ArticleNature communications2022
Clustering of single-cell multi-omics data with a multimodal deep learning method.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed.
- Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering.Interdisciplinary sciences, computational life sciences · 2026Article
- scURL: uncertainty-sharpened representation learning for single-cell multi-omics clustering.Bioinformatics (Oxford, England) · 2026Article
- The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics.Nature cell biology · 2026Article
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Numerical simulation of mitochondrial systems for ATP generation and membrane transport.Scientific reports · 2026Article
- scHashFormer: a hash-driven graph transformer for scalable scRNA-seq clustering.Briefings in bioinformatics · 2026Article
- AGCLD: an adaptive graph contrastive learning method with denoising for spatial domain identification.Briefings in bioinformatics · 2026Article
- UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.Briefings in bioinformatics · 2026Article
- Cross-Modal Denoising and Integration of Spatial Multi-Omics Data with CANDIES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrating Multi-View Features via Deep Generalized Canonical Correlation Analysis for Single-Cell Clustering.International journal of molecular sciences · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- SCONE: a subset-contrastive method for multi-omics network embedding.Briefings in bioinformatics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.Nature communications · 2026Article
- A deep adversarial network model for multi-task analysis of single-cell omics data.Briefings in bioinformatics · 2026Article
- A hybrid neighborhood enhanced contrastive learning and self-knowledge distillation method for scRNA-seq data clustering analysis.Bioinformatics (Oxford, England) · 2026Article
- A method to unravel complexity in human peripheral blood B-cell subsets through multiomic cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analyses.ImmunoHorizons · 2026Article
- scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment.Genome research · 2026Article
- Aligned cross-modal integration and regulatory heterogeneity characterization of single-cell multiomic data with deep contrastive learning.Genome medicine · 2026Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.