer Cell ID DrugSensitivity DrugSensitivity CellLevelTasks->DrugSensitivity Drug Sensitivity BiologicalMetrics Biological Relevance Metrics scGraphOntoRWR scGraphOntoRWR BiologicalMetrics->scGraphOntoRWR scGraph-OntoRWR LCAD LCAD BiologicalMetrics->LCAD LCAD Metric Evaluation Model Performance Ranking GOtermPrediction->Evaluation TissueSpecificity->Evaluation BatchIntegration->Evaluation CellTypeAnnotation->Evaluation CancerIdentification->Evaluation DrugSensitivity->Evaluation scGraphOntoRWR->Evaluation LCAD->Evaluation

This benchmarking approach evaluates representations across multiple axes, assessing both technical performance and biological relevance through novel metrics like scGraph-OntoRWR, which measures consistency of captured cell type relationships with established biological knowledge [2] [3].

Key Findings from Comparative Studies

Recent comprehensive benchmarks have yielded critical insights into input representation strategies:

  • No single superior approach: No single scFM consistently outperforms others across all tasks, indicating that optimal input representation may be task-dependent [2] [3].

  • Biological relevance varies: Models capture biological relationships with varying fidelity, as measured by ontology-informed metrics [2] [3].

  • Simplicity sometimes prevails: In many cases, simpler models with well-designed input representations can outperform more complex foundation models, particularly in resource-constrained environments [2] [34].

  • Perturbation prediction challenges: Current input representations struggle to enable accurate prediction of genetic perturbation effects, with simple additive models often outperforming foundation models [17] [34].

These findings suggest that while input representation optimization is crucial, it must be considered within the context of specific downstream applications and biological questions.

The Scientist's Toolkit: Research Reagent Solutions

Implementing effective input representations requires leveraging curated data resources and computational tools. The table below details essential research reagents for scFM development and evaluation.

Table 3: Essential Research Reagents for scFM Input Representation Optimization

Resource Name Type Primary Function Relevance to Input Representation
CZ CELLxGENE Data Repository Standardized access to annotated single-cell data Provides diverse training data; enables testing of representation generalizability [13] [1]
Human Cell Atlas Reference Atlas Comprehensive mapping of human cell types Ground truth for evaluating biological meaningfulness of representations [13]
PanglaoDB Curated Compendium Collated data from multiple single-cell studies Benchmark dataset for testing cross-study representation robustness [13] [1]
Gene Ontology Knowledge Base Structured biological knowledge Framework for evaluating biological relevance of gene embeddings [2] [3]
AIDA v2 Benchmark Dataset Independent, unbiased cellular atlas Validation dataset for mitigating data leakage risks in evaluation [2] [3]

These resources provide the essential raw materials and validation frameworks for developing and testing input representation strategies, ensuring that optimized approaches generalize across diverse biological contexts and technical conditions.

Implementation Protocols and Best Practices

Protocol for Systematic Input Representation Evaluation

Based on recent benchmarking studies, the following protocol provides a standardized approach for evaluating input representation strategies:

Phase 1: Data Preparation and Preprocessing

  • Curate diverse datasets representing multiple biological conditions, tissues, and experimental platforms
  • Apply consistent quality control metrics across all datasets
  • Implement multiple gene selection strategies in parallel (HVG, expression ranking, comprehensive)
  • Normalize expression values using standardized approaches

Phase 2: Multi-Task Evaluation

  • Extract embeddings using candidate representation strategies
  • Evaluate on gene-level tasks: GO term prediction and tissue specificity
  • Evaluate on cell-level tasks: batch integration, cell type annotation, cancer identification, and drug sensitivity prediction
  • Apply biological relevance metrics: scGraph-OntoRWR and LCAD

Phase 3: Analysis and Interpretation

  • Compare performance across tasks and datasets
  • Assess computational efficiency and scalability
  • Identify optimal representation strategies for specific application contexts

This protocol emphasizes the importance of evaluating representations across multiple biological contexts and using both technical and biologically-informed metrics [2] [3].

Decision Framework for Representation Strategy Selection

The following workflow diagram illustrates a systematic approach for selecting input representation strategies based on specific research contexts and constraints:

G Start Select Input Representation Strategy DataSize Dataset Size & Diversity? Start->DataSize TaskType Primary Downstream Task? DataSize->TaskType Large & Diverse SimpleModel Simple Model with Basic Embeddings DataSize->SimpleModel Small & Focused Comprehensive Comprehensive Gene Set with Specialized Tokens DataSize->Comprehensive Very Large & Diverse Resources Computational Resources? TaskType->Resources Cell Classification ExpressionRank Expression Ranking with Value Binning TaskType->ExpressionRank Perturbation Prediction TaskType->Comprehensive Multiple Diverse Tasks Resources->Comprehensive Ample Resources HVG HVG Selection with Value Projection Resources->HVG Limited Resources BioContext Biological Context Availability? KnowledgeEnhanced Biology-Enhanced Tokenization BioContext->KnowledgeEnhanced Available StandardApproach Standard Tokenization Approach BioContext->StandardApproach Limited

This decision framework emphasizes that optimal input representation depends on multiple factors including dataset characteristics, computational resources, and specific research goals. Rather than seeking a universally optimal approach, researchers should select representation strategies that align with their specific constraints and objectives [2] [3].

Future Directions and Emerging Paradigms

The optimization of input representations for scFMs remains an active area of research with several promising directions:

  • Dynamic gene selection: Context-aware selection strategies that adapt to specific biological questions or tissue types, moving beyond one-size-fits-all approaches.

  • Multi-modal integration: Unified representation strategies that seamlessly incorporate diverse data types (epigenomic, proteomic, spatial) within a common embedding space.

  • Knowledge-guided tokenization: More sophisticated incorporation of prior biological knowledge through specialized tokens and structured embeddings.

  • Geometry-aware embeddings: Representations that explicitly model the geometric relationships between genes and cells in latent space to better capture biological structure.

As the field matures, input representation strategies will likely become increasingly specialized for particular biological applications while maintaining the flexibility required for generalizable knowledge representation. The ultimate goal remains the development of representation strategies that faithfully encode biological meaning while enabling accurate prediction and discovery across diverse downstream tasks.

The emergence of single-cell foundation models (scFMs) has revolutionized biological knowledge representation, enabling researchers to extract profound insights from complex cellular data. A critical decision point in deploying these models lies in the choice between zero-shot inference and fine-tuning. This technical guide examines the performance-computation trade-offs between these approaches, drawing upon recent benchmarking studies and healthcare applications. We provide a structured framework to guide researchers and drug development professionals in selecting optimal adaptation strategies for scFMs across diverse biological scenarios, from cell atlas construction to clinical prediction tasks.

Single-cell foundation models represent a paradigm shift in computational biology, leveraging transformer architectures pretrained on millions of single-cell transcriptomes to learn universal representations of cellular states [1]. These models, including scGPT, Geneformer, and scFoundation, develop rich latent embeddings that capture complex gene regulatory networks and cellular heterogeneity [2]. However, a fundamental challenge persists: how to best adapt these general-purpose models to specialized biological tasks while balancing accuracy, computational cost, and data constraints.

The core adaptation strategies exist on a spectrum. Zero-shot learning utilizes pretrained model embeddings directly without weight updates, offering computational efficiency but potentially limited task-specific performance. In contrast, fine-tuning continues training the model on targeted datasets, updating parameters to excel at specific applications like cell type annotation or drug sensitivity prediction at increased computational expense [2] [42]. Understanding the precise trade-offs between these approaches is essential for efficient resource allocation in biological research and therapeutic development.

Quantitative Performance Comparison: Evidence from Benchmarking Studies

Recent comprehensive evaluations provide empirical evidence of the performance differentials between zero-shot and fine-tuned approaches across biological domains. The following tables synthesize key findings from large-scale benchmarking studies and healthcare applications.

Table 1: Performance comparison (F1 scores) of language models on clinical pathology report classification [43]

Model Type Model Scenario A (Easy) Scenario B (Medium) Scenario C (Hard)
Zero-Shot SLMs RoBERTa 0.34 0.01 0.02
PathologyBERT 0.40 0.01 0.04
Gatortron 0.34 0.01 0.13
Zero-Shot LLM Mistral 0.76 0.54 0.65
Fine-Tuned SLMs RoBERTa 0.96 0.78 0.61
PathologyBERT 0.95 0.81 0.60
Gatortron 0.97 0.85 0.78
BCCRoBERTa 0.97 0.84 0.71
BCCRTron 0.97 0.85 0.89

Table 2: Task-specific performance ranking of scFMs across biological applications [2] [4]

Model Cell Type Annotation Batch Integration Perturbation Prediction Drug Sensitivity Overall Ranking
scGPT 1 1 2 1 1
Geneformer 3 3 1 3 2
scFoundation 2 2 4 4 3
UCE 4 5 3 2 4
scBERT 5 4 5 5 5

The data reveals several critical patterns. First, fine-tuned small language models (SLMs) consistently outperform zero-shot large language models (LLMs) on specialized tasks, demonstrating the value of targeted adaptation [43]. Second, performance gaps widen with task complexity—while zero-shot LLMs maintain respectable performance on simpler classification tasks, their advantage diminishes significantly on complex, data-scarce problems where fine-tuned domain-specific models excel by substantial margins [44] [43].

Computational Cost Analysis: Infrastructure and Resource Requirements

The performance advantages of fine-tuning must be evaluated against significantly higher computational demands. Parameter-efficient fine-tuning (PEFT) methods have emerged as crucial intermediates balancing this trade-off.

Table 3: Computational requirements for different adaptation approaches [45] [42] [46]

Method GPU Memory Training Time Storage Overhead Data Requirements
Zero-Shot Low (inference only) Minutes Minimal (base model) None
Prompt Tuning Low 1-2 hours Small (~1% of base) Hundreds of examples
PEFT (LoRA) Medium 2-8 hours Moderate (~10% of base) 1,000-10,000 examples
Full Fine-Tuning High Hours to days Large (full model copy) 10,000+ examples

Fine-tuning approaches vary significantly in their resource profiles. Full fine-tuning updates all model parameters, requiring substantial GPU memory (often 40-80GB for moderate models) and generating complete model copies for each task [42]. Parameter-efficient methods like Low-Rank Adaptation (LoRA) and its quantized variant (QLoRA) dramatically reduce requirements by introducing small, trainable adapter modules while freezing the base model [45] [42]. QLoRA enables fine-tuning of 65B parameter models on single 48GB GPUs through 4-bit quantization, representing a 10,000-fold reduction in trainable parameters [45].

Experimental Protocols for Model Evaluation

Rigorous evaluation protocols are essential for meaningful comparison between adaptation strategies. Recent benchmarking studies have established standardized methodologies for assessing biological knowledge representation in scFMs.

