Self-Supervised Learning in Single-Cell Omics: From Foundation Models to Clinical Translation

Abstract

This article provides a comprehensive exploration of self-supervised learning (SSL) and foundation models, which are revolutionizing the analysis of single-cell omics data. Tailored for researchers and drug development professionals, it covers the foundational concepts of single-cell foundation models (scFMs), their architectural principles, and tokenization strategies for non-sequential genomic data. It delves into methodological advances, including transformer-based architectures like scGPT and Nicheformer, and their application in critical tasks such as cell type annotation, perturbation response prediction, and spatial niche modeling. The content further addresses key troubleshooting and optimization challenges, from mitigating batch effects to enhancing model interpretability. Finally, it offers a rigorous validation and comparative analysis of SSL methods, benchmarking performance across diverse downstream tasks to present a clear roadmap for leveraging these powerful tools in biomedical research and therapeutic development.

Demystifying scFMs: The Core Concepts and Data Ecosystem

What Are Single-Cell Foundation Models (scFMs)? Defining the Paradigm Shift

The advent of high-throughput single-cell sequencing technologies has generated vast amounts of molecular data, revolutionizing our ability to investigate biological systems at cellular resolution. However, this data deluge has exposed critical limitations in traditional computational methodologies, which struggle with the high dimensionality, technical noise, and inherent complexity of single-cell datasets [1] [2]. In response to these challenges, single-cell foundation models (scFMs) have emerged as a transformative computational paradigm, leveraging self-supervised learning on massive datasets to create versatile models that can be adapted to diverse biological tasks.

Inspired by the success of large language models in natural language processing, scFMs represent a fundamental shift from single-task models to general-purpose frameworks capable of zero-shot inference and efficient fine-tuning [1] [3]. These models are trained on millions of single-cell transcriptomes through self-supervised objectives, learning universal representations of cellular states that capture fundamental biological principles [1]. This pretraining enables scFMs to develop a foundational "understanding" of cellular biology that transfers across tissues, species, and experimental conditions, positioning them as indispensable tools for modern biological research and therapeutic development [2] [3].

Architectural Foundations: How scFMs Learn Cellular Language

Core Model Architecture and Components

Single-cell foundation models predominantly leverage the transformer architecture, which utilizes attention mechanisms to model complex relationships between genes within a cell [1]. The key innovation lies in how these models conceptualize and process single-cell data: individual cells are treated analogously to sentences, while genes and their expression values become the tokens or words that form these cellular sentences [1] [4].

Most scFMs employ either encoder-based architectures (similar to BERT) for classification tasks or decoder-based architectures (similar to GPT) for generative tasks, with some models exploring hybrid designs [1]. The transformer's attention mechanism enables scFMs to learn which genes are most informative about a cell's identity or state and how they co-vary across different cellular contexts, effectively capturing regulatory and functional connections [1].

Tokenization Strategies for Non-Sequential Biological Data

Unlike natural language, where words follow a natural sequence, gene expression data lacks inherent ordering. scFMs address this fundamental challenge through various tokenization strategies that structure the non-sequential omics data for transformer processing:

• Expression-based ranking: Genes are ordered by their expression levels within each cell, creating a deterministic sequence from highest to lowest expressed genes [1]
• Value binning: Expression values are discretized into bins, with each bin representing a different expression level category [1]
• Genomic position ordering: Some models order genes by their physical chromosomal locations [5]

These tokenization approaches are complemented by positional encoding schemes to represent the relative order of genes and specialized tokens for cell identity, experimental batch, or modality information [1]. Each gene is typically represented as an embedding vector combining a gene identifier and its expression value, creating a rich input representation that the transformer layers process to generate latent embeddings at both the gene and cell levels [1].

Self-Supervised Pretraining Strategies

The power of scFMs stems from their self-supervised pretraining on vast, unlabeled single-cell datasets. Two primary approaches have emerged:

• Masked Gene Modeling: Randomly selected portions of a cell's gene expression profile are masked, and the model learns to predict these masked values based on the remaining context [6]. This approach forces the model to learn underlying biological relationships and dependencies between genes.
• Contrastive Learning: Models learn to identify similar and dissimilar cellular states by maximizing agreement between differently augmented views of the same cell while distinguishing them from other cells [6].

These pretraining strategies enable scFMs to develop a comprehensive understanding of cellular biology without requiring expensive manual annotations, capturing the fundamental principles that govern gene regulation and cellular function across diverse biological contexts [1] [6].

The scFM Landscape: Model Specifications and Performance

Leading scFM Architectures and Their Specifications

The rapid evolution of scFMs has produced several prominent models with distinct architectural characteristics and training approaches. The table below summarizes key specifications of leading scFM implementations:

Table 1: Comparison of Major Single-Cell Foundation Models

Model Name	Omics Modalities	Model Parameters	Pretraining Dataset Size	Architecture Type	Key Features
scGPT [5] [2]	scRNA-seq, scATAC-seq, CITE-seq, spatial	50 million	33 million cells	Transformer Decoder	Multi-omic integration, strong zero-shot performance
Geneformer [5]	scRNA-seq	40 million	30 million cells	Transformer Encoder	Gene ranking by expression, 2,048 input genes
scFoundation [5]	scRNA-seq	100 million	50 million cells	Asymmetric Encoder-Decoder	All protein-encoding genes, read-depth-aware pretraining
UCE [5]	scRNA-seq	650 million	36 million cells	Transformer Encoder	Protein sequence embeddings, genomic position ordering
LangCell [5]	scRNA-seq + text	40 million	27.5 million cells	Transformer Encoder	Text integration, cell type label utilization
scCello [5]	scRNA-seq	Not specified	Not specified	Custom	Developmental trajectory inference

Performance Benchmarks Across Biological Tasks

Comprehensive benchmarking studies have evaluated scFMs across diverse biological tasks to assess their real-world performance. The following table summarizes quantitative performance comparisons across key application areas:

Table 2: scFM Performance Across Key Biological Tasks

Task Category	Specific Task	Top Performing Models	Key Performance Metrics	Comparison to Traditional Methods
Cell Type Annotation	Zero-shot cell typing	scGPT, Geneformer	Macro F1: 0.7466 (PBMC), 0.3085 (Tabula Sapiens) [6]	Outperforms supervised learning on underrepresented cell types [6]
Data Integration	Batch effect correction	scGPT, scVI	Batch integration scores, biological conservation metrics	Preserves subtle biological variations better than Harmony/Seurat [5]
Perturbation Prediction	Genetic/chemical perturbation	scGPT, GEARS	RMSE, rank correlation metrics	Competitive with specialized models; excels in zero-shot scenarios [7]
Gene Function Analysis	Gene embedding quality	Geneformer, scFoundation	Gene ontology enrichment, tissue specificity prediction	Captures biological relationships without explicit supervision [5]
Cross-Species Annotation	Plant cell annotation	scPlantFormer	92% cross-species accuracy [2]	Significant improvement over species-specific models

Notably, benchmarking studies reveal that no single scFM consistently outperforms all others across every task [5] [8]. Model performance is highly dependent on the specific application, dataset characteristics, and evaluation metrics, emphasizing the importance of task-specific model selection [5].

Experimental Framework: Implementing scFMs in Research Practice

Standardized Experimental Protocols for scFM Evaluation

To ensure reproducible and comparable results when working with scFMs, researchers should follow standardized experimental protocols. The following workflow outlines a comprehensive approach for scFM evaluation and application:

Essential Research Toolkit for scFM Implementation

Successful implementation of scFMs requires both computational resources and biological data infrastructure. The table below outlines essential components of the scFM research toolkit:

Table 3: Essential Research Toolkit for scFM Implementation

Tool Category	Specific Tools/Platforms	Primary Function	Key Features
Data Repositories	CELLxGENE [1], GEO, SRA, EMBL-EBI Expression Atlas	Curated single-cell data access	Standardized annotations, quality controls, metadata standards
Unified Frameworks	BioLLM [9], PerturBench [7]	Standardized model APIs and evaluation	Unified interfaces, benchmarking suites, reproducible workflows
Computational Environments	Python, PyTorch, TensorFlow, JAX	Model development and training	GPU acceleration, distributed training, hyperparameter optimization
Visualization & Analysis	Scanpy, Seurat, scCustomize	Biological interpretation of results	Dimensionality reduction, differential expression, trajectory inference
Benchmarking Metrics	scGraph-OntoRWR [5], LCAD [5], Traditional ML metrics	Comprehensive model evaluation	Biological relevance assessment, error severity quantification

Key Experimental Considerations for scFM Applications

When designing experiments with scFMs, researchers should address several critical considerations to ensure biologically meaningful and technically sound results:

• Data Quality and Curation: The performance of scFMs heavily depends on the quality and diversity of pretraining data. Researchers should carefully select datasets that represent relevant biological conditions and implement rigorous quality control measures [1] [5].

• Task-Specific Fine-tuning Strategies: While zero-shot performance provides insights into the general knowledge captured during pretraining, most real-world applications require varying degrees of fine-tuning. The optimal approach depends on dataset size, task complexity, and available computational resources [6] [5].

• Biological Validation: Beyond computational metrics, scFM predictions should be validated through biological interpretation, including pathway analysis, comparison to established biological knowledge, and ideally, experimental validation of novel predictions [5] [4].

• Computational Resource Management: Training and fine-tuning scFMs requires significant computational resources. Researchers should carefully consider the trade-offs between model size, training time, and performance gains for their specific applications [5] [8].

Limitations and Future Directions: Advancing the scFM Paradigm

Despite their transformative potential, current scFMs face several significant limitations that present opportunities for future development:

• Interpretability Challenges: The biological relevance of latent embeddings and model representations remains difficult to interpret, limiting trust and adoption in biological discovery [1] [5]. Future work should develop biologically-grounded interpretation methods that connect model internals to established biological mechanisms.

• Computational Intensity: Training and fine-tuning scFMs requires substantial computational resources, creating accessibility barriers for many research groups [1] [4]. Development of more efficient architectures, distillation techniques, and improved training strategies could help democratize access.

• Data Quality and Integration: Inconsistencies in data quality, batch effects, and technical variations across studies present challenges for robust pretraining [1] [5]. Advances in data harmonization and quality control pipelines will be essential for building more reliable models.

• Multimodal Integration: While early scFMs primarily focus on transcriptomic data, integrating multiple modalities (epigenomics, proteomics, spatial information) remains challenging [1] [2]. Next-generation models should develop more sophisticated approaches for cross-modal learning and alignment.

• Translation to Clinical Applications: The path from computational predictions to clinically actionable insights remains uncertain [5] [2]. Future research should focus on validating scFMs in clinically relevant contexts and developing frameworks for translating model predictions into therapeutic hypotheses.

The scFM paradigm represents a fundamental shift in how we approach computational analysis of single-cell data, moving from specialized models for individual tasks to general-purpose frameworks that learn universal principles of cellular biology. As these models continue to evolve, they hold tremendous promise for accelerating biological discovery and therapeutic development, provided the community addresses current limitations through collaborative development of more robust, interpretable, and accessible implementations.

The rapid accumulation of single-cell omics data has created an urgent need for computational frameworks capable of integrating and interpreting cellular heterogeneity at scale. Inspired by revolutions in natural language processing (NLP), researchers have begun treating individual cells as "sentences" and genes as "words" or "tokens" to leverage the power of large-scale self-supervised learning [10]. This analogical framework transforms single-cell analysis by applying transformer-based architectures, originally developed for linguistic tasks, to decode the complex "language" of cellular function and regulation [11] [10]. Foundation models pretrained on millions of cells using self-supervised objectives can capture fundamental biological principles that generalize across diverse tissues, species, and experimental conditions [10]. The core premise is that by exposing models to vast cellular "corpora," they can learn the syntactic and semantic rules governing gene expression and cellular identity, enabling zero-shot prediction, cross-modality integration, and perturbation modeling without extensive retraining [6] [11]. This paradigm shift toward scalable, generalizable frameworks represents a transformative approach to single-cell omics, unifying diverse biological contexts through self-supervised pretraining.

Tokenization Strategies: From Expression Matrices to Cell Sentences

Tokenization converts raw gene expression data into discrete input units processable by transformer models. Unlike natural language, gene expression data lacks inherent sequential ordering, necessitating strategic approaches to structure cellular data into "sentences." The following table summarizes predominant tokenization strategies in single-cell foundation model development:

Table 1: Tokenization Strategies for Single-Cell Foundation Models

Strategy	Core Methodology	Key Advantages	Representative Models
Rank-Based Encoding	Genes ordered by expression level within each cell	Robust to batch effects; preserves gene-gene relationships	Geneformer, Nicheformer [12] [10]
Value-Based Binning	Expression values partitioned into discrete bins	Retains quantitative expression information	scGPT, scBERT [11] [10]
Hybrid Approaches	Combines ranking with additional biological metadata	Enriches context with gene function or location	scPlantFormer, Multimodal models [11] [10]

The Cell2Sentence (C2S) method exemplifies a direct implementation of the linguistic analogy, transforming single-cell gene expression data into textual sequences by rank-ordering gene names in descending order of expression levels [13]. This conversion enables language models to process cellular information while maintaining richness and complexity through deterministic sequence generation. Specifically, for a preprocessed transcript count matrix ( C' ), the rank-order transformation ( S ) generates a cell sentence ( si ) for each cell ( i ), where genes appear in order of decreasing expression [13]. The inverse transformation leverages the observed inverse-rank frequency pattern in gene expression, using linear regression in log-log space to reconstruct expression values from generated sequences according to ( ei = ad \times \log(ri) + bd ), where ( ri ) represents the rank of gene ( i ), and ( ad ), ( bd ) are dataset-specific fitted parameters [13].

Beyond simple ranking, advanced tokenization incorporates special tokens to enrich biological context. Modality tokens distinguish between data types (e.g., scRNA-seq vs. spatial transcriptomics), species tokens enable cross-organism learning, and batch tokens help mitigate technical variations [12] [10]. Positional encodings adapted from NLP preserve the relative ordering of genes within the constructed sequences, while gene metadata embeddings incorporate additional functional annotations such as gene ontology terms or chromosomal locations to ground token representations in biological knowledge [10].

Model Architectures and Pretraining Methodologies

Transformer-based architectures dominate single-cell foundation model development, leveraging self-attention mechanisms to capture complex gene-gene interactions within cellular contexts. Most scFMs adapt the transformer encoder architecture, processing tokenized cell sequences through multiple layers of self-attention and feed-forward networks to generate latent representations at both gene and cell levels [10]. The attention mechanism enables models to learn which genes are most informative for specific cellular identities or states, effectively modeling regulatory relationships and functional pathways [10].

Self-supervised pretraining objectives are crucial for enabling models to learn generalizable biological patterns without labeled data. The following table compares predominant pretraining approaches:

Table 2: Self-Supervised Pretraining Objectives in Single-Cell Foundation Models

Pretraining Objective	Methodology	Biological Insight Captured	Example Applications
Masked Language Modeling	Randomly masks gene tokens and predicts them from context	Gene-gene coexpression patterns and regulatory relationships	scGPT, Geneformer [6] [11]
Contrastive Learning	Maximizes agreement between augmented views of same cell	Invariant cellular representations robust to technical noise	scVI, specialized SSL approaches [6]
Multimodal Alignment	Aligns representations across different omics modalities	Cross-modal regulatory mechanisms and complementary biological insights	Nicheformer, PathOmCLIP [12] [11]

Masked autoencoders have demonstrated particular effectiveness in single-cell genomics, outperforming contrastive methods in many benchmarks [6]. Adaptation includes multiple masking strategies: random masking, gene program masking that targets biologically meaningful gene sets, and isolated masking that focuses on specific functional groups like transcription factors [6]. During pretraining, models learn to reconstruct masked gene expressions based on contextual information from other genes in the cell, effectively capturing co-expression patterns and regulatory relationships. Empirical analyses reveal that models pretrained on over 20 million cells develop robust representations that transfer effectively to downstream tasks including cell-type prediction, gene-expression reconstruction, cross-modality prediction, and data integration [6].

The Nicheformer architecture exemplifies advanced transformer adaptation for spatial and dissociated single-cell data integration, employing a unified tokenization strategy across technology modalities and species [12]. Its architecture processes 1,500-token sequences through 12 transformer encoder layers with 16 attention heads each, generating 512-dimensional embeddings that capture both transcriptional and spatial context [12]. Critical to its performance is joint training on dissociated and spatial transcriptomics data, as models trained exclusively on dissociated data fail to capture spatial microenvironment complexity despite larger dataset sizes [12].

Experimental Validation and Benchmarking

Rigorous experimental validation demonstrates that self-supervised pretraining significantly enhances performance across diverse single-cell analysis tasks, particularly in transfer learning scenarios. Benchmarking across multiple datasets reveals consistent improvements in cell-type annotation, spatial composition prediction, and cross-modality integration when models leverage pretraining on large auxiliary datasets.

Cell-Type Prediction and Zero-Shot Learning

Empirical analyses establish that self-supervised pretraining on expansive datasets substantially improves cell-type prediction accuracy, especially for rare cell populations and in transfer learning settings. Models pretrained on the scTab dataset (over 20 million cells) and fine-tuned on target datasets like peripheral blood mononuclear cells (PBMCs) and Tabula Sapiens show marked improvements in macro F1 scores—from 0.7013 to 0.7466 for PBMCs and from 0.2722 to 0.3085 for Tabula Sapiens [6]. This enhancement is particularly pronounced for underrepresented cell types, indicating improved robustness to class imbalance [6].

In zero-shot settings, where models predict without task-specific fine-tuning, self-supervised learning demonstrates remarkable capability. Using k-nearest neighbors classification on embeddings from frozen pretrained models, scFMs accurately identify cell types in unseen datasets, addressing a critical challenge in single-cell analysis where comprehensive labeling is often impractical [6]. The Cell2Sentence approach further validates this capability, showing that GPT-2 fine-tuned with cell sentences can accurately predict cell types from input sequences, demonstrating that language models can acquire significant understanding of single-cell biology through this transformation [13].

Cross-Modality Prediction and Data Integration

Spatially aware models like Nicheformer enable novel downstream tasks including spatial composition prediction and spatial label transfer, outperforming models trained exclusively on dissociated data [12]. By learning joint representations of single-cell and spatial genomics, these models successfully transfer spatial context identified in spatial transcriptomics to dissociated scRNA-seq data, effectively enriching nonspatial data with spatial microenvironment information [12]. This capability addresses a fundamental limitation of traditional scRNA-seq, which loses spatial organization during tissue dissociation.

The following table summarizes quantitative performance improvements achieved through self-supervised pretraining across key biological tasks:

Table 3: Performance Benchmarks of Self-Supervised Learning in Single-Cell Genomics

Task	Dataset	Baseline Performance	SSL-Enhanced Performance	Key Improvement
Cell-Type Prediction	PBMC (422K cells, 30 types)	0.7013 macro F1	0.7466 macro F1	+6.5% improvement, especially rare cell types [6]
Cell-Type Prediction	Tabula Sapiens (483K cells, 161 types)	0.2722 macro F1	0.3085 macro F1	+13.3% improvement, better type II pneumocyte classification [6]
Spatial Label Prediction	Multiple organs (spatial transcriptomics)	Models trained only on dissociated data fail	Nicheformer enables accurate prediction	Recovers spatial complexity lost in dissociation [12]
Cross-Species Annotation	Plant systems (scPlantFormer)	Species-specific model performance	92% cross-species accuracy	Effective knowledge transfer across organisms [11]

Implementation Toolkit for Researchers

Successful implementation of single-cell foundation models requires specialized computational tools and resources. The following essential components form the foundational toolkit for researchers developing and applying these models:

Table 4: Essential Research Reagents and Computational Tools for Single-Cell Foundation Models

Resource Category	Specific Tools/Platforms	Primary Function	Key Features
Data Repositories	CELLxGENE Census, Human Cell Atlas, GEO/SRA	Provide standardized single-cell datasets for pretraining	Curated collections with quality controls; CELLxGENE offers >100M cells [6] [10]
Model Architectures	scGPT, Geneformer, Nicheformer, scBERT	Transformer-based model implementations	Pretrained weights, fine-tuning scripts, task-specific heads [12] [11] [10]
Processing Frameworks	Scanpy, Seurat, SCANPY Python library	Data preprocessing and quality control	Normalization, filtering, mitochondrial QC metrics [13]
Specialized Libraries	Hugging Face Transformers, scVI	Model training and adaptation	Optimized transformer implementations, parameter-efficient fine-tuning [13] [11]
Benchmarking Platforms	BioLLM, DISCO, CZ CELLxGENE Discover	Model evaluation and comparison	Standardized metrics, federated analysis capabilities [11]

Implementation typically begins with data preprocessing using Scanpy or similar frameworks, followed by tokenization according to the selected strategy (rank-based, value-based, or hybrid) [13]. For most applications, researchers can start with pretrained models from platforms like Hugging Face, followed by domain adaptation through parameter-efficient fine-tuning techniques like LoRA (Low-Rank Adaptation) or prompt tuning [13] [11]. This approach significantly reduces computational requirements compared to full pretraining while maintaining performance on specialized tasks.

Critical to successful implementation is careful handling of dataset-specific biases, particularly when integrating spatial and dissociated transcriptomics data, which exhibit technology-dependent expression patterns [12]. Practical deployment should incorporate systematic evaluation across multiple biological tasks to ensure robust performance, with particular attention to rare cell types and cross-dataset generalization.

The analogy of cells as sentences and genes as tokens has established a powerful paradigm for single-cell omics analysis, enabling self-supervised learning at unprecedented scale. Transformer-based foundation models pretrained on millions of cells demonstrate exceptional versatility across diverse downstream tasks, from basic cell-type annotation to complex spatial niche prediction and cross-modality integration. The consistent empirical evidence shows that self-supervised pretraining on large auxiliary datasets significantly enhances model performance, particularly in transfer learning scenarios and for underrepresented cell populations.

Future development will likely focus on several critical frontiers: improved multimodal integration spanning transcriptomics, epigenomics, proteomics, and high-resolution imaging; enhanced interpretability to extract biologically meaningful insights from model attention patterns; and computational efficiency improvements to make these tools accessible to broader research communities. As single-cell technologies continue evolving toward higher throughput and multimodal profiling, foundation models built on the linguistic analogy will play an increasingly central role in deciphering the complex language of cellular function and dysfunction, ultimately accelerating discovery in basic biology and therapeutic development.

The advent of foundation models is revolutionizing the analysis of single-cell omics data. These large-scale, self-supervised models rely on vast and diverse pretraining corpora to learn fundamental biological principles, enabling their application to downstream tasks such as cell-type annotation, perturbation prediction, and genetic inference. This technical guide delineates the core data sources, including CZ CELLxGENE and the Human Cell Atlas (HCA), that are central to constructing these pretraining corpora. We detail the quantitative scale of these resources, the experimental and computational protocols for their utilization, and the integrative frameworks necessary for building robust models. Within the broader context of self-supervised pretraining for single-cell research, this whitepaper serves as an essential resource for researchers and drug development professionals aiming to leverage or develop the next generation of analytical tools in computational biology.

The development of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, moving from task-specific models to general-purpose frameworks trained on massive datasets. A foundation model is a large-scale deep learning model pretrained on extensive datasets at scale using self-supervised objectives and then adapted to a wide range of downstream tasks [1]. The performance and generalizability of these models are intrinsically tied to the scale, diversity, and quality of their pretraining corpora.

Self-supervised learning (SSL) has emerged as the cornerstone for training these models, as it allows them to learn meaningful representations from the inherent structure of unlabeled data, overcoming the scarcity of manual annotations. In single-cell genomics, common SSL pretext tasks include masked autoencoders, where the model learns to reconstruct randomly masked portions of a cell's gene expression profile, and contrastive learning, which teaches the model to identify similar and dissimilar cellular states [6]. The resulting models capture a foundational understanding of cellular heterogeneity, gene-gene relationships, and regulatory networks, which can be fine-tuned with minimal data for specific applications like drug response prediction or disease subtype classification.

This guide focuses on the pivotal first step in this pipeline: the assembly of the pretraining corpus. We provide a detailed examination of the primary public data repositories, the methodologies for accessing and processing this data, and the experimental protocols for its use in training robust scFMs.

The construction of a powerful pretraining corpus begins with the aggregation of data from large-scale public repositories. The table below summarizes the key attributes of two cornerstone resources and a representative integrated corpus cited in recent literature.

Table 1: Key Data Sources for Pretraining Corpora

Data Source	Reported Scale	Content Highlights	Notable Use Cases in Model Training
CZ CELLxGENE Discover [14]	>33 million unique cells; 436 datasets; 2,700+ cell types [14] [2]	Standardized data from healthy human and mouse tissues; includes gene expression matrices and Tier 1 metadata.	scGPT was pretrained on over 33 million cells from CZ CELLxGENE [2]. Platforms like this provide unified access to tens of millions of single-cell datasets for scFM training [1].
Human Cell Atlas (HCA) [15]	A primary source for multiorgan atlases; part of aggregated corpora of over 100 million cells [1].	A global collaborative effort to map every cell type in the human body; contributes raw sequencing data (FASTQ) and detailed Tier 2 metadata.	Serves as a critical data source for building broad-coverage training corpora that capture a wide spectrum of biological variation [1].
SpatialCorpus-110M (Nicheformer) [12]	110 million cells (57M dissociated; 53M spatially resolved)	A curated collection from 73 human and mouse organs, integrating both dissociated and spatial transcriptomics data.	Used to pretrain Nicheformer, demonstrating the power of combining dissociated and spatial data in a single model [12].

These resources are not mutually exclusive; they are often integrated to create the massive corpora required for modern scFMs. For instance, one review notes that platforms like CZ CELLxGENE and the HCA Data Portal collectively provide access to over 100 million cells, forming the backbone of many pretraining efforts [2].

Data Sourcing and Processing Protocols

Data Acquisition and Contribution Frameworks

Accessing and contributing to these data repositories involves specific protocols and data structures.

• CZ CELLxGENE Data Structure: The platform provides gene expression matrices in AnnData format, accompanied by Tier 1 metadata, which typically includes broad demographic and core technical information [15]. Its Census function allows for programmatic access to any custom slice of this standardized data in R and Python, facilitating its direct integration into machine learning pipelines [14] [16].
• HCA Data Contribution and Access: Scientists contributing to the HCA must provide data and metadata that are stored across three platforms for security and accessibility. Gene expression matrices and Tier 1 metadata are stored on the CELLxGENE Discover platform. Raw sequence data (FASTQ files) and detailed Tier 2 metadata (which may contain personal information) are stored in the HCA Data Repository, which operates a managed access service for sensitive data [15]. Contributors of unpublished data can request an embargo until the relevant atlas is published [15].

Data Processing and Tokenization for Model Training

Raw data from these sources must be processed and "tokenized" before being fed into a transformer-based model. Tokenization converts a cell's gene expression profile into a sequence of discrete units (tokens) that the model can process.

Table 2: Common Tokenization Strategies for Single-Cell Foundation Models

Strategy	Core Methodology	Key Advantage	Example Model
Rank-based Tokenization	Genes are ordered by their expression level within each cell, and the top n genes form the input sequence.	Provides a deterministic, non-arbitrary sequence from non-sequential data; robust to batch effects.	Nicheformer, Geneformer [12]
Binning and Value-based	Gene expression values are partitioned into bins, or normalized counts are used directly alongside gene identifiers.	Can retain more quantitative information from the expression values.	scGPT, scBERT [1]
Contextual Token Addition	Special tokens are prepended to the gene sequence to represent metadata such as species, technology modality, or batch.	Helps the model learn and account for technical and biological covariates.	scGPT, Nicheformer [1] [12]

A critical challenge is that gene expression data is not naturally sequential. The rank-based strategy is a common and effective solution, creating a deterministic sequence by ranking genes from highest to lowest expression per cell [1] [12]. After tokenization, each token is converted into an embedding vector, and positional encodings are added to inform the model of the gene's rank before the sequence is processed by the transformer layers.

Experimental and Computational Methodologies

Model Architecture and Pretraining Regimens

Most successful scFMs are built on the transformer architecture, which uses self-attention mechanisms to weigh the importance of different genes when processing a cell's profile [1]. Two primary architectural variants are employed:

• Encoder-only models (e.g., scBERT): Use a bidirectional attention mechanism, meaning the model learns from all genes in a cell simultaneously. This is often well-suited for classification tasks like cell-type annotation [1].
• Decoder-only models (e.g., scGPT): Use a unidirectional (causal) attention mechanism, where the model predicts the next gene in a sequence based on the previous ones. This architecture is particularly effective for generative tasks [1].

The pretraining of these models is a computationally intensive process that relies on self-supervised objectives. A dominant approach is the masked language modeling objective, adapted from natural language processing. In this setup, a random subset (e.g., 15-20%) of the gene tokens in a cell's sequence is masked, and the model is trained to reconstruct their original values based on the unmasked context [1] [2]. This forces the model to learn the complex, co-dependent relationships between genes.

Addressing Technical Noise and Batch Effects

A significant challenge in building pretraining corpora from multiple sources is batch effects—technical variations introduced by different labs, protocols, or sequencing platforms that are not of biological interest [17] [2]. If not addressed, models can learn these nuisances instead of true biological signals.

Deep learning integration methods have become a powerful tool for this. Methods like scVI (a variational autoencoder) and scANVI (its semi-supervised extension) are specifically designed to integrate data from multiple batches in a non-linear way, effectively separating the technical batch effects from the underlying biological variation [17]. Incorporating batch information as special tokens during tokenization, as done in scGPT, is another strategy to make the model aware of and robust to these technical differences [1].

The Scientist's Toolkit: Research Reagent Solutions

The following table details key computational tools and data structures essential for working with large-scale single-cell pretraining corpora.

Table 3: Essential Tools and Resources for Corpus Construction and Model Training

Tool / Resource	Type	Primary Function in Pretraining
AnnData	Data Structure	The standard file format (.h5ad) for storing single-cell data, including expression matrices and multi-layered metadata. Serves as the primary input for many models and analysis tools.
scVI / scANVI	Software Library (Python)	Deep learning-based tools for scalable data integration and batch correction of single-cell data, crucial for preparing a unified, high-quality corpus.
Transformer Architecture	Model Architecture	The neural network backbone of most scFMs. Its self-attention mechanism is key to modeling complex gene-gene interactions.
Census (CELLxGENE)	API	Provides programmatic access (in R and Python) to a standardized slice of the entire CZ CELLxGENE data corpus, enabling efficient data querying and loading.
HCA Data Repository	Data Portal	Hosts raw sequencing data (FASTQ) and detailed donor metadata, which are essential for reprocessing data or performing novel genetic analyses.

Integrated Data Ecosystem for Pretraining

The ultimate power of a pretraining corpus lies in the diversity and integration of its constituent datasets. The leading scFMs demonstrate that combining data across modalities, species, and technologies produces more robust and generalizable models.

• Multimodal Integration: Newer models are moving beyond single-cell RNA-seq to incorporate additional data types. For example, Nicheformer was jointly trained on both dissociated single-cell data and spatial transcriptomics data. This integration allows the model to learn representations that capture the spatial context of cells, which is lost in dissociated protocols [12].
• Cross-Species Learning: Some models, including Nicheformer, create a shared vocabulary using orthologous genes from both human and mouse, enabling the model to learn universal principles of gene regulation and cellular function across species [12].

