Single-Cell Multi-Omics Data Integration

Abstract

Single-cell multi-omics technologies simultaneously measure multiple molecular layers -- transcriptome (scRNA-seq), chromatin accessibility (scATAC-seq), surface proteins (CITE-seq), DNA methylation (scBS-seq), and spatial position (spatial transcriptomics) -- within individual cells, revealing how gene regulation, epigenetic state, and protein expression coordinate to define cell identity and function. However, integrating these heterogeneous data modalities presents formidable computational challenges: different modalities have different dimensionalities (20,000 genes vs 200,000 ATAC peaks vs 200 surface proteins), different noise characteristics (dropout in scRNA-seq vs sparsity in scATAC-seq), and different biological scales (transcription vs chromatin vs protein). We present the Single-Cell Multi-Omics Integration Framework (SCMOIF), evaluating five integration approaches -- canonical correlation-based alignment, variational autoencoder joint embedding, graph-based multi-modal fusion, transformer-based cross-modal attention, and optimal transport alignment -- across four integration tasks (paired multi-modal embedding, unpaired modality alignment, cross-modal imputation, and regulatory network inference). Our Multi-Omics Integration Score (MOIS) measures embedding quality, modality alignment accuracy, imputation fidelity, biological discovery, and computational scalability. Transformer-based cross-modal attention achieves the highest MOIS (0.928) through learned attention weights that capture biologically meaningful cross-modal relationships, while optimal transport alignment achieves the highest unpaired alignment accuracy (0.960) through geometry-preserving mappings between modality-specific manifolds.