Benchmarking Framework for scFM Embeddings

The comprehensive evaluation pipeline encompasses multiple biological tasks and assessment metrics [2]:

  • Embedding Extraction: Generate zero-shot gene and cell embeddings from pretrained scFMs without weight updates
  • Task Evaluation:
    • Gene-level tasks: Gene function prediction, gene-gene interaction inference
    • Cell-level tasks: Cell type annotation, batch integration, perturbation response prediction, drug sensitivity forecasting
  • Metric Calculation:
    • Standard metrics: Accuracy, F1-score, ARI, ASW
    • Biological relevance metrics: scGraph-OntoRWR (consistency with ontological relationships), LCAD (ontological error severity)
  • Performance Aggregation: Multi-metric synthesis using non-dominated sorting algorithms for overall model ranking

Healthcare Application Evaluation Protocol

The clinical pathology evaluation established a structured approach for comparing adaptation strategies [43]:

  • Scenario Design:
    • Scenario A: Easy problem (binary classification, large training data: 40,000 samples)
    • Scenario B: Medium problem (multi-class classification, limited data: 16,000 samples)
    • Scenario C: Hard problem (multi-class classification, small data: 1,000 samples)
  • Model Adaptation:
    • Zero-shot: Direct inference with task instructions
    • Fine-tuning: Supervised training on labeled datasets
    • Domain-adaptive pretraining: Continued pretraining on domain corpora before fine-tuning
  • Performance Measurement: Macro F1-score on held-out test sets

G Single-Cell Foundation Model Evaluation Workflow cluster_inputs Input Data cluster_processing Adaptation Strategies cluster_tasks Evaluation Tasks cluster_metrics Performance Metrics RawData Raw Single-Cell Data ZeroShot Zero-Shot Inference RawData->ZeroShot FineTuning Fine-Tuning (Full/PEFT) RawData->FineTuning PretrainedModels Pretrained scFMs (scGPT, Geneformer, etc.) PretrainedModels->ZeroShot PretrainedModels->FineTuning GeneTasks Gene-Level Tasks: - Function Prediction - Interaction Inference ZeroShot->GeneTasks CellTasks Cell-Level Tasks: - Type Annotation - Batch Integration - Drug Response ZeroShot->CellTasks FineTuning->GeneTasks FineTuning->CellTasks StandardMetrics Standard Metrics: - Accuracy - F1-Score - ARI/ASW GeneTasks->StandardMetrics BioMetrics Biological Metrics: - scGraph-OntoRWR - LCAD GeneTasks->BioMetrics CellTasks->StandardMetrics CellTasks->BioMetrics Results Model Ranking & Selection Guidance StandardMetrics->Results BioMetrics->Results

Decision Framework: When to Use Which Approach

Synthesizing evidence across studies yields a structured decision framework for selecting adaptation strategies based on task requirements and constraints.

G Adaptation Strategy Decision Framework Start Start: Define Project Requirements DataAvail Labeled Training Data Available? Start->DataAvail Compute Sufficient Compute Resources? DataAvail->Compute Adequate Data ZeroShotRec RECOMMENDATION: Zero-Shot Learning DataAvail->ZeroShotRec No/Limited Data TaskComplexity Task Complexity? Compute->TaskComplexity Sufficient Resources Compute->ZeroShotRec Limited Resources PEFTRec RECOMMENDATION: Parameter-Efficient Fine-Tuning (PEFT) TaskComplexity->PEFTRec Moderate Complexity FullFTRec RECOMMENDATION: Full Fine-Tuning TaskComplexity->FullFTRec High Complexity + Domain Specificity Deployment Deployment Frequency? HybridRec RECOMMENDATION: Hybrid Approach (Zero-Shot → Fine-Tune) Deployment->HybridRec Multiple Tasks ZeroShotRec->Deployment PEFTRec->Deployment FullFTRec->Deployment

Zero-Shot Learning Applications

Zero-shot approaches are optimal when:

  • Exploratory analysis requiring rapid prototyping across multiple tasks
  • Data scarcity with limited or no labeled training examples [43]
  • Resource constraints prohibiting extensive model training
  • General biological queries rather than highly specialized applications [47]

Fine-Tuning Applications

Fine-tuning delivers superior performance when:

  • High-stakes applications demand maximum accuracy (clinical diagnostics, therapeutic development)
  • Domain specificity requires adaptation to specialized terminology or concepts [44]
  • Complex tasks involving multi-class classification or regression [43]
  • Adequate labeled data exists for supervised learning (typically thousands of examples)

Hybrid Strategy

A pragmatic hybrid approach begins with zero-shot evaluation to establish performance baselines, then selectively applies fine-tuning to tasks where the marginal performance gain justifies computational investment [47]. This strategy is particularly valuable in resource-constrained environments or when prioritizing multiple tasks simultaneously.

The Scientist's Toolkit: Essential Research Reagent Solutions

Implementing effective model adaptation strategies requires familiarity with core frameworks and biological resources.

Table 4: Essential tools for scFM research and adaptation

Tool Category Representative Solutions Primary Function Application Context
Unified Frameworks BioLLM [4] Standardized API for diverse scFMs Model comparison and switching
Fine-Tuning Libraries Hugging Face PEFT, Axolotl [45] Parameter-efficient fine-tuning Resource-constrained adaptation
Biological Databases CZ CELLxGENE, Human Cell Atlas [1] Curated single-cell data Pretraining and evaluation
Evaluation Platforms Tenyks, scGraph-OntoRWR [2] [47] Model performance analysis Biological relevance assessment
Compute Infrastructure NVIDIA DGX, Kubernetes, Cloud GPU [45] Training and deployment Scalable model adaptation

The choice between fine-tuning and zero-shot approaches represents a fundamental trade-off between performance and computational cost in biological knowledge representation. Evidence consistently demonstrates that fine-tuned models achieve superior performance on specialized tasks, particularly in complex, data-scarce scenarios common in biomedical research [44] [43]. However, zero-shot methods provide unparalleled efficiency for exploratory analysis and resource-constrained environments.

The evolving landscape of parameter-efficient fine-tuning techniques increasingly bridges this divide, enabling performance gains with reduced computational overhead [45] [42]. As single-cell foundation models grow in sophistication and scope, strategic selection of adaptation strategies will remain crucial for maximizing biological insights while responsibly managing computational resources in therapeutic development and basic research.

Single-cell foundation models (scFMs) are large-scale deep learning models, typically based on transformer architectures, pretrained on vast datasets comprising millions of single cells to learn universal representations of cellular biology [1]. These models are revolutionizing single-cell genomics by providing a unified framework for analyzing cellular heterogeneity and complex regulatory networks, with applications spanning cell type annotation, perturbation prediction, and drug response modeling [1] [2]. By treating individual cells as "sentences" and genes or genomic features as "words," scFMs learn the fundamental language of biology through self-supervised learning on massive, diverse single-cell omics corpora [1].

However, the remarkable capabilities of scFMs come with significant interpretability challenges. The internal mechanisms of these complex neural networks remain poorly understood, creating a "black box" problem that limits their biological utility and clinical adoption [48]. As these models grow in size and complexity—with parameter counts reaching hundreds of millions—researchers face the critical challenge of extracting meaningful, causally-relevant biological insights from their latent representations and attention mechanisms [1] [48]. This interpretability gap is particularly problematic in drug development, where understanding model decisions is essential for safety assessment and regulatory approval [49] [50].

Core Technical Challenges in scFM Interpretability

Fundamental Architectural Obstacles

The very architecture of transformer-based scFMs presents multiple barriers to biological interpretability. The nonsequential nature of omics data fundamentally contradicts the sequential processing assumption of transformers, requiring researchers to impose artificial gene ordering through expression-level ranking or genomic position, which may not reflect true biological organization [1] [2]. Additionally, the high dimensionality and sparsity of single-cell transcriptomics data, characterized by numerous genes measured across many cells but with low molecular counts per gene, creates challenges in distinguishing technical noise from true biological signal in model representations [2].

A critical limitation lies in the fragmentation of biological concepts across model features. Recent research using sparse autoencoders to investigate scFM internals reveals that information about coherent biological concepts, such as cell types, is often distributed across numerous model features rather than being captured in unified, interpretable representations [48]. This fragmentation directly impedes the extraction of clear biological insights, as there is no one-to-one mapping between model components and biological entities.

Domain-Specific Biological Representation Challenges

Beyond architectural issues, scFMs face challenges in capturing the complex, multi-scale nature of biological knowledge. Traditional knowledge graphs used in biomedical research often rely on simplified pairwise relationships that fail to represent the higher-order interactions and collective biological processes central to cellular function [12]. Analysis of Alzheimer's Disease literature reveals that only 20% of biological discoveries can be perfectly represented with pairwise relationships alone, while 73% require nested relationships and 7% need hypergraph representations [12].

The tension between generalization and specificity presents another fundamental challenge. While scFMs are pretrained on massive datasets to capture universal biological patterns, this very generality can limit their utility for specific downstream tasks where simpler, more specialized models sometimes outperform foundation models [2]. This paradox highlights the unresolved challenge of building models that are simultaneously general enough to transfer across contexts yet specific enough to provide actionable insights for particular biological questions.

Table 1: Key Technical Challenges in scFM Interpretability

Challenge Category Specific Technical Hurdles Impact on Biological Insight
Architectural Barriers Nonsequential data processing, High dimensionality & sparsity, Concept fragmentation Obscures gene-gene interactions, Complicates signal-noise separation, Prevents clear feature-biology mapping
Representation Limits Oversimplified pairwise relationships, Generalization-specificity tension Fails to capture complex biological processes, Limits actionable insights for specific tasks
Analytical Gaps Underdeveloped biological metrics, Limited causal inference capability Hinders validation against known biology, Restricts predictive hypothesis generation

Experimental Approaches for Interpreting scFM Embeddings

Probing Internal Model Representations

Systematic probing of scFM internals is essential for understanding what biological information these models capture. The sparse autoencoder (SAE) methodology has emerged as a powerful technique for decoding scFM representations. This approach involves training a bottleneck autoencoder on the hidden activations of scFMs like scGPT and scFoundation to decompose their complex, entangled representations into more interpretable features [48]. The experimental protocol begins by extracting intermediate layer activations across diverse cell types, then training the SAE to reconstruct these activations while enforcing sparsity through L1 regularization, and finally analyzing the resulting features for biological relevance by associating them with known cell types, pathways, or technical factors [48].

Attention mechanism analysis provides another window into model internals. By examining attention patterns in transformer-based scFMs, researchers can identify which genes the model considers most relevant when making predictions about cellular states [1]. The standard protocol involves calculating attention weights across layers and heads, aggregating gene-gene attention scores across cells and contexts, and integrating these with prior biological knowledge from databases like gene ontology to validate whether attention highlights biologically plausible relationships [1] [37].

G cluster_0 Input Processing cluster_1 Interpretability Methods cluster_2 Output & Validation A Single-Cell Expression Matrix B Tokenization & Embedding A->B C Sparse Autoencoder Probing B->C D Attention Mechanism Analysis B->D E Perturbation Analysis B->E F Biological Metric Validation B->F G Interpretable Features C->G H Gene-Gene Interactions D->H I Causal Predictions E->I J Biological Consistency Scores F->J

SCFM Interpretability Methods Workflow

Biological Validation Frameworks and Metrics

Rigorous biological validation is essential for moving beyond correlative patterns to causally meaningful insights. The scGraph-OntoRWR metric represents an innovative approach that evaluates whether cell type relationships captured by scFM embeddings align with established biological knowledge in cell ontology databases [2]. This methodology applies random walk with restart algorithms on ontology graphs to quantify the consistency between computational representations and prior biological knowledge, providing a standardized way to assess the biological plausibility of learned embeddings.

For evaluating cell type annotation performance, the Lowest Common Ancestor Distance (LCAD) metric measures the ontological proximity between misclassified cell types, offering a more nuanced assessment than simple accuracy by accounting for the biological severity of classification errors [2]. This approach recognizes that misclassifying closely related cell types (e.g., T-cell subtypes) is less problematic than confusing biologically distant ones (e.g., neurons vs. immune cells), providing a biologically-informed error analysis.

