{"doi":"10.1101/2021.04.01.438068","title":"Learning single-cell chromatin accessibility profiles using meta-analytic marker genes","abstract":"Abstract Motivation Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. Results In this study, we perform a systematic comparison of 7 scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC . Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner. Contact jesse.gillis@utoronto.ca Supplementary information Supplementary data are available at xxxxxx online. Key points 7 scATAC-seq datasets of mouse brain are systematically compared to benchmark the efficacy of neuronal cell-type annotation from gene sets. Redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation beyond the heterogeneity of scATAC-seq data. We reannotated all scATAC-seq data for detailed cell types using robust marker genes and their meta scATAC-seq profiles are publicly available at a new Meta scATAC-seq server. Predicted profiles from only DNA sequence using a deep neural network are visualized together to explore sequence-dependent and -independent epigenetic regulation.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":220530,"datarank":0.13189060423567595,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.027918527151684126,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.027918527151684126,"corpus_percentile":29.27980196487971,"corpus_rank":9143,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6068,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":820021,"name":"Ziqi Tang","orcid":"0000-0001-7585-915X","position":1,"is_corresponding":false},{"id":38337,"name":"Stephan Fischer","orcid":"0000-0002-7034-4103","position":2,"is_corresponding":false},{"id":820022,"name":"Chandana Rajesh","orcid":"0000-0002-0441-6527","position":3,"is_corresponding":false},{"id":820023,"name":"Rohit Tripathy","orcid":"0000-0003-0808-1194","position":4,"is_corresponding":false},{"id":298873,"name":"Peter K. Koo","orcid":"0000-0001-8722-0038","position":5,"is_corresponding":false},{"id":107897,"name":"Jesse Gillis","orcid":"0000-0002-0936-9774","position":6,"is_corresponding":false},{"id":266831,"name":"Risa Karakida Kawaguchi","orcid":"0000-0003-1764-5721","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-18T23:53:46.965811Z","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":[]}