{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:33Z","timestamp":1772138013885,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 MH117103"],"award-info":[{"award-number":["R01 MH117103"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 DA044297"],"award-info":[{"award-number":["R01 DA044297"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 MH116441"],"award-info":[{"award-number":["U01 MH116441"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.<\/jats:p>","DOI":"10.1093\/bib\/bbac063","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T07:11:01Z","timestamp":1644390661000},"source":"Crossref","is-referenced-by-count":10,"title":["<i>LRcell<\/i>\n                    : detecting the source of differential expression at the sub\u2013cell-type level from bulk RNA-seq data"],"prefix":"10.1093","volume":"23","author":[{"given":"Wenjing","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumeet","family":"Sharma","sequence":"additional","affiliation":[{"name":"Graduate Program in Neuroscience, Emory University, 1462 Clifton Road NE, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, Emory University, 1365 Clifton Road, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shannon L","family":"Gourley","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, School of Medicine, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Yerkes National Primate Research Center, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaohui S","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA 30322, USA"},{"name":"Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"2022051813171153200_ref1","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/s41587-019-0114-2","article-title":"Determining cell type abundance and expression from bulk tissues with digital cytometry","volume":"37","author":"Newman","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2022051813171153200_ref2","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1038\/s41586-019-1195-2","article-title":"Single-cell transcriptomic analysis of Alzheimer\u2019s disease","volume":"570","author":"Mathys","year":"2019","journal-title":"Nature"},{"key":"2022051813171153200_ref3","volume-title":"medRxiv","author":"Ruzicka","year":"2020"},{"key":"2022051813171153200_ref4","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat Method"},{"key":"2022051813171153200_ref5","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1093\/bioinformatics\/btt090","article-title":"DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-seq data","volume":"29","author":"Gong","year":"2013","journal-title":"Bioinformatics"},{"key":"2022051813171153200_ref6","first-page":"1","article-title":"Bulk tissue cell type deconvolution with multi-subject single-cell expression reference","volume":"10","author":"Wang","year":"2019","journal-title":"Nat Commun"},{"key":"2022051813171153200_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-10802-z","article-title":"Accurate estimation of cell-type composition from gene expression data","volume":"10","author":"Tsoucas","year":"2019","journal-title":"Nat Commun"},{"key":"2022051813171153200_ref8","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/j.meegid.2011.08.014","article-title":"Semi-supervised non-negative matrix factorization for gene expression deconvolution: a case study","volume":"12","author":"Gaujoux","year":"2012","journal-title":"Infect Genet Evol"},{"key":"2022051813171153200_ref9","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1186\/s13059-019-1778-0","article-title":"TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis","volume":"20","author":"Li","year":"2019","journal-title":"Genome Biol"},{"key":"2022051813171153200_ref10","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/1471-2105-14-89","article-title":"Digital sorting of complex tissues for cell type-specific gene expression profiles","volume":"14","author":"Zhong","year":"2013","journal-title":"BMC Bioinform"},{"key":"2022051813171153200_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02290-6","article-title":"A benchmark for RNA-seq deconvolution analysis under dynamic testing environments","volume":"22","author":"Jin","year":"2021","journal-title":"Genome Biol"},{"key":"2022051813171153200_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-20288-9","article-title":"Benchmarking of cell type deconvolution pipelines for transcriptomics data","volume":"11","author":"Cobos","year":"2020","journal-title":"Nat Comm"},{"key":"2022051813171153200_ref13","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"SC3: consensus clustering of single-cell RNA-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Method"},{"key":"2022051813171153200_ref14","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"PNAS"},{"key":"2022051813171153200_ref15","first-page":"1326","article-title":"Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system","volume":"352","author":"Marques","year":"2016","journal-title":"Sci Am Assoc Adv Sci"},{"key":"2022051813171153200_ref16","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","article-title":"The