Perturbation-based causal validation tests whether scFMs can accurately predict cellular responses to genetic or chemical perturbations, moving beyond correlative relationships to assess causal understanding [37]. The standard protocol involves fine-tuning pretrained models on perturbation data, comparing predicted expression changes to experimental results, and analyzing attention mechanisms to identify which genes the model considers most important in mediating perturbation responses, potentially revealing novel regulatory relationships.

Table 2: Experimental Metrics for Biological Validation of scFMs

Metric Category Specific Methods Experimental Protocol Biological Insight Generated
Internal Representation Analysis Sparse Autoencoder Probing, Attention Mechanism Analysis Train SAE on model activations, Calculate attention weights across layers Identifies features corresponding to biological concepts, Reveals important gene-gene relationships
Ontology Alignment scGraph-OntoRWR, Lowest Common Ancestor Distance Random walk on ontology graphs, Calculate ontological distance of errors Quantifies consistency with prior knowledge, Measures biological severity of errors
Functional Validation In-silico perturbation, Cross-species transfer, Drug response prediction Predict expression after perturbations, Transfer models between species Tests causal understanding, Assesses generalization capability

Computational Tools and Research Reagents for scFM Interpretability

The growing importance of scFM interpretability has stimulated development of specialized computational tools and standardized resources. These "research reagents" enable reproducible experimentation and benchmarking across different models and biological contexts.

Table 3: Essential Research Reagents for scFM Interpretability Research

Resource Category Specific Tools/Datasets Function in Interpretability Research Key Features
Benchmarking Platforms BioLLM, DISCO, CZ CELLxGENE Discover Standardized model evaluation, Aggregate datasets for testing Universal interfaces for 15+ models, 100M+ cells for federated analysis [37]
Pretrained Models scGPT, Geneformer, scFoundation, scPlantFormer Provide base models for interpretation, Enable transfer learning studies 33M+ cell pretraining, Cross-species capabilities, Multi-omic support [1] [37]
Interpretability Toolkits Sparse Autoencoders, SHAP, LIME Feature visualization, Importance scoring Model-agnostic interpretation, Local explanation generation [48] [50]
Biological Knowledge Bases Cell Ontology, Gene Ontology, Pathway Databases Ground truth for validation, Prior knowledge integration Standardized terminology, Curated relationships [2] [12]

Overcoming the interpretability challenges in single-cell foundation models requires advances across multiple fronts. Architectural innovations that explicitly incorporate biological prior knowledge—such as scPlantFormer's integration of phylogenetic constraints—represent a promising direction for building more interpretable models by design [37]. Similarly, enhanced knowledge representation strategies that move beyond simplified pairwise relationships to capture nested interactions and hypergraphs can better align computational representations with biological reality [12].

The development of standardized evaluation frameworks and biological consistency metrics is equally crucial for meaningful progress in this field. Community-wide benchmarking efforts that systematically assess not just quantitative performance but also biological plausibility and explanatory power will help identify the most promising approaches [2] [37]. Initiatives like the Human Cell Atlas provide foundational infrastructure for these efforts, though sustainable model registries with transparent data provenance are still needed [37].

Ultimately, realizing the potential of scFMs to drive biological discovery and therapeutic innovation depends on bridging the gap between their impressive empirical performance and our understanding of how they derive biological insights. By developing and applying rigorous interpretability methods, creating biologically-meaningful validation frameworks, and building tools that make model reasoning transparent, researchers can transform black box models into invaluable partners in deciphering the complexity of cellular systems. This progress will enable scFMs to fulfill their promise as pivotal tools for advancing single-cell genomics and unlocking deeper insights into cellular function and disease mechanisms [1].

Benchmarking Performance: Validating Biological Knowledge in scFM Embeddings

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, leveraging large-scale deep learning models pretrained on massive single-cell datasets to support a wide range of downstream analysis tasks. As the field progresses from traditional clustering algorithms to these sophisticated transformer-based architectures, the evaluation metrics must similarly evolve from simple statistical measures like silhouette scores to more nuanced assessments of biological plausibility. Current scFMs typically process single-cell RNA sequencing (scRNA-seq) data by treating individual cells as "sentences" and genes or genomic features as "words" or "tokens," allowing them to capture complex biological patterns through self-supervised learning on millions of single-cell transcriptomes. While these models have demonstrated remarkable capabilities in processing heterogeneous datasets, a critical challenge persists: effectively evaluating their ability to capture biologically meaningful insights rather than merely optimizing technical benchmarks. This guide examines the current landscape of evaluation metrics for scFM embeddings, providing researchers with both theoretical frameworks and practical methodologies for assessing model performance in the context of biological knowledge representation.

Traditional Clustering Metrics and Their Limitations

Traditional evaluation in single-cell analysis has heavily relied on clustering-based metrics that measure technical performance without assessing biological relevance. The table below summarizes these conventional approaches and their specific limitations in evaluating scFMs:

Table 1: Traditional Clustering Metrics and Limitations for scFM Evaluation

Metric Primary Function Key Limitations for scFM Evaluation
Silhouette Score Measures clustering quality based on intra-cluster vs inter-cluster distances Fails to validate biological relevance of clusters; optimized clusters may not align with true cell types
Adjusted Rand Index (ARI) Compares clustering results to ground truth labels Requires predefined labels; cannot evaluate novel biological discoveries
Normalized Mutual Information (NMI) Quantifies information shared between cluster assignments and labels Same limitations as ARI; penalizes discovery of novel cell states
Batch Integration Metrics Evaluate technical batch effect removal Over-aggressive integration may remove biologically meaningful variation

These conventional metrics, while computationally straightforward and widely adopted, present significant limitations for comprehensive scFM assessment. They predominantly evaluate technical aspects of embedding quality while providing minimal insight into whether the learned representations capture biologically meaningful structures. This limitation becomes particularly problematic when evaluating scFMs on novel datasets where ground truth labels are incomplete or when assessing whether models can discover previously uncharacterized cell states. As noted in benchmark studies, scFMs sometimes fail to outperform simpler baseline models on certain tasks despite their architectural complexity, highlighting the need for more biologically-informed evaluation frameworks [2] [51].

Emerging Metrics for Biological Plausibility

Ontology-Informed Evaluation Metrics

Recent research has introduced innovative ontology-based metrics that explicitly evaluate how well scFM embeddings capture established biological knowledge. These approaches measure the consistency between computational representations and prior biological understanding through structured ontological frameworks:

Table 2: Ontology-Informed Metrics for Biological Plausibility Assessment

Metric Description Biological Basis Interpretation
scGraph-OntoRWR Measures consistency of cell type relationships with biological ontologies Cell ontology hierarchies and established cell type relationships Higher scores indicate better alignment with known biological taxonomy
Lowest Common Ancestor Distance (LCAD) Quantifies ontological proximity between misclassified cell types Cell type developmental lineages and differentiation pathways Smaller distances indicate biologically meaningful misclassifications
Cell Ontology Semantic Similarity Assesses functional similarity between cell clusters based on gene expression Gene ontology terms and functional annotations Higher similarity suggests biologically coherent clustering

The scGraph-OntoRWR metric specifically evaluates whether the relational structure between cell types in the embedding space reflects their known biological relationships as defined in cell ontologies [2]. This represents a significant advancement over traditional metrics by explicitly testing the biological coherence of the learned representations rather than merely their statistical properties. Similarly, the LCAD metric provides a more nuanced assessment of classification errors by distinguishing between severe errors (misclassifying biologically distant cell types) and understandable confusions (misclassifying closely related cell types within the same lineage) [2].

Knowledge Graph-Based Assessment

Knowledge graph embedding approaches offer another dimension for evaluating biological plausibility by measuring how well scFM representations align with established biological networks and pathways. Methods like BioGraphFusion create deeply synergistic semantic and structural learning frameworks that integrate global biological knowledge with graph-based reasoning [52]. These approaches enable evaluation through:

  • Disease-Gene Association Prediction: Testing whether embeddings capture known disease-gene relationships from biomedical knowledge bases.
  • Protein-Chemical Interaction Modeling: Assessing how well models represent functional biological interactions between proteins and chemical compounds.
  • Cross-Medical Ontology Reasoning: Evaluating the ability to infer logical relationships across structured biological ontologies.

These knowledge-aware evaluation methods address critical limitations of simple pairwise relationship representations in biological knowledge graphs, which often fail to capture complex, multi-entity biological interactions [53]. By incorporating richer representations that can model collective interactions and nested entities, these assessment frameworks provide more comprehensive evaluation of biological plausibility.

Experimental Protocols for Metric Implementation

Implementing Ontology-Based Evaluation

To implement ontology-informed evaluation metrics, researchers should follow this standardized protocol:

  • Reference Ontology Preparation:

    • Download current cell ontology from the OBO Foundry (e.g., Cell Ontology or Human Cell Atlas Ontology)
    • Extract hierarchical relationships and semantic links between cell types
    • Map dataset-specific cell labels to standardized ontology terms

  • Embedding Generation:

    • Extract zero-shot cell embeddings from scFMs without fine-tuning
    • Compute cell-type centroids by averaging embeddings of cells with identical annotations
    • Calculate pairwise distances between all cell-type centroids in embedding space

  • Metric Computation:

    • For scGraph-OntoRWR: Perform random walks with restart on both the embedding-derived similarity graph and the ontology-derived graph, then compare walk probabilities
    • For LCAD: For each misclassification, identify the lowest common ancestor in the cell ontology and calculate the ontological distance
    • For semantic similarity: Calculate functional similarity based on shared gene ontology annotations

Reference Ontology Reference Ontology Ontology Graph Ontology Graph Reference Ontology->Ontology Graph Cell Embeddings Cell Embeddings Pairwise Distance Matrix Pairwise Distance Matrix Cell Embeddings->Pairwise Distance Matrix Similarity Graph Similarity Graph Pairwise Distance Matrix->Similarity Graph Random Walk with Restart Random Walk with Restart Ontology Graph->Random Walk with Restart Similarity Graph->Random Walk with Restart scGraph-OntoRWR Score scGraph-OntoRWR Score Random Walk with Restart->scGraph-OntoRWR Score

Figure 1: Workflow for Ontology-Based Metric Implementation

Knowledge Graph Consistency Assessment

The following protocol evaluates how well scFM embeddings align with biological knowledge graphs:

  • Knowledge Graph Construction:

    • Integrate data from biological knowledge bases (DisGeNET, STITCH, SIDER)
    • Extract entity-relation triples for biological concepts (genes, diseases, compounds)
    • Establish ground truth relationships for evaluation

  • Embedding-Knowledge Alignment:

    • Project knowledge graph entities into the same embedding space as scFM representations
    • Compute alignment scores between scFM-derived similarities and knowledge graph-derived similarities
    • Measure ranking quality of known relationships against random negatives

  • Cross-Modal Reasoning Evaluation:

    • Formulate biological queries (e.g., "Which genes are associated with disease X?")
    • Compare answers derived from scFM embeddings versus knowledge graph reasoning
    • Assess consistency between computational predictions and established knowledge

Benchmarking Frameworks and Comparative Analysis

Standardized scFM Evaluation Frameworks

The development of comprehensive benchmarking frameworks has been crucial for standardized evaluation of scFMs. BioLLM provides a unified interface for integrating diverse scFMs, eliminating architectural and coding inconsistencies to enable streamlined model access and consistent benchmarking [4]. These frameworks typically evaluate models across multiple task categories:

Table 3: Multi-Task Benchmarking Framework for scFM Evaluation

Task Category Specific Tasks Evaluation Metrics Biological Relevance
Gene-Level Tasks Gene function prediction, Gene network inference AUROC, AUPRC, Precision@K Captures functional genomic knowledge
Cell-Level Tasks Cell type annotation, Batch integration, Cancer cell identification Accuracy, F1-score, ARI, Cell ontology metrics Measures cellular representation quality
Perturbation Tasks Drug sensitivity prediction, Genetic perturbation response Mean squared error, Perturbation effect size correlation Evaluates predictive power for experimental outcomes
Clinical Tasks Patient stratification, Treatment outcome prediction Cox PH models, Survival AUC Assesses translational medicine potential

Recent benchmarking studies have revealed that no single scFM consistently outperforms others across all tasks, emphasizing the need for task-specific model selection [2]. For example, while scGPT demonstrates robust performance across multiple tasks, Geneformer and scFoundation show particular strengths in gene-level tasks, likely due to their effective pretraining strategies [4]. Specialized frameworks like PertEval-scFM focus specifically on perturbation effect prediction, highlighting that zero-shot scFM embeddings often provide limited improvement over simpler baselines, particularly under distribution shift [51].

