Single-Cell Omics Deep Learning

• Single-cell omics deep learning is a field combining deep neural architectures with diverse cellular data including transcriptomics, epigenomics, proteomics, and spatial profiles.
• It employs models like autoencoders, VAEs, transformers, and graph neural networks to automate feature extraction, impute missing data, and enable non-linear data fusion.
• State-of-the-art frameworks deliver improved scalability and performance in tasks such as imputation, clustering, batch correction, and cell-type annotation.

Single-cell omics deep learning encompasses algorithmic and architectural advances in the application of deep neural networks to high-dimensional, sparse, and multimodal biological data at cellular resolution. Such data include single-cell transcriptomes (scRNA-seq), epigenomes (scATAC-seq, methylation), proteomes (e.g., CITE-seq), and spatial transcriptomics, frequently measured in millions of cells. Deep learning has supplanted conventional machine learning as the analytic backbone, due to its capacity for automated feature extraction, nonlinear data fusion, batch correction, imputation, and advanced downstream biological inference. This article surveys modal coverage, model innovations, computational frameworks, interpretability, integration methodology, and major challenges, referencing recent developments and empirical benchmarks.

1. Modalities and Problem Structure

Single-cell deep learning is characterized by heterogeneous input modalities:

• Genome (DNA) sequencing
• Epigenome profiling (chromatin accessibility, methylation)
• Transcriptome quantification (scRNA-seq)
• Proteomics (surface proteins, CITE-seq)
• Spatial transcriptomics (MERFISH, Visium)

Typical datasets consist of raw count matrices $X\in\mathbb{R}^{N\times G}$ (cells × genes) with $>70\%$ zero entries, corresponding to dropout noise (Azad et al., 2019). Multimodal assays produce parallel matrices for different feature sets. Data dimensionality ($G\sim10^4$, $N\sim10^5$–$10^6$) necessitates scalable architectures and memory-efficient training pipelines (Sun et al., 28 Oct 2025, D'Ascenzo et al., 2 Jun 2025).

2. Deep Learning Architectures for Single-Cell Omics

Key model classes include:

• Autoencoders (AEs): Unsupervised dimensionality reduction via encoder/decoder pairs. Dense, sparse, or regularized variants are used for denoising, imputation, and visualization (Azad et al., 2019, Molho et al., 2022).
• Variational Autoencoders (VAEs): Probabilistic latent variable models optimizing

$$L_{\text{VAE}} = \mathbb{E}_{q_\phi(z|x)}[\log p_\theta(x|z)] - \beta\,\mathrm{KL}(q_\phi(z|x)\,\|\,p(z))$$

with Negative Binomial or Zero-Inflated decoders for count data (Molho et al., 2022, Sun et al., 28 Oct 2025).

• Graph Neural Networks (GNNs): Encode cell–cell, gene–gene, or spatial adjacencies using message-passing, commonly for domain identification and cell-cell communication (Molho et al., 2022, Ge et al., 2024, Avelar et al., 15 Apr 2025).
• Transformers and Attention: Applied to cell-gene or cell-peak token sequences; enable long-range dependencies and multimodal fusion (e.g., scMamba, scFusionTTT) (Yuan et al., 25 Jun 2025, Meng et al., 2024).
• GANs and Adversarial Nets: Used primarily for data augmentation, imputation, and domain adaptation (Molho et al., 2022, Ge et al., 2024).
• Capsule networks, recurrent models: Deployed for specific tasks such as cell-type deconvolution or trajectory inference (Molho et al., 2022).

Model innovation includes patch-based tokenization (scMamba), O($n\cdot d$)-complexity TTT blocks (scFusionTTT), regularized disentanglement (scMRDR), and subset-contrastive learning for large graphs (SCONE).

3. Multi-Omics Integration Strategies

Deep learning frameworks surpass classic statistical methods in fusing multiple omics layers:

• Modality-Specific Encoders + Fusion: e.g., scMamba employs separate transformer backbones per modality with patch/position-aware embeddings, aligned in latent space through contrastive loss without prior feature selection (Yuan et al., 25 Jun 2025).
• Disentangled Latents: scMRDR uses $\beta$-VAE with explicit decomposability into modality-shared and modality-specific codes, augmented by adversarial alignment and isometric regularization (Sun et al., 28 Oct 2025).
• Graph-Based and Contrastive Methods: SCONE applies contrastive learning on memory-efficient overlapping cell subsets, encoding KNN graphs per modality (Avelar et al., 15 Apr 2025).
• Test-Time Training (TTT) Layers: scFusionTTT propagates both expression and order-dependent symbol embeddings across RNA and protein branches, with linear-complexity fusion and masked autoencoding (Meng et al., 2024).
• Product-of-Experts and POE-VAEs: MultiVI, Cobolt, scMM integrate via joint approximate posteriors and alignment penalties (Ge et al., 2024).