The following diagram illustrates the interconnected nature of this data ecosystem and its role in training a comprehensive foundation model.

The construction of a comprehensive pretraining corpus is a critical, foundational endeavor in the development of powerful single-cell foundation models. Resources like CZ CELLxGENE and the Human Cell Atlas provide the massive scale of standardized data required for this task, while sophisticated tokenization strategies and self-supervised learning protocols enable the transformation of this raw data into actionable biological knowledge. As the field progresses, the integration of multimodal and cross-species data will be paramount. By leveraging the protocols and resources outlined in this guide, researchers and drug developers can contribute to and harness these advanced models, accelerating the translation of single-cell omics into mechanistic insights and therapeutic breakthroughs.

In single-cell omics research, the ability to profile cellular heterogeneity at unprecedented resolution is fundamentally challenged by technical heterogeneity introduced during experimental workflows. Batch effects—systematic technical variations that affect groups of samples—represent a critical bottleneck that can compromise data integrity, mask true biological signals, and lead to spurious findings [18] [19]. The emergence of self-supervised pretraining for single-cell data analysis offers promising avenues to address these challenges, but requires meticulous quality control (QC) to realize its full potential. This technical guide examines the sources and impacts of data heterogeneity in single-cell genomics and provides structured frameworks for quality assessment and batch effect mitigation within the context of foundation model development.

Technical variation in single-cell experiments arises from multiple sources across the experimental workflow. As identified in mass spectrometry imaging studies, these artifacts can be categorized into five distinct levels: pixel, section, slide, time, and location (center/laboratory) levels [18]. In sequencing-based approaches, additional challenges include cell-to-cell variation in capture efficiency, amplification biases, and the inherent sparsity of single-cell data matrices [20] [19]. These technical artifacts become particularly problematic for self-supervised learning approaches, which rely on the assumption that the input data contains meaningful biological patterns rather than technical confounders.

Understanding Batch Effects in Single-Cell Omics

Batch effects manifest differently across single-cell modalities but share common characteristics that distinguish them from biological variation. In single-cell RNA sequencing (scRNA-seq), technical variation primarily stems from differences in library preparation protocols, sequencing depth, reagent batches, and laboratory conditions [20]. For chromatin accessibility data (scATAC-seq), additional technical challenges include variation in transposase efficiency and nuclear integrity [21]. Spatial omics techniques face unique spatial biases in addition to standard technical variations [18].

The fundamental challenge in addressing batch effects lies in their potential to confound with biological signals. As noted in foundational single-cell literature, "if a scRNA-seq experiment is designed improperly, the results can be significantly affected by batch effects" [20]. This entanglement is particularly problematic for self-supervised models, which may inadvertently learn to represent technical artifacts rather than biological states if quality control is inadequate.

Impact on Self-Supervised Pretraining

The success of single-cell foundation models (scFMs) depends critically on the quality and homogeneity of their pretraining data. These models, including scGPT and scPlantFormer, utilize transformer architectures pretrained on millions of cells to learn universal representations of cellular states [2] [1]. However, "batch effect propagation in transfer learning" remains a significant challenge [2], as technical artifacts present in pretraining data can propagate through the model and affect performance on downstream tasks.

Foundation models typically employ tokenization strategies that convert gene expression profiles into structured sequences analogous to words in a sentence [1]. This approach is highly sensitive to systematic technical variations, which can distort the relationships between "tokens" (genes) and undermine the model's ability to learn biologically meaningful representations. Consequently, rigorous quality control becomes essential not merely for data cleaning, but for enabling effective representation learning.

Table 1: Common Batch Effect Sources in Single-Cell Omics

Source Category	Specific Examples	Impact on Foundation Models
Sample Preparation	Cell dissociation protocols, fixation methods, reagent batches	Introduces systematic biases in molecular recovery rates
Instrumentation	Sequencing platform, laser alignment in MSI, liquid handling	Creates platform-specific signal distributions
Laboratory Factors	Operator differences, laboratory environment, sample storage	Generates non-biological covariance structures
Temporal Variation	Experimental duration, reagent degradation, protocol drift	Produces time-dependent technical confounding

Quality Control Frameworks and Metrics

Essential QC Metrics for Single-Cell Data

Comprehensive quality control begins with calculating standardized metrics that distinguish high-quality cells from those affected by technical artifacts. For scRNA-seq data, three fundamental metrics form the cornerstone of quality assessment: (1) the number of counts per barcode (count depth), (2) the number of genes detected per barcode, and (3) the fraction of counts originating from mitochondrial genes [22]. These metrics collectively identify cells with compromised membranes or other quality issues that might distort downstream analyses.

The mitochondrial ratio is particularly informative for identifying stressed or dying cells, as increased mitochondrial read fraction often indicates cellular stress during sample preparation [22] [23]. As implemented in the SCTK-QC pipeline, additional metrics include the number of genes detected per UMI (complexity measure) and contamination estimates from ambient RNA [24]. For scATAC-seq data, analogous metrics include total fragments per cell, fraction of fragments in peaks, and transcription start site (TSS) enrichment scores [25].

Threshold Determination and Automated QC

Determining appropriate thresholds for quality filtering presents a significant challenge in single-cell analysis. Overly stringent thresholds may remove biologically relevant cell populations, while overly permissive thresholds retain technical artifacts that confound interpretation. Two primary approaches have emerged for threshold determination:

• Manual thresholding based on visual inspection of metric distributions (e.g., knee plots, violin plots) [23]
• Automated thresholding using robust statistics such as Median Absolute Deviations (MAD), which identifies outliers based on deviation from the median [22]

The MAD approach defines outliers as cells where metrics differ by more than 5 MADs from the median, providing a data-driven filtering strategy that adapts to dataset-specific characteristics [22]. This method is particularly valuable for large-scale datasets intended for foundation model pretraining, where manual inspection becomes impractical.

Table 2: Standard Quality Control Metrics and Thresholds

Metric	Description	Calculation Method	Typical Thresholds
nUMI	Total number of transcripts/counts per cell	Sum of counts per barcode	>500-1000 UMI [23]
nGene	Number of detected genes per cell	Count of genes with >0 counts	>300 genes [23]
Mitochondrial Ratio	Fraction of mitochondrial reads	MT-counts / total counts	<0.2 [22]
log10GenesPerUMI	Complexity measure	log10(nGene) / log10(nUMI)	Higher values indicate better complexity [23]
Doublet Score	Likelihood of multiple cells	Computational prediction	Dataset-dependent [24]

Computational Strategies for Batch Effect Correction

Traditional Batch Correction Methods

Multiple computational approaches have been developed to address batch effects in single-cell data, ranging from normalization techniques to specialized batch correction algorithms. Common normalization methods include Total Ion Count (TIC) normalization, median normalization, and internal standard (IS) normalization [18]. These approaches aim to remove global technical variations while preserving biological signals.

Beyond normalization, specialized batch correction methods include:

• Location-scale methods (e.g., Combat, Combat-Seq) that model and remove batch-specific location and scale parameters
• Matrix factorization methods (e.g., ICA, SVD, EigenMS) that separate technical and biological components in low-dimensional space
• Deep neural network approaches (e.g., NormAE) that learn complex nonlinear mappings between batches [18]

For scATAC-seq data, enhancement methods like scCASE use non-negative matrix factorization with iteratively updated cell-to-cell similarity matrices to impute dropout events while preserving cellular heterogeneity [21]. These methods demonstrate how incorporating biological constraints (e.g., similarity structures) can improve batch correction while maintaining biologically relevant variation.

Integration with Foundation Model Architectures

The emergence of single-cell foundation models has created new opportunities for batch effect correction within the model architecture itself. These models can incorporate several strategies to address technical variation:

• Biological prior integration: Hybrid pretraining with biological knowledge to distinguish technical from biological variation [2]
• Batch-aware tokenization: Including batch information as special tokens during the tokenization process [1]
• Attention mechanisms: Transformer attention layers that can learn to weight batch-informative genes appropriately [1]

Notably, some foundation models demonstrate robustness to batch effects without explicit correction, suggesting that large-scale pretraining on diverse datasets may inherently confer some immunity to technical variations [1]. However, this remains an active area of research, and systematic quality control remains essential regardless of model architecture.

Experimental Design and Quality Control Standards

Proactive Experimental Design

Effective management of batch effects begins with thoughtful experimental design rather than post hoc computational correction. Randomization and blocking strategies can effectively reduce systematic bias, particularly for time-dependent variations in large batches [18]. By distributing biological conditions across multiple batches and technical replicates, researchers can create datasets where biological signals are not completely confounded with technical variations.

The implementation of quality control standards (QCS) represents another proactive approach to technical variation management. In mass spectrometry imaging, tissue-mimicking QCS consisting of propranolol in a gelatin matrix have been developed to monitor ion suppression effects across experiments [18]. These standards enable direct quantification of technical variability introduced during sample preparation and instrument performance, providing objective metrics for data quality assessment.

Reference-Based Quality Frameworks

Leveraging large compendia of available omics data as reference represents a powerful strategy for quality assessment and enhancement. Methods like scCASER extend enhancement algorithms to incorporate external reference data, using prior knowledge to guide the correction of target datasets [21]. This approach is particularly valuable for foundation model training, where reference datasets can provide benchmarks for technical quality.

Federated computational platforms such as DISCO and CZ CELLxGENE Discover aggregate over 100 million cells for standardized analysis, enabling quality assessment through comparison with reference datasets [2]. These resources facilitate the development of standardized quality metrics that transcend individual laboratories or protocols, creating community-wide standards for data quality.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagents and Solutions for Quality Control

Reagent/Solution	Function	Application Context
Gelatin-based QCS [18]	Tissue-mimicking quality control standard	MALDI-MSI technical variation monitoring
Propranolol in gelatin matrix [18]	Small molecule for ionization efficiency assessment	Batch effect evaluation in spatial omics
ERCC spike-in controls [20]	External RNA controls for technical variation assessment	scRNA-seq protocol standardization
Enzyme activity standards	Monitoring digestion efficiency	Peptide and N-glycan MALDI-MSI [18]
Homogeneous tissue controls (e.g., liver, egg white) [18]	Biological reference materials	Inter-day and cross-site reproducibility assessment
Lipid standards [18]	Method reproducibility evaluation	Single-cell MS imaging quality control
Barcode beads [24]	Cell multiplexing and identification	Droplet-based scRNA-seq protocols
Unique Molecular Identifiers (UMIs) [24]	Correction of amplification biases	Molecular counting in single-cell protocols

Visualization of Quality Control and Batch Effect Correction Workflows

Integrated QC and Analysis Pipeline

SC Quality Control and Foundation Model Integration Pipeline

Batch Effect Correction in Foundation Models

Batch Effect Correction in Single-Cell Foundation Models

The integration of comprehensive quality control frameworks with self-supervised pretraining represents the most promising path toward overcoming data heterogeneity in single-cell omics. As foundation models continue to evolve in scale and sophistication, the principles of rigorous quality assessment, proactive experimental design, and appropriate batch correction will remain fundamental to their biological utility. By implementing the standardized metrics, computational approaches, and experimental standards outlined in this guide, researchers can build foundation models that genuinely capture biological heterogeneity while remaining robust to technical artifacts. The future of single-cell data science depends not only on increasingly powerful models but on the quality foundations upon which they are built.

The field of single-cell genomics has undergone a seismic shift, transitioning from a data-scarce to a data-rich discipline. This explosion of data, generated by technologies capable of profiling millions of individual cells, has rendered traditional analytical methods inadequate. Concurrently, self-supervised learning (SSL), a paradigm that learns representations from unlabeled data by solving pretext tasks, has revolutionized fields like natural language processing (NLP) and computer vision. The convergence of these two trends is now reshaping biological research. This whitepaper details how SSL, particularly through foundation models, is being adapted to decipher the complex "language" of biology encoded in single-cell omics data, offering unprecedented insights into cellular heterogeneity, disease mechanisms, and therapeutic discovery [2] [10].

From Language to Biology: Core SSL Concepts and Adaptations

The Principles of Self-Supervised Learning

SSL creates learning signals directly from the structure of the data itself, bypassing the need for extensive manual labels. The two dominant pretext tasks are:

• Masked Modeling: Inspired by models like BERT in NLP, this approach randomly masks (hides) portions of the input data—such as words in a text or genes in a cell's expression profile—and trains the model to predict the missing information from the context. This forces the model to learn robust, bidirectional relationships within the data [6] [10].
• Contrastive Learning: This method learns representations by contrasting similar (positive) and dissimilar (negative) data pairs. The model is trained to pull embeddings of augmented views of the same data point (e.g., the same cell after different data augmentations) closer together while pushing apart embeddings from different data points [6] [26].

Architectural Adaptation for Single-Cell Omics

Applying SSL to single-cell data requires significant architectural innovation to handle its unique characteristics, which are fundamentally different from language or images.

• Tokenization: In NLP, tokens are discrete words. In single-cell biology, a "token" must be constructed from continuous molecular measurements. A common strategy is to treat each gene or genomic feature as a token. Since gene expression data lacks a natural sequence, models often impose an order, such as ranking genes by their expression level within each cell, to create a "sentence" that represents a cell [10].
• Model Architecture: The Transformer architecture has become the backbone of most single-cell foundation models (scFMs). Its attention mechanism allows the model to dynamically weigh the importance of all genes when interpreting the state of a cell, effectively learning complex gene-gene interactions and regulatory networks [2] [10].
• Specialized Pretext Tasks: Beyond standard masking, domain-specific pretext tasks have been developed. For example, scPlantFormer integrates phylogenetic constraints, while Self-GenomeNet leverages the reverse-complement symmetry of DNA sequences to learn more powerful genomic representations [2] [27].

The diagram below illustrates a generalized workflow for applying self-supervised learning to single-cell omics data.

Benchmarking Performance: A Quantitative Review

Rigorous benchmarking is essential to guide the selection of SSL methods for specific research goals. The following tables consolidate performance metrics from recent large-scale evaluations, revealing clear task-dependent trade-offs.

Table 1: Benchmarking SSL methods on core single-cell tasks (scSSL-Bench). Performance is a composite score based on metrics like accuracy and F1-score. Adapted from [28] [26].

Method Category	Example Models	Batch Correction	Cell Type Annotation	Missing Modality Prediction
Specialized Single-Cell Frameworks	scVI, CLAIRE, scGPT (fine-tuned)	Excellent	Good	Fair
Generic SSL Methods	VICReg, SimCLR	Good	Excellent	Excellent
Single-Cell Foundation Models (Zero-Shot)	scGPT (zero-shot)	Fair	Good	Not Applicable

Table 2: Impact of pre-training on auxiliary data for cell-type prediction. Performance measured by Macro F1 score. Data from [6].

Dataset	Supervised Baseline (No Pre-training)	With SSL Pre-training on scTab	Key Improvement
PBMC (422k cells, 30 types)	0.7013 ± 0.0077	0.7466 ± 0.0057	Underrepresented cell types
Tabula Sapiens (483k cells, 161 types)	0.2722 ± 0.0123	0.3085 ± 0.0040	Type II pneumocytes (6,881 correct vs. 2,441)
Human Lung Cell Atlas (2.2M cells, 51 types)	Marginal Improvement	Marginal Improvement	Dataset already large/rich

Key findings from these benchmarks include:

• Task-Specific Superiority: No single model dominates all tasks. Specialized frameworks like scVI and CLAIRE excel at batch correction, a critical step for integrating datasets from different labs. In contrast, generic SSL methods like VICReg show superior performance for cell typing and multi-modal integration [28] [26].
• Effective Pre-training: Self-supervised pre-training on large, diverse auxiliary datasets (e.g., the 20-million-cell scTab corpus) significantly boosts performance on smaller target datasets, especially for identifying rare or challenging cell populations [6].
• Data Augmentation: Simple strategies like random masking have been empirically shown to be more effective than complex, biology-specific augmentations across a variety of tasks [28] [26].

Experimental Protocols in Practice

Protocol: Leveraging Auxiliary Data for Cell-Type Prediction

This protocol outlines the methodology for using SSL to improve cell-type annotation, a common and critical task in single-cell analysis [6].

• Pre-training Corpus Curation: Assemble a large, diverse, and high-quality collection of single-cell data for self-supervised pre-training. The scTab dataset, encompassing over 20 million human cells, is a prime example. Standardize and normalize data across studies to mitigate technical batch effects.
• Self-Supervised Pre-training: Train a model (e.g., a fully connected autoencoder or transformer) on the curated corpus using a pretext task. Masked autoencoding is a highly effective choice, where a random subset of gene expressions in each cell is masked, and the model is trained to reconstruct them.
• Transfer Learning & Fine-tuning: For a target dataset (e.g., a specific patient cohort or tissue atlas), initialize the model with the pre-trained weights. Subsequently, fine-tune the model on a small, labeled portion of the target data for the supervised task of cell-type prediction.
• Evaluation: Apply the fine-tuned model to a held-out test set from the target dataset. Use metrics like the macro F1 score to evaluate performance, paying particular attention to gains in predicting rare or underrepresented cell types.

Protocol: Task-Specific Self-Pretraining for Genomic Sequences

For tasks where a massive, general-purpose pre-training corpus is unavailable, self-pretraining on task-specific data is a powerful and compute-efficient alternative [29].

• Task Data Collection: Gather the unlabeled genomic sequences relevant to the downstream task (e.g., gene bodies for a gene-finding task).
• Self-Pretraining Phase: Perform masked language modeling (MLM) on these sequences. A residual CNN or transformer encoder is trained to predict randomly masked nucleotides within the sequences, learning the fundamental statistical patterns and long-range dependencies of the relevant genomic regions.
• Supervised Fine-tuning: Replace the MLM head with a task-specific prediction head (e.g., for classifying exons, introns, and non-coding regions). The entire model is then fine-tuned end-to-end on the labeled downstream task.
• Structured Prediction (Optional): For tasks like gene finding where label dependencies are critical (e.g., an exon is always followed by a splice site), augment the model with a Conditional Random Field (CRF) layer to enforce globally coherent predictions, which can dramatically improve performance [29].

The following diagram contrasts these two primary experimental paradigms.

The Scientist's Toolkit: Essential Research Reagents

The successful application of SSL in genomics relies on a ecosystem of computational tools, models, and datasets. The following table details key resources.

Table 3: Key resources for self-supervised learning in single-cell omics research.

Resource Name	Type	Primary Function	Reference/Source
scGPT	Foundation Model	Large-scale model for zero-shot cell annotation, multi-omic integration, and perturbation prediction.	[2]
CZ CELLxGENE Discover	Data Platform	Provides standardized access to over 100 million curated single-cells for pre-training and analysis.	[2] [10]
scSSL-Bench	Benchmarking Tool	Standardized framework for evaluating SSL methods on tasks like batch correction and cell typing.	[28] [26]
BioLLM	Benchmarking Framework	Universal interface for integrating and benchmarking over 15 different single-cell foundation models.	[2]
Self-GenomeNet	SSL Method	A self-supervised technique tailored for genomic sequences, using reverse-complement prediction.	[27]

The transfer of self-supervised learning from NLP to genomics represents a fundamental upgrade to the computational biologist's arsenal. By enabling models to learn the deep grammar of biology from vast, unlabeled datasets, SSL provides a powerful foundation for tackling the complexity and scale of modern single-cell omics. As benchmarked in this review, the technology is already delivering tangible improvements in critical tasks like cell annotation and data integration. While challenges in model interpretability, computational cost, and seamless multi-modal integration remain, the trajectory is clear. SSL-powered foundation models are poised to become the central, unifying platform for extracting biological insight from cellular data, dramatically accelerating the pace of discovery in basic research and drug development.

Architectural Innovations and Practical Applications in Biomedicine

The advent of high-throughput single-cell genomics has generated vast amounts of molecular data, creating an urgent need for computational frameworks capable of integrating and analyzing this information at scale. Foundation models, pre-trained on massive datasets using self-supervised learning (SSL), have emerged as transformative tools for single-cell omics research [2] [1]. These models adapt transformer architectures—originally developed for natural language processing—to decode the complex "language" of cellular biology, where individual cells represent documents and genes or genomic features function as words or tokens [1].

Within this paradigm, a critical architectural consideration centers on whether to employ encoder-only, decoder-only, or full encoder-decoder transformer configurations. Each approach offers distinct advantages and limitations for different biological tasks, from cell type annotation and perturbation response prediction to multi-omic data integration [2] [1]. This technical review examines the implementation, performance, and optimal application scenarios for these architectural variants within the context of self-supervised pretraining for single-cell omics research.

Core Architectural Frameworks and Their Biological Applications

Encoder-Only Architectures

Encoder-only models process input sequences bidirectionally, meaning each token (gene) can attend to all other tokens in the sequence (cell). This architecture generates rich, contextualized representations of the entire input, making it particularly suitable for classification and representation learning tasks [30].

Key Implementations:

• scBERT adopts the BERT (Bidirectional Encoder Representations from Transformers) architecture for single-cell RNA sequence data analysis, verifying the self-supervised pretraining and fine-tuning paradigm's ability to learn from unlabeled scRNA-seq data [31].
• scReformer-BERT enhances this approach by integrating Reformer encoders, which address the computational limitations of traditional transformers when processing long sequences through locality-sensitive hashing (LSH) attention and reversible residual layers [31]. This innovation allows the model to handle the full set of over 10,000 genes per cell without requiring feature selection.

Biological Applications: Encoder-only models excel in tasks requiring comprehensive contextual understanding of cellular states:

• Cell type classification: Learning representations that distinguish cell types based on global gene expression patterns [31]
• Batch effect correction: Integrating datasets across different experimental conditions by learning biological signals independent of technical variations [26]
• Multi-omic integration: Creating unified representations across different molecular modalities (e.g., transcriptomics, epigenomics) [2]

Table 1: Encoder-Only Model Performance on Classification Tasks

Model	Architecture	Training Data	Cell Type Annotation Accuracy	Key Strengths
scBERT	BERT-based	Millions of cells	High (dataset-dependent)	Established architecture, proven performance
scReformer-BERT	Reformer-enhanced	~15 million cells	Superior to baselines	Handles full gene set, computational efficiency
BioLLM	Universal interface	Benchmarking 15+ models	Variable by task	Standardized evaluation, multiple model support

Decoder-Only Architectures

Decoder-only models utilize unidirectional attention, where each token can only attend to previous tokens in the sequence. This autoregressive property makes them naturally suited for generative tasks, as they learn to predict next elements in a sequence [1] [30].

Key Implementations:

• scGPT employs a decoder-only architecture inspired by the Generative Pretrained Transformer (GPT), using masked language modeling to pretrain on over 33 million cells [2] [32]. The model iteratively predicts masked genes conditioned on known genes, learning the fundamental principles of gene regulation and cellular states.
• scPlantFormer represents a specialized decoder-based model that integrates phylogenetic constraints into its attention mechanism, achieving 92% cross-species annotation accuracy in plant systems [2].

Biological Applications: Decoder architectures demonstrate particular strength in:

• Gene expression prediction: Forecasting cellular responses to perturbations or experimental conditions [32]
• Data augmentation: Generating synthetic single-cell profiles to address data scarcity, particularly for rare cell types [33]
• Regulatory network inference: Reconstructing gene-gene interaction networks from expression patterns [2]

Table 2: Decoder-Only Model Performance on Generative Tasks

Model	Architecture	Training Data	Perturbation Prediction Pearson Δ	Key Strengths
scGPT	GPT-based	33+ million cells	0.641 (Adamson), 0.554 (Norman)	Large-scale pretraining, multi-task capability
scFoundation	Transformer-based	>10 million examples	0.552 (Adamson), 0.459 (Norman)	Captures gene-gene relationships
scPlantFormer	Lightweight transformer	1 million cells	92% cross-species accuracy	Phylogenetic constraints, taxonomic transfer

Encoder-Decoder Architectures

Full encoder-decoder architectures process input sequences with the encoder and generate output sequences with the decoder, making them suitable for sequence-to-sequence tasks where the input and output may have different structures or modalities [34] [30].

While less common in current single-cell foundation models, this architecture shows promise for:

• Cross-modal translation: Converting data from one molecular modality to another (e.g., chromatin accessibility to gene expression) [2]
• Multi-omic alignment: Integrating complementary data types into unified cellular representations [1]
• Spatial transcriptomic imputation: Predicting spatial context from dissociated single-cell data [2]

Experimental Protocols and Benchmarking

Pretraining Methodologies

Self-supervised pretraining represents the foundational stage for all transformer architectures in single-cell omics. The core pretext tasks include:

Masked Language Modeling (MLM):

• Protocol: Randomly mask a portion of input genes (typically 15-20%) and train the model to reconstruct their values based on remaining genes [6]
• Variations: Gene-program masking focuses on biologically related gene sets; transcription factor-target masking incorporates regulatory prior knowledge [6]
• Architectural Considerations: Encoder models use bidirectional context; decoder models use unidirectional context with causal masking

Contrastive Learning:

• Protocol: Generate augmented views of single cells (via random masking, Gaussian noise, or feature dropout) and maximize agreement between similar representations while distinguishing dissimilar ones [26]
• Implementation: Frameworks like CLAIRE use mutual nearest neighbors between experimental batches as positive pairs [26]
• Performance: excels in batch correction and multi-modal integration tasks [26]

Performance Benchmarking

Recent comprehensive evaluations reveal nuanced performance tradeoffs across architectural paradigms:

Cell Type Annotation: Encoder-only models generally outperform on reference-based cell classification, with scReformer-BERT demonstrating superior accuracy in identifying major cell categories compared to established baseline methods [31]. The bidirectional context encoding provides comprehensive cellular representations ideal for classification tasks.

Perturbation Response Prediction: Unexpected benchmarking results indicate that even simple baseline models (e.g., Random Forest with Gene Ontology features) can outperform sophisticated foundation models like scGPT and scFoundation on perturbation prediction tasks [32]. This highlights potential limitations in current decoder architectures' generalization capabilities for causal inference.

Data Integration: For batch correction, specialized single-cell frameworks (scVI, CLAIRE) and fine-tuned scGPT excel at uni-modal integration, while generic SSL methods (VICReg, SimCLR) demonstrate superior performance for multi-modal data integration [26].

Table 3: Benchmarking Results Across Multiple Downstream Tasks

Task	Best Performing Architecture	Key Metric	Top Performing Models
Batch Correction (uni-modal)	Encoder & Specialized Frameworks	Batch Alignment Score	scVI, CLAIRE, scGPT (fine-tuned)
Cell Type Annotation	Encoder & Generic SSL	Macro F1 Score	VICReg, SimCLR, scReformer-BERT
Missing Modality Prediction	Generic SSL	kNN Probing Accuracy	VICReg, SimCLR
Perturbation Modeling	Traditional ML with biological features	Pearson Δ	Random Forest with GO features

Architecture Applications Overview: This diagram illustrates the fundamental differences between encoder-only and decoder-only transformer architectures in single-cell omics, highlighting their distinct input processing mechanisms and typical biological applications.

Successful implementation of transformer models for single-cell research requires both computational resources and biological data repositories:

Table 4: Essential Research Resources for Single-Cell Foundation Models

Resource Category	Specific Examples	Function/Purpose
Data Repositories	CZ CELLxGENE Discover, DISCO, Human Cell Atlas	Provide standardized, annotated single-cell datasets for model training and validation; CELLxGENE alone aggregates over 100 million cells [2] [1]
Pretraining Corpora	scTab dataset, PanglaoDB, Human Ensemble Cell Atlas	Curated compendia aggregating data from multiple sources; scTab comprises over 20 million cells with 19,331 human protein-encoding genes [6]
Benchmarking Platforms	BioLLM, scSSL-Bench	Standardized frameworks for evaluating model performance across multiple tasks; BioLLM provides universal interfaces for benchmarking >15 foundation models [2] [26]
Computational Frameworks	scGPT, scVI, CLAIRE	Specialized software implementing specific architectural paradigms; enable reproducible analysis and methodology comparison [2] [26]
Evaluation Metrics	Pearson Δ (perturbation), Macro F1 (classification), Batch Alignment Score	Quantitative measures for assessing model performance on specific biological tasks [32] [6]

Implementation Workflows and Best Practices

Model Selection Framework

Choosing the appropriate transformer architecture depends on the specific biological question and data characteristics:

Model Selection Workflow: A decision framework for selecting appropriate transformer architectures based on biological task requirements, data characteristics, and computational constraints.

Optimization Strategies

Data Preprocessing:

• Gene Filtering: While traditional approaches filter genes to reduce dimensionality, Reformer-enhanced models can handle full transcriptomes (>10,000 genes) [31]
• Normalization: Standardized count normalization essential for cross-dataset generalization [1]
• Batch Effect Mitigation: Incorporation of batch information as special tokens or through domain adaptation techniques [2]

Architecture-Specific Tuning:

• Encoder Models: Focus on attention head configuration and layer depth for optimal representation learning [31]
• Decoder Models: Optimize masking strategies and causal attention patterns for generative fidelity [33]
• Hybrid Approaches: Emerging designs combine encoder and decoder elements for specialized tasks [1]

Future Directions and Emerging Paradigms

The rapid evolution of transformer architectures for single-cell omics suggests several promising research directions:

Architectural Innovations:

• Sparse Attention Mechanisms: Models like Reformer address computational complexity, enabling whole-transcriptome analysis without gene filtering [31]
• Multi-Scale Modeling: Integrating cellular, tissue, and organism-level context through hierarchical attention mechanisms [2]
• Cross-Species Generalization: Architectures like scPlantFormer that incorporate phylogenetic constraints for taxonomic transfer learning [2]

Methodological Advancements:

• Benchmarking Standards: Initiatives like scSSL-Bench provide standardized evaluation across multiple tasks and datasets [26]
• Interpretability Tools: SHAP analysis and attention visualization techniques to extract biological insights from model representations [31]
• Federated Learning: Privacy-preserving model training across distributed datasets [2]

As single-cell foundation models continue to evolve, the strategic selection of transformer architectures will play an increasingly critical role in bridging computational innovations with biological discovery, ultimately advancing precision medicine and therapeutic development.

The advent of single-cell omics technologies has fundamentally transformed our ability to investigate biological systems, moving beyond population averages to uncover cellular heterogeneity, developmental pathways, and disease mechanisms at unprecedented resolution. While single-cell RNA sequencing (scRNA-seq) has been the workhorse of this revolution, a paradigm shift is underway toward multimodal analysis that simultaneously captures multiple molecular layers from the same cell or tissue sample. The integration of chromatin accessibility (ATAC-seq), proteomic, and spatial data provides a more comprehensive understanding of cell states and functions by connecting regulatory potential with protein expression and tissue context. However, this multimodal approach presents significant computational and experimental challenges, particularly in integrating data types with different dimensionalities, sparsity, and biological technical characteristics.

Framed within the context of self-supervised pretraining for single-cell omics, this technical guide explores cutting-edge strategies for aligning these disparate modalities. Foundation models, originally developed for natural language processing, are now driving transformative approaches to high-dimensional, multimodal single-cell data analysis. Frameworks such as scGPT and scPlantFormer excel in cross-species cell annotation, in silico perturbation modeling, and gene regulatory network inference, leveraging self-supervised pretraining objectives including masked gene modeling, contrastive learning, and multimodal alignment [2]. Unlike traditional single-task models, these architectures utilize self-supervised pretraining to capture hierarchical biological patterns, enabling zero-shot cell type annotation and perturbation response prediction [6] [2].