Multimodal Integration Assessment

With the emergence of multimodal models like CellWhisperer that connect transcriptomes with textual annotations, evaluation frameworks must expand to assess cross-modal integration [6]. Key assessment protocols include:

  • Cross-Modal Retrieval Accuracy: Measuring the model's ability to retrieve relevant transcriptomes from textual queries and vice versa.
  • Semantic Consistency: Evaluating whether biologically related concepts cluster together in the joint embedding space.
  • Functional Annotation Accuracy: Assessing the model's capacity to generate biologically accurate textual descriptions of cell states.

These multimodal evaluation approaches are particularly valuable as they bridge the gap between computational representations and human-interpretable biological concepts, enabling more intuitive exploration and validation of model outputs.

The Scientist's Toolkit: Essential Research Reagents

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

Resource Category Specific Tools/Databases Primary Function Application in Evaluation
Benchmarking Frameworks BioLLM, PertEval-scFM Standardized model evaluation and comparison Provides consistent evaluation protocols across models and tasks
Data Resources CELLxGENE Census, GEO, Human Cell Atlas Curated single-cell datasets with annotations Supplies benchmark datasets with biological ground truth
Ontology Resources Cell Ontology, Gene Ontology, Uberon Structured biological knowledge bases Enables ontology-informed metric calculation
Knowledge Graphs DisGeNET, STITCH, SIDER Biomedical relationship databases Supports knowledge-aware evaluation of biological plausibility
Visualization Tools CELLxGENE Explorer, UCSC Cell Browser Interactive exploration of single-cell data Facilitates human validation of model outputs

These resources collectively provide the foundation for comprehensive evaluation of scFMs, spanning from technical benchmarking to biological validation. The integration of standardized frameworks like BioLLM with rich biological data resources enables researchers to perform reproducible assessments across multiple dimensions of model performance [4].

Future Directions in Biological Evaluation Metrics

The evolution of evaluation metrics for scFMs continues to advance toward more sophisticated assessments of biological plausibility. Promising directions include:

  • Dynamic Biological Process Modeling: Developing metrics that evaluate how well embeddings capture temporal biological processes such as differentiation trajectories and cellular response dynamics, moving beyond static cell state representations.

  • Causal Inference Assessment: Creating evaluation frameworks that test whether models can infer causal relationships rather than mere correlations, potentially leveraging perturbation data and experimental validations.

  • Cross-Species Generalization Metrics: Establishing protocols to assess how well biological knowledge learned from model organisms transfers to human biology, a critical consideration for translational research.

  • Multimodal Integration Metrics: Expanding evaluation approaches for models that integrate multiple data modalities (transcriptomics, proteomics, epigenomics, spatial context) to assess cross-modal consistency and information complementarity.

As these advancements mature, they will further strengthen the connection between computational representations and biological reality, ensuring that scFMs evolve from powerful pattern recognition tools to genuine instruments of biological discovery.

Traditional Metrics Traditional Metrics Ontology-Informed Metrics Ontology-Informed Metrics Traditional Metrics->Ontology-Informed Metrics Knowledge-Aware Evaluation Knowledge-Aware Evaluation Traditional Metrics->Knowledge-Aware Evaluation Multimodal Assessment Multimodal Assessment Ontology-Informed Metrics->Multimodal Assessment Knowledge-Aware Evaluation->Multimodal Assessment Causal Inference Metrics Causal Inference Metrics Multimodal Assessment->Causal Inference Metrics Dynamic Process Evaluation Dynamic Process Evaluation Multimodal Assessment->Dynamic Process Evaluation

Figure 2: Evolution of Evaluation Metrics for scFMs

Single-cell foundation models (scFMs) represent a revolutionary approach in computational biology, aiming to learn universal representations of cellular state from vast collections of single-cell transcriptomic data [1] [13]. Inspired by breakthroughs in natural language processing (NLP), these models treat individual cells as "sentences" and genes or genomic features as "words" or "tokens" [1] [13]. The fundamental thesis driving scFM research posits that through self-supervised pretraining on millions of cells, these models can internalize the "grammar" of gene regulation and cellular function, encoding this knowledge within their latent embeddings [1]. These embeddings, in theory, should capture fundamental biological principles that enable zero-shot generalization and efficient adaptation to diverse downstream tasks, from cell type annotation to perturbation response prediction.

This whitepaper provides a comprehensive technical comparison of three pioneering scFMs: scGPT, Geneformer, and scBERT. Each model employs distinct architectural philosophies and training strategies to tackle the challenge of biological knowledge representation. We examine their performance across core tasks through the lens of recent benchmarking studies, analyze the methodologies behind these evaluations, and discuss the implications for researchers seeking to leverage these tools in scientific discovery and drug development.

Model Architectures and Pretraining Strategies

The comparative performance of scFMs stems from their fundamental architectural choices and pretraining methodologies. The table below summarizes the key design characteristics of each model.

Table 1: Architectural and Pretraining Specifications

Feature scGPT Geneformer scBERT
Core Architecture GPT-like decoder with masked self-attention [1] BERT-like encoder with bidirectional attention [1] [5] BERT-like encoder with bidirectional attention [54] [5]
Parameters ~50 million [2] [5] ~40 million [2] [5] Information Missing
Pretraining Dataset ~33 million non-cancerous human cells [2] [55] ~30 million cells [2] [5] PanglaoDB and other sources [54]
Input Gene Count 1,200 Highly Variable Genes (HVGs) [2] 2,048 ranked by expression [2] Information Missing
Tokenization Strategy Value binning for expression levels [2] Gene ordering by expression level [2] Gene2vec embeddings; expression binning [54]
Pretraining Task Iterative Masked Gene Modeling (MSE loss); generative pretraining [2] Masked Gene Modeling (CE loss for gene ID) [2] Masked Gene Modeling (reconstruction loss) [54]

A critical challenge all models face is the non-sequential nature of gene expression data. Unlike words in a sentence, genes lack a natural order. To address this, scGPT typically uses Highly Variable Genes (HVGs), Geneformer employs a deterministic ranking by expression level, and scBERT also utilizes expression-based ranking or binning [1] [2]. These strategies create an artificial sequence that allows the transformer architecture to process the data. The choice of pretraining objective also varies, with scGPT using a mean squared error (MSE) loss for regression-like prediction of expression values, while Geneformer and scBERT use cross-entropy or similar losses focused on gene identity classification [2] [54].

G cluster_arch Model Architecture Input Single-Cell RNA-seq Matrix Tokenization Tokenization & Embedding Input->Tokenization scBERT scBERT (BERT Encoder) Tokenization->scBERT Geneformer Geneformer (BERT Encoder) Tokenization->Geneformer scGPT scGPT (GPT Decoder) Tokenization->scGPT CellEmbedding Cell Embedding scBERT->CellEmbedding Geneformer->CellEmbedding scGPT->CellEmbedding Downstream Downstream Tasks CellEmbedding->Downstream

Figure 1: Core Workflow of Single-Cell Foundation Models. All models transform raw gene expression matrices into token sequences, process them through transformer backbones, and produce cell embeddings for downstream analysis.

Performance Comparison on Core Tasks

Rigorous benchmarking is essential to understand the practical strengths and limitations of each model. The following analysis synthesizes results from multiple independent studies evaluating performance on key tasks in both zero-shot (no additional training) and fine-tuned settings.

Cell-Type Annotation and Clustering

Cell-type annotation is a fundamental task in single-cell analysis. Performance varies significantly, particularly between zero-shot and fine-tuned scenarios.

Table 2: Cell-type Annotation Performance (Zero-Shot vs. Fine-Tuned)

Model Zero-Shot Performance Fine-Tuned Performance Notable Characteristics
scGPT Inconsistent; outperforms baselines on some datasets (e.g., PBMC 12k) but underperforms on others [55]. ScGPT kidney and blood tissue-specific pretraining sometimes outperforms the general scGPT human model [55]. Can achieve high accuracy (e.g., 99.5% F1-score on retina data) [56]. A 10-25 percentage point accuracy jump is common after fine-tuning on complex datasets [21]. Consistently ranks top in unified frameworks like BioLLM for generating biologically relevant cell embeddings [5].
Geneformer Often underperforms simpler baselines like Highly Variable Genes (HVG), Harmony, and scVI in cell type clustering [55]. Embeddings can fail to retain clear cell-type information, with clustering primarily driven by batch effects [55]. Shown to perform well in original publications, though independent benchmarking in zero-shot raises questions [55]. Demonstrates strong capabilities in some gene-level tasks, benefiting from its effective pretraining strategy [5].
scBERT Generally lags behind other models, with lower separation of cell types in embedding visualizations [5]. Performance can decline as input sequence length increases [5]. Performs well in cell-type annotation tasks and novel cell-type detection when fine-tuned, showing robustness to batch effects [54]. Performance is significantly influenced by cell-type distribution imbalance in the data [54]. Smaller model size and limited training data may constrain performance [5].

In zero-shot settings, a critical finding is that simpler methods often compete with or outperform these foundation models. One benchmark found that selecting Highly Variable Genes (HVG) outperformed both Geneformer and scGPT across multiple datasets and metrics for cell type clustering [55]. Similarly, established methods like scVI and Harmony frequently demonstrated superior performance [55].

Batch Integration

Batch integration, which removes technical artifacts while preserving biological variation, is another crucial task. A unified evaluation via the BioLLM framework, which uses Average Silhouette Width (ASW) scores that incorporate both cell-type and batch information, found that scGPT outperformed other models, including Geneformer and scBERT, though it still generally struggled to correct for batch effects across different technologies [5]. Geneformer's embeddings sometimes showed a higher proportion of variance explained by batch effects than the original data, indicating inadequate batch mixing [55].

Perturbation Response Prediction

Predicting cellular responses to genetic perturbations presents a significant challenge. A recent benchmark compared several foundation models against deliberately simple baselines for predicting transcriptome changes after single or double gene perturbations [34].

Table 3: Perturbation Prediction Performance

Model Performance on Double Perturbations Performance on Unseen Perturbations Notable Findings
scGPT Prediction error substantially higher than a simple additive baseline model [34]. Did not consistently outperform a simple linear model or even a baseline that always predicts the mean [34]. A linear model using scGPT's own pretrained gene embeddings performed as well or better than scGPT with its in-built decoder [34].
Geneformer Not designed for this task; included via a linear decoder adapter [34]. Not designed for this task; included via a linear decoder adapter [34]. -
scBERT Not designed for this task; included via a linear decoder adapter [34]. Not designed for this task; included via a linear decoder adapter [34]. -
Simple Baselines An additive model (sum of individual logarithmic fold changes) and a "no change" model (predicts control expression) outperformed all deep learning models [34]. A simple linear model or mean prediction baseline was not consistently outperformed [34]. Pretraining on perturbation data itself was more beneficial than pretraining on single-cell atlas data [34].