State-of-the-art benchmarks show scMamba achieving $>10\%$ higher aggregate integration scores and cell-pair alignment accuracy (FOSCTTM), and scFusionTTT outperforming baselines with ARI~0.90 and NMI~0.91 on several multimodal datasets (Yuan et al., 25 Jun 2025, Meng et al., 2024).

4. Key Tasks: Imputation, Clustering, Annotation, Trajectory

Deep models address primary analytic tasks:

• Imputation: AE/VAEs (DCA, DeepImpute) and GANs (scIGANs) yield highest Pearson correlations ($>$0.85) in dropout recovery and preserve rare cell-type variance (Azad et al., 2019, Ge et al., 2024, Molho et al., 2022).
• Clustering and Cell-Type Annotation: DEC structures minimize KL divergence between empirical and target cluster assignments; transformer-based pretraining enables zero-shot annotation (scMulan, scGPT) (Ge et al., 2024). scFusionTTT achieves superior ARI, NMI across diverse datasets.
• Batch Correction: MMD or adversarial domain adaptation aligns batch embeddings; conditional models leverage side information (Ge et al., 2024).
• Trajectory and Pseudotime: Neural ODEs and UMAP-based latent trajectory inference benefit from deep joint integration (scMamba) (Yuan et al., 25 Jun 2025).
• Cell–Cell Interaction and Spatial Mapping: GNNs model neighborhood signaling; attention-based graph transformers recover ligand-receptor relationships with high precision (Ge et al., 2024, Molho et al., 2022).
• Functional Prediction, Augmentation: GAN-based architectures generate in silico cell profiles for rare types or perturbation screens (Molho et al., 2022).

5. Interpretability: Mechanistic Attribution and Transparent Modeling

Biological trust and actionable insights necessitate interpretable deep learning:

• Intrinsic Attention Weights: Transformer-based models (scBERT, TOSICA) yield interpretable gene–cell or motif–base attributions via attention matrices (Wagle et al., 2024).
• Gradient, Saliency, and Layer-Wise Relevance: Methods such as Integrated Gradients and LRP quantify feature importance, often post-hoc (Wagle et al., 2024).
• Sparse and Structured Decoders: Architectures like VEGA and ExpiMap link latent dimensions to predefined gene modules or pathways (Wagle et al., 2024).
• Shapley Values and Archetypal Analysis: Feature attribution per cell-type or cluster label enhances marker discovery (Wagle et al., 2024).
• Regulatory Networks: Model architectures embedding TF–gene interaction priors provide explicit regulatory mapping (Wagle et al., 2024).
• Benchmarking Limitations: Standard datasets and metrics for interpretability are nascent; coverage remains incomplete for multivariate, spatial, and perturbation contexts.

6. Scalability and Computational Efficiency

Handling large-scale atlases (10⁵–10⁶ cells):

• Efficient Data Loading: scDataset leverages block sampling and batched fetching for AnnData, resulting in $18$–$48\times$ throughput increases over previous loaders (D'Ascenzo et al., 2 Jun 2025).
• Linear Complexity Models: scMamba and scFusionTTT utilize state-space duality layers and order-aware embeddings for linear scaling in time and memory, robust to full-genome tokenization (Yuan et al., 25 Jun 2025, Meng et al., 2024).
• Subset Sampling for Graphs: SCONE avoids O($n^2$) graph costs by working on overlapping size-$k_s$ subsets (Avelar et al., 15 Apr 2025).
• Multi-GPU and Hardware-Optimized Backends: Atlas-scale integration now feasible on single nodes with controlled memory footprint (scMamba <60GB on 300K cells) (Yuan et al., 25 Jun 2025).

7. Challenges, Benchmarks, and Future Directions

Current limitations include:

• Interpretability Versus Expressivity: Black-box models hinder mechanistic explanation, even as performance rises; structured sparsity and multi-task learning are proposed solutions (Azad et al., 2019, Wagle et al., 2024).
• Modality Alignment and Data Heterogeneity: Distinct error models across omics complicate integration, especially with unpaired measurements (Sun et al., 28 Oct 2025).
• Benchmark Dataset Needs: Community standards for integration, annotation, functional prediction, and interpretability have emerged (e.g., ARI, NMI, F1, batch-correction scores) but require further expansion (Ge et al., 2024, Molho et al., 2022).
• Scalability and Data Scarcity: Annotation frameworks using agentic foundation models (DeepSeq) leverage web search and prompt engineering, reaching~82.5% accuracy for cell-type prediction—yet still lag human curation (Dajani et al., 14 Jun 2025).
• Agentic AI and Autonomous Workflows: Evaluation of AI agents (Grok-3-beta, ReAct, AutoGen) in single-cell omics reveals self-reflection and robust code synthesis as critical for workflow completion and traceability (Liu et al., 16 Aug 2025).

Ongoing research targets biologically informed architectures, continual benchmarking, and efforts to bridge interpretability with data-driven expressivity, thereby advancing translational, multi-omic cellular mapping at scale.