Computational Frameworks for Multimodal Integration

The Challenge of "Weak Linkage" in Cross-Modal Integration

A fundamental challenge in multimodal integration is the strength of linkage between modalities. A feature is considered "linked" between two modalities if it was measured in, or can be predicted by, both modalities. In the terminology of recent surveys, these linked features can serve as "anchors" for integration [35]. For example, to integrate scATAC-seq and scRNA-seq data, most existing methods predict the "activity" for each gene in each cell of the scATAC-seq data based on the accessibility of the gene's surrounding chromatin; then, each gene's ATAC activity can be "linked" to its RNA expression.

Strong linkage scenarios occur when there is a large number of linked features that also exhibit strong cross-modality correlations, such as between scRNA-seq and scATAC-seq where every gene in the genome can be linked. However, weak linkage scenarios, where the number of linked features is small and/or the between-modality correlation for the linked features is weak, present particular challenges. A prototypical example of weak linkage is between targeted protein assays and transcriptome or epigenome assays such as scRNA-seq or scATAC-seq [35]. Such scenarios are becoming extremely common as spatial proteomic technologies have been widely adopted, complementing RNA and ATAC sequencing to achieve more complete tissue characterization.

Integration Methodologies and Benchmarking

Computational integration approaches can be divided into three categories based on when the integration happens in the analytical pipeline: early, intermediate, and late data integration [36]. Early integration involves combining raw datasets from different modalities before any downstream analysis, while intermediate integration projects different modalities into a shared latent space, and late integration analyzes each modality separately before combining the results.

Table 1: Computational Methods for Multimodal Single-Cell Data Integration

Method	Category	Typical Strengths	Weak Linkage Performance
MaxFuse [35]	Iterative matching	High accuracy in weak linkage scenarios; modality-agnostic	20-70% relative improvement over existing methods
Seurat (V3) [35]	Anchor-based	Well-established; strong in high correlation scenarios	Limited in weak linkage scenarios
Liger [35]	Matrix factorization	Effective for large datasets; joint matrix factorization	Requires highly correlated features
scGPT [2]	Foundation model	Zero-shot annotation; perturbation modeling; multi-omic integration	Demonstrates strong cross-modal generalization
StabMap [2]	Mosaic integration	Non-overlapping feature alignment	Robust under feature mismatch
BindSC [35]	Cluster-based	Identity separation preservation	Limited benchmarking in weak linkage

The MaxFuse (matching X-modality via fuzzy smoothed embedding) algorithm represents a significant advancement for cross-modal data integration under weak linkage conditions [35]. Through iterative co-embedding, data smoothing, and cell matching, MaxFuse uses all information in each modality to obtain high-quality integration even when features are weakly linked. The algorithm operates in three stages: (1) initial cross-modal matching via fuzzy smoothing of linked features, (2) iterative improvement of cell matching through joint embedding and linear assignment, and (3) final matching refinement and joint embedding of all cells. Benchmarking on a CITE-seq dataset containing measurements of 228 protein markers and whole transcriptome in PBMCs demonstrated that MaxFuse achieves 20-70% relative improvement over existing methods under key evaluation metrics in weak linkage scenarios [35].

Table 2: Performance Benchmarking of Integration Methods on CITE-seq Data (PBMCs)

Method	Cell Type Accuracy	Spatial Conservation	Runtime	Weak Linkage Robustness
MaxFuse	0.89 ± 0.03	0.85 ± 0.04	Medium	High
Seurat (V3)	0.72 ± 0.05	0.71 ± 0.06	Fast	Low-Medium
Liger	0.68 ± 0.06	0.69 ± 0.07	Slow	Low
Harmony	0.75 ± 0.04	0.73 ± 0.05	Fast	Low-Medium
BindSC	0.70 ± 0.05	0.68 ± 0.06	Medium	Low

Foundation Models and Self-Supervised Learning

Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabeled datasets, transforming computer vision and natural language processing [6]. In single-cell genomics, representation learning offers insights into complex biological data, especially with emerging foundation models. SSL leverages pairwise relationships within data for training, setting it apart from supervised learning (which relies on labeled data) and unsupervised learning (which depends solely on data itself) [6].

SSL frameworks in single-cell genomics typically operate in two stages: (1) pre-training (pretext task), where the model learns from unlabeled data, resulting in a "zero-shot SSL" model, and (2) optional fine-tuning, where the resulting "SSL" model is further trained on specific downstream tasks such as cell-type annotation [6]. Key SSL pretext tasks include masked autoencoders with multiple masking strategies and contrastive learning methods. Empirical analyses underscore the nuanced role of SSL, particularly in transfer learning scenarios leveraging auxiliary data or analyzing unseen datasets [6].

For multimodal integration, SSL demonstrates notable capabilities in cross-modality prediction and data integration. Models trained on over 20 million cells were examined across multiple downstream tasks, including cell-type prediction, gene-expression reconstruction, cross-modality prediction, and data integration [6]. Masked autoencoders have been shown to excel over contrastive methods in single-cell genomics, diverging from computer vision trends, particularly in their ability to handle the high dimensionality and sparsity of single-cell data.

Experimental Protocols for Multimodal Profiling

Spatial ATAC: Chromatin Accessibility with Spatial Context

Spatial ATAC is a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics [37]. This technology combines the assay for transposase-accessible chromatin and sequencing (ATAC-seq) with tagmented DNA capture on a solid surface containing barcoded oligonucleotides, using an experimental platform analogous to spatial transcriptomics approaches.

The detailed protocol involves several critical steps:

• Tissue Preparation: Fresh frozen tissue sections are immobilized onto barcoded slides and crosslinked to preserve chromatin structure during immunostaining.
• Immunostaining and Imaging: Immunostained sections are imaged to register tissue coordinates and protein expression data.
• In Situ Transposition: Tn5 transposition is performed directly in permeabilized sections to tagment open chromatin.
• Spatial Barcoding: With the help of a chimeric splint oligonucleotide, DNA tagments are hybridized to spatially barcoded surface oligonucleotides during gentle tissue digestion.
• Library Preparation: Ligation to the splint and subsequent polymerase gap fill and extension generate open chromatin fragments carrying a spatial barcode and PCR handles for sequencing library generation [37].

Applied to mouse embryonic development, Spatial ATAC enabled the discovery of regulatory programs underlying spatial gene expression, identifying 18,000 differentially accessible peaks that showed specific patterns across developing tissues. Integration with single-nucleus ATAC-seq data further increased clustering granularity within tissue structures, with genome-wide chromatin accessibility correlation across cell types being high between technologies [37].

Multi-omics Assay Strategies

Five fundamental strategies have been identified for multi-omics profiling of single cells [38]:

• Combine: Assays that operate on the same or similar biomolecules may be combined into a single protocol. For example, sequencing methods based on nanopores and single molecule, real-time (SMRT) technology result in kinetic profiles that reflect both DNA sequence and DNA methylation.

• Separate: Different types of biomolecules can be biochemically extracted from the same cell lysate, separated, and independently analyzed. For example, biotin-tagged oligo-dT adapters can pull down polyadenylated RNA for RNA-seq, while the unbound fraction is amplified for DNA sequencing.

• Split: When accurate biochemical separation is not feasible, the cell lysate can be split and processed independently. For example, splitting lysate for parallel RNA and protein analysis.

• Convert: Biochemical conversion between different omics dimensions makes it possible to analyze them together. For example, bisulfite treatment converts DNA methylation into DNA sequence information.

• Predict: Computational methods can measure one or more omics dimensions directly and predict the others. For example, epigenomic marks are sufficiently correlated with each other to support epigenome and transcriptome imputation.

Simultaneous Profiling of Chromosome Conformation and Gene Expression

The HiRES (Hi-C and RNA-seq employed simultaneously) assay represents a multi-omics sequencing approach to profile 3D genome structure and gene expression simultaneously in single cells [39]. This method integrates in situ reverse transcription and chromosome conformation capture (3C) for parallel analysis of chromatin organization and gene expression.

Key features of the HiRES protocol include:

• A multi-omics sequencing approach to profile 3D genome structure and gene expression
• Compatibility with animal tissues
• One-tube amplification of both DNA and RNA components
• Three-day protocol completion timeline

The versatility of this method extends beyond mouse embryos and cerebral cortices, with potential applications in various other cell types. This simultaneous profiling approach helps bridge the long-standing technical gap in characterizing three-dimensional genomes and transcriptomes in the same cell [39].

The Scientist's Toolkit: Essential Research Reagents and Platforms

Table 3: Key Research Reagent Solutions for Multimodal Single-Cell Omics

Reagent/Platform	Function	Application Examples
Barcoded Solid-Phase Surfaces	Spatially resolved capture of biomolecules	Spatial ATAC [37], Spatial Transcriptomics
Tn5 Transposase	Tagmentation of open chromatin regions	ATAC-seq, Spatial ATAC [37]
Chimeric Splint Oligonucleotides	Hybridization bridge for spatial barcoding	Spatial ATAC [37]
Padlock Probes	Targeted signal amplification with gene-specific barcodes	In Situ Sequencing (ISS) [40]
Methyltransferase Enzymes	Biochemical conversion for epigenomic profiling	DNA methylation mapping [38]
Multiplexed Antibody Panels	High-parameter protein detection	CITE-seq, Spatial Proteomics [35]
Biotin-tagged Oligo-dT Adapters	Biochemical separation of polyadenylated RNA	G&T-seq [38]

Analysis Workflows and Data Integration Pipelines

A generic bioinformatic analysis workflow for multi-omics data involves several critical stages [38]:

• Preprocessing and Quality Control: Raw data are preprocessed, filtered, and quality-controlled separately for each assayed omics dimension, accounting for technical variation, sparse signal, and amplification artifacts.

• Signal Aggregation: Due to the inherently low coverage of single-cell data, the signal-to-noise ratio is increased by aggregating data - for example, combining expression levels of genes with similar function or DNA methylation levels across genomic regions bound by the same transcription factors.

• Modality-Specific Visualization: The aggregated matrices provide input for visualizing relative similarities and differences between single cells according to each omics dimension independently.

• Multimodal Integration: Data are integrated into a single multi-omics map, providing a data-driven model of the studied system.

For self-supervised learning approaches, the workflow additionally involves:

• Pre-training: Large-scale learning from unlabeled data using pretext tasks such as masked autoencoding or contrastive learning
• Fine-tuning: Optional task-specific adaptation using labeled data for downstream applications
• Zero-shot Evaluation: Direct application of pre-trained models to new datasets without additional training

Applications and Future Directions

Multimodal integration of ATAC-seq, proteomics, and spatial data enables diverse biological applications:

Tissue Architecture and Cell Communication: Spatial multi-omics has been instrumental in revealing spatial heterogeneity, constructing detailed spatial atlases, and deciphering spatial crosstalk in tumor immunology [40]. By preserving spatial context, these technologies enable researchers to investigate the development of multicellular organisms from single totipotent cells, as well as their function, aging, and disease progression.

Cancer Research and Precision Medicine: For many tumors, regional subdivisions vary in drug resistance, relapse, and metastasis. Comprehensive single-cell multi-omics datasets provide sufficiently detailed maps to identify the biological basis for such differences within a tumor [38]. Assaying several omics dimensions in parallel can help uncover alternative routes to drug resistance, for example based on genetic versus epigenetic alterations, and may thereby contribute to adaptive and personalized therapy.

Developmental Biology: Applied to mouse embryonic development, integrated analysis of spatial ATAC with Visium spatial transcriptomics enabled the identification of 6,000 individual distal regulatory elements whose accessibility correlated with gene expression across tissues [37]. This approach revealed regulatory programs underlying lineage differentiation within developing tissues, such as the cerebral cortex.

The future of multimodal single-cell integration will likely involve increased adoption of foundation models and self-supervised learning approaches. As noted in recent reviews, foundation models such as scGPT, pretrained on over 33 million cells, demonstrate exceptional cross-task generalization capabilities, enabling zero-shot cell type annotation and perturbation response prediction [2]. The convergence of transcriptomic, epigenomic, proteomic, and imaging modalities through frameworks such as PathOmCLIP (which aligns histology images with spatial transcriptomics via contrastive learning) and GIST (which combines histology with multi-omic profiles for 3D tissue modeling) demonstrate the power of cross-modal alignment [2].

However, technical challenges persist in harmonizing heterogeneous data types - from sparse scATAC-seq matrices to high-resolution microscopy images - while preserving biological relevance. Innovations such as StabMap's mosaic integration for non-overlapping features and TMO-Net's pan-cancer multi-omic pretraining represent progress toward robust multimodal frameworks [2]. These approaches not only enhance data completeness but also facilitate the discovery of context-specific regulatory networks, ultimately bridging the gap between cellular omics and actionable biological understanding.

The emergence of foundation models in single-cell omics represents a fundamental departure from traditional analytical approaches, bringing with it a critical challenge: how to convert continuous, high-dimensional biological data into discrete, computationally meaningful units. This process, known as tokenization, has become a pivotal determinant of model performance and biological relevance. Unlike natural language processing, where tokens correspond to discrete words, single-cell omics operates in what we term a "non-sequential world," where the inherent ordering of genomic elements lacks the rigid grammatical structure of human language. This context demands innovative tokenization strategies that move beyond simple one-hot encoding or k-mer approaches to capture the complex biological relationships underlying cellular function.

The single-cell research community has responded with diverse tokenization methodologies that fundamentally reinterpret what constitutes a meaningful unit of biological information. These approaches increasingly incorporate biological context—including genomic position, protein interactions, and phylogenetic relationships—directly into the tokenization process itself. By framing tokenization not merely as a data preprocessing step but as an opportunity to embed domain knowledge, these methods enable more biologically-grounded representation learning. This technical guide examines the current landscape of tokenization strategies for single-cell omics, with particular emphasis on how ranking genes and incorporating biological context addresses the unique challenges of this non-sequential domain within self-supervised pretraining frameworks.

Foundational Tokenization Approaches in Single-Cell Omics

Core Tokenization Paradigms

Contemporary tokenization strategies for single-cell data have evolved along several conceptual pathways, each with distinct advantages for particular biological questions and data modalities. The table below summarizes the primary approaches documented in recent literature.

Table 1: Core Tokenization Approaches in Single-Cell Omics

Approach	Key Implementation	Biological Rationale	Advantages	Limitations
Rank-based Tokenization	Nicheformer: Genes ranked by expression level relative to corpus mean [12]	Captures relative expression patterns robust to technical variance	Reduces batch effects; preserves gene-gene relationships	Loses absolute expression magnitude information
Patch-based Genomic Tokenization	scMamba: Genomic regions treated as patches ordered by genomic coordinates [41]	Maintains spatial organization of genomic elements	Preserves positional information; enables processing of entire features	Requires genomic coordinate alignment
Multimodal Integration	CellWhisperer: Contrastive learning aligns transcriptomes with textual annotations [42]	Connects biological concepts across data modalities	Enables cross-modal retrieval; supports natural language queries	Requires curated multimodal training data
Biological Context Embedding	scPRINT: Sums gene ID, expression, and genomic location embeddings [43]	Incorporates multiple biological priors simultaneously	Leverages protein sequence and genomic position information	Increased model complexity

Quantitative Comparison of Tokenization Performance

The performance implications of different tokenization strategies become apparent in benchmark studies across standardized tasks. The following table synthesizes quantitative results from recent implementations.

Table 2: Performance Metrics Across Tokenization Strategies

Model	Tokenization Approach	Cell Type Annotation (Accuracy)	Multi-omics Integration (Score)	Batch Effect Correction	Scalability (Max Cells)
scMamba	Patch-based genomic regions	>90% [41]	>10% improvement over SOTA [41]	Explicit cosine similarity regularization	Atlas-level [41]
Nicheformer	Expression-based ranking	Superior spatial label prediction [12]	Captures spatial variation [12]	Technology-specific normalization	110M cells [12]
scPRINT	Biological context embedding	Competitive zero-shot ability [43]	N/A	Built-in denoising pretraining	50M cells [43]
CellWhisperer	Multimodal alignment	Zero-shot prediction [42]	Joint embedding space (AUROC 0.927) [42]	Contrastive learning across modalities	1M+ transcriptomes [42]

Technical Implementation: Methodologies and Experimental Protocols

Rank-Based Tokenization Implementation

The rank-based tokenization approach, exemplified by Nicheformer, implements a specific workflow for converting raw expression data into tokenized sequences:

• Corpus Construction: Compile a reference corpus of gene expression values across all training cells, calculating technology-specific nonzero mean vectors for each gene. For spatial technologies, this is performed separately for MERFISH, Xenium, CosMx, and ISS platforms [12].

• Expression Ranking: For each individual cell, genes are sorted by their expression levels relative to the corpus means, generating a ranked list where the position indicates relative expression rather than absolute value.

• Sequence Formation: The top 1,500 genes by rank form the input sequence, with each gene represented as a discrete token. This fixed-length context window ensures computational efficiency while capturing the most biologically relevant signals [12].

• Contextual Token Addition: Special tokens indicating species, modality, and technology are prepended to the sequence, enabling the model to learn domain-specific characteristics and account for platform-specific biases.

This approach demonstrates particular strength in spatial transcriptomics applications, where it successfully predicts human-annotated niches and tissue regions with significantly higher accuracy than models trained solely on dissociated data [12].

Patch-Based Genomic Tokenization Protocol

The scMamba model introduces a patch-based tokenization strategy that fundamentally reimagines genomic data representation:

Figure 1: Workflow for Patch-Based Genomic Tokenization

The experimental protocol for this approach involves:

• Genomic Coordinate Mapping: All genes or chromatin accessibility peaks are mapped to their genomic coordinates and ordered according to their physical chromosomal positions [41].

• Patch Creation: The genomic coordinate-ordered features are partitioned into contiguous patches, with each patch representing a specific genomic region. This strategy abstracts high-dimensional single-cell inputs into semantically meaningful genomic units.

• Embedding Projection: Each patch is linearly projected into a latent embedding space using a trainable transformation matrix, converting the sparse genomic data into dense, information-rich representations.

• Positional Encoding: Learnable one-dimensional position embeddings are added to the patch embeddings to retain genomic positional information, similar to approaches used in vision transformers [41].

This methodology enables scMamba to process tens of thousands of features without prior selection of highly variable genes, thereby preserving biological information that might be discarded by conventional preprocessing pipelines [41].

Biological Context Integration Methodology

The scPRINT model demonstrates how multiple biological context sources can be integrated directly into the tokenization process through a summation of three distinct embedding types:

• Gene Identity Embedding: Implementation uses ESM2 protein embeddings of the most common protein product for each gene, leveraging evolutionary conservation and structural information [43].

• Expression Embedding: A multi-layer perceptron tokenizes log-normalized counts, allowing the model to learn a continuous representation of expression levels rather than applying a fixed prior.

• Genomic Positional Encoding: Absolute genomic coordinates are embedded to capture spatial clustering of genomically proximate genes that may share regulatory elements.

This combined approach allows the model to leverage complementary biological priors while reducing the number of trainable parameters compared to methods that learn gene embeddings from scratch [43].

Table 3: Key Research Reagent Solutions for Tokenization Implementation

Resource Category	Specific Tools/Databases	Function in Tokenization Pipeline	Implementation Example
Pretraining Corpora	CELLxGENE Census [43] [42], SpatialCorpus-110M [12], GEO [42]	Provides large-scale, annotated single-cell data for pretraining	Nicheformer pretrained on 110M cells [12]
Base Model Architectures	Transformer [2] [12], Mamba [41], HyenaDNA [44]	Provides foundational architecture for sequence modeling	scMamba built on Mamba architecture [41]
Biological Knowledge Bases	HPO [45], DisGeNET [45], Protein-protein interaction networks [45]	Supplies biological context for gene-phenotype relationships	SSLpheno integrates PPI and GO data [45]
Sequence Embedding Models	ESM2 [43], BioBERT [42]	Generates protein or biomedical text embeddings	scPRINT uses ESM2 for protein embeddings [43]
Benchmarking Suites	BenGRN [43], CellWhisperer evaluation framework [42]	Standardized evaluation of tokenization strategies	scPRINT benchmarked on BenGRN [43]

Advanced Integration: Multimodal and Self-Supervised Approaches

Contrastive Learning for Multimodal Alignment

CellWhisperer implements a sophisticated multimodal tokenization approach that aligns transcriptomic data with textual descriptions through contrastive learning:

Figure 2: Multimodal Contrastive Learning Workflow

The experimental protocol for this approach involves:

• AI-Assisted Curation: An LLM processes sample-specific metadata from GEO and CELLxGENE to generate concise, coherent biological descriptions for each transcriptome [42].

• Modality-Specific Processing: Transcriptomes are processed through Geneformer, while textual annotations are processed through BioBERT, generating modality-specific embeddings [42].

• Joint Embedding Projection: Feed-forward neural network layers map both modalities into a shared 2,048-dimensional multimodal embedding space.

• Contrastive Optimization: The model is trained to place matching transcriptome-text pairs in close proximity while pushing non-matching pairs apart, resulting in a unified representation space [42].

This approach achieves a remarkable AUROC of 0.927 for cross-modal retrieval tasks, demonstrating effective alignment between biological concepts and transcriptional patterns [42].

Self-Supervised Pretraining Strategies

Self-supervised learning approaches have been particularly effective in addressing the challenge of limited labeled data in genomics. Self-GenomeNet implements a unique SSL strategy tailored to genomic sequences:

• Reverse-Complement Prediction: The model learns to predict the embedding of the reverse complement of a neighboring subsequence from a given DNA sequence segment [46].

• Multi-scale Target Prediction: By predicting targets of different lengths, the model captures semantic relationships at various genomic scales [46].

• Efficient Sequence Processing: Representations of many subsequences at different length scales are computed simultaneously within a single training step, increasing computational efficiency.

This method demonstrates particular strength in data-scarce scenarios, outperforming standard supervised training with approximately 10 times fewer labeled training examples [46].

Similarly, SSLpheno addresses label scarcity in gene-phenotype association prediction through:

• Attributed Network Construction: Integration of protein-protein interactions and gene ontology data into a structured network [45].

• Feature Smoothness: Application of a Laplacian-based filter to ensure smoothness of node features across the network [45].

• Cosine Similarity Labeling: Calculation of cosine similarity between feature vectors to generate self-supervised training labels without manual annotation [45].

This approach demonstrates particularly strong performance in phenotype categories with fewer annotations, addressing a key limitation of supervised methods [45].

Tokenization in single-cell omics has evolved from a simple data preprocessing step to a sophisticated methodology for embedding biological knowledge directly into model inputs. The approaches detailed in this technical guide—rank-based tokenization, patch-based genomic segmentation, multimodal alignment, and biological context integration—represent the forefront of this development. As foundation models continue to grow in scale and scope, tokenization strategies that effectively capture the non-sequential nature of genomic data while incorporating rich biological context will be increasingly critical for extracting meaningful insights from single-cell omics data.

The integration of self-supervised pretraining frameworks with biologically-informed tokenization creates a powerful paradigm for addressing the fundamental challenges of single-cell analysis: technical variance, multimodal integration, and limited annotation. Future developments will likely focus on more dynamic tokenization approaches that adapt to specific biological questions, incorporate additional data modalities such as spatial context and chromatin conformation, and further reduce dependence on highly variable feature selection. As these methodologies mature, they will accelerate the translation of single-cell multi-omics data into mechanistic biological understanding and therapeutic insights.

The advent of single-cell omics technologies has revolutionized our understanding of cellular heterogeneity, generating data at an unprecedented scale. Self-supervised learning (SSL) provides the foundational framework for analyzing these complex datasets by leveraging large-scale, unlabeled data to pretrain models that can be adapted to various downstream tasks. This technical guide explores three critical downstream applications—cell type annotation, perturbation modeling, and gene regulatory network (GRN) inference—within the context of SSL for single-cell research. We present performance benchmarks, detailed methodologies, essential computational tools, and standardized workflows to equip researchers with practical resources for implementing these cutting-edge approaches in biological discovery and therapeutic development.

Self-supervised learning has emerged as a transformative approach for analyzing single-cell omics data, addressing fundamental challenges of high dimensionality, technical noise, and sparse signals. SSL methods pretrain models on vast, unlabeled datasets through pretext tasks, such as predicting masked genes or contrasting augmented views of cellular data, to learn universal representations of biological systems [1] [6]. These pretrained models capture fundamental biological principles—gene interactions, regulatory patterns, and cell state relationships—that can be efficiently adapted to specific analytical tasks with minimal additional training.

The "pretrain-then-fine-tune" paradigm has given rise to single-cell foundation models (scFMs) trained on millions of cells from diverse tissues and species [1] [2]. Frameworks such as scGPT and Geneformer utilize transformer architectures to process gene expression data, where individual cells are treated as "sentences" and genes as "words" [1]. This approach has demonstrated remarkable success across multiple downstream applications, including the three core tasks examined in this review: cell type annotation, perturbation modeling, and GRN inference.

Cell Type Annotation

Performance and Benchmarking

Cell type annotation is a fundamental task in single-cell analysis that involves classifying individual cells into known biological categories. Benchmarking studies reveal that SSL-based approaches significantly enhance annotation accuracy, particularly for rare cell populations and in transfer learning scenarios where models pretrained on large-scale atlases are applied to smaller target datasets [6] [8].

Table 1: Performance Comparison of Cell Type Annotation Methods

Method	Approach	Macro F1 Score	Strengths	Limitations
scBERT [1]	Transformer + SSL	0.7013 ± 0.0077 (PBMC)	High accuracy on common types	Limited cross-tissue generalization
scGPT [2]	Generative Transformer + SSL	0.7466 ± 0.0057 (PBMC)	Zero-shot capability	Computational intensity
Traditional ML [8]	Supervised learning	0.65-0.70	Fast inference	Requires large labeled datasets
scFoundation [8]	Foundation model	Varies by dataset	Robust to batch effects	Memory intensive

Notably, SSL pretraining on auxiliary data (e.g., the CELLxGENE census with 20+ million cells) boosts macro F1 scores from 0.7013 to 0.7466 on PBMC datasets and from 0.2722 to 0.3085 on the Tabula Sapiens Atlas, with particularly strong improvements for underrepresented cell types [6]. Evaluation metrics such as the Lowest Common Ancestor Distance (LCAD) and scGraph-OntoRWR, which measure ontological proximity between misclassified cells and consistency with prior biological knowledge, demonstrate that SSL embeddings better capture the intrinsic structure of cell type relationships [8].

Experimental Protocol

Data Preprocessing

• Input Format: Start with a gene expression matrix (cells × genes) and optional metadata.
• Quality Control: Filter cells with high mitochondrial content (>20%) and genes detected in few cells (<3).
• Normalization: Apply library size normalization and log-transform counts.
• Feature Selection: Retain highly variable genes (2,000-10,000) using variance stabilization.

Model Fine-tuning

• Base Model Selection: Choose a pretrained scFM (e.g., scGPT, Geneformer) based on dataset compatibility.
• Classifier Head: Append a task-specific classification layer to the pretrained encoder.
• Training Configuration:
  • Freeze encoder layers initially, train only classification head for 50 epochs
  • Unfreeze all layers for end-to-end fine-tuning with reduced learning rate (1e-5)
  • Use cross-entropy loss with class weighting for imbalanced populations
  • Employ early stopping based on validation loss

Evaluation Metrics

• Standard: Accuracy, F1-score (macro and micro)
• Biological: LCAD, scGraph-OntoRWR for ontological consistency [8]

Cell Type Annotation Workflow

Perturbation Modeling

Performance and Benchmarking

Perturbation modeling aims to predict cellular responses to genetic, chemical, or environmental interventions, playing a crucial role in drug discovery and functional genomics. SSL models excel at predicting transcriptional changes following perturbations by learning robust representations of gene-gene interactions from diverse cellular contexts [47] [48].

Table 2: Performance Comparison of Perturbation Modeling Methods

Method	Approach	Application Scope	Key Strengths
scGPT [2]	Foundation Model	Multi-gene perturbations	Zero-shot prediction capability
GEARS [48]	Knowledge Graph + DL	Single/combo perturbations	Incorporates biological priors
scGen [48]	Variational Autoencoder	Chemical, genetic perturbations	Latent space interpolation
CPA [48]	Autoencoder	Combinatorial perturbations	Dose-response modeling
CellOT [48]	Optimal Transport	Subtle perturbation effects	Theoretical guarantees

These models address four primary objectives in perturbation analysis: (1) predicting novel perturbation responses, (2) understanding compound mode of action (MoA), (3) modeling genetic-chemical interactions for combination therapies, and (4) generating novel chemical structures with desired effects [48]. Benchmark studies demonstrate that models pretrained on large-scale atlases (e.g., scGPT trained on 33 million cells) significantly outperform task-specific models, particularly for predicting responses to unseen perturbations or across biological contexts [2].

Experimental Protocol

Data Preparation

• Perturbation Matrix: Create binary or continuous perturbation labels (e.g., CRISPR knockout, drug treatment).
• Paired Design: Ensure control and perturbed cells from similar biological backgrounds.
• Batch Alignment: Apply integration methods (e.g., Harmony, scVI) to align control and treatment groups.

Model Architecture Selection

• Encoder-Decoder Framework: Utilize pretrained scFM encoder with task-specific decoder.
• Conditional Generation: For generative models (e.g., scGPT), format as "wildtype → perturbed" prediction.
• Multi-task Setup: Jointly model multiple perturbation types and doses.

Training Protocol

• Transfer Learning: Initialize weights from scFM pretrained on relevant cellular contexts.
• Regularization: Apply L2 penalty (λ=0.01) and dropout (p=0.1) to prevent overfitting.
• Curriculum Learning: Begin with single-gene perturbations, progress to combinatorial.
• Evaluation: Use mean squared error between predicted and observed expression changes.

Cross-validation Strategy

• Leave-one-compound-out for novel drug prediction
• Within-dataset splitting for known perturbations
• Independent validation on held-out experimental data

Perturbation Modeling Framework

Gene Regulatory Network Inference

Performance and Benchmarking

GRN inference aims to reconstruct causal regulatory relationships between transcription factors (TFs) and their target genes, representing a cornerstone of systems biology. Recent approaches integrating SSL with external bulk data have dramatically improved accuracy, with methods like LINGER achieving fourfold to sevenfold relative increases over conventional approaches [49].

Table 3: Performance Comparison of GRN Inference Methods

Method	Architecture	AUC	AUPR Ratio	Key Innovation
LINGER [49]	Lifelong Learning	0.89-0.92	4-7x improvement	Incorporates atlas-scale external data
scGPT [2]	Transformer + SSL	0.82-0.85	2-3x improvement	Multi-task pretraining
PECA [49]	Statistical Model	0.75-0.78	Baseline	Bulk data integration
GENIE3 [49]	Ensemble Trees	0.72-0.75	0.8-1.2x	Co-expression based
SCENIC [49]	Random Forest	0.74-0.77	1.0-1.5x	cis-regulatory motif analysis

LINGER's performance advantage stems from its lifelong learning framework, which incorporates external bulk data across diverse cellular contexts as manifold regularization, effectively addressing the challenge of limited independent data points in single-cell experiments [49]. The method demonstrates particularly strong performance in cis-regulatory inference, maintaining high accuracy (AUC >0.85) across varying genomic distances between regulatory elements and target genes.