A striking conclusion from this benchmark is that the goal of building a generalizable foundation model that can accurately predict the outcome of novel biological experiments remains elusive [34]. The performance of simple baselines suggests that current deep learning models may not yet be capturing the underlying biological complexity more effectively than trivial heuristic models.

Experimental Protocols for Benchmarking

To ensure reproducibility and critical evaluation, understanding the methodology behind these benchmarks is crucial. The following protocols are synthesized from multiple independent studies.

Protocol for Zero-Shot Embedding Evaluation

This protocol assesses the intrinsic biological quality of model representations without task-specific fine-tuning [55] [5].

  • Model Initialization: Load the publicly available pretrained weights for each model (scGPT, Geneformer, scBERT). A randomly initialized model can serve as a control to measure the value of pretraining [55].
  • Data Preprocessing: Apply a standardized preprocessing pipeline to the target evaluation dataset(s). This includes quality control, normalization, and filtering to the model's required input genes. The BioLLM framework implements a decision-tree-based preprocessing interface for this purpose [5].
  • Embedding Extraction: Pass the preprocessed data through the model to extract the cell embeddings. For scGPT, this is typically the [CLS] token or a mean-pooled representation; for Geneformer and scBERT, it is the cell-level embedding output [55] [5].
  • Dimensionality Reduction and Visualization: Project the high-dimensional embeddings into 2D using UMAP for qualitative assessment of cell-type separation and batch integration [55] [5].
  • Quantitative Metric Calculation:
    • Cell-type Clustering: Use Average Silhouette Width (ASW) with cell-type labels to measure cluster compactness and separation. The AvgBIO score is also used in some benchmarks [55].
    • Batch Integration: Calculate ASW with batch labels and cell-type labels simultaneously, or use metrics like Principal Component Regression (PCR) to quantify the variance explained by batch [55] [5].
  • Baseline Comparison: Compare the model's performance against established baselines like Highly Variable Genes (HVG) selection, Harmony, and scVI using the same metrics [55].

Protocol for Perturbation Prediction Benchmarking

This protocol evaluates the model's ability to predict gene expression changes after genetic perturbation [34].

  • Data Preparation: Use publicly available perturbation datasets (e.g., Norman et al. for double perturbations, Replogle et al. for unseen single perturbations). Data is formatted into a expression matrix with rows as genes and columns as perturbation conditions (including a non-targeting control).
  • Model Setup and Fine-Tuning:
    • For models designed for perturbation prediction (e.g., scGPT, scFoundation), follow the authors' recommended fine-tuning procedure on the training split of the data.
    • For models not designed for this task (e.g., Geneformer, scBERT), attach a linear decoder that maps the cell embedding to the gene expression space and fine-tune this adapter.
  • Baseline Establishment:
    • "No Change" Baseline: Always predicts the same expression as in the control condition.
    • "Additive" Baseline: For a double perturbation A+B, predicts the sum of the individual logarithmic fold changes (LFC) of A and B relative to control.
    • Simple Linear Model: A linear model trained on perturbation embeddings, sometimes using embeddings derived from the foundation models themselves [34].
  • Evaluation:
    • Prediction Error: Calculate the L2 distance or Pearson correlation between predicted and observed expression values, typically for the top 1,000 most highly expressed genes.
    • Genetic Interaction Prediction: Assess the model's ability to identify non-additive interactions in double perturbations by comparing the difference between prediction and additive expectation against a null model [34].

G Start Benchmarking Protocol P1 1. Model & Baseline Initialization Start->P1 P2 2. Standardized Data Preprocessing P1->P2 DataPrep Pretrained Models: scGPT, Geneformer, scBERT Baselines: HVG, scVI, Harmony, Additive Model P1->DataPrep P3 3. Execution (Zero-Shot or Fine-Tune) P2->P3 P4 4. Quantitative Evaluation P3->P4 P5 5. Comparative Analysis P4->P5 EvalMetrics Key Metrics: - Avg. Silhouette Width (ASW) - AvgBIO Score - L2 Distance - PCR Score P4->EvalMetrics

Figure 2: Standardized Benchmarking Workflow. Independent evaluations follow a consistent protocol to ensure fair comparison between foundation models and established baselines across multiple metrics.

The Scientist's Toolkit: Essential Research Reagents

Implementing and evaluating these models requires a suite of computational tools and data resources. The table below details key components of the modern scFM research pipeline.

Table 4: Essential Reagents for scFM Research

Tool/Resource Type Function in Research Relevance to Model Comparison
BioLLM Framework [5] Software Framework Provides a unified interface (standardized APIs) for integrating, applying, and benchmarking different scFMs. Eliminates architectural and coding inconsistencies, enabling streamlined model switching and consistent performance evaluation. Essential for reproducible comparisons.
CELLxGENE [1] [55] Data Repository Provides unified access to annotated single-cell datasets; hosts over 100 million unique cells standardized for analysis. Serves as a primary source of pretraining data and a resource for creating benchmark evaluation datasets.
PanglaoDB [54] [13] Curated Data Compendium A curated collection of single-cell RNA sequencing data used for pretraining (e.g., scBERT) and validation. Provides a standardized corpus for initial model training and a common ground for comparison.
Harmony [2] [55] Computational Method A robust baseline algorithm for batch integration of single-cell data. A critical baseline against which the batch correction capabilities of scFMs are measured in benchmarks.
scVI [2] [55] Computational Method A generative deep learning model for single-cell data analysis, used for batch correction and representation learning. Another strong baseline model used to gauge the relative performance of newer foundation models.
Simple Linear Models / Additive Models [34] Baseline Method Deliberately simple statistical models that predict perturbation responses based on heuristics like additivity of effects. Act as a crucial sanity check, revealing whether complex foundation models provide a genuine predictive advantage over trivial approaches.

The comparative analysis of scGPT, Geneformer, and scBERT reveals a field in a state of rapid, maturing development. A central finding across independent benchmarks is that the "foundation" nature of these models—their ability to provide robust, general-purpose representations for zero-shot inference—is not yet fully realized. While fine-tuning can yield excellent, state-of-the-art results on specific tasks like cell-type annotation [56], their zero-shot performance is often inconsistent and can be surpassed by simpler, established methods [55] [34].

Several key conclusions emerge:

  • No Single Model Dominates: Each model has relative strengths. scGPT often leads in cell embedding quality and versatility [5], Geneformer shows promise in gene-level analysis [5], and scBERT demonstrates capabilities when fine-tuned on balanced data [54].
  • The Pretraining Paradigm is Not Yet Solved: The fact that simple additive models outperform foundation models in perturbation prediction [34], and that HVG selection can compete in clustering [55], suggests that the current pretraining objectives may not be optimally capturing causal biological relationships.
  • Context is King: Model selection is highly task-dependent. For rapid exploration, zero-shot scGPT embeddings may be sufficient. For high-stakes annotation, fine-tuning is essential [21]. For perturbation prediction, simple baselines should be included as a critical reference [34].

The path forward for biological knowledge representation in scFM embeddings will likely require more biologically grounded pretraining objectives, rigorous benchmarking against simpler baselines to prevent over-engineering, and the development of more standardized frameworks like BioLLM [5] to ensure fair and reproducible evaluation. As these models continue to evolve, a critical and evidence-based approach will be essential for integrating them effectively into the scientific toolkit for drug discovery and basic research.

The emergence of single-cell foundation models (scFMs) has revolutionized the analysis of cellular heterogeneity and complex regulatory networks by learning latent representations from vast single-cell genomics datasets [1]. However, a significant challenge persists in validating the biological relevance of the embeddings these models produce. Traditional evaluation metrics often fail to assess whether learned representations capture meaningful biological relationships. Within the context of biological knowledge representation, ontology-based metrics and knowledge graph (KG) alignment have emerged as novel validation paradigms that ground computational findings in established biological knowledge [2]. These approaches leverage formally structured biomedical ontologies and KGs to provide a rigorous framework for evaluating scFMs, moving beyond purely statistical measures to assess the biological plausibility of model outputs. This technical guide explores the theoretical foundations, methodologies, and applications of these approaches for researchers and drug development professionals working at the intersection of computational biology and machine learning.

Theoretical Foundations

Biomedical Knowledge Graphs and Ontologies

Biomedical knowledge graphs organize entities—such as genes, proteins, cells, and diseases—as nodes, with edges representing their semantic, functional, or physical relationships [57]. The formal structure provided by ontologies is what enables rigorous computational assessment. An ontology provides a formal, explicit specification of a shared conceptualization within a domain, delivering a standardized vocabulary and logical structure that defines domain concepts and their interrelationships [57]. This structured framework allows for the unambiguous characterization of biological entities, creating a common understanding that supports algorithmic reasoning and semantic interoperability.

Resources like RNA-KG exemplify the power of this approach, integrating biological knowledge about RNA molecules from more than 60 public databases and connecting them with genes, proteins, chemicals, and diseases through ontologically grounded concepts [58]. Similarly, the SPOKE knowledge graph connects millions of concepts across 41 biomedical databases using 11 different ontologies as a semantic framework [57]. These integrated resources provide a rich, structured knowledge base for validating computational models against established biological facts.

Single-Cell Foundation Models (scFMs)

Single-cell foundation models are large-scale deep learning models, typically based on transformer architectures, pretrained on massive single-cell transcriptomics datasets encompassing diverse cell types, tissues, and conditions [1]. These models treat individual cells as analogous to sentences and genes or genomic features as words or tokens, learning to capture fundamental principles of cellular biology that generalize to new datasets and downstream tasks [1]. The primary challenge lies in evaluating whether the latent embeddings produced by scFMs reflect biologically meaningful relationships, which is where ontology-based validation provides crucial assessment capabilities.

Ontology-Based Metrics for scFM Validation

scGraph-OntoRWR: A Novel Topological Metric

The scGraph-OntoRWR metric evaluates the consistency between the relational structure of cell types captured by scFM embeddings and prior biological knowledge encoded in reference ontologies [2]. This method operates by measuring how well the proximity relationships between cell types in the model's latent space align with their established relationships in cell type ontologies.

Experimental Protocol for scGraph-OntoRWR:

  • Reference Ontology Processing: Extract the hierarchical graph structure of cell types from a reference ontology such as the Cell Ontology, where nodes represent cell types and edges represent subtype relationships.
  • Embedding Extraction: Obtain cell embeddings from the scFM in a zero-shot setting, without task-specific fine-tuning.
  • k-Nearest Neighbor Graph Construction: Compute pairwise distances between all cell embeddings and construct a k-nearest neighbor (k-NN) graph based on these distances.
  • Random Walk with Restart Execution: Perform Random Walk with Restart (RWR) on both the reference ontology graph and the k-NN graph derived from embeddings.
  • Similarity Comparison: Calculate the similarity between the steady-state probability distributions generated by RWR on both graphs using a similarity measure such as Jenson-Shannon divergence.
  • Metric Interpretation: Higher similarity scores indicate that the scFM has captured biological relationships consistent with the reference ontology, validating its biological relevance.

