{"doi":"10.1016/j.crmeth.2023.100563","title":"Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures","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.","journal":"Cell Reports Methods","year":2023,"id":328989,"datarank":0.4636563680037475,"base_score":3.091042453358316,"endowment":3.091042453358316,"self_citation_contribution":0.4636563680037475,"citation_network_contribution":0.0,"self_endowment_contribution":0.4636563680037475,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9518,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":708930,"name":"Hao Zhang","orcid":"0000-0002-2928-2692","position":1,"is_corresponding":false},{"id":851448,"name":"Zilong Bai","orcid":"0000-0002-3891-8015","position":2,"is_corresponding":false},{"id":1051030,"name":"Dylan Mann‐Krzisnik","orcid":"0000-0003-2874-7272","position":3,"is_corresponding":false},{"id":240709,"name":"Fei Wang","orcid":"0000-0001-9459-9461","position":4,"is_corresponding":false},{"id":635992,"name":"Yue Li","orcid":"0000-0003-3844-4865","position":5,"is_corresponding":false},{"id":559631,"name":"Manqi Zhou","orcid":"0000-0001-6238-3228","position":0,"is_corresponding":true}],"reference_count":91,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:08:56.940647Z","pmid":"37671028","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}