{"doi":"10.1101/300681","title":"Single cell RNA-seq denoising using a deep count autoencoder","abstract":"Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNAseq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a zero-inflated negative binomial noise model, and nonlinear gene-gene or gene-dispersion interactions are captured. Our method scales linearly with the number of cells and can therefore be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2018,"id":8051,"datarank":3.2680173027205104,"base_score":4.465908118654584,"endowment":4.465908118654584,"self_citation_contribution":0.6698862177981877,"citation_network_contribution":2.5981310849223225,"self_endowment_contribution":0.6698862177981877,"citer_contribution":2.5981310849223225,"corpus_percentile":null,"corpus_rank":null,"citation_count":86,"citer_count":79,"citers_with_citation_signal":62,"citers_with_endowment":62,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0429,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2018-04-13","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":2925,"name":"Lukas M. Simon","orcid":"0000-0001-6148-8861","position":1,"is_corresponding":false},{"id":37752,"name":"Maria Mircea","orcid":"0000-0002-1935-8665","position":2,"is_corresponding":false},{"id":5186,"name":"Nikola S. Mueller","orcid":"0000-0001-8659-4548","position":3,"is_corresponding":false},{"id":42,"name":"Fabian Joachim Theis","orcid":"0000-0002-2419-1943","position":4,"is_corresponding":false},{"id":484,"name":"Gökçen Eraslan","orcid":"0000-0001-9579-2909","position":0,"is_corresponding":true}],"reference_count":46,"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":[]}