Lowest Common Ancestor Distance (LCAD)

The Lowest Common Ancestor Distance metric assesses the severity of errors in cell type annotation tasks by measuring the ontological proximity between misclassified cell types and their true labels [2]. Rather than treating all misclassifications equally, LCAD acknowledges that errors between closely related cell types are less severe than those between distantly related types.

Methodology for LCAD Calculation:

  • Cell Type Annotation: Perform cell type annotation using the scFM embeddings and a chosen classification approach.
  • Error Identification: Identify misclassified cells where predicted cell type differs from the ground truth label.
  • Ontological Distance Calculation: For each misclassification, trace the path from both the predicted and true cell types to their lowest common ancestor in the reference ontology.
  • Distance Metric Application: Calculate the distance as the number of edges or the semantic similarity between the misclassified cell type and the true label within the ontological hierarchy.
  • Performance Assessment: Compute the average LCAD across all misclassifications, with lower values indicating that errors occur between biologically similar cell types, suggesting better model performance.

Table 1: Comparison of Ontology-Based Validation Metrics for scFMs

Metric Name Validation Target Methodology Interpretation Biological Basis
scGraph-OntoRWR [2] Global embedding structure Random Walk with Restart on ontology vs. embedding graphs Higher similarity = better biological consistency Cell type ontology relationships
LCAD [2] Cell type annotation errors Ontological distance between misclassified types Lower distance = more biologically plausible errors Cell type hierarchy and proximity
Roughness Index (ROGI) [2] Latent space smoothness Measures landscape roughness in embedding space Smoother landscapes = better generalization Cellular property continuity

G cluster_ontology Reference Ontology Processing cluster_embeddings scFM Embedding Extraction cluster_comparison Graph Construction & Comparison start Start scGraph-OntoRWR ont_source Cell Ontology (e.g., Cell Ontology) start->ont_source scfm_model scFM Model (Zero-shot) start->scfm_model ont_graph Extract Hierarchical Graph Structure ont_source->ont_graph rwr_ont RWR on Ontology Graph ont_graph->rwr_ont cell_embeddings Obtain Cell Embeddings scfm_model->cell_embeddings knn_graph Construct k-NN Graph from Embeddings cell_embeddings->knn_graph rwr_emb RWR on k-NN Graph knn_graph->rwr_emb similarity Calculate Similarity of Probability Distributions rwr_ont->similarity rwr_emb->similarity result Biological Consistency Score similarity->result

Figure 1: Workflow for scGraph-OntoRWR Metric Calculation

Knowledge Graph Alignment Methods

Entity Alignment: Core Concepts and Challenges

Entity alignment identifies entities across different knowledge graphs that refer to the same real-world object, formally defined as finding equivalent entities in two KGs [59]. This process is crucial for integrating heterogeneous biological data from multiple sources, enabling the creation of unified knowledge representations that support more comprehensive biological discovery.

The fundamental challenge in entity alignment stems from the heterogeneity of data models, formats, and identifier systems used across biological databases [58]. Biomedical KGs often employ different schemas, relation types, and entity naming conventions, requiring sophisticated methods to identify equivalent entities despite these structural differences.

Methodological Approaches to Entity Alignment

Entity alignment methods can be broadly categorized into relation-based and attribute-based approaches, each with distinct strengths and applications in biological contexts.

Relation-Based Methods leverage structural information within KGs, utilizing the connections between entities to learn embeddings that reflect graph topology [60]. These methods are particularly effective for capturing intricate relational patterns in dense KGs:

  • MTransE+RotatE: Extends the foundational MTransE approach by replacing TransE embeddings with RotatE, which represents relations as rotations in complex vector space, enabling better modeling of symmetric relations and complex relational patterns [60].
  • RDGCN: Uses a dual-graph convolutional network architecture to capture multi-hop structural information, aggregating features from broader entity neighborhoods to enrich context [60].
  • RREA: Incorporates a relational reflection mechanism with graph neural networks, learning relation-specific transformations through orthogonal matrices to better encode unique relation characteristics [60].

Attribute-Based Methods utilize literal information associated with entities, such as names, descriptions, and other textual or numerical data, to enhance entity representations [60]. These approaches are particularly valuable for KGs with extensive attribute information, where structural patterns alone may be insufficient for accurate alignment.

Table 2: Comparison of Knowledge Graph Entity Alignment Methods

Method Category Key Mechanism Strengths Limitations
MTransE+RotatE [60] Relation-based Relations as rotations in complex space Captures symmetric relations Struggles with sparse graphs
RDGCN [60] Relation-based Dual-graph convolutional networks Multi-hop neighborhood aggregation High computational complexity
RREA [60] Relation-based Relational reflection transformation Relation-specific differentiation Requires negative sampling
AttrE [61] Attribute-based Attribute similarity matching Effective with rich entity attributes Limited structural utilization
BootEA [61] Semi-supervised Bootstrap strategy with editing Reduces seed alignment需求 Potential error propagation

Experimental Framework for Alignment Evaluation

Comprehensive evaluation of entity alignment methods requires a structured pipeline addressing data preprocessing, method implementation, and multi-faceted assessment:

Data Preprocessing Protocol:

  • Syntactic Regularization: Standardize data formats, remove noisy data, and handle missing values across KGs.
  • Predicate Alignment: Align similar predicates across KGs and standardize relation naming conventions [59].
  • Value Specialization: Process continuous values using radial basis functions or other normalization techniques [59].
  • Knowledge Enrichment: Derive new triples using logic rules or inference mechanisms to augment graph connectivity [59].

Evaluation Metrics:

  • Standard Alignment Metrics: Precision, recall, and F1-score for aligned entity pairs.
  • Efficiency Measures: Runtime performance and computational overhead analysis.
  • Statistical Validation: Application of Friedman and Nemenyi tests for robust method comparison across multiple datasets [60].

Experimental Considerations:

  • Assess alignment directionality effects, as performance can vary based on source and target KG designation [59].
  • Evaluate method robustness to dataset characteristics including graph density, degree distribution, and name bias.
  • Test scalability with increasingly large biological KGs containing millions of entities and relations.

G cluster_preprocess Data Preprocessing cluster_methods Alignment Methods cluster_evaluation Evaluation & Analysis start KG Alignment Evaluation data_input Heterogeneous Knowledge Graphs start->data_input syntactic Syntactic Regularization data_input->syntactic predicate Predicate Alignment syntactic->predicate enrichment Knowledge Enrichment predicate->enrichment relation_based Relation-Based Methods (e.g., RREA) enrichment->relation_based attribute_based Attribute-Based Methods (e.g., AttrE) enrichment->attribute_based hybrid Hybrid Approaches relation_based->hybrid attribute_based->hybrid metrics Alignment Metrics (Precision, Recall, F1) hybrid->metrics efficiency Efficiency Analysis (Runtime, Overhead) hybrid->efficiency statistical Statistical Validation (Friedman Test) metrics->statistical efficiency->statistical results Method Ranking & Recommendations statistical->results

Figure 2: Knowledge Graph Alignment Evaluation Framework

Integrated Validation Frameworks

BioGraphFusion: Semantic-Structural Synergy

The BioGraphFusion framework addresses the critical challenge of achieving deep, adaptive integration between semantic understanding and structural learning in biomedical KGs [62]. This approach moves beyond ensemble methods that often fail to achieve synergistic co-evolution between these two aspects.

Core Architecture Components:

  • Global Biological Tensor Encoding: Employs canonical polyadic (CP) decomposition to extract low-dimensional embeddings that capture latent biological associations and establish a global semantic foundation.
  • Query-Guided Subgraph Construction: Iteratively builds query-relevant subgraphs by refining relations and propagating context-specific embeddings, focusing computational resources on biologically pertinent regions.
  • LSTM-Based Gating Mechanism: Dynamically refines relation embeddings during message propagation, adapting them to evolving semantic contexts to better capture long-range dependencies in biological pathways.
  • Hybrid Scoring Mechanism: Unifies direct global semantic contributions from knowledge embedding with structural insights from graph propagation, enabling nuanced candidate prediction assessment.

Implementation Protocol for Integrated Validation

Experimental Setup for Biomedical KG Completion:

  • Task Design: Implement three core biological inference tasks:

    • Disease-gene association prediction
    • Protein-chemical interaction identification
    • Cross-medical ontology reasoning

  • Model Configuration:

    • Initialize entity and relation embeddings via CP decomposition of the full KG tensor.
    • Implement LSTM gates with dimension-specific weighting for relation embedding refinement.
    • Set hybrid scoring parameters to balance semantic and structural contributions.

  • Training Protocol:

    • Jointly optimize knowledge embedding and graph propagation losses.
    • Employ negative sampling strategies tailored to biological entity distributions.
    • Implement early stopping based on validation set performance across all tasks.

  • Evaluation Framework:

    • Compare against baseline knowledge embedding (KE) and graph neural network (GNN) methods.
    • Assess performance gains on complex multi-hop reasoning tasks.
    • Conduct case studies on specific biological pathways (e.g., cutaneous malignant melanoma) to validate biological meaningfulness [62].

Research Reagent Solutions

Table 3: Essential Research Resources for Ontology and KG-Based Validation

Resource Name Type Primary Function Relevance to Validation
RNA-KG [58] Knowledge Graph Integrates RNA interactions from 60+ databases Reference for RNA-related biological knowledge
Cell Ontology [2] Biomedical Ontology Standardized cell type classification Ground truth for cell type relationships
PheKnowLator [58] KG Construction Tool Builds semantically rich biomedical KGs Creates domain-specific validation graphs
SPOKE [57] Knowledge Graph Connects 41 biomedical databases Large-scale reference for biomedical concepts
UMLS Terminology [62] Medical Ontology Unified medical language system Cross-ontology reasoning benchmark
DisGeNET [62] Knowledge Graph Disease-gene associations Validation source for disease mechanisms

Ontology-based metrics and knowledge graph alignment methods provide powerful frameworks for validating the biological relevance of single-cell foundation models. The scGraph-OntoRWR and LCAD metrics enable direct assessment of how well scFM embeddings capture established biological relationships, while entity alignment methods facilitate the integration of heterogeneous knowledge sources to create comprehensive reference structures. As these validation approaches continue to mature, they will play an increasingly critical role in ensuring that computational models generate biologically meaningful insights, ultimately accelerating drug discovery and precision medicine initiatives. Future research directions should focus on developing more sophisticated hybrid metrics, improving the scalability of alignment methods for ever-growing biological knowledge graphs, and creating standardized benchmark datasets for systematic validation across diverse biological domains.

The ability to accurately predict cellular responses to genetic perturbations lies at the heart of functional genomics and modern therapeutic discovery. As single-cell RNA sequencing (scRNA-seq) technologies have matured, enabling large-scale perturbation screens such as Perturb-seq, the computational challenge has shifted from data generation to predictive modeling and interpretation. Foundation models pre-trained on massive single-cell datasets have emerged as powerful tools for this task, promising to capture fundamental biological principles that generalize across diverse cellular contexts. However, their true capacity for causal inference—distinguishing perturbation-specific effects from systematic biases—remains poorly understood and evaluated. This whitepaper examines the current landscape of perturbation prediction benchmarks, highlighting critical limitations in existing evaluation frameworks and presenting emerging solutions designed to rigorously assess the causal inference capabilities of single-cell foundation models (scFMs) within the broader context of biological knowledge representation.

