{"doi":"10.1101/gr.251603.119","title":"netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis","abstract":"Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene-gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains.","journal":"Genome Research","year":2020,"id":118312,"datarank":3.5494359608828923,"base_score":4.804021044733257,"endowment":4.804021044733257,"self_citation_contribution":0.7206031567099886,"citation_network_contribution":2.8288328041729036,"self_endowment_contribution":0.7206031567099886,"citer_contribution":2.8288328041729036,"corpus_percentile":null,"corpus_rank":null,"citation_count":121,"citer_count":102,"citers_with_citation_signal":79,"citers_with_endowment":79,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9406,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":29920,"name":"Bianca Dumitrascu","orcid":"0000-0001-8328-2354","position":1,"is_corresponding":false},{"id":29300,"name":"Barbara E. Engelhardt","orcid":"0000-0002-6139-7334","position":2,"is_corresponding":false},{"id":106817,"name":"Benjamin J. Raphael","orcid":"0000-0003-1274-048X","position":3,"is_corresponding":false},{"id":406943,"name":"Rebecca Elyanow","orcid":"0000-0001-8877-7188","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:13:58.531532Z","pmid":"31992614","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":[]}