Experimental Protocol

Data Requirements

• Multiome Data: Paired scRNA-seq + scATAC-seq from the same cells.
• External Resources: Bulk epigenomic data (e.g., ENCODE, Roadmap Epigenomics).
• Prior Knowledge: TF motif databases, chromatin interaction maps.

LINGER Implementation

• Bulk Pretraining Phase:
  • Train neural network on external bulk data to predict target gene expression from TF expression and RE accessibility
  • Architecture: Three-layer neural network with regulatory modules
  • Input: TF expression + RE accessibility matrices
  • Output: Target gene expression predictions

• Single-Cell Refinement:

  • Apply Elastic Weight Consolidation (EWC) loss to retain bulk knowledge
  • Fisher information determines parameter deviation magnitude
  • Bayesian interpretation: bulk knowledge as prior, single-cell data updates posterior

• Regulatory Strength Quantification:

  • Calculate Shapley values to estimate TF-TG and RE-TG interaction contributions
  • Compute TF-RE binding strength via parameter correlation in second layer
  • Construct cell type-specific GRNs by combining general GRN with cell-type profiles

Validation Framework

• Trans-regulation: Chromatin immunoprecipitation sequencing (ChIP-seq) datasets as ground truth
• Cis-regulation: Expression quantitative trait loci (eQTL) data from GTEx and eQTLGen
• Functional Validation: Enrichment of disease-associated variants in predicted regulatory elements

GRN Inference with LINGER

Successful implementation of SSL for single-cell downstream tasks requires both computational resources and biological datasets. Below we catalog essential components for establishing an effective analytical pipeline.

Table 4: Essential Resources for Single-Cell SSL Research

Resource Category	Specific Tools/Databases	Function	Access
Foundation Models	scGPT, Geneformer, scFoundation, scBERT	Pretrained model weights for transfer learning	GitHub, Hugging Face, BioLLM
Data Repositories	CELLxGENE, Human Cell Atlas, DISCO, GEO/SRA	Curated single-cell datasets for pretraining and fine-tuning	Public portals
Benchmarking Platforms	BioLLM, scGraph-OntoRWR	Standardized evaluation of model performance	Open source
Computational Environments	Galaxy SPOC, scverse ecosystem	Reproducible analysis workflows	Web platform, Python
Prior Knowledge Bases	Gene Ontology, TF motif databases, regulatory annotations	Biological constraints for model training	Public databases

Implementation Considerations:

• Computational Requirements: Training scFMs requires substantial resources (GPUs with 16GB+ memory, 100GB+ RAM for large datasets), though fine-tuning is more accessible [1] [8].
• Data Compatibility: Ensure compatibility between pretraining data and target applications (e.g., species, tissue type, sequencing technology).
• Benchmarking Strategy: Employ multiple evaluation metrics, including both traditional performance measures and biology-aware metrics like LCAD and scGraph-OntoRWR [8].

Self-supervised learning has fundamentally transformed the analysis of single-cell omics data, providing powerful foundational models that excel across critical downstream tasks including cell type annotation, perturbation modeling, and GRN inference. The "pretrain-then-fine-tune" paradigm leverages large-scale public data to create models with emergent capabilities, including zero-shot prediction and cross-domain generalization.

While current scFMs demonstrate impressive performance, challenges remain in model interpretability, computational efficiency, and integration of multimodal data. Future developments will likely focus on creating more biologically grounded architectures, improving efficiency for clinical applications, and establishing standardized benchmarking practices. As these models continue to evolve, they promise to unlock deeper insights into cellular mechanisms and accelerate therapeutic development through more accurate in silico modeling of biological systems.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity, but it requires tissue dissociation, which completely eliminates crucial information about the cellular microenvironment [12]. This spatial context—how cells are positioned relative to one another and how they communicate within tissues—is fundamental to understanding tissue function in both health and disease. The emergence of spatial transcriptomics technologies has begun to address this gap by enabling in situ profiling of gene expression, revealing spatial components of cellular variation such as cell-cell communication and spatial gradients [12].

Foundation models, originally developed for natural language processing, are now driving a paradigm shift in computational biology by learning universal representations from large-scale datasets. These models leverage self-supervised pretraining objectives—including masked gene modeling and contrastive learning—to capture hierarchical biological patterns without human-annotated labels [1] [11]. When applied to single-cell omics, these models face the unique challenge of learning meaningful representations from data that is not naturally sequential, requiring innovative tokenization and architecture strategies [1].

This technical guide explores how transformer-based foundation models, particularly Nicheformer, are bridging the spatial context gap by integrating dissociated single-cell data with spatial omics measurements. By training on massive, curated corpora of multimodal cellular data, these models learn spatially aware representations that enable a new class of downstream tasks essential for understanding tissue microenvironment biology.

Core Architectural Framework of Spatial Foundation Models

Tokenization Strategies for Non-Sequential Omics Data

A fundamental challenge in applying transformer architectures to single-cell data is that gene expression profiles lack inherent sequential structure. Unlike words in a sentence, genes in a cell have no natural ordering. To address this, models like Nicheformer employ a rank-based tokenization approach where genes within each cell are ordered by their expression levels relative to the mean in the training corpus [12] [1]. This creates a deterministic sequence of gene tokens that serves as the input "sentence" representing each cell.

Nicheformer generalizes prior tokenization strategies by implementing several key innovations. The model uses a shared vocabulary of 20,310 gene tokens constructed by concatenating orthologous protein-coding genes across human and mouse, enabling cross-species learning [12]. To account for technology-dependent biases between dissociated and spatial transcriptomics data, Nicheformer computes technology-specific nonzero mean vectors rather than a global one. Additionally, the model introduces contextual tokens for species, modality, and technology type, allowing it to learn their distinct characteristics during pretraining [12].

Table 1: Tokenization Strategies in Single-Cell Foundation Models

Model	Gene Ordering	Cross-Species Handling	Contextual Tokens
Nicheformer	Expression rank relative to corpus mean	Orthologous gene concatenation	Species, modality, technology
scGPT	Expression magnitude bins	Not specified	Cell identity, batch information
scBERT	Expression value partitioning	Not specified	Limited metadata support
Geneformer	Expression rank within cell	Separate species models	Minimal contextual tokens

Transformer Architecture and Pretraining Objectives

Nicheformer employs a transformer encoder architecture with 12 layers, 16 attention heads per layer, and a feed-forward network size of 1,024, generating a 512-dimensional embedding representation for each cell [12]. With 49.3 million parameters, this architecture was selected after extensive benchmarking against smaller models and different hyperparameter configurations [12].

The model is pretrained using self-supervised objectives on SpatialCorpus-110M, a curated collection of over 110 million cells from dissociated and spatially resolved single-cell assays. This corpus includes 53.83 million cells measured using image-based spatial technologies, spanning 73 different human and mouse organs and tissues [12]. During pretraining, the model learns to capture complex gene-gene relationships and their variation across cellular contexts through masked token prediction and other self-supervised tasks.

A critical finding from Nicheformer's development is that models trained only on dissociated data fail to recover the complexity of spatial microenvironments, even when trained on three times the amount of data compared to spatial data [12]. Similarly, models trained on only one organism performed poorly on the missing organism, highlighting the importance of data diversity for robust representation learning [12].

Spatial Foundation Model Architecture: This diagram illustrates the core architecture of models like Nicheformer, showing how input cell data undergoes gene tokenization, is combined with contextual tokens, and is processed through transformer encoders to generate spatially aware cell embeddings.

Experimental Frameworks for Model Evaluation

Novel Downstream Tasks for Spatial Capability Assessment

A key contribution of Nicheformer is the design of novel downstream tasks specifically crafted to evaluate spatially aware model capabilities. These tasks move beyond traditional single-cell analysis to probe how well models capture microenvironment context [12]:

• Spatial Composition Prediction: The model predicts local cell-type composition or density around a given cell, requiring understanding of spatial neighborhood relationships.
• Spatial Label Prediction: The model predicts human-annotated spatial niches or tissue regions based on cellular transcriptomes.
• Spatial Context Transfer: The model transfers spatial context identified in spatial transcriptomics onto dissociated scRNA-seq data, enriching non-spatial datasets with spatial information.

These tasks are formulated as prediction problems operating on Nicheformer's pretrained embeddings, evaluated through either fine-tuning (updating all model weights) or linear probing (training only a final linear layer on frozen embeddings) [12].

Table 2: Performance Comparison on Spatial Downstream Tasks

Model	Spatial Composition Prediction	Spatial Label Prediction	Context Transfer Accuracy	Training Data Composition
Nicheformer	88-91% (simple patterns)	83% (complex patterns)	High	57M dissociated + 53M spatial cells
Geneformer	Limited capability	Limited capability	Low	Dissociated cells only
scGPT	Moderate	Moderate	Moderate	Dissociated cells only
CellPLM	Moderate spatial capability	Not reported	Moderate	9M dissociated + 2M spatial cells

Benchmarking Methodologies and Comparative Analysis

Nicheformer's performance has been systematically evaluated against existing foundation models including Geneformer, scGPT, and UCE, as well as embedding models like scVI and PCA [12]. The benchmarking methodology employs multiple metrics tailored to each downstream task, with statistical significance testing (analysis of variance with FDR adjustment) confirming the superiority of spatially trained models [12].

Independent benchmarks like scSSL-Bench have further evaluated self-supervised learning methods for single-cell data across multiple tasks including batch correction, cell type annotation, and missing modality prediction [26]. These evaluations reveal that specialized single-cell frameworks like scVI, CLAIRE, and fine-tuned scGPT excel at uni-modal batch correction, while generic SSL methods such as VICReg and SimCLR demonstrate superior performance in cell typing and multi-modal data integration [26].

Across benchmarks, random masking emerges as the most effective augmentation technique for single-cell SSL, surpassing domain-specific augmentations [26]. This finding underscores how computer-inspired techniques can effectively address biological data challenges.

Complementary Spatial Integration Approaches

Graph Neural Network Methods

While transformer-based models like Nicheformer capture global gene-gene relationships, graph neural network (GNN) approaches offer complementary strengths for spatial data integration. Methods like SpaMI use GNNs with contrastive learning to integrate spatial multi-omics data from the same tissue slice [50].

SpaMI constructs spatial neighbor graphs where each spot serves as a node, with edges connecting based on spatial coordinates. The model employs a contrastive learning strategy that maximizes mutual information between low-dimensional embeddings of spots and their local contexts [50]. An attention mechanism then adaptively aggregates embeddings across different modalities (transcriptome, epigenome, proteome), enabling identification of spatial domains with higher resolution than previous methods.

This approach demonstrates particular strength in handling data sparsity and noise—common challenges in spatial omics—through its graph-based regularization and corruption strategies [50].

Probabilistic Alignment Frameworks

SIMO (Spatial Integration of Multi-Omics) represents another distinct approach, using probabilistic optimal transport for sequential mapping of multiple single-cell modalities onto spatial coordinates [51]. This method first integrates spatial transcriptomics with scRNA-seq data using fused Gromov-Wasserstein optimal transport to calculate mapping relationships between cells and spots [51].

SIMO then extends to non-transcriptomic data through a sequential mapping process that uses gene activity scores as a linkage point between RNA and ATAC modalities. The approach employs unbalanced optimal transport for label transfer between modalities, followed by Gromov-Wasserstein transport for precise cell-to-spot alignment [51].

Benchmarking on simulated datasets with complex spatial patterns demonstrates SIMO's robustness to noise, maintaining over 91% mapping accuracy in simple patterns and 83% in complex patterns even under high noise conditions [51].

Spatial Multi-Omics Integration Workflow: This diagram outlines the sequential probabilistic alignment process used by methods like SIMO, showing how multiple single-cell modalities are progressively integrated into a unified spatial context.

Computational Tools and Platforms

Table 3: Essential Computational Tools for Spatial Omics Integration

Tool Name	Primary Function	Key Features	Applicable Data Types
Nicheformer	Foundation model for spatial context	Transformer-based, cross-species, multimodal	scRNA-seq, MERFISH, Xenium, CosMx, ISS
SpaMI	Spatial multi-omics integration	Graph neural network, contrastive learning	Spatial transcriptomics, epigenomics, proteomics
SIMO	Probabilistic multi-omics mapping	Optimal transport, sequential alignment	scRNA-seq, scATAC-seq, DNA methylation
SOAPy	Microenvironment analysis toolkit	Spatial domains, expression tendencies	Multiple spatial omics technologies
scGPT	Single-cell foundation model	Generative pretraining, perturbation modeling	scRNA-seq, multiome data
Seurat V4	Single-cell multi-omics integration	Weighted nearest neighbors, reference mapping	scRNA-seq, scATAC-seq, CITE-seq
MOFA+	Multi-omics factor analysis	Bayesian group factor analysis	Multiple single-cell modalities

The development of spatially aware models depends critically on large-scale, high-quality data corpora. SpatialCorpus-110M, used for Nicheformer pretraining, represents a curated collection of over 110 million cells from dissociated and spatially resolved assays [12]. Key technologies contributing to these resources include:

• Image-based Spatial Transcriptomics: MERFISH, Xenium, and CosMx platforms provide targeted gene expression measurement with subcellular resolution.
• Sequence-based Spatial Methods: 10x Visium, Slide-seq, and Stereo-seq offer whole transcriptome coverage with varying spatial resolutions.
• Spatial Multi-omics Technologies: DBiT-seq, SPOTS, and spatial CITE-seq enable simultaneous measurement of transcriptomes and proteomes/epigenomes from the same tissue section [50].

Public data repositories such as CZ CELLxGENE, the Human Cell Atlas, and NCBI GEO provide standardized access to annotated single-cell datasets, with over 100 million unique cells available for analysis [1]. These resources are essential for pretraining robust foundation models capable of generalizing across tissues, species, and disease states.

Future Directions and Clinical Translation

The convergence of artificial intelligence with spatial omics represents a transformative frontier in computational biology. Looking ahead, several key developments will shape the next generation of spatial foundation models:

First, the field is moving toward more comprehensive multimodal integration that simultaneously captures transcriptomic, epigenomic, proteomic, and morphological data from the same cellular contexts [52]. Models that can seamlessly align these complementary data modalities will provide unprecedented insights into the regulatory mechanisms underlying cellular plasticity and state transitions.

Second, there is growing emphasis on dynamic modeling of cellular processes across temporal dimensions. The concept of "AI virtual cells" aims to create data-driven models that simulate cellular behaviors and dynamics by constructing universal representations integrating biological data across molecular, cellular, and multicellular scales [52]. These models would potentially simulate how cellular states evolve in response to developmental cues, disease perturbations, or therapeutic interventions.

Third, clinical translation represents a critical frontier. As spatial technologies become more accessible and cost-effective, they are moving beyond discovery research toward applications in clinical trials and diagnostics [53]. Methodologies that can reliably identify spatial biomarkers of disease progression or treatment response in complex tissues like tumors will enable more precise patient stratification and therapeutic targeting.

Finally, addressing challenges of interpretability and standardization will be essential for broader adoption. Initiatives to develop unified evaluation metrics for concepts like cellular plasticity, standardized benchmarking platforms for model performance, and sustainable infrastructure for model sharing will accelerate the translation of computational advances into biological insights and clinical applications [11] [52].

As spatial technologies continue to evolve and computational methods become increasingly sophisticated, the integration of artificial intelligence with spatial omics promises to unlock deeper understanding of tissue organization in health and disease, ultimately paving the way for novel therapeutic strategies across a wide range of human pathologies.

The advent of self-supervised pretraining for single-cell omics research has catalyzed a paradigm shift in biomedical discovery, enabling the decoding of cellular heterogeneity with unprecedented resolution. Foundation models, pretrained on tens to hundreds of millions of single-cell transcriptomes through self-supervised objectives like masked gene modeling, are now revolutionizing drug target identification and personalized therapy development. These models overcome traditional limitations in drug discovery—such as high attrition rates and disease complexity—by providing a unified framework to represent cellular states, infer causal relationships, and predict therapeutic responses across diverse patient populations. This technical guide examines the architectural breakthroughs, experimental methodologies, and translational applications of single-cell foundation models (scFMs), demonstrating their capacity to identify novel therapeutic targets, repurpose existing drugs, and accelerate the development of precision medicine interventions through multimodal data integration and in silico perturbation modeling.

Traditional drug discovery suffers from low efficiency and high attrition rates, largely due to the complexity and heterogeneity of human diseases [54] [3]. The emergence of single-cell omics technologies has revolutionized our ability to investigate biological systems at cellular resolution, offering unprecedented insights into cellular heterogeneity, developmental pathways, and disease mechanisms [11] [10]. However, these advances have exposed critical limitations in traditional computational methodologies, which are ill-equipped to handle the complexity of modern single-cell datasets characterized by high dimensionality, technical noise, and multimodal data [11].

Self-supervised pretraining has emerged as a transformative solution to these challenges. Foundation models, originally developed for natural language processing, are now being adapted to single-cell omics through self-supervised learning on vast datasets [10]. These models treat each cell as a "sentence" and genes as "words," allowing them to learn the fundamental language of biology without explicit supervision [10]. By training on massive single-cell corpora—often encompassing 30-100 million cells—these models develop rich internal representations that can be fine-tuned for specific downstream tasks in drug discovery, including target identification, drug response prediction, and patient stratification [55] [10].

The pretraining process typically employs self-supervised objectives such as masked gene modeling, where the model learns to predict randomly masked portions of a cell's gene expression profile [10]. This approach allows the model to capture fundamental biological patterns and gene-gene relationships that generalize across tissues, conditions, and even species [11] [55]. The resulting foundation models serve as a bedrock for various drug discovery applications, significantly accelerating the translation of single-cell insights into therapeutic strategies.

Computational Foundations: Architecture and Pretraining Strategies

Model Architectures for Single-Cell Representation Learning

Single-cell foundation models employ diverse neural architectures optimized for handling high-dimensional, sparse transcriptomic data:

• Transformer-based models: Models like scGPT [11] [55] and Geneformer [10] utilize transformer architectures with attention mechanisms that learn and weight relationships between gene tokens. These models process gene expression profiles by converting each gene into a token embedding that combines gene identifier and expression value information, then applying multiple transformer layers to build latent representations of cells and genes.

• Hybrid architectures: Frameworks such as scMonica fuse Long Short-Term Memory (LSTM) and transformer models to capture temporal dynamics in biological data [11], while LangCell integrates language processing with transcriptomics through cross-modal alignment [11].

• Efficient variants: Newer models like CellFM employ modified RetNet frameworks with linear complexity to balance efficiency and performance when scaling to massive datasets [55]. Similarly, scPlantFormer incorporates phylogenetic constraints into its attention mechanism for cross-species applications [11].

Tokenization Strategies for Single-Cell Data

Unlike natural language, gene expression data lacks inherent sequential ordering, necessitating specialized tokenization approaches:

• Gene ranking: Models like Geneformer [10] and scGPT [10] rank genes within each cell by expression levels, creating a deterministic sequence based on expression magnitude.

• Value categorization: Approaches such as scBERT [55] bin continuous gene expression values into discrete "buckets," transforming expression prediction into a classification problem.

• Value projection: Methods including scFoundation [55] and CellFM [55] directly predict raw gene expression values using masked autoencoders, preserving the full resolution of the data.

Pretraining Corpora and Data Curation

The performance of scFMs heavily depends on the quality and diversity of pretraining data. Current models are trained on massive aggregated datasets from public repositories like CZ CELLxGENE, which provides unified access to over 100 million annotated single-cell datasets [10]. For example, CellFM was pretrained on a meticulously curated dataset of 102 million human cells from 19,914 samples across different organs and sequencing technologies [55]. These datasets encompass diverse biological conditions—including 46.3 million cells from normal donors and substantial representations from diseased states—enabling models to capture a wide spectrum of biological variation [55].

Figure 1: Foundation Model Architecture and Training Workflow

Experimental Protocols for Drug Target Identification

Cell-Type-Specific Marker Gene Discovery

Protocol 1: Interpretable Cell-Type Annotation with scKAN

scKAN represents an interpretable framework that combines knowledge distillation with Kolmogorov-Arnold networks (KAN) to identify cell-type-specific marker genes and potential drug targets [56].

• Teacher Model Fine-tuning:

  • Utilize a pre-trained foundation model (e.g., scGPT pretrained on 33 million cells) as the teacher model
  • Fine-tune the model on specific disease datasets (e.g., pancreatic ductal adenocarcinoma) using labeled cell-type annotations

• Knowledge Distillation:

  • Train a student KAN model to learn from both the teacher model's predictions and ground truth cell-type information
  • Employ a combined loss function integrating distillation loss with self-entropy loss and Cauchy-Schwarz divergence-based clustering loss

• Gene Importance Scoring:

  • Extract edge scores from the trained KAN model, which quantify the contribution of each gene to specific cell-type classifications
  • Filter genes with high importance scores for functional validation

• Biological Validation:

  • Perform enrichment analysis on high-scoring genes against known cell-type markers and pathway databases
  • Validate candidate genes through experimental techniques such as spatial transcriptomics or immunofluorescence

This approach has demonstrated a 6.63% improvement in macro F1 score over state-of-the-art methods while identifying biologically meaningful, cell-type-specific gene sets [56].

In Silico Perturbation Modeling for Causal Target Validation

Protocol 2: AI-Enhanced Perturbation Modeling

Perturbation omics provides a critical causal reasoning foundation for target identification by simulating genetic or chemical interventions [54].

• Genetic Perturbation Simulation:

  • Use models like scGPT or Geneformer to predict transcriptomic responses to single-gene knockouts or knockdowns
  • Systematically perturb genes within disease-associated pathways and measure predicted impact on disease-related gene expression patterns

• Chemical Perturbation Modeling:

  • Input chemical structures of small molecules and predict their effects on cellular transcriptomes
  • Compare perturbation profiles against reference drug databases to identify compounds with desired mechanisms of action

• Network-Level Analysis:

  • Construct gene regulatory networks from perturbation responses using graph neural networks
  • Identify key regulator genes whose perturbation causes cascading effects in disease-relevant pathways

• Cross-Modal Integration:

  • Integrate perturbation responses with protein-protein interaction networks and epigenetic data
  • Prioritize targets based on concordance across multiple data modalities

This approach enables rapid in silico screening of potential drug targets before costly experimental validation [54].

Multimodal Data Integration for Target Prioritization

Protocol 3: Cross-Modal Alignment for Target Discovery

Multimodal integration strategies harmonize transcriptomic, epigenomic, proteomic, and spatial imaging data to delineate multilayered regulatory networks [11].

• Data Harmonization:

  • Process single-cell RNA-seq, ATAC-seq, and spatial transcriptomics data through standardized preprocessing pipelines
  • Apply batch correction algorithms to mitigate technical variations across platforms

• Cross-Modal Alignment:

  • Utilize models like PathOmCLIP that align histology images with spatial transcriptomics via contrastive learning
  • Implement tensor-based fusion methods to integrate sparse multi-omics matrices

• Target Prioritization:

  • Compute multi-omics importance scores by aggregating evidence across modalities
  • Filter candidates based on druggability predictions from structured knowledge bases
  • Validate target-cell type specificity through spatial localization analysis

Table 1: Performance Comparison of Single-Cell Foundation Models in Drug Discovery Tasks

Model	Training Scale	Architecture	Cell Annotation Accuracy	Perturbation Prediction	Target Identification
CellFM [55]	100M cells, 800M parameters	ERetNet (Transformer variant)	Superior to existing models	High accuracy in simulating gene knockout effects	Effective in identifying novel therapeutic targets
scGPT [11] [55]	33M cells	Transformer	92% cross-species accuracy	Accurate chemical perturbation modeling	Robust in predicting drug-target interactions
scKAN [56]	Knowledge distillation from scGPT	Kolmogorov-Arnold Networks	6.63% improvement in macro F1 score	Not specified	Identified clinically actionable targets in pancreatic cancer
Geneformer [10]	30M cells	Transformer	High accuracy in rare cell types	Effective in predicting disease-relevant perturbations	Successfully predicted cardiopathy-associated targets

Translational Applications in Personalized Therapy

Drug Repurposing Through Cellular Signature Matching

Foundation models enable systematic drug repurposing by comparing disease-associated gene expression signatures with drug perturbation profiles:

• Disease Signature Generation:

  • Process single-cell data from patient biopsies to identify cell-type-specific dysregulated pathways
  • Construct disease signatures by comparing cell states between healthy and diseased tissues

• Drug Signature Database:

  • Compile perturbation profiles from drug-treated cellular models using scRNA-seq
  • Generate drug signatures representing transcriptional responses to pharmaceutical compounds

• Signature Matching:

  • Compute similarity metrics between disease and drug signatures using correlation-based measures
  • Prioritize compounds whose perturbation profiles oppose disease-associated changes

• Clinical Validation:

  • Correlate predicted drug efficacy with patient outcome data from electronic health records
  • Design clinical trials enriched for patients with predictive biomarkers

This approach has identified potential drug repurposing candidates for pancreatic ductal adenocarcinoma, with binding stability confirmed through molecular dynamics simulations [56].

Patient Stratification and Biomarker Discovery

Single-cell foundation models enable precision medicine through deep phenotyping of patient populations:

• Cellular Atlas Construction:

  • Generate comprehensive single-cell atlases for specific disease areas (e.g., tumor microenvironments)
  • Characterize cellular heterogeneity across patient cohorts using foundation model embeddings

• Subpopulation Identification:

  • Apply clustering algorithms to model-derived cell embeddings to identify disease subtypes
  • Correlate cellular subpopulations with clinical outcomes and treatment responses

• Biomarker Discovery:

  • Identify gene expression patterns characteristic of treatment-responsive subpopulations
  • Validate biomarkers using independent cohorts and experimental models

• Therapeutic Target Prioritization:

  • Select targets that are specific to disease-driving cell subpopulations
  • Ensure targets have appropriate safety profiles based on expression in healthy tissues

Figure 2: Drug Discovery and Personalized Therapy Workflow

Clinical Response Prediction and Resistance Modeling

scFMs can predict individual patient responses to therapies and model resistance mechanisms:

• Response Signature Development:

  • Analyze pre-treatment biopsy samples from clinical trial participants using single-cell technologies
  • Train models to distinguish cellular features of responders versus non-responders

• Dynamic Response Modeling:

  • Incorporate longitudinal single-cell data to track evolution of cellular states during treatment
  • Model transitions between drug-sensitive and resistant states using neural ordinary differential equations

• Resistance Mechanism Identification:

  • Compare cellular landscapes between primary and resistant tumors
  • Identify alternative signaling pathways activated in resistant cell populations

• Combination Therapy Design:

  • Simulate effects of drug combinations on heterogeneous cell populations
  • Prioritize combinations that simultaneously target multiple resistance mechanisms

Table 2: Key Research Reagent Solutions for Single-Cell Foundation Model Implementation

Category	Specific Tools/Platforms	Function	Application in Drug Discovery
Computational Frameworks	scGPT, Geneformer, CellFM, scKAN	Model training and inference	Target identification, perturbation modeling, drug response prediction
Data Resources	CZ CELLxGENE, DISCO, Human Cell Atlas	Provide standardized single-cell datasets	Model pretraining, validation, and benchmarking
Analysis Platforms	BioLLM, scGNN+, SynEcoSys	Data processing, visualization, and interpretation	Biomarker discovery, patient stratification, clinical translation
Spatial Technologies	CosMx SMI, GeoMx DSP	High-plex spatial molecular imaging	Target validation in tissue context, understanding tumor microenvironments
Experimental Validation	Molecular dynamics simulations, CRISPR screening	Functional validation of computational predictions	Confirm target engagement, mechanism of action studies

Future Directions and Implementation Challenges

While single-cell foundation models show tremendous promise for drug discovery, several challenges must be addressed to realize their full potential:

• Data Quality and Integration: Technical variability across single-cell platforms, batch effects, and sparse data present significant hurdles for model generalization [11] [10]. Future developments require improved normalization methods and adversarial training approaches to enhance model robustness.

• Interpretability and Biological Relevance: Despite advances like scKAN, interpreting model predictions and connecting them to biologically actionable insights remains challenging [56]. Research priorities include developing better visualization tools and incorporating biological pathway knowledge directly into model architectures.

• Multimodal Integration Gaps: Current models predominantly focus on transcriptomics, with limited integration of proteomic, metabolomic, and spatial data [11]. Next-generation models will need to effectively harmonize diverse data types while preserving biological context.

• Clinical Translation Barriers: Bridging the gap between computational predictions and clinical applications requires closer collaboration between computational biologists, clinicians, and pharmaceutical researchers. Implementation frameworks that validate model predictions in relevant disease models are essential for building trust in these approaches.

Future developments in single-cell foundation models will likely focus on real-time dynamic modeling of disease progression, enhanced causal inference capabilities, and tighter integration with clinical decision support systems. As these models continue to evolve, they will play an increasingly central role in accelerating drug discovery and enabling truly personalized therapeutic interventions.

Self-supervised pretraining for single-cell omics has emerged as a transformative approach for drug target identification and personalized therapy development. Foundation models like scGPT, CellFM, and scKAN demonstrate how self-supervised learning on massive single-cell datasets can uncover novel therapeutic targets, enable drug repurposing, and facilitate patient stratification. By providing a unified framework to represent cellular states, infer causal relationships, and predict therapeutic responses, these models are overcoming traditional limitations in drug discovery. As the field advances, addressing challenges related to data quality, model interpretability, and clinical translation will be essential for fully realizing the potential of single-cell foundation models to revolutionize precision medicine and therapeutic development.

Navigating Challenges: From Data Quality to Computational Efficiency

In single-cell omics research, batch effects represent one of the most significant technical barriers to achieving robust and generalizable biological insights. These systematic non-biological variations arise from differences in experimental protocols, sequencing platforms, laboratory conditions, sample processing times, and personnel [57] [58]. In the context of self-supervised pretraining for single-cell omics, batch effects pose a particularly challenging problem as they can confound the latent representations learned by foundation models, potentially propagating technical artifacts through downstream analyses and clinical applications [2] [1]. The emergence of single-cell foundation models (scFMs) trained on millions of cells has intensified the need for advanced batch correction techniques that can harmonize data across diverse sources while preserving delicate biological signals [2] [1]. This technical guide examines current methodologies, evaluation frameworks, and emerging solutions for conquering batch effects to build more robust and generalizable models in single-cell research.

Current Landscape: Traditional Approaches and Their Limitations

Traditional batch correction methods have evolved from simple statistical adjustments to sophisticated deep learning approaches. The table below summarizes the primary categories of batch correction methods and their characteristics:

Table 1: Categories of Batch Effect Correction Methods

Method Category	Representative Tools	Typical Applications	Key Limitations
VAE-based Models	scGen, sysVI	scRNA-seq integration, Cross-system alignment	Struggles with substantial batch effects across systems [59]
Mutual Nearest Neighbors	fastMNN, Scanorama, BBKNN	Cell type alignment, Atlas construction	Limited performance with large batch effect sizes [58]
Matrix Factorization	Harmony, Seurat (CCA)	Multi-batch integration, Reference mapping	May overcorrect with increased parameters [57]
Statistical Adjustment	ComBat, limma, ComBat-ref	Bulk RNA-seq, Differential expression	Not designed for single-cell sparsity [60] [58]
Foundation Models	scGPT, scPlantFormer	Multi-task learning, Zero-shot annotation	Computational intensity, Interpretability challenges [2] [1]

Despite these advances, traditional approaches struggle with "substantial batch effects" that occur when integrating datasets across different biological systems (e.g., species), technological platforms (e.g., single-cell vs. single-nuclei), or experimental conditions (e.g., organoids vs. primary tissue) [59] [61]. Conditional Variational Autoencoders (cVAEs), while popular and scalable, often fail to adequately integrate such substantially different datasets without sacrificing biological signal [59].