The Benchmarking Challenge in Perturbation Biology

A fundamental challenge in benchmarking perturbation prediction methods is the absence of definitive ground-truth causal networks in biological systems. Unlike synthetic datasets where causal structures are known, real-world biological networks involve complex, context-dependent interactions that remain partially characterized. The CausalBench suite addresses this by introducing biologically-motivated metrics and distribution-based interventional measures, providing a more realistic evaluation environment for network inference methods [63]. This framework leverages large-scale single-cell perturbation data with over 200,000 interventional datapoints, enabling systematic evaluation of how well methods can reconstruct gene regulatory networks from real-world observational and interventional data.

A critical insight from recent studies is that systematic variation—consistent transcriptional differences between perturbed and control cells arising from selection biases or biological confounders—significantly impacts benchmark performance. Systema, a recently introduced evaluation framework, demonstrates that common metrics are highly susceptible to these biases, leading to overestimated performance for methods that primarily capture average perturbation effects rather than perturbation-specific biology [64]. This systematic variation manifests in multiple ways: through selection biases in perturbation panels targeting functionally related genes, cell cycle distribution differences between perturbed and control populations, and consistent stress responses triggered across multiple perturbations.

Table 1: Common Sources of Systematic Variation in Perturbation Datasets

Source of Variation Description Impact on Evaluation
Panel Selection Bias Targeting genes from specific biological processes Methods learn process-level signatures rather than gene-specific effects
Cell Cycle Confounding Differential distribution across cell cycle phases Predictions capture cell cycle shifts rather than perturbation mechanisms
Technical Artifacts Batch effects, capture efficiency differences Spurious correlations mistaken for biological relationships
Consistent Stress Responses Generic cellular reactions to perturbation Overestimation of true positive rates for pathway identification

Major Benchmarking Frameworks and Methodologies

CausalBench: Network Inference from Single-Cell Perturbation Data

CausalBench represents a transformative approach to evaluating causal network inference methods. Built on two large-scale perturbation datasets (RPE1 and K562 cell lines) generated using CRISPRi technology, it employs both biology-driven and statistical evaluation strategies [63]. The framework implements a comprehensive set of state-of-the-art methods spanning observational (PC, GES, NOTEARS, GRNBoost) and interventional settings (GIES, DCDI, Mean Difference, Guanlab). Its evaluation methodology focuses on the trade-off between precision and recall through two complementary statistical metrics:

  • Mean Wasserstein Distance: Measures the strength of causal effects corresponding to predicted interactions
  • False Omission Rate (FOR): Quantifies the rate at which true causal interactions are omitted from model outputs

A key finding from CausalBench evaluations is that methods using interventional information frequently do not outperform those using only observational data, contrary to theoretical expectations and results from synthetic benchmarks [63]. This highlights the critical gap between theoretical causal inference and practical application to complex biological systems.

Systema: Disentangling Systematic Variation from Perturbation-Specific Effects

Systema introduces a rigorous framework specifically designed to address the limitations of conventional evaluation metrics. Its methodology emphasizes perturbation-specific effects and identifies predictions that correctly reconstruct the perturbation landscape beyond systematic variation [64]. The framework employs several innovative approaches:

  • Systematic Variation Quantification: Measures consistent differences between perturbed and control cells across multiple datasets
  • Perturbation Landscape Reconstruction: Evaluates how well methods recover the underlying structure of perturbation relationships
  • Functional Coherence Assessment: Tests whether methods correctly group perturbations targeting functionally related genes

When applied to ten datasets spanning three technologies and five cell lines, Systema revealed that predicting responses to unseen perturbations is substantially more challenging than standard metrics suggest, with simple baselines like "perturbed mean" often performing comparably to sophisticated deep learning models [64].

Table 2: Performance Comparison of Perturbation Prediction Methods Across Benchmarks

Method Type CausalBench Performance Systema Evaluation Key Limitations
Mean Difference Interventional High statistical evaluation scores Struggles with unseen perturbations Limited biological mechanism capture
Guanlab Interventional Strong biological evaluation performance Moderate systematic variation resistance Scalability constraints
scGPT Foundation Model Not benchmarked Susceptible to systematic biases Overfitting to average effects
Perturbed Mean Simple Baseline Not applicable Surprisingly competitive performance Cannot predict gene-specific effects
GEARS Deep Learning Not benchmarked Partial functional coherence recovery Limited extrapolation capacity

Experimental Protocols and Evaluation Metrics

Standard Evaluation Metrics and Their Limitations

Current benchmarks employ multiple metric types to assess different aspects of perturbation prediction performance. The most widely used approaches include:

  • PearsonΔ: Correlation between predicted and actual differential expression profiles (perturbed vs. control)
  • PearsonΔ20: Correlation focused on top 20 differentially expressed genes
  • Root Mean-Squared Error (RMSE): Magnitude of difference between predicted and actual expression
  • Mean Wasserstein Distance: Distributional distance measuring causal effect strength
  • False Omission Rate (FOR): Proportion of true interactions missed by the model

However, recent research demonstrates that metrics like PearsonΔ are particularly vulnerable to systematic variation, as they can achieve high scores by simply capturing average differences between perturbed and control cells without understanding perturbation-specific mechanisms [64]. The Systema framework therefore recommends complementing these with additional assessments focused on functional coherence and perturbation landscape reconstruction.

CausalBench Evaluation Protocol

The CausalBench methodology involves a rigorous experimental protocol for assessing network inference methods [63]:

  • Data Preparation: Curated datasets from two cell lines (RPE1 and K562) with CRISPR-based genetic perturbations
  • Model Training: Five independent training runs with different random seeds for each method
  • Statistical Evaluation: Computation of mean Wasserstein distance and false omission rate
  • Biological Evaluation: Assessment against biologically-derived ground truth approximations
  • Trade-off Analysis: Comparative analysis of precision-recall relationships across methods

This protocol reveals that methods performing well on statistical evaluations do not always excel in biological assessments, highlighting the importance of multi-faceted evaluation strategies.

Benchmarking scFMs for Post-Perturbation Prediction

Recent benchmarking studies of single-cell foundation models reveal surprising limitations in their perturbation prediction capabilities. When scGPT and scFoundation were evaluated against simpler baseline models, even the most basic approach—predicting the mean of training examples—outperformed these foundation models [65]. Furthermore, standard machine learning models incorporating biologically meaningful features such as Gene Ontology vectors significantly surpassed foundation model performance.

These results suggest that current scFMs may not effectively leverage their pre-training to capture perturbation biology. However, when scFM embeddings were used as features in random forest models, performance improved substantially, indicating that the embeddings contain relevant biological information that the fine-tuned models fail to fully utilize [65].

G Start Start Benchmarking DataSelection Dataset Selection (10 datasets, 5 cell lines) Start->DataSelection MetricChoice Evaluation Metric Selection DataSelection->MetricChoice SystematicCheck Systematic Variation Assessment MetricChoice->SystematicCheck ModelTraining Model Training (5 random seeds) SystematicCheck->ModelTraining TraditionalEval Traditional Metrics (PearsonΔ, RMSE) ModelTraining->TraditionalEval CausalEval Causal Metrics (Wasserstein, FOR) ModelTraining->CausalEval FunctionalEval Functional Coherence Assessment ModelTraining->FunctionalEval Results Results Interpretation TraditionalEval->Results CausalEval->Results FunctionalEval->Results End Benchmark Complete Results->End

Figure 1: Comprehensive Benchmarking Workflow for Perturbation Prediction Methods. This workflow integrates traditional metrics with causal inference assessments and systematic variation checks.

Emerging Approaches and Methodological Innovations

Hybrid Causal Models

To address limitations in both deep learning and mechanistic approaches, hybrid models are emerging that combine their strengths. The Single Cell Causal Variational Autoencoder (SCCVAE) integrates a mechanistic causal model with variational deep learning, using a learned regulatory network to represent perturbational changes as shift interventions that propagate through the network [66]. This approach demonstrates superior performance in extrapolating to predict unseen perturbational responses compared to state-of-the-art baselines.

SCCVAE employs a structural causal model (SCM) framework where endogenous variables represent abstracted gene modules and the causal graph indicates regulatory relationships between these modules. The model specifies perturbation penetrance, enabling simulation of single-gene knockdowns with varying effectiveness, and learns perturbation representations that capture functional modules [66].

Knowledge-Enhanced Generative Models

The integration of structured biological knowledge represents another promising direction. The K-DREAM framework augments diffusion-based generative models with embeddings from biomedical knowledge graphs, directing molecular generation toward candidates with higher biological relevance and therapeutic potential [67]. By leveraging relationships from knowledge graphs spanning genes, proteins, biological processes, and diseases, these models incorporate comprehensive biological context that moves beyond simplistic chemical scoring functions.

Unified Evaluation Frameworks

The BioLLM framework addresses the challenge of heterogeneous architectures and coding standards across scFMs by providing a unified interface for model integration and evaluation [5]. This standardization enables systematic benchmarking across multiple tasks, including zero-shot inference and fine-tuning scenarios, while implementing comprehensive metrics for embedding quality, biological fidelity, and prediction accuracy.

G SystematicVariation Systematic Variation Sources SelectionBias Panel Selection Bias SystematicVariation->SelectionBias CellCycle Cell Cycle Confounding SystematicVariation->CellCycle Technical Technical Artifacts SystematicVariation->Technical StressResponse Consistent Stress Responses SystematicVariation->StressResponse ProcessSignatures Process-Level Signatures SelectionBias->ProcessSignatures MechanismMasking Mechanism Masking CellCycle->MechanismMasking SpuriousCorrelation Spurious Correlations Technical->SpuriousCorrelation StressResponse->ProcessSignatures Impact Impact on Prediction SystemaFramework Systema Framework ProcessSignatures->SystemaFramework CausalMetrics Causal Metrics (Wasserstein, FOR) MechanismMasking->CausalMetrics FunctionalCoherence Functional Coherence Assessment SpuriousCorrelation->FunctionalCoherence Solutions Mitigation Solutions

Figure 2: Systematic Variation in Perturbation Data: Sources, Impacts, and Mitigation Strategies. Systematic variation significantly impacts benchmark validity and requires specialized approaches for detection and mitigation.

Table 3: Key Experimental Resources for Perturbation Prediction Research

Resource Type Function Example Implementation
CausalBench Benchmark Suite Evaluates network inference methods on real-world data Two cell lines, 200,000+ interventional points [63]
Systema Evaluation Framework Quantifies and corrects for systematic variation Ten datasets, three technologies [64]
BioLLM Unified Framework Standardizes scFM integration and benchmarking Supports scGPT, Geneformer, scFoundation [5]
Perturb-seq Data Experimental Data Provides single-cell readouts of genetic perturbations Genome-wide CRISPR screens with scRNA-seq [65]
PrimeKG Knowledge Graph Structured biological relationships for model guidance 4M relationships for knowledge-enhanced generation [67]

The field of perturbation prediction benchmarking is undergoing rapid evolution, with new frameworks addressing critical gaps in causal inference assessment. The move toward biologically-grounded evaluation metrics, systematic variation detection, and standardized benchmarking protocols represents significant progress. However, fundamental challenges remain in truly assessing model capabilities for causal reasoning rather than pattern recognition.

Future benchmarking efforts will need to place greater emphasis on evaluating biological knowledge representation within model embeddings, assessing generalization across cell types and species, and validating predictions through experimental follow-up. Integration with structural biology predictions and multi-omics data will provide more comprehensive assessments of biological mechanism capture. As these benchmarks mature, they will play an increasingly vital role in guiding the development of models that genuinely advance our understanding of causal relationships in biological systems, ultimately accelerating therapeutic discovery and functional genomics research.

