ArticleCell reports methods2023
Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- scENature communications · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics.bioRxiv : the preprint server for biology · 2026Article
- Single-cell multiomics data integration and generation with scPairing.Cell reports methods · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- Benchmarking single-cell cross-omics imputation methods for surface protein expression.Genome biology · 2025Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data.Genome biology · 2024Article
- Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer.International journal of molecular sciences · 2023Review
- GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.Genome biology · 2023Article
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Abstract
The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.
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