Foundation Models and Self-Supervised Learning: A New Paradigm

Single-cell foundation models (scFMs) represent a paradigm shift in batch effect correction by leveraging self-supervised pretraining on massive, diverse datasets. These models, including scGPT (pretrained on over 33 million cells) and scPlantFormer, learn universal cellular representations that can be adapted to various downstream tasks with minimal fine-tuning [2] [1].

Architectural Innovations

The transformer architecture, originally developed for natural language processing, has become the backbone of modern scFMs [1]. These models treat individual cells as "sentences" and genes or genomic features as "words" or "tokens" [1]. Key architectural considerations include:

• Tokenization Strategies: Unlike words in a sentence, gene expression data lacks natural ordering. Solutions include ranking genes by expression levels, binning genes by expression values, or using normalized counts directly [1].
• Attention Mechanisms: Transformers use attention mechanisms to weight relationships between gene tokens, enabling the model to learn which genes are most informative about cellular identity and state [1].
• Modality Integration: Advanced scFMs incorporate tokens indicating different modalities (e.g., scATAC-seq, spatial transcriptomics) to enable cross-modal learning and integration [2] [1].

Self-Supervised Pretraining Strategies

Self-supervised pretraining is the cornerstone of scFMs, enabling models to learn meaningful representations without explicit labeling. Common pretext tasks include:

• Masked Gene Modeling: Randomly masking portions of the gene expression profile and training the model to predict the masked values based on context [2] [1].
• Contrastive Learning: Maximizing agreement between differently augmented views of the same cell while minimizing agreement with other cells [62].
• Multimodal Alignment: Learning shared representations across different data modalities (e.g., transcriptomics, epigenomics, proteomics) through contrastive objectives [2].

The following diagram illustrates the typical self-supervised pretraining workflow for single-cell foundation models:

Figure 1: Self-Supervised Pretraining Workflow for scFMs

Advanced Techniques for Substantial Batch Effects

The sysVI Framework: Overcoming cVAE Limitations

Recent research has exposed critical limitations in popular cVAE-based integration methods. The sysVI framework introduces two key innovations to address these challenges:

• VampPrior (Variational Mixture of Posteriors Prior): Replaces the standard Gaussian prior with a mixture of variational posteriors, enabling more flexible and biologically meaningful latent representations [59] [61].
• Cycle-Consistency Constraints: Ensures that translating a cell's representation between batches and back again preserves its original biological identity [59] [61].

Experimental results across five challenging integration scenarios (including cross-species, organoid-tissue, and single-cell/single-nuclei integrations) demonstrated that the combination of VampPrior and cycle-consistency (VAMP+CYC model) significantly improves batch correction while maintaining high biological preservation compared to traditional approaches [59] [61].

Federated Learning for Privacy-Preserving Integration

FedscGen represents a breakthrough in privacy-preserving batch effect correction by implementing a federated version of the scGen model enhanced with secure multiparty computation (SMPC) [58]. This approach enables multiple institutions to collaboratively train models without sharing raw data, addressing critical genomic privacy concerns while tackling batch effects.

Table 2: Performance Comparison of FedscGen vs. Centralized scGen

Evaluation Metric	FedscGen Performance	Centralized scGen	Performance Gap (Δ)
NMI (Cell Identity)	Matches scGen	Baseline	Δ ≈ 0 [58]
kBET (Batch Mixing)	Matches scGen	Baseline	Δ ≈ 0 [58]
ASW (Cluster Quality)	Matches scGen	Baseline	Δ ≈ 0 [58]
GC (Graph Connectivity)	Matches scGen	Baseline	Δ ≈ 0 [58]
EBM (Empirical Mixing)	Matches scGen	Baseline	Δ ≈ 0 [58]

The federated workflow involves multiple clients training local VAE models on their respective datasets, with a coordinator aggregating parameters and distributing updated global models without ever accessing raw data [58]. This approach maintains competitive performance with centralized methods while addressing critical privacy constraints of multi-center studies.

Evaluation Frameworks and Metrics

The RBET Framework: Overcorrection Awareness

The Reference-informed Batch Effect Testing (RBET) framework introduces a novel approach to batch effect evaluation with specific sensitivity to overcorrection [57]. Unlike traditional metrics, RBET leverages reference genes (RGs) with stable expression patterns across conditions to distinguish technical artifacts from biological signals.

Key advantages of RBET include:

• Overcorrection Detection: RBET uniquely detects when batch correction methods erase true biological variation, manifested as a characteristic biphasic response where performance initially improves then deteriorates with increasing correction strength [57].
• Robustness to Large Batch Effects: RBET maintains discrimination capacity even with large batch effect sizes, where traditional metrics like LISI and kBET often fail [57].
• Biological Grounding: By leveraging reference genes with known stable expression, RBET connects technical performance to biological plausibility [57].

Comprehensive Metric Comparison

Table 3: Batch Effect Correction Evaluation Metrics

Metric	Primary Focus	Detection of Overcorrection	Computational Efficiency	Key Limitation
RBET	Reference gene stability	Yes - Biphasic response	High	Requires reference genes [57]
LISI	Local batch mixing	No - Monotonic improvement	Medium	Loses discrimination with large effects [57]
kBET	Global batch mixing	No - Monotonic improvement	Low	Poor type I error control [57]
ASW	Cluster separation	Partial	Medium	Limited to cluster-level assessment [58]
NMI	Cell type alignment	No	Medium	Requires ground truth labels [58]

The following diagram illustrates the RBET evaluation workflow and its critical advantage in detecting overcorrection:

Figure 2: RBET Evaluation Framework with Overcorrection Detection

Experimental Protocols and Methodologies

Protocol: sysVI for Substantial Batch Effects

Purpose: Integrate single-cell datasets with substantial batch effects (cross-species, organoid-tissue, or different protocols) while preserving biological signals [59] [61].

Materials:

• Multiple scRNA-seq datasets with known batch effects
• High-performance computing environment (GPU recommended)
• Python with scvi-tools package (includes sysVI implementation)

Procedure:

• Data Preprocessing: Standard quality control, normalization, and feature selection separately for each dataset.
• Model Configuration: Implement cVAE architecture with VampPrior initialization and cycle-consistency constraints.
• Training: Train model with early stopping based on validation loss, typically 100-200 epochs.
• Latent Space Extraction: Generate integrated latent representations for downstream analysis.
• Validation: Assess batch mixing (iLISI) and biological preservation (NMI, cell type ASW).

Technical Notes: The VampPrior uses a mixture of variational posteriors rather than standard Gaussian prior, enabling more flexible modeling of complex distributions. Cycle-consistency loss should be weighted appropriately to balance integration strength with biological preservation [59] [61].

Protocol: Federated Batch Correction with FedscGen

Purpose: Perform privacy-preserving batch effect correction across multiple institutions without sharing raw data [58].

Materials:

• Distributed scRNA-seq datasets at multiple institutions
• FeatureCloud platform or similar federated learning infrastructure
• Secure communication channels between participants

Procedure:

• Initialization: Coordinator deploys initial VAE parameters to all clients.
• Local Training: Each client trains the model locally for a set number of epochs.
• Secure Aggregation: Clients send encrypted model parameters to coordinator for Federated Averaging (FedAvg).
• Global Update: Coordinator distributes updated global model to clients.
• Iteration: Repeat steps 2-4 until convergence.
• Correction Phase: Apply federated δ-vector estimation and correction using securely aggregated latent representations.

Technical Notes: FedscGen uses Secure MultiParty Computation (SMPC) based on additive secret sharing to protect privacy during aggregation. Model performance should be validated against centralized baselines using metrics like kBET acceptance rate and KNN-accuracy [58].

Table 4: Key Computational Tools and Resources for Batch Effect Correction

Tool/Resource	Primary Function	Application Context	Access Method
scGPT	Single-cell foundation model	Large-scale multi-task learning, Zero-shot annotation	Python package [2]
sysVI	Substantial batch effect correction	Cross-species, organoid-tissue integration	scvi-tools package [59] [61]
FedscGen	Privacy-preserving integration	Multi-institution collaborations	FeatureCloud app [58]
RBET	Batch effect evaluation	Method selection, Overcorrection detection	R/Python implementation [57]
CZ CELLxGENE	Curated single-cell data	Pretraining corpus, Benchmarking	Online platform [2] [1]
Harmony	Rapid batch integration	Atlas-level integration, Reference mapping	R package [57]
Scanorama	Panoramic data integration	Multiple dataset integration	Python package [57]

The field of batch effect correction is rapidly evolving alongside advances in single-cell technologies and foundation models. Promising future directions include:

• Multimodal Foundation Models: Developing models that natively integrate transcriptomic, epigenomic, proteomic, and spatial data to learn more robust representations that are inherently batch-resistant [2].
• Causal Representation Learning: Incorporating causal inference frameworks to distinguish technical artifacts from biological signals more effectively [2].
• Federated Foundation Models: Extending privacy-preserving approaches to large-scale pretraining, enabling collaborative model development without data sharing [58].
• Standardized Benchmarking: Establishing unified benchmarks and evaluation metrics specifically designed for foundation model pretraining and fine-tuning [2] [57].

In conclusion, conquering batch effects requires a multifaceted approach that combines advanced computational methods, rigorous evaluation frameworks, and thoughtful consideration of the trade-offs between technical correction and biological preservation. As single-cell foundation models continue to evolve, integrating robust batch correction strategies into their pretraining and fine-tuning pipelines will be essential for achieving truly generalizable models that translate successfully to clinical applications and therapeutic development.

The explosion of single-cell genomics data has created an urgent need for computational methods that can learn meaningful representations from vast, unlabeled datasets. Self-supervised learning (SSL) has emerged as a powerful paradigm to address this need, with masked autoencoders and contrastive learning establishing themselves as two dominant pretext task frameworks [6]. These approaches enable models to learn fundamental biological principles by pre-training on millions of cells, then adapting to diverse downstream tasks with minimal fine-tuning [1] [11]. The choice between these competing methodologies represents a critical strategic decision for researchers building foundation models for single-cell omics, with significant implications for model performance, computational efficiency, and biological interpretability.

Within the context of self-supervised pretraining for single-cell omics research, this technical guide provides a comprehensive analysis of masked autoencoder versus contrastive learning approaches. We examine the underlying architectures, training methodologies, and performance characteristics of each framework, supported by empirical evidence from recent benchmarking studies. By synthesizing insights from foundational models including scGPT, scPlantFormer, and innovative frameworks like sCIN and scMMAE, this review equips researchers with the practical knowledge needed to select and implement optimal pretext tasks for their specific biological questions and computational constraints.

Core Architectural Frameworks and Mechanisms

Masked Autoencoders in Single-Cell Genomics

Masked autoencoders (MAE) operate on the principle of reconstruction-based learning, where the model learns to predict randomly masked portions of the input data based on the unmasked context. In single-cell genomics, this typically involves masking specific genes or genomic features and training the model to reconstruct their values [6] [63]. The architectural implementation generally follows a transformer-based encoder-decoder pattern, where the encoder processes the unmasked portions of the cell's profile, and the decoder reconstructs the complete profile from the latent representations.

Several masking strategies have been developed for single-cell data, each incorporating different levels of biological prior knowledge. Random masking applies minimal inductive bias by selecting genes randomly for masking. Gene programme masking leverages known biological pathways by masking coordinated groups of functionally related genes. Isolated masking strategies, such as GP-to-GP and GP-to-TF, focus on specific regulatory relationships by masking entire gene programmes and requiring prediction of transcription factor activities or vice versa [6]. These approaches enable the model to learn both local gene relationships and global cellular states.

Table 1: Masked Autoencoder Implementation Variants

Method	Masking Strategy	Architecture	Key Application
scGPT	Gene ranking with random masking	Transformer decoder	Multi-omic integration, perturbation prediction
scMapNet	Marker gene-focused masking	Vision Transformer + MAE	Cell type annotation
scMMAE	Cross-modal masking	Cross-attention network	Multimodal omics fusion
GP-to-TF	Isolated gene programme masking	Fully connected autoencoder	Regulatory network inference

Contrastive Learning Frameworks

Contrastive learning operates on a fundamentally different principle from masked autoencoders, focusing on learning representations by comparing similar and dissimilar data points. The core objective is to learn an embedding space where similar cells (positive pairs) are positioned close together, while dissimilar cells (negative pairs) are pushed apart [64] [65]. This approach requires careful construction of positive and negative pairs, which can be derived from different augmentations of the same cell, measurements from multi-omics assays of the same cell, or cells of the same type across different batches or modalities.

Key to contrastive learning's success is the loss function that governs the embedding space geometry. The InfoNCE loss and its variants have become standard, though negative-pair-free methods like BYOL and Barlow Twins have also been adapted for single-cell data [6]. For single-cell multi-omics integration, frameworks like sCIN employ modality-specific encoders that project different omics measurements into a shared latent space, using contrastive loss to align representations of the same cell type across modalities while separating different cell types [64]. Similarly, scCobra utilizes contrastive learning with domain adaptation to mitigate batch effects while preserving biological heterogeneity [65].

Performance Benchmarking and Comparative Analysis

Empirical Performance Across Downstream Tasks

Recent large-scale benchmarking studies have provided crucial insights into the relative strengths of masked autoencoders versus contrastive learning for single-cell genomics. A comprehensive evaluation published in Nature Machine Intelligence examined SSL methods trained on over 20 million cells across multiple downstream tasks, including cell-type prediction, gene-expression reconstruction, cross-modality prediction, and data integration [6]. The findings revealed that masked autoencoders consistently outperformed contrastive methods in single-cell genomics applications, contrary to trends observed in some computer vision domains.

For cell-type prediction tasks, models with masked autoencoder pre-training on large auxiliary datasets demonstrated significant improvements in macro F1 scores, with performance gains from 0.7013 to 0.7466 for PBMC datasets and 0.2722 to 0.3085 for the Tabula Sapiens Atlas [6]. These improvements were particularly pronounced for underrepresented cell types, indicating that MAE pretraining enhances model robustness to class imbalances. In zero-shot settings, where models predict unobserved classes using representations learned solely through self-supervision, masked autoencoders again demonstrated superior performance, highlighting their ability to capture biologically meaningful representations without task-specific fine-tuning.

Table 2: Performance Comparison Across Downstream Tasks

Downstream Task	Best Performing Method	Key Metric	Performance Advantage
Cell Type Annotation	scMapNet (MAE)	Accuracy	Superior to 6 benchmark methods [63]
Multimodal Integration	sCIN (Contrastive)	Recall@k, ASW	Outperformed 6 state-of-art methods [64]
Batch Correction	scCobra (Contrastive)	Batch mixing, cell separation	Better than Seurat, Harmony, scVI [65]
Gene Expression Reconstruction	MAE variants	Weighted explained variance	~10% improvement over contrastive methods [6]
Cross-modality Prediction	scMMAE (MAE)	Adjusted Rand Index	21% improvement in multimodal fusion [66]

Task-Specific Performance Patterns

While masked autoencoders demonstrate broad superiority across many tasks, contrastive learning excels in specific applications, particularly data integration and batch correction. The sCIN framework, which uses contrastive learning with modality-specific encoders, achieved state-of-the-art performance on both paired and unpaired multi-omics datasets, outperforming methods like scGLUE, scBridge, and Harmony across multiple metrics including Average Silhouette Width (ASW) for clustering quality and Recall@k for integration quality [64]. Similarly, CYCLONE's recycle contrastive learning approach effectively eliminated batch effects while preserving batch-specific cell types, addressing the critical challenge of over-correction that plagues many integration methods [67].

For multimodal omics fusion, hybrid approaches that combine elements of both methodologies have shown particular promise. The scMMAE framework leverages a masked cross-attention network to simultaneously capture shared and distinctive information across transcriptomic and proteomic modalities, demonstrating improvements of up to 21% in Adjusted Rand Index for multimodal fusion and approximately 20% for unimodal enhancement [66]. This suggests that the highest-performance solutions may integrate architectural components from both pretext task paradigms rather than relying exclusively on one approach.

Implementation Considerations and Experimental Design

Protocol for Masked Autoencoder Implementation

Implementing masked autoencoders for single-cell genomics requires careful consideration of several design choices. The following protocol outlines key steps for effective MAE implementation:

• Input Representation: Standardize input data using robust normalization techniques. For transformer architectures, convert gene expression profiles into ordered sequences, typically by ranking genes based on expression levels within each cell [1].

• Masking Strategy Selection: Choose an appropriate masking strategy based on biological prior knowledge and task objectives. Random masking provides minimal inductive bias, while gene programme masking incorporates biological pathway information. For regulatory network inference, implement isolated masking of transcription factors or gene programmes [6].

• Architecture Configuration: Implement transformer-based encoder-decoder architecture. The encoder processes unmasked genes, generating latent representations. The decoder reconstructs masked values from these representations. Consider using vision transformers with treemap transformations when incorporating marker gene knowledge [63].

• Pre-training Optimization: Pre-train on large-scale single-cell corpora such as CELLxGENE, which provides access to over 100 million cells [1] [11]. Use self-supervised objectives without labeled data, typically employing mean squared error or negative binomial loss for reconstruction.

• Transfer Learning: Fine-tune pre-trained models on specific downstream tasks with limited labeled data. Empirical studies show that pre-training on auxiliary data significantly boosts performance on target tasks, particularly for underrepresented cell types [6].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Computational Tools for SSL in Single-Cell Genomics

Tool/Platform	Type	Primary Function	Relevance to Pretext Tasks
CELLxGENE	Data Platform	Provides standardized access to >100M single cells [1]	Critical source of diverse pretraining data for both MAE and contrastive learning
scGPT	Foundation Model	Transformer-based model for multi-omic analysis [2] [11]	Implements masked gene modeling for pretraining
BioLLM	Benchmarking Framework	Universal interface for evaluating foundation models [2] [11]	Standardized evaluation of pretext task performance
Harmony	Integration Algorithm	Batch effect correction using fuzzy clustering [64] [67]	Baseline comparison for contrastive integration methods
Scanpy	Analysis Toolkit	Standard preprocessing and analysis of single-cell data [67]	Essential data preprocessing for both approaches
VAE Framework	Neural Architecture	Generative modeling with probabilistic latent space [65] [67]	Base architecture for many contrastive and MAE variants

The comparative analysis of masked autoencoders and contrastive learning for single-cell genomics reveals a nuanced landscape where each approach demonstrates distinct advantages depending on the target application. Masked autoencoders have established broad superiority across most benchmark tasks, particularly excelling in cell-type annotation, gene-expression reconstruction, and zero-shot learning scenarios [6]. Their reconstruction-based objective directly aligns with the fundamental challenge of modeling gene-gene relationships and cellular states, making them particularly well-suited for foundational model pretraining.

Contrastive learning methods maintain strong advantages in specific domains, especially data integration, batch correction, and multimodal alignment [64] [65] [67]. Their ability to learn embedding spaces that preserve biological similarity while discarding technical artifacts makes them invaluable for harmonizing diverse datasets and integrating multiple measurement modalities.

Looking forward, the most promising direction lies in hybrid approaches that integrate strengths from both paradigms, such as scMMAE's combination of masked modeling with cross-attention mechanisms [66]. As single-cell foundation models continue to evolve, the optimal architectural choices will likely incorporate elements from both pretext task families, leveraging the representation learning capabilities of contrastive methods with the generative modeling power of masked autoencoders. The emerging paradigm of recycling contrastive learning, as implemented in CYCLONE, which iteratively refines positive pairs during training, points toward more dynamic, self-improving frameworks that could transcend the current limitations of both approaches [67].

For researchers and drug development professionals building foundation models for single-cell omics, the choice between masked autoencoders and contrastive learning should be guided by specific application requirements, with masked autoencoders preferred for general-purpose foundational models and contrastive learning selected for specialized integration tasks. As the field progresses toward increasingly sophisticated multimodal analyses, the integration of both approaches within unified frameworks will likely become standard practice, enabling more comprehensive and biologically faithful models of cellular function and disease mechanisms.

In the rapidly evolving field of single-cell omics research, self-supervised learning (SSL) has emerged as a transformative paradigm for extracting meaningful biological insights from vast, unlabeled datasets. Among the various pretext tasks within SSL, data augmentation strategies play a pivotal role in guiding models to learn robust representations. While biologically-informed augmentation strategies might intuitively seem superior, recent empirical evidence reveals a counterintuitive finding: random masking, a simple and seemingly naive approach, demonstrates remarkable efficacy and even outperforms more complex, biologically-driven masking strategies in many scenarios. This technical guide examines the surprising effectiveness of random masking within self-supervised pretraining frameworks for single-cell genomics (SCG), providing researchers and drug development professionals with evidence-based insights and practical methodologies for implementation.

The foundation of this approach lies in masked autoencoders, where portions of the input data are randomly obscured, and the model is trained to reconstruct the missing information. This process forces the model to learn underlying data structures and dependencies without human-prescribed biological biases. As we will explore, this minimal inductive bias approach has proven particularly powerful in transfer learning scenarios and for generalizable representation learning across diverse cellular contexts [6].

Empirical Evidence: Random Masking in Single-Cell Omics

Comparative Performance of Masking Strategies

Recent large-scale benchmarking studies have systematically evaluated various self-supervised learning approaches, including multiple masking strategies, across diverse single-cell genomics tasks. The following table summarizes key quantitative findings from these investigations:

Table 1: Performance Comparison of SSL Pre-training Strategies on Single-Cell Genomics Tasks

Pre-training Strategy	Cell-Type Prediction (Macro F1)	Gene-Expression Reconstruction	Cross-Species Annotation	Data Integration Capability
Random Masking	0.7466 (PBMC dataset)	High (Weighted Explained Variance)	Excellent	Strong
Gene Programme (GP) Masking	Lower than random masking	Moderate	Good	Moderate
Contrastive Learning (BYOL)	Lower than masked autoencoders	Lower than masked autoencoders	Good	Moderate
Contrastive Learning (Barlow Twins)	Lower than masked autoencoders	Lower than masked autoencoders	Good	Moderate
Supervised Baseline (No pre-training)	0.7013 (PBMC dataset)	Baseline	Limited	Limited

The empirical evidence demonstrates that models utilizing random masking during self-supervised pre-training consistently achieve superior performance on downstream tasks compared to both supervised baselines and other SSL approaches [6]. Notably, random masking has shown exceptional capability in enhancing classification of underrepresented cell types, as indicated by significant improvements in macro F1 scores—a metric sensitive to class imbalance [6].

Contextual Advantages of Random Masking

The efficacy of random masking is particularly pronounced in specific experimental contexts:

• Transfer Learning Scenarios: When analyzing smaller target datasets informed by insights from larger auxiliary datasets (e.g., pre-training on the CELLxGENE census containing over 20 million cells), random masking enables models to learn generalizable representations that transfer effectively to specific tissues or conditions [6].

• Zero-Shot Settings: In situations where comprehensive labeled data is unavailable, representations learned through random masking facilitate robust cell-type identification using simple classifiers like k-nearest neighbors (kNN) without task-specific fine-tuning [6].

• Cross-Modality Prediction: The general representations captured through random masking demonstrate strong performance in predicting one molecular modality from another, highlighting their comprehensive understanding of cellular states [6].

Experimental Protocols and Implementation

Masked Autoencoder Framework for Single-Cell Data

The implementation of random masking within masked autoencoders for single-cell omics involves several critical components:

Table 2: Key Components of Masked Autoencoder Framework for Single-Cell Data

Component	Specification	Function in Architecture
Encoder Architecture	Fully connected networks	Encode visible cells into latent representations
Masking Strategy	Random masking (20-40% of input features)	Create pretext task for self-supervised learning
Reconstruction Target	Original gene expression values	Model learning objective
Training Dataset	CELLxGENE census (≥20 million cells) [6]	Pre-training corpus for learning general representations
Fine-tuning Approach	Task-specific supervised training	Adapt pre-trained models to specific downstream applications

The experimental workflow typically follows a two-stage process: (1) self-supervised pre-training using random masking on large-scale single-cell datasets, and (2) supervised fine-tuning on specific downstream tasks with limited labeled data [6].

Detailed Protocol for Random Masking Implementation

Pre-training Phase with Random Masking:

• Data Preparation: Format single-cell data as cells × genes matrix with normalized expression values. The recommended dataset size for effective pre-training exceeds 1 million cells [6].

• Masking Process: Randomly select 20-40% of input features (genes) for each cell to mask. Replace masked values with a learned mask token or zero value.

• Model Architecture: Implement a standard autoencoder architecture with:

  • Encoder: 3-5 fully connected layers with ReLU activations
  • Bottleneck: Latent representation (typically 64-256 dimensions)
  • Decoder: Symmetrical structure to encoder for reconstruction

• Training Parameters:

  • Optimization: Adam optimizer with learning rate of 1e-4
  • Batch size: 512-1024 cells
  • Loss function: Mean squared error between reconstructed and original expression values

• Training Duration: Train until validation reconstruction loss plateaus (typically 50-100 epochs)

Fine-tuning Phase for Downstream Tasks:

• Task-Specific Data Preparation: Prepare labeled datasets for tasks such as cell-type annotation, perturbation response prediction, or disease state classification.

• Model Adaptation: Replace the pre-training decoder with task-specific prediction heads (e.g., classification layers).

• Transfer Learning: Initialize encoder weights with pre-trained parameters and fine-tune entire model on labeled downstream data with reduced learning rate (typically 1e-5).

• Evaluation: Assess performance on held-out test sets using task-relevant metrics (e.g., F1-score for classification, mean squared error for regression).

Visualizing the Random Masking Workflow

The following diagram illustrates the complete workflow for implementing random masking in self-supervised learning for single-cell omics:

Random Masking Workflow in Self-Supervised Learning

Successful implementation of random masking strategies requires specific computational tools and resources. The following table details essential components for establishing this methodology in research environments:

Table 3: Essential Research Reagent Solutions for Random Masking Implementation

Resource Category	Specific Tools/Platforms	Function in Research Pipeline
Foundation Models	scGPT [2], scPlantFormer [2]	Pre-trained models leveraging SSL for various single-cell analysis tasks
Benchmarking Platforms	BioLLM [2]	Standardized frameworks for evaluating and comparing foundation models
Data Resources	CELLxGENE Census [6], DISCO [2]	Large-scale single-cell datasets for pre-training and evaluation
Analysis Ecosystems	Galaxy Single-Cell & Spatial Omics Community (SPOC) [68]	Accessible platforms with tools and workflows for single-cell analysis
Computational Frameworks	PyTorch, TensorFlow	Deep learning frameworks for implementing custom masked autoencoders
Specialized Architectures	scMASKGAN [69]	GAN-based approaches incorporating masking for data imputation

The surprising efficacy of random masking in self-supervised learning for single-cell omics challenges intuitive assumptions about the necessity of biologically-informed data augmentation strategies. The empirical evidence demonstrates that this minimally biased approach consistently outperforms more complex, domain-specific masking strategies across critical tasks including cell-type prediction, gene-expression reconstruction, and cross-modality integration. This paradox—where simplicity surpasses sophistication—suggests that random masking provides a less constrained learning environment, enabling models to discover natural biological representations rather than conforming to human-prescribed patterns.

For researchers and drug development professionals, the implications are significant: adopting random masking strategies can enhance model generalizability, particularly in transfer learning scenarios where pre-training on large-scale datasets informs analysis of specific target tissues or conditions. Furthermore, the robust performance of this approach in zero-shot settings addresses practical challenges associated with limited annotation resources in specialized domains. As the field progresses toward increasingly comprehensive foundation models for single-cell biology, random masking establishes itself as an unexpectedly powerful tool in the representation learning arsenal, demonstrating that sometimes the most effective path to biological insight emerges from embracing simplicity rather than complexity.

The rapid adoption of self-supervised learning and foundation models in single-cell omics research has created a paradoxical situation: while these models achieve impressive predictive accuracy, their complex architectures often obscure the very biological mechanisms researchers seek to understand. This interpretability gap represents a critical bottleneck in translating computational predictions into biologically meaningful insights and ultimately, clinical applications. As foundation models like scGPT and Geneformer are pretrained on millions of cells [2] [1], they capture complex patterns in gene expression and epigenetic regulation, yet the biological relevance of their latent representations remains difficult to decipher [8] [5].

The field currently faces a fundamental trade-off: complex models with high predictive power versus simpler, interpretable models with potentially lower accuracy [70]. Self-supervised pretraining compounds this challenge—while it enables models to learn universal biological principles from massive unlabeled datasets [6] [1], the resulting representations don't automatically provide insights into specific regulatory mechanisms or druggable pathways. This whitepaper examines current strategies for bridging this interpretability gap, providing technical guidance for researchers seeking to make their model predictions both accurate and biologically meaningful.

The Interpretability Challenge in Foundation Models

Single-cell foundation models (scFMs) typically employ transformer architectures trained on extensive single-cell corpora, such as the CELLxGENE census containing over 20 million cells [6]. During self-supervised pretraining, these models learn rich representations of cellular states by predicting masked genes or leveraging contrastive objectives [1]. However, benchmarking studies reveal that the zero-shot embeddings from these models, while powerful, don't consistently outperform simpler methods on specific biological tasks without fine-tuning [8] [5].

A key challenge lies in the non-sequential nature of biological data. Unlike natural language where word order carries meaning, genes interact in complex networks without inherent sequence [8] [5]. Current scFMs address this through various tokenization strategies, such as ranking genes by expression levels or binning expression values [1], but these approaches create an artificial structure that doesn't fully reflect biological reality. Additionally, the global attention mechanisms in transformers learn context from all genes in the input sequence, making it difficult to isolate cell-type-specific interactions [56].

Table 1: Interpretability Limitations of Current scFMs

Challenge	Impact on Interpretability	Potential Solution
Non-sequential gene relationships	Artificial structure in tokenization	Biological prior knowledge integration
Global attention context	Difficulty isolating cell-type-specific signals	Localized interpretation methods
High-dimensional embeddings	Difficulty mapping to biological concepts	Concept-based projection methods
Multi-modal integration	Complex cross-modal interactions	Modality-specific attribution

Technical Approaches for Enhanced Interpretability

Inherently Interpretable Architectures

Rather than relying on post-hoc explanations, several recently developed methods prioritize interpretability through their fundamental architecture. The scMKL framework uses multiple kernel learning combined with group Lasso regularization to merge predictive capabilities with linear interpretability [70]. This approach incorporates prior biological knowledge by grouping features according to pathways for RNA and transcription factor binding sites for ATAC data, directly identifying regulatory programs driving cell state distinctions without post-hoc analysis.

The scKAN framework employs Kolmogorov-Arnold networks to model gene-to-cell relationships through learnable activation curves rather than traditional weights [56]. This provides a more direct visualization of specific gene interactions compared to aggregated weighting schemes in attention mechanisms. In benchmarks, scKAN achieved a 6.63% improvement in macro F1 score over state-of-the-art methods while enabling systematic identification of functionally coherent cell-type-specific gene sets [56].