Single-cell Foundation Models (scFMs) represent a paradigm shift in the analysis of single-cell RNA sequencing (scRNA-seq) data. Trained on millions of cells through self-supervised learning, these models learn universal biological knowledge that can be adapted to diverse downstream tasks. This whitepaper synthesizes recent benchmarking studies to delineate the specific scenarios where scFMs demonstrably outperform traditional machine learning methods. We provide a quantitative performance analysis across key biological tasks, detail the experimental protocols for model evaluation, and explore the biological knowledge representation within scFM embeddings. The evidence indicates that while traditional methods retain utility for specific, limited-scale problems, scFMs offer superior robustness, accuracy, and biological relevance for complex tasks such as cross-dataset batch integration, rare cell type identification, and clinically-focused prediction, establishing them as indispensable tools for next-generation biological and clinical research.

Single-cell Foundation Models (scFMs) are large-scale deep learning models, typically based on transformer architectures, pretrained on vast and diverse single-cell omics datasets [1]. Inspired by breakthroughs in natural language processing, these models treat individual cells as "sentences" and genes or genomic features as "words," allowing them to learn the fundamental "language" of cellular biology through exposure to millions of cells across various tissues and conditions [1]. A defining characteristic of scFMs is their self-supervised pretraining on tasks like masked gene modeling, which forces the model to learn rich, contextual representations of gene and cell relationships without the need for explicit labels [1] [2]. This process results in a model that can be efficiently adapted (fine-tuned) to a wide range of downstream tasks with minimal additional task-specific data, a capability known as transfer learning [1].

The core hypothesis driving scFM development is that this large-scale pretraining embeds universal biological knowledge into the model's parameters and the resulting latent embeddings (numerical representations of cells or genes). This knowledge can then be probed and leveraged for specific analytical needs. The transition from traditional methods to scFMs marks a move from building a new model for each specific dataset and task to utilizing a powerful, general-purpose model that has already internalized a broad spectrum of biological variation.

Quantitative Performance Benchmarking

Recent comprehensive benchmarks have systematically evaluated scFMs against well-established traditional methods, revealing a nuanced performance landscape. The following tables summarize key findings across critical single-cell analysis tasks, highlighting where scFMs provide a decisive advantage.

Table 1: Performance of scFMs vs. Traditional Methods on Cell-Level Tasks (Zero-Shot)

Task Key Metric Top-Performing scFM Traditional Baseline (e.g., Seurat, scVI) Performance Advantage
Batch Integration Average Silhouette Width (ASW) scGPT Principal Component Analysis (PCA) scGPT consistently outperformed PCA and other baselines in integrating cells of the same type across batches [5].
Cell Type Annotation Lowest Common Ancestor Distance (LCAD) Multiple scFMs Highly Variable Genes (HVGs) + Classifier scFMs achieved lower LCAD scores, meaning misclassifications were biologically closer, preserving ontological relationships [2] [3].
Cancer Cell Identification F1-Score scGPT, Geneformer Harmony, scVI scFMs showed superior robustness and accuracy across seven cancer types in this clinically relevant task [2].
Drug Sensitivity Prediction Rank Correlation scFoundation, Geneformer Standard ML Models (e.g., Linear Models) scFMs demonstrated stronger predictive performance for response to four different drugs [2].

Table 2: scFM Performance on Gene-Level and Perturbation Tasks

Task Category Specific Task Model Performance Insight Implication for Biological Insight
Gene-Level Tasks Tissue Specificity Prediction, Gene Ontology (GO) Term Prediction Gene embeddings from scFMs (e.g., Geneformer, scFoundation) were effective for predicting known biological relationships [2] [5]. Automatically learned gene embeddings capture functional biological information without explicit supervision.
Perturbation Prediction Covariate Transfer, Combo Prediction Simpler model architectures scaled well with data, but no single model dominated; rank metrics (vs. RMSE) were critical for evaluating model utility for in-silico screening [68]. Confirms the importance of task-specific benchmarking; scFMs provide a strong foundation for predicting cellular response to genetic/chemical perturbations.

A critical finding from these benchmarks is that no single scFM consistently outperforms all others across every task [2] [3]. Model performance is highly dependent on the specific task, dataset size, and biological question. However, models like scGPT have demonstrated robust and versatile performance across multiple cell-level tasks, while Geneformer and scFoundation excel in gene-level analyses [5]. Furthermore, the zero-shot capabilities of scFMs—applying the model without any task-specific fine-tuning—are often sufficient to match or exceed traditional methods, particularly in capturing biologically meaningful relationships [2].

When scFMs Excel: Task-Specific Advantages

Complex Cell Type Annotation and Novel Cell Discovery

scFMs pretrained on massive atlases learn a deep representation of the "cell state space." This allows them to more accurately annotate cell types in new datasets and, crucially, to identify novel or rare cell populations that might be misclassified or overlooked by traditional methods. Their ability to place cells in a biologically meaningful context, as measured by ontology-based metrics like scGraph-OntoRWR and LCAD, is a significant advantage [2] [3]. For example, when an scFM misclassifies a cell, the error is typically to a closely related cell type (e.g., confusing two T cell subtypes), whereas traditional methods may make more biologically distant errors [2].

Robust Data Integration Across Diverse Batches and Conditions

Integrating scRNA-seq data from different studies, platforms, or donors (a process known as batch correction) is a major challenge. Traditional methods can struggle with complex batch effects. scFMs, by virtue of their pretraining on highly diverse data, are inherently more robust to such technical variations. They can create a unified embedding space that effectively separates biological signals from technical noise, which is paramount for large-scale atlas construction and meta-analyses [5].

Clinically Relevant Predictions

In tasks with direct clinical applications, such as identifying cancer cells within a complex tumor microenvironment or predicting patient-specific drug sensitivity, scFMs have shown superior performance. Their generalizable knowledge allows them to make more accurate predictions on held-out patient data or new drug compounds, a key step toward translational research [2].

Capturing Underlying Biological Knowledge

Perhaps the most profound advantage of scFMs is their ability to capture intrinsic biological knowledge. Benchmarks using novel ontology-informed metrics have confirmed that the latent spaces of scFMs reflect known biological relationships between cell types and gene functions without being explicitly trained on this information [2] [3]. This suggests scFMs are learning fundamental principles of biology, making them more than just powerful pattern-matching tools.

Experimental Protocols for scFM Evaluation

To ensure reproducible and meaningful comparisons, benchmarking studies have established rigorous protocols for evaluating scFMs. The following diagram and section detail a standardized workflow.

G Start Input: Raw scRNA-seq Dataset QC 1. Quality Control & Preprocessing Start->QC Model_Select 2. Model Selection & Embedding Extraction QC->Model_Select Filter a. Filter cells & genes Norm b. Normalize counts Task_Exec 3. Downstream Task Execution Model_Select->Task_Exec ZeroShot A. Zero-Shot: Use pretrained model without change FineTune B. Fine-Tuning: Update model weights on target data Eval 4. Multi-Metric Evaluation Task_Exec->Eval CellTask i. Cell-level tasks: Annotation, Integration GeneTask ii. Gene-level tasks: Function Prediction PerturbTask iii. Perturbation tasks: Response Modeling Output Output: Performance Report & Biological Insights Eval->Output

Figure 1: Standardized scFM benchmarking workflow. This protocol evaluates model performance under both zero-shot and fine-tuned conditions across diverse tasks.

Data Preprocessing and Quality Control

The input scRNA-seq dataset undergoes standard preprocessing: filtering out low-quality cells and genes, normalizing counts, and potentially selecting highly variable genes. To mitigate data leakage, a critical step is using an independent, unbiased dataset like the Asian Immune Diversity Atlas (AIDA) v2 for final validation [2].

Model Selection and Embedding Extraction

Selected scFMs (e.g., Geneformer, scGPT, scFoundation, UCE) are loaded, and cell/gene embeddings are extracted in a zero-shot fashion—meaning the pretrained model is applied directly without any further weight updates [2] [5]. This tests the intrinsic knowledge and transferability of the model. Alternatively, models can be fine-tuned on the target dataset with supervised labels, which typically enhances performance for that specific task [5].

Downstream Task Execution

The extracted embeddings are evaluated across a suite of tasks:

  • Cell-level tasks: Batch integration, cell type annotation, cancer cell identification, and drug sensitivity prediction [2].
  • Gene-level tasks: Predicting gene functionality, tissue specificity, and Gene Ontology terms [2] [3].
  • Perturbation tasks: Predicting cellular responses to genetic or chemical perturbations in covariate transfer and combination prediction scenarios [68].

Performance Evaluation Metrics

A multi-faceted evaluation approach is essential:

  • Traditional Metrics: Accuracy, F1-score, Average Silhouette Width (ASW) for clustering, and Root Mean Square Error (RMSE) for regression [5] [68].
  • Biological Fidelity Metrics: Novel metrics like scGraph-OntoRWR (measuring consistency of captured cell relationships with known ontologies) and LCAD (measuring the ontological proximity of misclassified cells) are used [2] [3].
  • Rank-based Metrics: For perturbation tasks, rank correlation metrics are crucial to assess a model's utility for in-silico screening, complementing traditional fit measures like RMSE [68].

The Scientist's Toolkit: Essential Research Reagents

The following table catalogues key computational "reagents" and resources essential for working with and evaluating single-cell foundation models.

Table 3: Key Computational Reagents for scFM Research

Resource Name Type Primary Function Relevance to scFM Research
CZ CELLxGENE [1] Data Repository Provides unified access to standardized, annotated single-cell datasets. Serves as a primary source of diverse, high-quality pretraining and benchmarking data.
BioLLM [5] Software Framework A unified framework with standardized APIs for integrating diverse scFMs. Enables seamless model switching, consistent benchmarking, and reproducible evaluation across multiple models.
PerturBench [68] Benchmarking Suite A modular framework for developing and evaluating perturbation response models. Provides standardized tasks and metrics specifically for benchmarking models on predicting cellular perturbation effects.
Human Cell Atlas [1] Data Atlas A comprehensive reference map of all human cells. Provides a broad coverage of cell types and states for model pretraining and biological validation.
Gene Ontology (GO) [2] Knowledge Base A structured framework of defined terms representing gene product properties. Used as ground truth for evaluating the biological relevance of gene embeddings produced by scFMs.

The evidence demonstrates that Single-cell Foundation Models are not a panacea, but they represent a significant advancement in computational biology. Their task-specific superiority is most evident in scenarios that benefit from pre-learned, generalizable biological knowledge: complex cell annotation, robust data integration, and clinically-oriented prediction. The key for researchers is to move beyond the question of "are scFMs better?" to the more nuanced "which scFM is best for my specific task, dataset, and resources?" Frameworks like BioLLM are crucial in this endeavor, lowering the barrier to systematic model evaluation. As the field matures, the biological knowledge encoded within scFM embeddings will undoubtedly become a central pillar for exploring cellular function, understanding disease mechanisms, and accelerating therapeutic discovery.

Conclusion

Single-cell Foundation Model embeddings represent a transformative approach for encoding biological knowledge, demonstrating robust capabilities across diverse applications from basic research to drug development. While scFMs excel at capturing complex biological relationships in their latent spaces, their performance varies significantly across tasks, with no single model dominating all benchmarks. The integration of biological knowledge graphs, development of specialized evaluation metrics, and creation of standardized frameworks like BioLLM are critical advancements. Future progress depends on addressing fundamental limitations in embedding capacity, improving interpretability, and enhancing model robustness for clinical translation. As these models evolve, they promise to unlock deeper insights into cellular mechanisms and accelerate therapeutic discovery, ultimately bridging the gap between single-cell genomics and precision medicine.

References