{"doi":"10.1101/2023.09.08.556842","title":"Identification of cell types, states and programs by learning gene set representations","abstract":"As single cell molecular data expand, there is an increasing need for algorithms that efficiently query and prioritize gene programs, cell types and states in single-cell sequencing data, particularly in cell atlases. Here we present scDECAF, a statistical learning algorithm to identify cell types, states and programs in single-cell gene expression data using vector representation of gene sets, which improves biological interpretation by selecting a subset of most biologically relevant programs. We applied scDECAF to scRNAseq data from PBMC, Lung, Pancreas, Brain and slide-tags snRNA of human prefrontal cortex for automatic cell type annotation. We demonstrate that scDECAF can recover perturbed gene programs in Lupus PBMC cells stimulated with IFNbeta and TGFBeta-induced cells undergoing epithelial-to-mesenchymal transition. scDECAF delineates patient-specific heterogeneity in cellular programs in Ovarian Cancer data. Using a healthy PBMC reference, we apply scDECAF to a mapped query PBMC COVID-19 case-control dataset and identify multicellular programs associated with severe COVID-19. scDECAF can improve biological interpretation and complement reference mapping analysis, and provides a method for gene set and pathway analysis in single cell gene expression data.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":12363,"datarank":0.21915745109887694,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.011213296930893322,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.011213296930893322,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0507,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-09-12","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":97865,"name":"Holly J. Whitfield","orcid":"0000-0002-7282-387X","position":1,"is_corresponding":false},{"id":97866,"name":"Malvika Kharbanda","orcid":"0000-0001-9726-3023","position":2,"is_corresponding":false},{"id":6411,"name":"Fabiola Curion","orcid":"0000-0003-2502-8803","position":3,"is_corresponding":false},{"id":97867,"name":"Dharmesh D. Bhuva","orcid":"0000-0002-6398-9157","position":4,"is_corresponding":false},{"id":42,"name":"Fabian Joachim Theis","orcid":"0000-0002-2419-1943","position":5,"is_corresponding":false},{"id":97868,"name":"Melissa J. Davis","orcid":"0000-0003-4864-7033","position":6,"is_corresponding":false},{"id":1697,"name":"Soroor Hediyeh-zadeh","orcid":"0000-0001-7513-6779","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"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":[]}