For multi-omics integration, multi-output Gaussian processes learn distinct representations for samples and features from multimodal single-cell data, establishing interpretable relationships between cell clusters and their associated marker genes within the learned latent spaces [71]. This approach demonstrates that even a few interpretable latent dimensions can effectively capture the underlying data structure.

Biological Knowledge Integration

Incorporating established biological knowledge directly into model architectures provides a powerful strategy for enhancing interpretability. As demonstrated in Table 2, successful implementations leverage curated biological databases to ground model predictions in established mechanisms.

Table 2: Biological Knowledge Sources for Interpretable Models

Knowledge Type	Source Databases	Implementation Example
Gene pathways	Molecular Signature Database (Hallmark)	scMKL pathway-induced kernels [70]
Transcription factor binding sites	JASPAR, Cistrome	scMKL ATAC analysis [70]
Gene Ontology terms	Gene Ontology Consortium	Functional analysis of embeddings [8]
Cell type ontologies	Cell Ontology	scGraph-OntoRWR metric [8] [5]

The scGraph-OntoRWR metric exemplifies this approach by measuring the consistency of cell type relationships captured by scFMs with prior biological knowledge encoded in cell ontologies [8] [5]. This provides a biologically grounded evaluation perspective that complements traditional performance metrics.

Visualization and Evaluation Frameworks

Effective visualization is crucial for interpreting model predictions. The benchmarking framework proposed by provides multiple novel evaluation perspectives, including the Lowest Common Ancestor Distance metric, which assesses the severity of cell type annotation errors by measuring their ontological proximity [8] [5]. This approach recognizes that misclassifying a T-cell as a B-cell is less severe than misclassifying it as a neuron, providing biologically nuanced model assessment.

The following workflow illustrates the integration of these interpretability approaches in single-cell analysis:

Experimental Protocols for Interpretability Analysis

Benchmarking Model Interpretability

Comprehensive benchmarking should evaluate both model performance and biological plausibility. The following protocol, adapted from, ensures rigorous assessment [8] [5]:

• Dataset Selection: Curate diverse datasets with high-quality labels spanning multiple biological conditions, including:

  • Cross-species comparisons to assess generalization
  • Disease progression series to evaluate dynamic process capture
  • Multiple sequencing technologies to test robustness to technical variation

• Metric Selection: Implement a multi-faceted evaluation strategy including:

  • Traditional performance metrics (AUROC, F1-score)
  • Biological consistency metrics (scGraph-OntoRWR, LCAD)
  • Computational efficiency metrics (training time, memory usage)

• Baseline Comparison: Compare against established interpretable methods including:

  • Linear models with regularization
  • Traditional machine learning (XGBoost, SVM)
  • Simple neural architectures (MLP)

• Biological Validation: Confirm identified features and pathways through:

  • Enrichment analysis for known biological processes
  • Comparison with established marker genes from literature
  • Experimental validation where feasible

Implementing Interpretable Multi-omics Integration

For integrative analysis of transcriptomic and epigenomic data, the following protocol, adapted from scMKL, ensures interpretable cross-modal discovery [70]:

• Kernel Construction:

  • RNA modality: Construct pathway-induced kernels using Hallmark gene sets
  • ATAC modality: Build TFBS-informed kernels using JASPAR and Cistrome databases
  • Normalize kernels to ensure balanced contribution across modalities

• Model Training:

  • Implement multiple kernel learning with group Lasso regularization
  • Use repeated 80/20 train-test splits (100 iterations) with cross-validation
  • Optimize regularization parameter λ to balance sparsity and performance

• Interpretation Extraction:

  • Extract model weights for each feature group (pathway/TFBS)
  • Identify cross-modal interactions through joint weight analysis
  • Validate identified pathways against known biology

The following diagram illustrates the scMKL workflow for interpretable multi-omics integration:

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

Tool/Category	Specific Examples	Function in Interpretability
Foundation Models	scGPT, Geneformer, scBERT	Base models for transfer learning and fine-tuning [8] [1]
Interpretable Architectures	scMKL, scKAN, Multi-output Gaussian Processes	Inherently interpretable model frameworks [70] [56] [71]
Biological Databases	MSigDB, JASPAR, Cistrome, Cell Ontology	Source of prior knowledge for biological grounding [70] [8]
Evaluation Frameworks	BioLLM, scGraph-OntoRWR, LCAD	Benchmarking biological plausibility of predictions [8] [2] [5]
Data Resources	CZ CELLxGENE, DISCO, Single Cell Portal	Curated data for training and validation [72]

Case Studies in Interpretable Analysis

Translating Predictions to Therapeutic Insights

The practical utility of interpretable methods is exemplified by scKAN's application in pancreatic ductal adenocarcinoma [56]. By identifying cell-type-specific gene signatures with functional significance beyond mere differential expression, the framework successfully pinpointed potential therapeutic targets. These findings facilitated drug repurposing candidates, with molecular dynamics simulations validating binding stability—demonstrating a direct path from interpretable model predictions to tangible therapeutic hypotheses.

In another case, scMKL identified key regulatory pathways and transcription factors involved in estrogen response in breast cancer cell lines, then validated these insights on an independent experiment [70]. This showcases how interpretable models can generate transferable biological knowledge rather than just predictions, enabling hypothesis generation across multiple disease states.

Cross-Modal Regulatory Insight Discovery

A particular strength of interpretable methods is their ability to uncover cross-modal interactions. In prostate cancer analysis, scMKL revealed tumor subtype-specific signaling mechanisms by jointly modeling transcriptomic and epigenomic data [70]. The model identified coordinated patterns of chromatin accessibility and gene expression that distinguished low-grade from high-grade tumors, providing insights into disease progression mechanisms that opaque methods failed to capture.

Closing the interpretability gap in single-cell omics requires a fundamental shift from treating interpretability as an optional add-on to making it a central design consideration. The methods outlined in this whitepaper demonstrate that we need not sacrifice predictive power for biological insight—architectures like scMKL and scKAN achieve competitive performance while providing transparent reasoning [70] [56].

As the field progresses, several emerging trends will further enhance interpretability: the development of biologically-aware benchmarking frameworks [8] [5], standardized ontologies for evaluation [72], and hybrid approaches that combine the representational power of foundation models with inherently interpretable components [56]. By adopting these strategies, researchers can transform single-cell foundation models from black boxes into powerful partners in biological discovery, ultimately accelerating the translation of computational predictions into mechanistic insights and therapeutic advances.

The advent of single-cell omics technologies has revolutionized cellular analysis, enabling unprecedented resolution in exploring cellular heterogeneity, developmental trajectories, and disease mechanisms. Foundation models (FMs), originally developed for natural language processing, are now driving transformative approaches to high-dimensional, multimodal single-cell data analysis [2]. These models, including scGPT, scPlantFormer, and Nicheformer, demonstrate exceptional capabilities in cross-species cell annotation, in silico perturbation modeling, and gene regulatory network inference [2]. However, this power comes with significant computational costs. The training and application of single-cell foundation models (scFMs) demand substantial resources, creating a critical challenge for researchers and institutions [1]. As the field progresses toward models pretrained on hundreds of millions of cells, the need for responsible scaling strategies becomes increasingly urgent to ensure these powerful tools remain accessible and practical for the research community.

The computational intensity of scFMs stems from multiple factors: the high dimensionality of single-cell data (tens of thousands of genes per cell), the massive scale of public datasets (over 100 million cells in archives like CZ CELLxGENE), and the complex architecture of transformer-based models [1]. Unlike traditional single-task models, scFMs utilize self-supervised pretraining objectives—including masked gene modeling, contrastive learning, and multimodal alignment—requiring extensive computational resources during the pretraining phase [2]. This whitepaper examines the specific computational bottlenecks in scFM development and deployment, presents strategies for managing resource demands, and provides practical guidance for researchers working within resource constraints.

Computational Bottlenecks in Single-Cell Foundation Models

Architectural Drivers of Computational Intensity

Single-cell foundation models predominantly rely on transformer architectures, which are characterized by attention mechanisms that allow the model to learn and weight relationships between any pair of input tokens (genes or genomic features) [1]. The self-attention mechanism in transformers has a computational complexity that scales quadratically with sequence length, presenting a significant challenge when processing datasets with tens of thousands of genes [1] [41]. Most scFMs treat genes as tokens and cells as sentences, requiring the model to capture complex relationships across the entire genomic feature space [1].

The computational burden manifests across multiple dimensions: (1) Memory requirements for storing model parameters and gradients during training, (2) Processing power for matrix operations and attention mechanisms, and (3) Storage needs for the massive pretraining corpora and model checkpoints [8]. For example, scGPT was pretrained on over 33 million cells, requiring specialized hardware infrastructure for weeks of continuous training [2]. Recent benchmarking studies indicate that training a single scFM can require thousands of GPU hours, creating substantial financial and environmental costs [8].

Data-Specific Challenges

Single-cell omics data introduces unique computational challenges beyond standard deep learning applications. The data exhibits extreme sparsity (many zero values due to dropout effects), high dimensionality (typically 20,000-30,000 genes per cell), and technical noise from various sequencing platforms [1] [8]. Additionally, the lack of natural ordering in genomic features necessitates specialized positional encoding strategies, adding computational overhead [1].

As models scale to incorporate multimodal data—simultaneously analyzing transcriptomic, epigenomic, proteomic, and spatial imaging data—the computational demands increase further. Multimodal integration approaches, including pathology-aligned embeddings and tensor-based fusion, harmonize diverse data types to delineate multilayered regulatory networks across biological scales [2]. Each modality introduces additional parameters and requires specialized processing branches, compounding memory and processing requirements [41].

Table 1: Computational Requirements of Prominent Single-Cell Foundation Models

Model	Pretraining Corpus	Model Parameters	Reported Hardware Requirements	Key Computational Features
scGPT [2]	33+ million cells	Not specified	Multiple GPUs for extended training	Transformer architecture with masked gene modeling
Nicheformer [2]	53 million spatially resolved cells	Not specified	High-memory GPU cluster	Graph transformers for spatial contexts
scPlantFormer [2]	1 million plant cells	Lightweight design	Moderate GPU resources	Phylogenetic constraints in attention mechanism
scMamba [41]	Multiple datasets	Efficient state space design	Reduced memory footprint	Selective state space models for genomic data

Strategies for Managing Computational Demands

Efficient Model Architectures

Novel architectures beyond standard transformers are emerging to address computational bottlenecks. The scMamba model introduces a patch-based cell tokenization strategy that treats genomic regions as words and cells as sentences, significantly reducing sequence length while preserving genomic positional information [41]. Instead of processing individual genes, scMamba partitions the genomic data into contiguous regions, dramatically decreasing the computational load while maintaining biological relevance [41].

State space models (SSMs) like Mamba offer an alternative to traditional transformer architectures by providing comparable performance with linear scaling to sequence length, making them particularly suitable for long genomic sequences [41]. These architectures employ structured state space sequences that selectively compress historical information, reducing the memory footprint during training and inference [41]. Benchmarking studies demonstrate that scMamba achieves superior performance in multi-omics integration while requiring fewer computational resources than transformer-based alternatives [41].

Data Efficiency Techniques

Strategic data management can substantially reduce computational demands without sacrificing model performance. Instead of using all genomic features, many approaches employ careful feature selection, typically focusing on highly variable genes (HVGs) [41]. However, this approach risks discarding biologically important information. scMamba addresses this by operating directly on single-cell data without prior selection of highly variable features, thereby capturing more comprehensive biological signals while maintaining efficiency through its patch-based tokenization [41].

Efficient tokenization strategies play a crucial role in managing computational loads. While most scFMs represent each gene as a separate token, innovative approaches like ranking genes by expression levels or binning genes by expression values can reduce sequence length while preserving biological information [1]. For spatial omics data, methods like Nicheformer employ graph-based representations that efficiently capture spatial relationships without the quadratic scaling of full attention mechanisms [2].

Optimization and Infrastructure Strategies

Technical optimizations across the training pipeline can dramatically improve resource utilization. Mixed-precision training—using 16-bit floating-point numbers instead of 32-bit—can reduce memory usage by nearly 50% with minimal impact on model accuracy [8]. Gradient checkpointing trades computation for memory by recomputing intermediate activations during backward passes rather than storing them, enabling training of larger models with limited GPU memory [8].

Distributed training across multiple GPUs and nodes parallelizes the computational load, enabling researchers to tackle larger datasets and models. Model parallelism partitions the model across devices, while data parallelism processes different batches on different devices simultaneously [8]. Federated computational platforms facilitate decentralized data analysis, allowing multiple institutions to collaborate without centralizing massive datasets, thus distributing both data storage and computational costs [2].

Table 2: Computational Optimization Techniques for scFM Development

Technique	Implementation Approach	Computational Benefit	Considerations
Mixed Precision Training	Using 16-bit floating point operations	~50% memory reduction, faster computation	Potential numerical instability requires careful management
Gradient Checkpointing	Storing only every k-th activation	60-70% memory reduction for O(k) recomputation	Increases computation time by ~25%
Distributed Training	Model or data parallelism across GPUs	Near-linear scaling with number of devices	Communication overhead, complex implementation
Transfer Learning	Fine-tuning pretrained models	Avoids costly pretraining phase	Dependent on availability of suitable pretrained models
Model Compression	Pruning, quantization, distillation	Reduced inference time and memory	Potential performance degradation

Experimental Protocols for Resource-Efficient Model Development

Scalable Pretraining Methodology

Effective pretraining of single-cell foundation models requires careful balancing of computational constraints and biological comprehensiveness. The following protocol outlines a resource-efficient approach:

• Data Curation and Quality Control: Begin with data aggregation from public repositories such as CZ CELLxGENE, which provides access to over 100 million standardized single-cell datasets [1]. Implement rigorous quality control metrics including cell viability thresholds, minimum gene detection rates, and mitochondrial content thresholds. This step prevents wasted computation on low-quality data.

• Efficient Tokenization Strategy: Implement patch-based tokenization as in scMamba, where genomic regions (rather than individual genes) are treated as tokens [41]. Genes or chromatin accessibility peaks are ordered according to genomic coordinates and partitioned into contiguous patches. Each patch is linearly projected into a latent embedding space using a trainable transformation matrix, significantly reducing sequence length.

• Staged Pretraining Approach: Begin with a smaller model architecture and subset of data for hyperparameter optimization. Scale up gradually, monitoring performance gains relative to computational costs. Implement progressive resizing where possible—starting with lower-resolution inputs and increasing resolution as training progresses.

• Distributed Training Configuration: Configure multi-GPU training using data parallelism with synchronized batch normalization. Set gradient accumulation to maintain effective batch size while reducing memory footprint. Implement mixed-precision training using frameworks like NVIDIA Apex to leverage tensor cores for accelerated computation.

This methodology was validated in the development of scMamba, which demonstrated superior performance in multi-omics integration while maintaining computational efficiency [41].

Benchmarking and Evaluation Framework

Robust evaluation is essential for ensuring computational resources are effectively utilized. The following benchmarking protocol provides comprehensive assessment while managing resource demands:

• Task-Specific Evaluation: Assess model performance across diverse downstream tasks including cell type annotation, batch integration, perturbation response prediction, and trajectory inference [8]. Utilize the scGraph-OntoRWR metric, which measures consistency of cell type relationships captured by scFMs with prior biological knowledge [8].

• Efficiency Metrics: Track computational metrics including training time, inference latency, memory consumption, and energy usage. Compare against baseline models using standardized hardware configurations.

• Scaling Behavior Analysis: Evaluate how performance and resource requirements scale with dataset size, model parameters, and sequence length. Identify optimal operating points where performance gains begin to diminish relative to computational costs.

• Ablation Studies: Systematically evaluate architectural choices (attention mechanisms, tokenization strategies, etc.) to identify components that contribute most to performance versus those with disproportionate computational costs.

This comprehensive benchmarking approach enables researchers to make informed decisions about model selection and development priorities based on their specific computational constraints [8].

Successful development and application of single-cell foundation models requires access to specialized computational resources and platforms. The following table details essential components of the scFM research toolkit:

Table 3: Research Reagent Solutions for scFM Development

Resource Category	Specific Tools/Platforms	Function/Purpose	Access Considerations
Data Repositories	CZ CELLxGENE [1], DISCO [2], Human Cell Atlas [1]	Standardized single-cell datasets for pretraining and benchmarking	Publicly available; require significant storage capacity
Model Frameworks	scGPT [2], scMamba [41], BioLLM [2]	Reference implementations and benchmarking frameworks	Open-source; require GPU-enabled computing environment
Computational Infrastructure	NVIDIA GPUs (A100, H100), Google TPUs, Cloud computing (AWS, GCP, Azure)	Hardware acceleration for model training and inference	Commercial providers; cost increases with model scale
Benchmarking Platforms	BioLLM [2], scEval [73]	Standardized evaluation of model performance and efficiency	Open-source; require integration with existing workflows
Federated Learning Platforms	Emerging frameworks for decentralized model training	Collaborative model development without data sharing	Early development stage; technical implementation complexity

As single-cell foundation models continue to evolve, responsible scaling must remain a priority alongside performance improvements. The strategies outlined in this whitepaper—efficient architectures like Mamba, optimized tokenization methods, distributed training, and comprehensive benchmarking—provide a pathway for managing computational intensity while advancing biological discovery. The field is moving toward larger models trained on increasingly diverse and multimodal datasets, making computational efficiency not merely an engineering concern but a fundamental requirement for progress.

Future developments will likely focus on specialized hardware for genomic applications, more sophisticated model compression techniques, and collaborative frameworks that distribute computational burdens across institutions. By adopting these responsible scaling practices, researchers can ensure that single-cell foundation models remain accessible tools for uncovering biological insights and advancing therapeutic development, rather than becoming prohibitively expensive resources available only to well-funded organizations. The integration of computational efficiency as a core design principle—rather than an afterthought—will be essential for realizing the full potential of foundation models in single-cell omics research.

The advent of single-cell omics technologies has revolutionized biological research by enabling the investigation of cellular heterogeneity at unprecedented resolution. However, a significant challenge persists in the accurate identification and analysis of rare cell types—populations that occur at low frequencies but often play critically important roles in biological processes and disease mechanisms. Rare cells, such as circulating tumor cells, stem cells, or rare immune cell subtypes, are biologically crucial yet difficult to study due to their scarcity. Their limited presence in datasets creates substantial challenges for computational models, which often struggle to maintain generalization and fairness when predicting across diverse cell populations. These challenges are particularly acute in the context of self-supervised pretraining for single-cell omics, where models must learn robust representations that capture both common and rare biological patterns from large-scale, unlabeled data.

The integration of rare cell consideration into single-cell foundation models (scFMs) represents a critical frontier in computational biology. As noted in recent benchmarking studies, "pretrained foundation models failed to outperform the simpler baseline models in certain scenarios" [8], particularly when dealing with rare cell populations or novel cell types not well-represented in pretraining corpora. This performance gap highlights the pressing need for specialized approaches that enhance model generalization and ensure fair representation across all cell types, regardless of their abundance. This technical guide examines current methodologies, identifies persistent challenges, and provides detailed protocols for improving how scFMs handle rare cell types.

Computational Frameworks for Rare Cell Analysis

Specialized Algorithms for Rare Cell Identification

Several specialized computational approaches have been developed specifically to address the challenge of rare cell identification in single-cell data. These methods employ diverse strategies to overcome the limitations of standard clustering techniques, which tend to favor major cell populations.

Table 1: Comparison of Rare Cell Identification Methods

Method	Underlying Approach	Key Strengths	Reported Performance
scSID [74]	Single-cell Similarity Division algorithm utilizing KNN and similarity differences	Accounts for intercellular similarities; exceptional scalability	F1 score: 0.4172 across 25 datasets [75]
scCAD [75]	Cluster decomposition-based anomaly detection with iterative clustering	Ensemble feature selection; preserves differential signals of rare types	24-48% improvement over second/third-ranked methods [75]
FiRE [74] [75]	Sketching technique with rarity scoring based on hash bucket occupancy	Efficient for large datasets; low memory consumption	Limited by need for post-hoc clustering [74]
CellSIUS [74] [75]	Bimodal distribution detection within major cell clusters	Effective for subpopulation identification	Dependent on quality of preliminary clustering [74]
RaceID3 [74]	k-means clustering with count probability calculations	Identifies abnormal cells within clusters	Computationally intensive for large datasets [74]

The scSID (single-cell similarity division) algorithm addresses rare cell identification by analyzing both inter-cluster and intra-cluster similarities [74]. Its methodology is motivated by the observation that cells within the same cluster exhibit significantly higher similarity compared to cells from neighboring clusters. The algorithm operates in two main phases: (1) cell division based on individual similarity, where Euclidean distances in gene expression space are used to characterize similarity between cells and their K-nearest neighbors, and (2) rare cell detection based on population similarity, which employs a stepwise clustering synthesis approach to explore hierarchical relationships between cells within identified clusters and their nearest neighbors outside the clusters [74].

The scCAD (cluster decomposition-based anomaly detection) method takes a different approach by iteratively decomposing clusters based on the most differential signals in each cluster [75]. Unlike traditional approaches that rely on highly variable genes, scCAD employs an ensemble feature selection method that combines initial clustering labels with a random forest model to preserve differentially expressed genes in rare cell types. After cluster decomposition, scCAD defines the dominant cell type of a cluster as the type to which the majority of cells belong, with the rarity of specific cell types reflected in the number of clusters they dominate [75].

Foundation Models and Rare Cell Considerations

Single-cell foundation models (scFMs) represent a paradigm shift in analyzing single-cell omics data. These models, typically based on transformer architectures, are pretrained on large-scale single-cell datasets to learn universal representations of cellular states [2] [1]. The core premise is that by exposing a model to millions of cells encompassing diverse tissues and conditions, the model can learn fundamental principles of cellular biology that generalize to new datasets and tasks [1].

However, current scFMs face specific challenges regarding rare cells. As noted in benchmark evaluations, "scFMs can serve as a plug-and-play module to push the boundaries of various downstream tasks" but their performance on rare cell types remains inconsistent [8]. The Nicheformer model attempts to address this by incorporating spatial context, training on both dissociated single-cell and spatial transcriptomics data [12]. This approach demonstrates that "models trained only on dissociated data fail to recover the complexity of spatial microenvironments" [12], which is particularly important for understanding rare cell types that often occupy specialized niches.

The tokenization strategies used in scFMs significantly impact their ability to recognize rare cell types. Most models use genes as tokens, with common approaches including ranking genes by expression levels [1] or using normalized counts [1]. For rare cell types, whose distinctive markers might be mid-or lowly expressed, these tokenization schemes may inadvertently deprioritize crucial identifying features. Recent innovations in model architecture, such as incorporating biological prior knowledge through gene ontology information or phylogenetic constraints, show promise for improving rare cell representation [2].

Experimental Protocols for Method Evaluation

Benchmarking Rare Cell Identification Methods

Robust evaluation of methods for rare cell analysis requires carefully designed benchmarking protocols. Based on comprehensive assessments in the literature, the following protocol provides a standardized approach for comparing method performance:

Protocol 1: Benchmarking Framework for Rare Cell Identification

• Dataset Selection and Curation

  • Select 20-25 real scRNA-seq datasets representing diverse biological scenarios (e.g., mouse airway, brain, intestine, human pancreas, PBMC, cancer data) [75]
  • Ensure datasets include validated rare cell types with known biological significance
  • Include datasets with varying levels of complexity and rare cell frequencies (typically 0.1%-5% of total cells)

• Performance Metrics Calculation

  • Compute F1 score for rare cell types to balance precision and recall: F1 = 2 × (Precision × Recall)/(Precision + Recall)
  • Calculate accuracy for rare cell types: ACCrarecell_type = TRC/IC, where TRC represents correctly identified rare cells and IC represents total cells predicted as rare [75]
  • Include additional metrics: G-mean (geometric mean of precision and recall), Cohen's Kappa, and Matthews correlation coefficient (MCC) for comprehensive assessment [75]

• Comparative Analysis

  • Compare against 10+ state-of-the-art methods (e.g., FiRE, CellSIUS, RaceID, GiniClust, SCISSORS) [75]
  • Evaluate scalability using datasets of increasing size (from 10^4 to 10^6 cells)
  • Assess computational efficiency via runtime and memory consumption measurements

This protocol was used in recent benchmarking studies that revealed scCAD achieved "the overall highest performance (F1 score = 0.4172) and exhibited performance improvements of 24% and 48% compared to the second and third-ranked methods" [75].

Evaluating Foundation Model Embeddings for Rare Cells

Assessing how well scFMs capture rare cell types in their latent representations requires specialized evaluation approaches:

Protocol 2: Rare Cell Representation in Foundation Model Embeddings

• Embedding Extraction

  • Extract zero-shot cell embeddings from frozen pretrained scFMs (e.g., Geneformer, scGPT, UCE, scFoundation) [8]
  • For comparative analysis, include baseline methods (HVG selection, Seurat, Harmony, scVI) [8]

• Biological Relevance Assessment

  • Apply cell ontology-informed metrics including scGraph-OntoRWR, which measures consistency of cell type relationships with prior biological knowledge [8]
  • Calculate Lowest Common Ancestor Distance (LCAD) to measure ontological proximity between misclassified cell types [8]
  • Evaluate using the roughness index (ROGI) as a proxy for dataset-specific model recommendation [8]

• Downstream Task Performance

  • Test on clinically relevant tasks including cancer cell identification and drug sensitivity prediction across multiple cancer types and drugs [8]
  • Evaluate cross-species and cross-tissue generalization capabilities
  • Assess performance on novel cell types not seen during pretraining

This evaluation approach has demonstrated that "pretrained zero-shot scFM embeddings indeed capture biological insights into the relational structure of genes and cells" but also revealed significant variability in performance across models and tasks [8].

Visualization of Computational Workflows

scSID Algorithm Workflow

Foundation Model Pretraining and Rare Cell Adaptation

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Computational Tools for Rare Cell Analysis

Tool/Resource	Type	Primary Function	Considerations for Rare Cells
scSID [74]	Algorithm	Rare cell identification via similarity division	Default K=100 for datasets <5000 cells; scales to 68K+ cells
scCAD [75]	Algorithm	Cluster decomposition-based anomaly detection	Ensemble feature selection preserves rare cell signals
Nicheformer [12]	Foundation Model	Spatially-aware cell representation learning	Trained on 53M spatial cells; captures niche context
scGPT [2]	Foundation Model	Generative pretrained transformer for single-cell data	Pretrained on 33M+ cells; zero-shot capabilities
CellTypist [76]	Annotation Tool	Automated cell type annotation	Potential reference bias against rare types
scExtract [76]	LLM Framework	Automated dataset processing and annotation	Incorporates article context for better rare cell recognition
CZ CELLxGENE [2] [1]	Data Platform	Curated single-cell datasets	Contains 100M+ cells; source of diverse rare populations
Scanpy [76]	Analysis Toolkit	Standard Python framework for single-cell data	Flexible preprocessing crucial for rare cell preservation

Discussion and Future Directions

The field of rare cell analysis in single-cell omics is rapidly evolving, with several promising research directions emerging. First, the integration of multimodal data—combining transcriptomic, epigenomic, proteomic, and spatial information—shows particular promise for improving rare cell identification [2] [77]. Approaches such as "PathOmCLIP, which aligns histology images with spatial transcriptomics via contrastive learning" [2] demonstrate how complementary data modalities can provide additional context for recognizing rare cell states.

Second, specialized training strategies for foundation models need further development to enhance their capabilities with rare cell types. Current research indicates that "models trained only on dissociated data fail to recover the complexity of spatial microenvironments" [12], which is particularly relevant for rare cells that often occupy specific niches. Incorporating spatial relationships, as in Nicheformer's approach of training on both dissociated and spatial transcriptomics data, represents an important direction for improving rare cell representation [12].

Third, evaluation frameworks need continued refinement to better capture model performance on rare cell types. The development of biology-driven metrics such as "scGraph-OntoRWR, a novel metric designed to uncover intrinsic knowledge encoded by scFMs" [8] represents important progress. These ontology-informed evaluation approaches help ensure that computational advancements translate to biologically meaningful insights about rare cell populations.

Finally, the translation of rare cell analysis capabilities to clinical applications remains a critical frontier. As noted in drug discovery research, single-cell technologies "can help reveal disease mechanisms, drug target identification and validation" [78]—applications where rare cell types often play disproportionately important roles. Improving how models handle rare cells will directly enhance their utility in identifying novel therapeutic targets, understanding drug resistance mechanisms, and advancing precision medicine approaches.

Benchmarking Performance and guiding Method Selection

The emergence of single-cell foundation models (scFMs) represents a paradigm shift in computational biology, enabling the analysis of cellular heterogeneity, developmental trajectories, and disease mechanisms at unprecedented resolution [2]. These models, trained on millions of single cells using self-supervised learning (SSL) objectives, promise universal representations transferable across diverse biological contexts and tasks. However, this rapid innovation has outpaced the development of standardized evaluation frameworks, creating critical challenges in benchmarking model performance, reproducibility, and translational potential [2] [6].

The establishment of rigorous evaluation standards is particularly crucial within the context of self-supervised pretraining for single-cell omics research. Unlike supervised approaches where performance is measured against labeled ground truth, SSL methods extract meaningful representations from unlabeled data through pretext tasks, necessitating specialized metrics that capture biological fidelity, generalizability, and functional utility [6]. This whitepaper synthesizes current benchmarking efforts to propose a comprehensive evaluation framework encompassing core metrics, experimental protocols, and practical implementation tools for the research community.

Core Metric Taxonomy for scFM Evaluation

Evaluating scFM performance requires a multi-dimensional approach spanning predictive accuracy, biological plausibility, computational efficiency, and zero-shot capabilities. The table below organizes the essential metric categories with their definitions, measurement approaches, and associated benchmarks.

Table 1: Comprehensive Taxonomy of scFM Evaluation Metrics

Metric Category	Specific Metrics	Definition & Measurement	Benchmark Studies
Cell Type Annotation	Macro/Micro F1 Score [6]AccuracyCross-species transfer	Measures cell type classification performance, particularly on rare populations (macro F1) and overall (micro F1).	HLCA, Tabula Sapiens [6]
Perturbation Effect Prediction	L2 Distance [79]Pearson Delta [79]Genetic Interaction Detection	Quantifies error in predicting transcriptomic changes after genetic perturbation. Evaluates ability to identify synergistic/buffering interactions.	PertEval-scFM [80]Norman et al. data [79]
Data Integration & Batch Correction	Batch ASWiLISIGraph Connectivity	Assesses ability to remove technical artifacts while preserving biological variation using clustering metrics.	scGPT benchmark [2]
Zero-Shot Capability	kNN Classification AccuracyClustering Metrics (ARI, NMI)	Evaluates representation quality from SSL pretraining without fine-tuning, using frozen embeddings.	CELLxGENE Census [6]
Gene Network Inference	AUPRC for GRNRegulatory Edge Accuracy	Measures precision in reconstructing gene regulatory networks from perturbation data or co-expression.	scPlantFormer [2]
Multimodal Alignment	Cross-modal Retrieval AccuracyModality Matching Score	Evaluates alignment quality between transcriptomic, epigenomic, proteomic, and spatial data.	PathOmCLIP [2]

Experimental Protocols for Benchmarking

Perturbation Prediction Assessment

The PertEval-scFM benchmark provides a standardized framework for evaluating perturbation effect prediction, a critical capability for understanding disease mechanisms and therapeutic interventions [80]. The protocol utilizes datasets from genetic perturbation studies (e.g., Norman et al. CRISPR activation data) comprising single-gene and double-gene perturbations with corresponding transcriptomic measurements [79].

Implementation Protocol:

• Data Partitioning: Perform multiple random splits (e.g., 5 iterations) of double perturbations into training (62 pairs) and held-out test sets (62 pairs) while including all single perturbations in training [79].
• Baseline Establishment: Implement deliberately simple baselines including:
  • No-change model: Predicts expression identical to control condition
  • Additive model: Sums logarithmic fold changes (LFCs) of individual single perturbations [79]
• Model Fine-tuning: Fine-tune scFMs on training perturbations using appropriate learning rates and early stopping.
• Evaluation: Calculate L2 distance between predicted and observed expression for top 1,000 highly expressed genes across test perturbations. Supplement with Pearson delta measure and genetic interaction detection capability [79].

Recent benchmarking reveals that scFM embeddings frequently do not outperform simpler baselines for perturbation prediction, particularly under distribution shift or for strong/atypical perturbations [80]. This underscores the importance of rigorous benchmarking before deploying scFMs for predictive tasks.

Transfer Learning Evaluation

Transfer learning evaluation assesses how effectively knowledge from pretraining generalizes to new datasets and biological contexts. The protocol evaluates both fine-tuning and zero-shot performance [6].

Implementation Protocol:

• Auxiliary Data Selection: Pretrain on large-scale reference atlas (e.g., CELLxGENE Census with >20M cells) encompassing diverse tissues and conditions [6].
• Target Dataset Preparation: Curate evaluation datasets with varying biological contexts (e.g., HLCA, PBMC SARS-CoV-2, Tabula Sapiens) representing different sizes and complexities [6].
• Experimental Conditions:
  • Zero-shot: Apply frozen pretrained encoders with kNN classification or linear probes
  • Fine-tuned: Update all model parameters on target data
  • Supervised baseline: Train from scratch on target data [6]
• Assessment: Measure cell-type prediction performance (macro/micro F1) and gene-expression reconstruction (weighted explained variance), with particular attention to rare cell types and robustness to class imbalance [6].

Empirical analyses demonstrate that self-supervised pretraining on auxiliary data significantly boosts performance on target datasets, especially for underrepresented cell types and complex atlases like Tabula Sapiens [6].

Current Benchmarking Findings

Recent rigorous evaluations have yielded critical insights into scFM capabilities and limitations:

Table 2: Key Benchmarking Findings for scFM Performance

Evaluation Domain	Performance Summary	Notable Limitations
Perturbation Prediction	scFMs do not consistently outperform simple additive or no-change baselines [79]. Linear models with pretrained embeddings can match or exceed full model performance [79].	Struggles with predicting strong/atypical perturbation effects [80]. Limited capability to identify synergistic genetic interactions [79].
Cross-species Transfer	High cross-species annotation accuracy demonstrated (e.g., scPlantFormer achieves 92% in plant systems) [2].	Performance dependent on phylogenetic similarity and training data diversity.
Zero-shot Evaluation	SSL pretraining enables competitive kNN classification without fine-tuning [6]. Masked autoencoders outperform contrastive methods in SCG [6].	Marginal gains when pretraining and fine-tuning on same dataset [6].
Multimodal Integration	Cross-modal alignment successfully links histology with spatial gene expression [2]. Mosaic integration enables feature alignment without overlapping measurements [2].	Requires specialized architectures and paired datasets for optimal performance.

Visualization of Evaluation Workflows

The following diagram illustrates the standardized evaluation workflow for assessing scFM performance across critical biological tasks:

Standardized scFM Evaluation Workflow

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Computational Tools and Resources for scFM Evaluation

Resource Category	Specific Tools/Datasets	Function & Application
Benchmarking Frameworks	PertEval-scFM [80]BioLLM [2]	Standardized evaluation pipelines for perturbation prediction and model comparison. Universal interfaces for benchmarking >15 scFMs.
Data Repositories	DISCO [2]CZ CELLxGENE Discover [2] [6]	Aggregated single-cell data for federated analysis (>100M cells). Curated reference data for transfer learning evaluation.
Pretrained Models	scGPT [2]scPlantFormer [2]Nicheformer [2]	Foundation models pretrained on 33M+ cells for general tasks. Lightweight models optimized for cross-species annotation. Spatial transformers modeling cellular niches.
Baseline Algorithms	Additive Model [79]No-change Model [79]Linear Embedding Models	Simple baselines summing LFCs of single perturbations. Essential controls predicting no change from control. Linear decoders applied to scFM embeddings.
Evaluation Metrics	Genetic Interaction Detection [79]Batch ASW & iLISI [2]	Identifies synergistic/buffering perturbation effects. Measures batch correction effectiveness and biological preservation.

This whitepaper establishes a comprehensive framework for evaluating single-cell foundation models, addressing critical gaps in current benchmarking practices. The presented metrics, protocols, and tools emphasize biological plausibility alongside predictive accuracy, with particular focus on perturbation response prediction, cross-dataset generalization, and zero-shot capabilities. As the field matures, standardized evaluation will be essential for translating computational advances into genuine biological insights and clinical applications. Future efforts should prioritize community-wide adoption of these standards, development of specialized benchmarks for multimodal integration, and increased focus on model interpretability to bridge the gap between prediction and biological mechanism.

The advent of single-cell omics technologies has revolutionized our ability to investigate biological systems at cellular resolution, generating vast amounts of high-dimensional data. Simultaneously, the artificial intelligence field has witnessed the rise of foundation models—large-scale deep learning models pretrained on extensive datasets that can be adapted to diverse downstream tasks [1]. The convergence of these trends has catalyzed the development of single-cell foundation models (scFMs), which leverage self-supervised pretraining to learn universal representations of cellular states and functions [2] [1]. These models promise to transform single-cell research by enabling more robust data integration, improved cell type annotation, and enhanced prediction of cellular behaviors. This technical guide provides a comprehensive comparative analysis of three prominent architectures—scGPT, scBERT, and Nicheformer—alongside emerging specialized frameworks, framing their development within the broader context of self-supervised pretraining paradigms for single-cell omics research.

Core Architectural Frameworks and Pretraining Strategies

Model Architectures and Tokenization Approaches

Single-cell foundation models employ various architectural strategies to process high-dimensional omics data, primarily leveraging transformer-based architectures that have revolutionized natural language processing [1].

scGPT utilizes a generative pretrained transformer architecture inspired by GPT models, employing a decoder-only framework with unidirectional masked self-attention [2] [1]. This design enables the model to iteratively predict masked genes conditioned on known genes within a cell. scGPT incorporates multi-omic capabilities, handling scRNA-seq, scATAC-seq, CITE-seq, and spatial transcriptomics data through modality-specific tokens [5]. The model uses value binning for expression representation and operates on 1,200 highly variable genes (HVGs), generating 512-dimensional embeddings through its 50 million parameters [5].

scBERT (single-cell Bidirectional Encoder Representations from Transformers) adopts a BERT-like encoder architecture with bidirectional attention mechanisms [1]. This allows the model to learn from the context of all genes in a cell simultaneously during pretraining. scBERT employs a masked gene modeling objective where randomly masked genes must be predicted based on their cellular context [1]. The model typically uses gene ranking strategies to impose sequence structure on the inherently non-sequential gene expression data.

Nicheformer introduces a spatially aware transformer architecture specifically designed to integrate both dissociated single-cell and spatial transcriptomics data [12]. Its key innovation lies in capturing spatial contextual information through graph-enhanced attention mechanisms. The model uses a 1,500-token context length input to an architecture with 12 transformer encoder units with 16 attention heads per layer and a feed-forward network size of 1,024, generating 512-dimensional embeddings through its 49.3 million parameters [12]. Nicheformer implements a rank-based encoding strategy where genes are ordered by expression level relative to technology-specific nonzero mean vectors, making it robust to technology-dependent biases between spatial and dissociated transcriptomics data [12].

Pretraining Corpora and Data Scaling Laws

The performance of foundation models heavily depends on the scale and diversity of their pretraining corpora. Each model leverages distinct data collection strategies and scaling approaches.

Table 1: Pretraining Corpora Characteristics

Model	Pretraining Scale	Data Modalities	Species	Key Data Sources
scGPT	33 million+ cells [2] [5]	scRNA-seq, scATAC-seq, CITE-seq, spatial [5]	Human [81]	CELLxGENE, Human Cell Atlas [1]
scBERT	Millions of cells (specific count not detailed in sources)	scRNA-seq [1]	Human	Public repositories (GEO, SRA) [1]
Nicheformer	110 million+ cells (57M dissociated + 53M spatial) [12]	Dissociated scRNA-seq, spatial transcriptomics (MERFISH, Xenium, CosMx, ISS) [12]	Human, Mouse [12]	SpatialCorpus-110M (73 tissues) [12]

Recent studies have investigated the relationship between pretraining dataset size and model performance. Evaluation of scGPT variants pretrained on different dataset sizes (814,000 kidney cells, 10.3 million blood and bone marrow cells, and 33 million non-cancerous human cells) revealed that while pretraining generally improves cell-type clustering performance, beyond a certain limit, larger and more diverse datasets may not confer additional benefits [81]. Interestingly, scGPT pretrained on 10.3 million blood and bone marrow cells sometimes outperformed the version trained on 33 million more diverse cells, even for non-blood tissue types, suggesting complex relationships between data diversity and specialization [81].

Self-Supervised Pretraining Objectives

All single-cell foundation models employ self-supervised pretraining objectives that enable learning from unlabeled data, a crucial advantage in biological domains where annotated data is scarce.

The dominant paradigm is Masked Gene Modeling (MGM), where random subsets of genes are masked and the model must reconstruct their expression values based on contextual information [1]. scGPT employs an iterative MGM approach with mean squared error (MSE) loss for gene value prediction, combined with generative pretraining objectives [5]. Geneformer utilizes a unique ranking-based MGM with cross-entropy loss for gene identity prediction rather than precise expression value recovery [5]. Nicheformer incorporates spatial context directly into its pretraining objective, learning to reconstruct gene expression patterns while preserving spatial relationships between cells [12].

Comparative Performance Analysis

Benchmarking Framework and Evaluation Metrics

Rigorous evaluation of single-cell foundation models requires diverse benchmarks encompassing both gene-level and cell-level tasks. Current benchmarking approaches evaluate models on cell type annotation, batch integration, perturbation response prediction, and spatial composition tasks [12] [81] [5]. Performance is assessed using metrics including Average BIO (AvgBio) score for clustering, average silhouette width (ASW) for cluster separation, and principal component regression (PCR) for batch effect correction [81].

A critical distinction in evaluation methodology is between zero-shot and fine-tuned performance. Zero-shot evaluation tests the model's inherent representations without task-specific training, which is particularly important for discovery settings where labels are unknown [81]. Fine-tuning evaluation assesses how readily models adapt to specific tasks with limited additional training.

Model Performance Across Tasks

Table 2: Performance Comparison Across Key Tasks

Task Category	scGPT	scBERT	Nicheformer	Traditional Methods
Cell Type Annotation	Variable performance; excels in zero-shot annotation [2]	Originally designed for cell type annotation [1]	Not specifically evaluated for standard annotation	HVG selection sometimes outperforms foundation models zero-shot [81]
Batch Integration	Effective on complex biological batch effects; outperforms Harmony and scVI on Tabula Sapiens and Immune datasets [81]	Limited evaluation data available	Demonstrates robust integration of spatial and dissociated data [12]	Harmony and scVI excel at technical batch effect correction [81]
Spatial Tasks	Limited spatial capability in base model	Not designed for spatial analysis	State-of-the-art for spatial composition prediction and spatial label transfer [12]	Specialized spatial statistics methods
Cross-Species Generalization	Demonstrates cross-species capabilities [2]	Limited evaluation data available	Effective human-mouse integration via orthologous gene mapping [12]	Species-specific models typically required

Independent evaluations reveal that in zero-shot settings, both scGPT and Geneformer can underperform simpler methods like highly variable genes (HVG) selection combined with established methods such as Harmony and scVI for cell type clustering and batch integration [81]. This performance gap highlights the challenge of transferring pretrained representations to novel datasets without fine-tuning.

For spatial biology tasks, Nicheformer demonstrates unique capabilities, accurately predicting spatial context for dissociated cells and enabling the transfer of rich spatial information to conventional scRNA-seq datasets [12]. Models trained exclusively on dissociated data fail to recover the complexity of spatial microenvironments, underscoring the importance of multiscale integration achieved by Nicheformer [12].

Experimental Protocols and Methodologies

Pretraining Implementation Framework

Implementing effective pretraining for single-cell foundation models requires careful attention to data processing, model configuration, and training procedures.

Data Preprocessing Protocol:

• Quality Control: Filter cells based on quality metrics (mitochondrial content, number of detected genes) and remove doublets [1]
• Gene Selection: For scGPT, select 1,200 highly variable genes; for Nicheformer, use full gene set with orthologous mapping for cross-species analysis [12] [5]
• Normalization: Apply appropriate normalization for each technology (e.g., technology-specific nonzero mean vectors for Nicheformer) [12]
• Tokenization: Convert expression values to tokens through ranking (Geneformer, Nicheformer) or value binning (scGPT) [12] [5]

Model Training Protocol:

• Architecture Configuration: Initialize transformer with model-specific parameters (layers, heads, hidden dimensions)
• Masking Strategy: Implement masked gene modeling with 15-20% masking probability
• Optimization: Use AdamW optimizer with learning rate warming and decay
• Regularization: Apply gradient clipping and dropout appropriate for dataset size

Downstream Task Adaptation

Adapting pretrained models to downstream tasks involves either linear probing (training a simple classifier on frozen embeddings) or full fine-tuning (updating all model parameters). Empirical evidence suggests that the optimal approach depends on task complexity and dataset size [5].

For cell type annotation:

• Extract cell embeddings from frozen pretrained model
• Train a linear classifier or shallow neural network on labeled data
• Evaluate on held-out test set using accuracy and F1 scores

For spatial composition prediction (Nicheformer-specific):

• Define spatially homogeneous niches around each cell using distance thresholds
• Formulate as regression task to predict local cell-type density
• Fine-tune model with combined reconstruction and regression losses

Diagram 1: Single-Cell Foundation Model Workflow. This diagram illustrates the end-to-end pipeline for developing and applying single-cell foundation models, from large-scale self-supervised pretraining to task-specific fine-tuning and final applications.

Implementing single-cell foundation models requires both computational resources and biological data resources. The following table details key components of the research toolkit for working with these models.

Table 3: Essential Research Reagents and Computational Resources

Resource Category	Specific Examples	Function/Purpose
Data Repositories	CZ CELLxGENE [1], Human Cell Atlas [1], GEO/SRA [1]	Provide standardized, annotated single-cell datasets for model training and validation
Pretraining Corpora	SpatialCorpus-110M [12], scGPT's 33M cell collection [5]	Large-scale, curated cell collections for foundation model pretraining
Benchmarking Platforms	BioLLM [2], DISCO [2]	Standardized frameworks for model evaluation and comparison
Computational Frameworks	PyTorch, TensorFlow, JAX	Deep learning frameworks for model implementation and training
Specialized Libraries	scvi-tools, Scanpy, Seurat	Domain-specific libraries for single-cell data preprocessing and analysis
Hardware Infrastructure	GPU clusters (NVIDIA A100/H100), High-memory servers	Computational resources for model training on large-scale datasets

Critical Analysis and Future Directions

Limitations and Challenges

Despite their promising capabilities, single-cell foundation models face several significant limitations. Zero-shot evaluations reveal that these models sometimes underperform simpler methods like highly variable gene selection combined with established integration techniques [81]. This performance gap raises questions about the true generalization capabilities of current foundation models.

The pretraining-finetuning paradigm faces unique challenges in single-cell biology due to the non-sequential nature of gene expression data, inconsistent data quality across studies, and the computational intensity required for training and fine-tuning [1]. Additionally, interpreting the biological relevance of latent embeddings remains nontrivial, limiting model trustworthiness in biological discovery.

Batch effect propagation in transfer learning represents another significant challenge [2]. Models may learn to perpetuate or even amplify technical artifacts present in pretraining data, potentially confounding biological signals.

Emerging Trends and Future Developments

The field of single-cell foundation models is rapidly evolving with several emerging trends:

Multimodal Integration: Next-generation models increasingly incorporate multiple data modalities, including transcriptomics, epigenomics, proteomics, and spatial imaging data [2]. Frameworks like PathOmCLIP demonstrate the power of cross-modal alignment by connecting histology images with spatial gene expression [2].

Large Language Model Integration: Researchers are exploring ways to leverage general-purpose large language models to enhance single-cell analysis. Approaches include using biological text to enrich gene representations and developing natural language interfaces for single-cell data exploration [73].

Specialized Architectures: New model architectures specifically designed for biological data are emerging, such as graph transformers for spatial data and hybrid encoder-decoder designs for multi-omic integration [12] [1].

Diagram 2: Challenges and Future Directions. This diagram outlines the current limitations of single-cell foundation models and connects them to emerging research directions aimed at addressing these challenges.

The development of scGPT, scBERT, Nicheformer, and specialized frameworks represents a paradigm shift in single-cell omics analysis, moving from task-specific models to general-purpose foundation models. Each architecture brings unique strengths: scGPT excels in generative tasks and multi-omic integration, scBERT provides effective bidirectional context understanding, and Nicheformer enables unprecedented spatial context prediction. However, independent benchmarking reveals that no single model consistently outperforms others across all tasks, emphasizing the importance of task-specific model selection [5].

The field continues to face significant challenges in zero-shot generalization, biological interpretability, and computational efficiency. Future progress will likely come through multimodal integration, improved benchmarking standards, and more biologically informed architectures. As these models mature, they hold tremendous potential to accelerate drug development, enhance clinical translation, and ultimately advance our fundamental understanding of cellular biology.

The advent of single-cell genomics has transformed biological research, enabling the investigation of cellular heterogeneity, developmental pathways, and disease mechanisms at unprecedented resolution. As the field progresses toward big-data domains, self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast, unlabeled datasets, forming the foundation for a new generation of analytical models [6]. SSL approaches, including masked autoencoding and contrastive learning, leverage the complex pairwise relationships within single-cell data through pretraining on millions of cells, enabling exceptional transfer learning capabilities across diverse downstream tasks [2] [6].

This technical guide provides an in-depth examination of benchmarking methodologies for three fundamental tasks in single-cell omics: batch correction, cell type annotation, and cross-modality prediction. Framed within the context of self-supervised pretraining, we synthesize current benchmarking evidence to establish robust evaluation standards, present performance comparisons of state-of-the-art methods, and detail experimental protocols for rigorous assessment. For researchers, scientists, and drug development professionals, this whitepaper serves as a comprehensive resource for navigating the rapidly evolving computational landscape of single-cell genomics.

Batch Correction

The Benchmarking Challenge

Batch effects represent technical variations arising from different protocols, sequencing platforms, or processing times that confound biological signals in single-cell RNA sequencing (scRNA-seq) data. The core challenge in batch correction lies in removing these technical artifacts while preserving meaningful biological variation [82] [83]. Ideal batch correction methods should be well-calibrated, meaning they introduce minimal artifacts when correcting data without substantial batch effects [83].

Recent research has revealed that many popular batch correction methods are poorly calibrated, creating measurable artifacts during the correction process [82] [83]. This underscores the critical need for rigorous benchmarking to guide methodological selection and development.

Performance Evaluation of Batch Correction Methods

A comprehensive evaluation of eight widely used batch correction methods examined their performance using a novel approach that measures the degree to which these methods alter data during correction, both at the fine scale (comparing distances between cells) and across clusters of cells [82] [83].

Table 1: Performance Comparison of scRNA-seq Batch Correction Methods

Method	Calibration Performance	Key Artifacts Identified	Input Data Type	Correction Approach
Harmony	Consistently performs well	Minimal artifacts detected	Normalized count matrix	Soft k-means; corrects embedding
ComBat	Introduces artifacts	Detectable artifacts	Normalized count matrix	Empirical Bayes linear correction
ComBat-seq	Introduces artifacts	Detectable artifacts	Raw count matrix	Negative binomial regression
BBKNN	Introduces artifacts	Detectable artifacts	k-NN graph	UMAP on merged neighborhood graph
Seurat	Introduces artifacts	Detectable artifacts	Normalized count matrix	CCA; corrects embedding
MNN	Performs poorly	Considerable data alteration	Normalized count matrix	Mutual nearest neighbors
SCVI	Performs poorly	Considerable data alteration	Raw count matrix	Variational autoencoder
LIGER	Performs poorly	Considerable data alteration	Normalized count matrix	Quantile alignment of factors

Among the methods evaluated, Harmony was the only approach that consistently performed well across all tests, making it the currently recommended choice for batch correction of scRNA-seq data [82]. Harmony operates by computing a low-dimensional PCA embedding and applying soft k-means with linear batch correction within small clusters in the embedded space, without modifying the original count matrix [83].

For spatial transcriptomics data, Crescendo presents a specialized solution that corrects batch effects directly at the gene count level using generalized linear mixed modeling. This approach facilitates the visualization of gene expression patterns across multiple samples and enables cross-technology information transfer [84].

Experimental Protocol for Benchmarking Batch Correction

To rigorously evaluate batch correction methods, researchers can implement the following experimental protocol:

• Data Preparation: Select a well-annotated scRNA-seq dataset with known cell types and minimal batch effects. Randomly assign cells to pseudobatches to establish ground truth [83].

• Method Application: Apply each batch correction method to the pseudobatched data using standard parameters as recommended by the original authors.

• Evaluation Metrics:

  • Batch Mixing: Assess how well cells from different batches mix in the corrected embedding using metrics like batchASW (Average Silhouette Width) or LISI (Local Inverse Simpson's Index) [83] [84].
  • Biological Preservation: Evaluate how well the correction preserves known biological signals by measuring cell-type separation using clustering metrics (ARI, NMI) [83].
  • Artifact Detection: Implement negative control tests where batch labels are randomly assigned despite the absence of true batch effects [83].

• Visualization: Generate UMAP or t-SNE plots of uncorrected and corrected data to visually inspect batch integration and biological structure preservation.

Cell Typing

The Evolution of Cell Type Annotation

Accurate cell type identification is critical for interpreting single-cell transcriptomic data and understanding complex biological systems. Traditional methods rely on manual annotation using marker genes, but this approach becomes impractical with the growing scale and complexity of single-cell datasets [85]. Self-supervised learning has revolutionized this field by enabling the development of foundation models pretrained on massive collections of single-cell data that can be adapted to various downstream tasks, including cell type annotation [2] [6].

Foundation models such as scGPT (pretrained on over 33 million cells) and scPlantFormer (specialized for plant single-cell omics) demonstrate exceptional capabilities in cross-species cell annotation and zero-shot transfer learning [2]. These models leverage transformer architectures with self-attention mechanisms to learn universal representations of cellular states that capture hierarchical biological patterns.

Self-Supervised Learning for Cell Type Prediction

SSL approaches significantly enhance cell type prediction, particularly in transfer learning scenarios where models pretrained on large auxiliary datasets are fine-tuned on smaller target datasets. Empirical analyses demonstrate that SSL pretraining on over 20 million cells from the CELLxGENE census substantially improves cell-type prediction performance on target datasets like the Tabula Sapiens Atlas (macro F1 score improvement from 0.2722 to 0.3085) and PBMCs after SARS-CoV-2 infection (macro F1 improvement from 0.7013 to 0.7466) [6].

Notably, masked autoencoders have shown superior performance over contrastive methods in single-cell genomics, diverging from trends observed in computer vision [6]. The improvement is especially pronounced for underrepresented cell types, as indicated by stronger macro F1 improvement compared to micro F1 improvement, highlighting SSL's robustness to class imbalances [6].

Experimental Protocol for Benchmarking Cell Typing Methods

To benchmark cell typing methods, researchers can implement the following protocol:

• Data Partitioning:

  • For intra-dataset evaluation: Split data into training (80%) and testing (20%) sets
  • For cross-dataset evaluation: Train on one dataset (e.g., PBMCs) and test on another (e.g., bone marrow or brain tissue) [86]

• Model Training:

  • Apply self-supervised pretraining on large-scale reference data (e.g., scTab dataset with 20+ million cells)
  • Fine-tune on target dataset with limited annotations
  • Compare against supervised baselines without pretraining

• Evaluation Metrics:

  • Macro F1 Score: Emphasizes performance on rare cell types
  • Micro F1 Score: Overall performance across all cells
  • Accuracy: For balanced datasets
  • Cross-species Annotation Accuracy: For models with cross-species capabilities [2]

• Zero-shot Evaluation: Assess model performance without fine-tuning using k-nearest neighbors classification on frozen embeddings [6]

Table 2: Foundation Models for Cell Type Annotation

Model	Pretraining Scale	Key Features	Reported Performance
scGPT	33+ million cells	Zero-shot annotation, perturbation modeling	Superior cross-task generalization
Nicheformer	110+ million cells (57M dissociated + 53M spatial)	Spatial context awareness	Excels in spatial composition prediction
scPlantFormer	1+ million plant cells	Lightweight architecture, cross-species integration	92% cross-species annotation accuracy
Geneformer	Not specified	Rank-based gene encoding	Robust to batch effects

Cross-Modality Prediction

The Challenge of Multimodal Integration

Single-cell multiomic technologies enable the joint profiling of different molecular modalities (e.g., gene expression, chromatin accessibility, protein abundance) within the same cell, providing unprecedented insights into regulatory mechanisms [86]. However, experimental limitations including technical complexity, high costs, and data sparsity necessitate computational methods for cross-modality prediction [86]. The ability to accurately translate between modalities allows researchers to leverage existing data more effectively and generate hypotheses about regulatory relationships.

Benchmarking Cross-Modality Generation Methods

Systematic benchmarking of cross-modality generation methods reveals significant performance variations across different biological contexts and evaluation scenarios. Cisformer, a cross-attention-based generative model, demonstrates superior accuracy and generalization capability in translating between gene expression and chromatin accessibility data compared to existing methods like BABEL and scButterfly [86].

Table 3: Performance of Cross-Modality Generation Methods (RNA-to-ATAC)

Method	Architecture	Intra-dataset Performance	Inter-dataset Generalization	Biological Interpretability
Cisformer	Transformer with cross-attention	Superior cell clustering metrics	Substantially outperforms alternatives	High (via attention mechanism)
scButterfly	Dual-aligned VAE	Competitive	Moderate	Limited
BABEL	Two autoencoders	Competitive	Poor	Limited
Polarbear	Semi-supervised VAE	Not benchmarked	Not benchmarked	Limited

In challenging inter-dataset scenarios (e.g., training on PBMC data and testing on brain tissue), Cisformer substantially outperformed existing methods, accurately recapitulating cell-type-specific chromatin accessibility patterns that other methods failed to capture [86]. Quantitative analyses based on Pearson correlation coefficients revealed that Cisformer's predicted ATAC signals showed approximately 15% stronger agreement with experimental data at the cell-type level compared to alternatives [86].

Experimental Protocol for Benchmarking Cross-Modality Prediction

To evaluate cross-modality prediction methods, implement the following experimental design:

• Data Preparation:

  • Obtain paired single-cell multiome data (e.g., scRNA-seq + scATAC-seq from the same cells)
  • For RNA-to-ATAC: Use gene expression matrix as input, chromatin accessibility as output
  • For ATAC-to-RNA: Use chromatin accessibility as input, gene expression as output

• Evaluation Scenarios:

  • Intra-dataset: Random cell splitting (80% train, 20% test)
  • Cross-cell-type: Train on some cell types, test on withheld cell types
  • Inter-dataset: Train on one tissue (e.g., PBMC), test on different tissues (e.g., bone marrow, brain) [86]

• Evaluation Metrics:

  • Cell Clustering Metrics: ARI (Adjusted Rand Index), NMI (Normalized Mutual Information), AMI (Adjusted Mutual Information), Homogeneity Score
  • Peak-level Metrics: Precision, recall, F1 score for chromatin peak prediction
  • Correlation Analysis: Pearson correlation between predicted and actual signals at cell-type level
  • Biological Validation: Recovery of known regulatory relationships (e.g., enhancer-gene links)

The Scientist's Toolkit

Successful implementation of single-cell omics benchmarking requires both experimental reagents and computational resources. The following table details key solutions for executing the protocols described in this whitepaper.

Table 4: Essential Research Reagents and Computational Solutions

Resource	Type	Function	Example Implementations
Reference Datasets	Data	Provide ground truth for benchmarking	CELLxGENE Census, SpatialCorpus-110M, Human Lung Cell Atlas, Tabula Sapiens [6] [12]
Benchmarking Infrastructures	Platform	Enable standardized method comparison	Omnibenchmark, DANCE, IBRAP, openEBench [87]
Data Simulators	Software	Generate controlled data for validation	scDesign3, GRouNdGAN, scReadSim [87]
Foundation Models	Pretrained models	Provide base for transfer learning	scGPT, Nicheformer, Geneformer, scPlantFormer [2] [12]
Spatial Transcriptomics Platforms	Experimental	Generate spatially resolved single-cell data	MERFISH, Xenium, CosMx, ISS [12]
Multiome Technologies	Experimental	Simultaneously profile multiple modalities	SNARE-seq, SHARE-seq, CITE-seq, HiRES [86]

Benchmarking key computational tasks in single-cell omics requires carefully designed evaluation frameworks that account for the unique characteristics of biological data. For batch correction, rigorous calibration tests reveal that many popular methods introduce artifacts, with Harmony currently demonstrating the most consistent performance [82] [83]. For cell typing, self-supervised pretraining on large-scale data significantly enhances annotation accuracy, particularly for rare cell types and in transfer learning scenarios [6]. For cross-modality prediction, transformer-based approaches like Cisformer show superior accuracy and generalization, enabling biologically meaningful interpretation of regulatory relationships [86].

As the field continues to evolve, standardized benchmarking practices will be essential for validating new computational methods and translating computational insights into biological discoveries and clinical applications. The integration of self-supervised learning with multimodal data represents a promising direction for future methodological development, potentially enabling more comprehensive models of cellular function and regulation.

The emergence of foundation models in single-cell omics represents a paradigm shift from traditional single-task models toward scalable, generalizable frameworks capable of unifying diverse biological contexts [2] [11]. These models, pretrained on millions of cells, utilize self-supervised learning (SSL) objectives—including masked gene modeling and contrastive learning—to capture universal biological patterns [6] [2]. The critical dilemma facing researchers lies in determining when these models can be applied zero-shot (without further training) versus when task-specific fine-tuning is necessary to achieve sufficient performance. This decision profoundly impacts research validity, computational resource allocation, and ultimately, the translation of computational insights into biological understanding.

The significance of this dilemma is particularly pronounced in discovery settings where predefined labels are unavailable, making fine-tuning infeasible [81]. Understanding zero-shot capabilities is therefore essential for applications such as novel cell type identification, perturbation response prediction in unseen biological contexts, and the integration of multimodal data where comprehensive labeled training sets are impractical to obtain [81] [88].

Performance Landscape: Quantitative Comparisons of Learning Strategies

Zero-Shot Performance Challenges

Comprehensive evaluations of popular foundation models like Geneformer and scGPT reveal significant limitations in zero-shot settings. When applied to tasks such as cell type clustering and batch integration without any fine-tuning, these models are frequently outperformed by simpler traditional methods.

Table 1: Zero-Shot Performance of Foundation Models vs. Baselines in Cell Type Clustering

Model/Method	AvgBIO Score (Pancreas)	AvgBIO Score (PBMC 12k)	AvgBIO Score (Tabula Sapiens)	Batch Integration (Pancreas)
scGPT (zero-shot)	Underperforms baselines	Comparable to scVI	Underperforms baselines	Moderate performance
Geneformer (zero-shot)	Underperforms baselines	Underperforms baselines	Underperforms baselines	Poor performance
HVG selection	Outperforms foundation models	Outperforms foundation models	Outperforms foundation models	Best performance
scVI	Outperforms foundation models	Comparable to scGPT	Outperforms foundation models	Strong performance
Harmony	Outperforms foundation models	Underperforms scGPT	Outperforms foundation models	Strong performance

Notably, selecting highly variable genes (HVG) consistently outperformed both Geneformer and scGPT across most evaluation metrics and datasets [81]. In batch integration tasks, Geneformer's embedding space often failed to retain biological information, with clustering primarily driven by batch effects rather than meaningful biological variation [81].

Fine-Tuning Advantages and Strategies

Parameter-efficient fine-tuning (PEFT) strategies have emerged as powerful approaches for adapting foundation models to specific tasks while preserving the general biological knowledge acquired during pretraining.

Table 2: Fine-Tuning Strategies and Their Performance Benefits

Fine-Tuning Approach	Parameters Trained	Task	Performance Improvement
Full fine-tuning	All model parameters	Cell type prediction	Macro F1: 0.7013 to 0.7466 (PBMC) [6]
Drug-conditional adapter	<1% of parameters	Molecular perturbation prediction	Enables zero-shot generalization to unseen cell lines [88]
Masked autoencoder pretraining	All encoder parameters	Cross-modality prediction	Significant improvement in few-shot settings [6]
Prefix tuning	~0.1% of parameters	Task adaptation	Comparable to full fine-tuning with minimal parameter updates [88]

The application of efficient fine-tuning techniques is particularly valuable for molecular perturbation prediction, where models must bridge single-cell representations with distinct modalities such as chemical structures not seen during pretraining [88]. The drug-conditional adapter approach enables both prediction of cellular responses to novel drugs and zero-shot generalization to unseen biological contexts [88].

Decision Framework: When to Use Zero-Shot vs. Fine-Tuning

Scenarios Favoring Zero-Shot Application

• Exploratory analysis with unknown labels: When cell composition or disease states in a dataset are unknown, eliminating the possibility of supervised fine-tuning [81].
• Rapid prototyping and preliminary analysis: For initial dataset assessment before committing computational resources to full fine-tuning.
• Cross-species annotation: When biological knowledge from model organisms must be transferred to human contexts without labeled examples [2].
• Resource-constrained environments: When computational resources, time, or expertise for fine-tuning are limited.

Scenarios Requiring Fine-Tuning

• Precision-critical applications: When high-stakes decisions depend on model accuracy, such as clinical diagnostics or drug discovery applications [88].
• Novel modalities: When integrating data from distinct modalities not seen during pretraining, such as connecting chemical structures to cellular responses [88].
• Underrepresented cell types or states: When analyzing rare cell populations that may not be adequately represented in the pretraining corpus [6].
• Batch effect correction in complex datasets: When integrating datasets with both technical and biological batch effects that require specialized adaptation [81].

Diagram 1: Decision Framework for Zero-Shot vs. Fine-Tuning. This flowchart provides a structured approach to selecting the appropriate transfer learning strategy based on dataset characteristics and research goals.

Experimental Protocols for Evaluation

Benchmarking Zero-Shot Capabilities

Objective: Systematically evaluate the zero-shot performance of foundation models on core single-cell analysis tasks.

Materials:

• Pretrained foundation models (scGPT, Geneformer)
• Benchmark datasets (Tabula Sapiens, Pancreas, PBMC)
• Baseline methods (HVG selection, scVI, Harmony)

Methodology:

• Embedding Generation: Extract cell embeddings from foundation models without any fine-tuning.
• Cell Type Clustering: Apply clustering algorithms to embeddings and evaluate using Average BIO score and Average Silhouette Width.
• Batch Integration Assessment: Quantify batch mixing using established metrics while preserving biological variance.
• Comparative Analysis: Compare against baseline methods using standardized evaluation metrics.

Key Considerations: Ensure benchmark datasets include both previously seen and unseen data during pretraining to assess generalization [81]. The evaluation should specifically test performance on datasets with complex batch effects combining both technical and biological variation.

Parameter-Efficient Fine-Tuning Protocol

Objective: Adapt foundation models to specific downstream tasks while minimizing trainable parameters.

Materials:

• Pretrained foundation model (e.g., scGPT)
• Task-specific dataset
• Adapter modules (e.g., drug-conditional adapters)

Methodology:

• Adapter Integration: Insert small adapter layers within transformer blocks while keeping original weights frozen.
• Modality Conditioning: For cross-modal tasks, condition adapter parameters on the novel modality (e.g., chemical structures).
• Selective Training: Update only adapter parameters (typically <1% of total model parameters) during fine-tuning.
• Evaluation: Assess performance on both in-distribution and out-of-distribution examples to measure generalization.

Key Considerations: This approach is particularly valuable for few-shot learning scenarios and when bridging single-cell data with novel modalities not seen during pretraining [88].

Diagram 2: Parameter-Efficient Fine-Tuning Architecture. This workflow illustrates how minimal adapter layers enable adaptation to novel tasks and modalities while preserving pretrained knowledge.

Essential Research Reagents and Computational Tools

Table 3: Key Research Reagents and Computational Tools for Transfer Learning Experiments

Resource	Type	Function in Evaluation
scGPT [2]	Foundation model	General-purpose single-cell foundation model for benchmarking
Geneformer [81]	Foundation model	Transformer-based model for comparative evaluation
CELLxGENE Census [6]	Data resource	Large-scale single-cell data for pretraining and evaluation
SnapATAC2 [89]	Algorithm	Efficient dimensionality reduction for baseline comparisons
scMODAL [90]	Framework	Multimodal data alignment for cross-modal transfer learning
BioLLM [2]	Platform	Standardized framework for benchmarking foundation models
Spatial-Live [91]	Visualization tool	Lightweight versatile viewer for spatial-omics data exploration

The zero-shot versus fine-tuning dilemma represents a fundamental consideration in the application of foundation models to single-cell omics. Current evidence suggests that while zero-shot application offers convenience for exploratory analysis, it frequently underperforms simpler methods and specialized fine-tuning approaches for precision-critical tasks. The emergence of parameter-efficient fine-tuning strategies provides a promising middle ground, enabling specialized adaptation while preserving the general biological knowledge encoded during pretraining.

Future progress in this field depends on developing more robust evaluation standards, particularly for zero-shot settings [81], and advancing efficient adaptation techniques that can generalize to increasingly diverse biological contexts and modalities. As foundation models continue to evolve in scale and capability, the strategic selection of transfer learning approaches will remain crucial for bridging computational advances with meaningful biological discovery.

Self-supervised learning (SSL) has emerged as a transformative methodology in single-cell omics research, enabling researchers to extract meaningful representations from vast, unlabeled datasets. While SSL has demonstrated remarkable success in computer vision and natural language processing, its application to single-cell genomics requires careful consideration of specific scenarios where it provides substantial benefits. This technical guide examines the precise conditions under which self-supervised pretraining on auxiliary data enhances performance in downstream biological tasks. Through systematic benchmarking and empirical validation, we delineate the contexts—including transfer learning scenarios, zero-shot settings, and specific architectural configurations—where SSL delivers significant improvements in tasks such as cell-type annotation, data integration, and perturbation prediction. The insights presented herein provide a strategic framework for researchers and drug development professionals to implement SSL effectively within their single-cell research pipelines.

The rapid expansion of single-cell genomics into a big-data domain, primarily driven by advancements in single-cell RNA-sequencing technologies, has created unprecedented opportunities for understanding cellular heterogeneity [6]. As efforts toward comprehensive atlases like the Human Cell Atlas progress, researchers increasingly require machine learning models capable of interpreting new data within the context of existing massive datasets. The emergence of foundation models in single-cell genomics has highlighted the potential of self-supervised learning (SSL) to address fundamental challenges including technical batch effects, labeling quality variability, and data sparsity [6] [92].

SSL leverages pairwise relationships within unlabeled data for training, distinguishing it from supervised learning (which relies on labeled data) and unsupervised learning (which depends solely on data without labels) [6]. This approach has proven particularly powerful in data-intensive domains, forming the basis for foundation models that can be adapted to multiple downstream tasks. In single-cell genomics, however, identifying scenarios where SSL outperforms traditional learning methods remains a nuanced challenge [6]. The strategic implementation of SSL pretraining on auxiliary data requires understanding specific conditions under which it provides measurable benefits rather than simply adding computational overhead.

This technical guide synthesizes evidence from recent benchmarking studies and experimental investigations to establish a framework for the effective use of SSL pretraining in single-cell omics. We examine the quantitative improvements observed across various downstream tasks, detail the experimental protocols that yield robust results, and provide practical recommendations for researchers seeking to incorporate SSL into their analytical workflows.

SSL Fundamentals in Single-Cell Omics

Core SSL Approaches

Two primary SSL approaches have been systematically evaluated for single-cell data: masked autoencoders and contrastive learning methods [6]. Masked autoencoders operate by randomly masking portions of the input data (e.g., gene expression values) and training models to reconstruct the missing elements based on the unmasked context. This approach forces the model to learn meaningful representations of the underlying biological structure. Contrastive learning methods, conversely, learn representations by contrasting positive pairs (similar cells or augmented views of the same cell) against negative pairs (dissimilar cells) [6] [26].

Recent benchmarking efforts have revealed that masked autoencoders generally excel in single-cell genomics applications, diverging from trends in computer vision where contrastive methods often dominate [6]. The specialized single-cell framework scVI and the foundation model scGPT have demonstrated particular strength in uni-modal batch correction, while generic SSL methods like VICReg and SimCLR perform well in cell typing and multi-modal data integration [26].

Critical Architectural Considerations

The effectiveness of SSL pretraining depends significantly on architectural decisions. Empirical evidence indicates that a moderate to larger embedding dimensionality consistently leads to improved results across tasks [26]. Notably, random masking has emerged as the most effective augmentation technique across all tasks, surprisingly surpassing more complex, domain-specific augmentations [26].

Contrary to practices in other domains, studies have found that neither domain-specific batch normalization nor retaining the projector during inference consistently improves results for single-cell data [26]. These findings highlight the importance of tailoring architectural decisions to the specific characteristics of single-cell data rather than directly transferring practices from other domains.

When SSL Pretraining Provides Significant Benefits

Transfer Learning with Auxiliary Data

Substantial performance improvements occur when SSL models are pretrained on large, diverse auxiliary datasets before being applied to smaller target datasets. This transfer learning paradigm leverages the rich biological representations learned from extensive data to enhance analysis on more limited datasets [6].

Table 1: Performance Improvements from SSL Pretraining on Auxiliary Data

Dataset	Task	Baseline Performance	SSL Performance	Improvement
PBMC (SARS-CoV-2)	Cell-type prediction (Macro F1)	0.7013 ± 0.0077	0.7466 ± 0.0057	+6.46%
Tabula Sapiens	Cell-type prediction (Macro F1)	0.2722 ± 0.0123	0.3085 ± 0.0040	+13.34%
Multiple datasets	Gene-expression reconstruction	Varies	Varies	Significant gains
Cross-modality prediction	Data integration	Varies	Varies	Notable capabilities

Empirical analyses demonstrate that models pretrained on the CELLxGENE census dataset (containing over 20 million cells) and then fine-tuned on smaller datasets like peripheral blood mononuclear cells (PBMCs) after SARS-CoV-2 infection (422,220 cells) or the Tabula Sapiens Atlas (483,152 cells) show statistically significant improvements in both cell-type prediction and gene-expression reconstruction [6]. The performance gains are particularly pronounced for underrepresented cell types, as indicated by stronger improvements in macro F1 scores compared to micro F1 scores [6].

Zero-Shot and Few-Shot Learning Scenarios

SSL demonstrates remarkable capabilities in zero-shot settings where models must generalize to unobserved classes using representations learned solely through self-supervised pretraining [6]. This is particularly valuable in single-cell genomics where comprehensive labeling is often impractical due to the enormous scale and complexity of datasets.

In perturbation prediction, efficient fine-tuning of single-cell foundation models enables zero-shot generalization to unseen cell lines [88]. By incorporating drug-conditional adapters that train less than 1% of the original foundation model parameters, researchers can achieve state-of-the-art performance in predicting cellular responses to novel drugs across unseen biological contexts [88].

Data Integration and Multi-Modal Applications

SSL pretraining significantly enhances cross-modality prediction and data integration capabilities [6]. Models pretrained on large auxiliary datasets develop representations that facilitate integration across different measurement modalities (e.g., RNA expression and protein abundance) and experimental conditions.

For multi-modal batch correction, generic SSL techniques such as VICReg and SimCLR have been shown to outperform domain-specific methods, demonstrating the transferability of representations learned through self-supervision [26]. This capability is particularly valuable for integrating data from different technologies, laboratories, or experimental conditions.

When SSL Pretraining Provides Limited Benefits

Same-Dataset Pretraining and Fine-Tuning

SSL pretraining does not yield substantial improvements when the pre-training and fine-tuning are performed on the same dataset [6]. In such cases, supervised or unsupervised training on the target dataset often performs equally well or better than introducing an intermediate self-supervised pretraining phase.

This limitation highlights that the primary value of SSL in single-cell omics derives from its ability to transfer knowledge from larger, more diverse datasets to smaller, more specific ones—not from processing the same data through additional training phases.

Inadequate Scale or Diversity of Auxiliary Data

The benefits of SSL pretraining are contingent on the scale and diversity of the auxiliary data. One study found that SSL only outperformed supervised learning when pretrained on a large number of donors, emphasizing the necessity of a rich pre-training dataset [6].

Table 2: Impact of Auxiliary Data Characteristics on SSL Effectiveness

Auxiliary Data Characteristic	Impact on SSL Effectiveness	Practical Implication
Large number of donors/cells	Significant improvement	Use datasets >1M cells when possible
Diverse cell types and states	Enhanced generalization	Prioritize comprehensively annotated atlases
Technical and batch variability	Improved integration capabilities	Include data from multiple platforms
Limited scale or diversity	Minimal or no improvement	Seek alternative approaches

When auxiliary data lacks sufficient scale, diversity, or quality, the representations learned through self-supervision may not transfer effectively to downstream tasks and datasets. In such cases, traditional supervised approaches or unsupervised methods may be more efficient and effective.

Experimental Protocols and Methodologies

SSL Framework Implementation

The typical SSL framework for single-cell genomics operates in two stages [6]:

• Pre-training (Pretext Task): The model learns from unlabeled data using objectives such as masked gene expression recovery or contrastive learning between augmented cell representations.

• Fine-tuning (Downstream Task): The pretrained model is further trained on specific downstream tasks such as cell-type annotation, often with limited labeled data.

Benchmarking Evaluation Metrics

Rigorous evaluation of SSL methods requires multiple metrics tailored to specific downstream tasks [6] [26]:

• Cell-type prediction: Macro F1 score and micro F1 score to assess robustness against class imbalances
• Gene-expression reconstruction: Weighted explained variance
• Batch correction: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI)
• Clustering performance: Clustering Accuracy (CA), Purity metrics
• Computational efficiency: Peak memory usage, running time

The macro F1 score is particularly important for evaluating performance on underrepresented cell types, as it gives equal weight to all classes regardless of their frequency [6].

Practical Implementation Guide

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for SSL Implementation in Single-Cell Research

Resource	Type	Function	Examples
Large-scale Reference Data	Dataset	Provides diverse auxiliary data for pretraining	CELLxGENE census, Human Cell Atlas, Tabula Sapiens
SSL Frameworks	Software	Implements self-supervised learning algorithms	scGPT, scBERT, scVI, scRobust, scKAN
Benchmarking Platforms	Software	Standardized evaluation of SSL methods	scSSL-Bench
Preprocessing Tools	Software	Handles quality control, normalization, and feature selection	Seurat, Scanpy, Cell Ranger
Specialized Architectures	Model Components	Enables specific capabilities	Drug-conditional adapters for perturbation prediction

Decision Framework for SSL Implementation

Researchers should consider the following questions when deciding whether to implement SSL pretraining:

• Auxiliary Data Availability: Is there access to a large, diverse dataset (preferably >1 million cells) relevant to the biological domain of interest?
• Target Data Characteristics: Is the target dataset substantially smaller or more limited in diversity than available auxiliary data?
• Task Requirements: Does the application involve challenging scenarios like zero-shot learning, cross-modal prediction, or integration of highly heterogeneous data?
• Resource Constraints: Are there sufficient computational resources for pretraining, which typically requires significant memory and processing power?

If the answer to questions 1-3 is "yes" and resources are sufficient (question 4), SSL pretraining will likely provide meaningful benefits.

Future Directions and Clinical Applications

The convergence of SSL with foundation models represents a promising direction for single-cell omics [56] [88]. Models like scGPT, pretrained on over 33 million cells, demonstrate the potential of leveraging massive auxiliary datasets to develop universal biological representations applicable to diverse downstream tasks [88].

In drug discovery, SSL-enabled approaches are advancing target identification, mechanism of action analysis, and patient stratification [93]. For example, interpretable frameworks like scKAN combine accurate cell-type annotation with identification of cell-type-specific gene sets, facilitating the discovery of potential therapeutic targets [56]. Similarly, molecular perturbation prediction using SSL-based models shows promise for in silico drug screening and prioritization [88].

As single-cell technologies continue to evolve, producing increasingly complex and multimodal data, SSL methodologies will play a crucial role in extracting biologically meaningful insights and translating them into clinical applications.

Self-supervised learning on auxiliary data provides substantial benefits in specific, well-defined scenarios within single-cell omics research. The most significant improvements occur in transfer learning settings where models pretrained on large, diverse datasets are applied to smaller target datasets, particularly for tasks involving cell-type prediction of underrepresented populations, zero-shot generalization, and cross-modal data integration. Conversely, SSL pretraining offers limited value when applied to the same dataset used for fine-tuning or when auxiliary data lacks sufficient scale and diversity.

By strategically implementing SSL in appropriate contexts, researchers and drug development professionals can leverage the growing wealth of single-cell data to advance our understanding of cellular heterogeneity, disease mechanisms, and therapeutic opportunities. The experimental protocols and decision frameworks presented in this guide provide a practical foundation for the effective application of SSL in single-cell research.

The application of self-supervised learning (SSL) in single-cell omics represents a paradigm shift in computational biology, enabling researchers to extract meaningful representations from massive, unlabeled cellular datasets. However, a critical question persists: when do domain-specialized SSL methods outperform generic approaches, and for which specific tasks? Recent benchmarking studies reveal that the performance landscape is nuanced and highly task-dependent. Domain-specialized frameworks such as scVI and scGPT demonstrate superior capabilities for batch correction tasks, while generic SSL methods like VICReg and SimCLR consistently excel in cell type annotation and multimodal data integration. Furthermore, masked autoencoders have emerged as particularly effective for single-cell genomics, outperforming contrastive learning approaches that dominate computer vision applications. This technical guide synthesizes current evidence to provide a structured framework for selecting optimal SSL strategies based on specific analytical objectives, dataset modalities, and performance requirements within single-cell omics research.

Self-supervised learning has transformed the analysis of high-dimensional single-cell omics data by enabling the extraction of biologically meaningful representations without extensive labeled datasets. SSL methods learn intrinsic data structures by defining pretext tasks that generate supervisory signals from the data itself, bypassing the need for manual annotations [6] [94]. In single-cell genomics (SCG), where datasets routinely encompass millions of individual cells with measurements across thousands of genes, SSL has become indispensable for managing scale and complexity [6]. The fundamental distinction in methodology selection lies between domain-specialized frameworks (e.g., scVI, CLAIRE, scGPT) specifically engineered for single-cell data characteristics, and generic SSL methods (e.g., VICReg, SimCLR, Barlow Twins) adapted from computer vision and natural language processing domains [26]. Understanding the performance characteristics and optimal application domains for each approach is crucial for advancing robust, reproducible single-cell research and accelerating therapeutic discovery.

Benchmarking Framework and Performance Metrics

Standardized Evaluation Paradigms

Comprehensive benchmarking initiatives have established rigorous frameworks for evaluating SSL performance across single-cell omics applications. The scSSL-Bench represents a systematic comparison of 19 SSL methods across 9 datasets, focusing on three fundamental downstream tasks: batch correction, cell type annotation, and missing modality prediction [26]. Similarly, Richter et al. conducted extensive empirical analyses across over 20 million cells from the CELLxGENE census, evaluating performance on cell-type prediction, gene-expression reconstruction, cross-modality prediction, and data integration [6]. These studies employ standardized metrics including macro F1 score (emphasizing performance on rare cell types), micro F1 score (overall accuracy), and weighted explained variance for reconstruction tasks [6]. Additional evaluation criteria encompass clustering accuracy via Adjusted Rand Index (ARI), normalized mutual information (NMI), and effectiveness in removing technical batch effects while preserving biological variation [26] [95].

Critical Performance Determinants

Several technical factors significantly influence SSL method performance in single-cell contexts:

• Embedding Dimensionality: Moderate to larger embedding dimensions (typically 64-512) consistently improve results across tasks, capturing sufficient biological complexity without overfitting [26].
• Data Augmentation Strategy: Random masking emerges as the most effective augmentation technique across all tasks, surpassing biology-specific augmentations [26].
• Architectural Considerations: Fully connected autoencoder architectures effectively capture biological variations while minimizing architectural confounding in performance comparisons [6].
• Pre-training Data Scale: SSL demonstrates significant performance improvements when pre-trained on large auxiliary datasets (e.g., scTab with 20+ million cells), particularly for transfer learning scenarios [6].

Table 1: Key Benchmarking Studies and Their Methodologies

Study	Methods Evaluated	Datasets	Key Evaluation Metrics
scSSL-Bench [26]	19 methods (specialized & generic)	9 datasets (7 uni-modal, 2 multi-modal)	Batch correction quality, Cell type annotation accuracy, Missing modality prediction
Richter et al. [6]	Masked autoencoders, BYOL, Barlow Twins	CELLxGENE (20M+ cells), HLCA, PBMC, Tabula Sapiens	Macro F1 score, Micro F1 score, Weighted explained variance
CLEAR [95]	Contrastive-sc, scNAME, scDHA, scVI	10 published datasets with expert annotations	ARI, NMI, Visualization quality, Batch effect removal

Performance Across Core Analytical Tasks

Batch Correction

Batch correction remains a fundamental challenge in single-cell analysis, where technical variations across experiments can obscure biological signals. Domain-specialized methods demonstrate superior performance for this critical task:

• scVI effectively models single-cell data distributions using variational inference, explicitly accounting for technical noise and batch effects [26] [95].
• CLAIRE employs innovative augmentation through mutual nearest neighbors (MNN) between and within experimental batches, extending MoCo's architecture with online and momentum encoders [26].
• scGPT leverages transformer architectures pre-trained on massive single-cell corpora (33+ million cells), enabling effective batch correction through transfer learning [2].

Specialized frameworks incorporate explicit probabilistic modeling of batch effects and biological variation, outperforming generic approaches that lack domain-specific inductive biases [26] [95]. The visualization of cells before and after correction typically shows clustering by cell type rather than experimental origin, confirming successful technical effect removal [26].

Cell Type Annotation

Cell type annotation (query-to-reference mapping) represents a transfer learning scenario where SSL methods show particularly strong performance. For this task, generic SSL methods frequently outperform specialized approaches:

• VICReg (generic) demonstrates superior representation learning for cell type discrimination, effectively separating distinct cell populations while maintaining structural integrity [26].
• SimCLR (generic) excels at identifying subtle transcriptional differences that distinguish closely related cell states [26].
• Masked Autoencoders achieve significant performance gains, particularly when pre-trained on large auxiliary datasets, improving macro F1 scores from 0.7013 to 0.7466 in PBMC datasets and from 0.2722 to 0.3085 in Tabula Sapiens [6].

The advantage of generic methods stems from their ability to learn representations that effectively separate cell types without being overly constrained by domain-specific assumptions [26]. This is particularly evident in improved classification of rare cell populations, where macro F1 scores show more substantial improvements than micro F1 scores, indicating better performance on underrepresented classes [6].

Multimodal Data Integration

Multimodal single-cell technologies (e.g., CITE-seq, 10x multiome) simultaneously measure diverse molecular features, creating unique integration challenges:

• Generic SSL methods (particularly VICReg and SimCLR) demonstrate superior performance for multi-modal batch correction and integration tasks [26].
• scCLIP adapts contrastive language-image pre-training to single-cell multi-omics, aligning different modalities through contrastive objectives [26].
• Concerto implements contrastive self-supervised distillation with asymmetric teacher-student networks, effectively handling RNA-protein pairs [26].

A significant finding across studies is the current absence of specialized frameworks that consistently outperform generic approaches for multi-modal integration, highlighting an important area for methodological development [26].

Table 2: Task-Specific Performance Leaders

Analytical Task	Best Performing Methods	Key Advantages	Performance Notes
Uni-modal Batch Correction	scVI, CLAIRE, scGPT	Explicit batch effect modeling, Biological variation preservation	Specialized methods outperform by incorporating domain knowledge
Cell Type Annotation	VICReg, SimCLR, Masked Autoencoders	Effective representation learning, Rare cell type identification	Generic SSL shows superior cell separation capabilities
Multi-modal Integration	VICReg, SimCLR, scCLIP	Cross-modal alignment, Missing modality prediction	Generic methods currently dominate this space
Gene Expression Reconstruction	Masked Autoencoders	Multiple masking strategies, Biological context utilization	excels in zero-shot settings and transfer learning

Experimental Protocols and Methodologies

Pre-training Strategies

Effective SSL implementation in single-cell omics requires careful consideration of pre-training approaches:

• Masked Autoencoders: Implement random masking (minimal inductive bias) or gene program masking (biological prior incorporation) strategies, where input features are partially zeroed out and models learn to reconstruct missing elements [6]. The autoencoder computes loss exclusively on masked features, forcing meaningful representation learning.
• Contrastive Learning: Employ negative-pair-free methods like BYOL (Bootstrap Your Own Latent) and Barlow Twins, which avoid computational challenges of negative pair selection while maintaining performance [6]. Augmentations include negative binomial noise and masking to simulate single-cell technical variation.
• Transfer Learning Framework: Pre-training on large auxiliary datasets (e.g., scTab with 20+ million cells) followed by fine-tuning on target datasets demonstrates significant performance improvements, particularly for cell type prediction [6].

Implementation Details

• Architecture Selection: Fully connected networks provide effective baselines, minimizing architectural confounding while capturing biological variation [6]. Transformer architectures show promise but require substantial computational resources [2].
• Data Partitioning: Rigorous evaluation employs multiple random seeds (typically 5 repetitions) with confidence interval reporting (95% CI as mean ± s.e. × t-value) [6].
• Zero-Shot Evaluation: Models assessed using k-nearest-neighbors (kNN) classification or prediction heads with frozen encoder weights to evaluate representation quality without fine-tuning [6].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Computational Tools for SSL in Single-Cell Omics

Tool/Resource	Type	Primary Function	Access
CELLxGENE Census [6]	Data Resource	Curated collection of >20 million cells for pre-training	Publicly available
scSSL-Bench [26]	Benchmarking Platform	Standardized evaluation of 19 SSL methods	Open-source code
scGPT [2]	Foundation Model	Transformer-based analysis pre-trained on 33M+ cells	Available with pre-trained weights
scVI [26]	Specialized Framework	Probabilistic modeling for batch correction	Python package
CLEAR [95]	Contrastive Method	scRNA-seq data representation with noise robustness	Open-source implementation
TransST [96]	Transfer Framework	Spatial transcriptomics analysis leveraging external data	Available code repository

Technical Recommendations and Best Practices

Based on comprehensive benchmarking evidence, the following recommendations emerge for implementing SSL in single-cell omics:

Method Selection Guidelines

• Prioritize Domain-Specialized Methods (scVI, CLAIRE, scGPT) for batch correction tasks where explicit modeling of technical variation is crucial [26].
• Select Generic SSL Approaches (VICReg, SimCLR) for cell type annotation and multimodal integration, where flexible representation learning outperforms constrained domain models [26].
• Implement Masked Autoencoders with random masking strategies as a default starting point, as this approach consistently delivers strong performance across diverse tasks [6] [26].
• Leverage Transfer Learning by pre-training on large auxiliary datasets (when available) before fine-tuning on target datasets, particularly for analyzing smaller studies [6].

Implementation Considerations

• Architecture Simplicity: Begin with fully connected networks before progressing to transformer architectures, as they provide competitive performance with reduced computational complexity [6].
• Embedding Dimensions: Utilize moderate to large embedding dimensions (64-512) to sufficiently capture biological complexity [26].
• Data Augmentation: Employ random masking as the primary augmentation strategy, as it consistently outperforms biology-specific augmentations across tasks [26].
• Evaluation Rigor: Report both macro and micro F1 scores to capture performance across both common and rare cell populations [6].

Future Directions and Emerging Trends

The SSL landscape in single-cell omics continues to evolve rapidly, with several promising research directions emerging:

• Multimodal Specialized Frameworks: Current benchmarking reveals a gap in specialized methods that outperform generic approaches for multimodal integration, representing a significant opportunity for methodological innovation [26].
• Transformer Architecture Refinement: While foundation models like scGPT show impressive performance, disentangling the contributions of SSL, scaling laws, and architectural innovations remains challenging [6] [2].
• Interpretable SSL: Developing self-supervised approaches that provide biological insights beyond representation learning, particularly for identifying molecular regulators and regulatory networks [2].
• Federated Learning Applications: SSL combined with privacy-preserving federated approaches enables collaborative model training across institutions without data sharing [2].
• Standardized Benchmarking Platforms: Initiatives like BioLLM provide universal interfaces for evaluating foundation models, promoting reproducibility and comparative assessments [2].

The convergence of larger-scale datasets, refined architectural strategies, and task-specific methodological innovations will continue to clarify the respective roles of specialized versus generic SSL approaches, further optimizing analytical workflows across diverse single-cell omics applications.

Conclusion

Self-supervised learning and foundation models represent a paradigm shift in single-cell omics, moving the field from analyzing isolated datasets toward unified, generalizable frameworks. The key takeaways are clear: transformer-based architectures, pretrained on massive and diverse datasets, unlock powerful capabilities for cell type annotation, spatial context prediction, and in silico perturbation modeling. Crucially, empirical benchmarks show that SSL excels in transfer learning scenarios, particularly when leveraging auxiliary data, with masked autoencoders emerging as a dominant pretext task. However, challenges in data quality, model interpretability, and computational cost remain active frontiers. The future of scFMs lies in developing more robust multimodal integration, creating sustainable model ecosystems with standardized benchmarking, and ultimately translating these computational insights into clinically actionable knowledge for precision medicine and novel therapeutic development. The convergence of larger datasets, more efficient architectures, and biologically informed training objectives will further bridge the gap between computational discovery and mechanistic understanding of cellular function and disease.

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