molecular signatures database (MSigDB) hallmark gene set collection","volume":"1","author":"Liberzon","year":"2015","journal-title":"Cell Syst"},{"key":"2022051813171153200_ref17","first-page":"1","article-title":"scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured","volume":"22","author":"Sun","year":"2021","journal-title":"Genome Biol"},{"key":"2022051813171153200_ref18","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.cell.2018.07.028","article-title":"Molecular diversity and specializations among the cells of the adult mouse brain","volume":"174","author":"Saunders","year":"2018","journal-title":"Cell"},{"key":"2022051813171153200_ref19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/s41591-018-0223-3","article-title":"Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia","volume":"25","author":"Swarup","year":"2019","journal-title":"Nat Med"},{"key":"2022051813171153200_ref20","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1038\/nrneurol.2010.17","article-title":"Microglia in neurodegenerative disease","volume":"6","author":"Perry","year":"2010","journal-title":"Nat Rev Neurol"},{"key":"2022051813171153200_ref21","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1038\/mp.2015.9","article-title":"Gene networks specific for innate immunity define post-traumatic stress disorder","volume":"20","author":"Breen","year":"2015","journal-title":"Mol Psychiatry"},{"key":"2022051813171153200_ref22","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2022051813171153200_ref23","first-page":"1761","article-title":"A novel, five-marker alternative to CD16\u2013CD14 gating to identify the three human monocyte subsets. Frontiers in immunology","volume":"10","author":"Ong","year":"2019","journal-title":"Frontiers"},{"key":"2022051813171153200_ref24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1161\/ATVBAHA.116.308198","article-title":"Monocyte conversion during inflammation and injury","volume":"37","author":"Kratofil","year":"2017","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"2022051813171153200_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41398-018-0355-8","article-title":"Cell type-specific gene expression patterns associated with posttraumatic stress disorder in World Trade Center responders","volume":"9","author":"","year":"2019","journal-title":"Transl Psychiatry"},{"key":"2022051813171153200_ref26","first-page":"1","article-title":"Voom: precision weights unlock linear model analysis tools for RNA-seq read counts","volume":"15","author":"Law","year":"2014","journal-title":"Genome Biol BioMed Central"},{"key":"2022051813171153200_ref27","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2022051813171153200_ref28","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.cmet.2016.08.020","article-title":"Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes","volume":"24","author":"Segerstolpe","year":"2016","journal-title":"Cell Metab"},{"key":"2022051813171153200_ref29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1093\/bioinformatics\/btn592","article-title":"LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data","volume":"25","author":"Sartor","year":"2009","journal-title":"Bioinformatics"},{"key":"2022051813171153200_ref30","doi-asserted-by":"crossref","first-page":"3228","DOI":"10.1093\/bioinformatics\/btab257","article-title":"Identification of cell-type-specific marker genes from co-expression patterns in tissue samples","volume":"37","author":"Qiu","year":"2021","journal-title":"Bioinformatics"},{"key":"2022051813171153200_ref31","doi-asserted-by":"crossref","first-page":"e6970","DOI":"10.7717\/peerj.6970","article-title":"Detection of condition-specific marker genes from RNA-seq data with MGFR","volume":"7","author":"El Amrani","year":"2019","journal-title":"PeerJ PeerJ Inc"},{"key":"2022051813171153200_ref32","doi-asserted-by":"crossref","first-page":"D721","DOI":"10.1093\/nar\/gky900","article-title":"CellMarker: a manually curated resource of cell markers in human and mouse","volume":"47","author":"Zhang","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022051813171153200_ref33","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"key":"2022051813171153200_ref34","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1038\/s41593-020-0621-y","article-title":"Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons","volume":"23","author":"Nagy","year":"2020","journal-title":"Nat Neurosci"},{"key":"2022051813171153200_ref35","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1038\/nmeth.4380","article-title":"Simultaneous epitope and transcriptome measurement in single cells","volume":"14","author":"Stoeckius","year":"2017","journal-title":"Nat Method"},{"key":"2022051813171153200_ref36","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol"},{"key":"2022051813171153200_ref37","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/nbt.3838","article-title":"Reproducible RNA-seq analysis using recount2","volume":"35","author":"Collado-Torres","year":"2017","journal-title":"Nat Biotechnol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/3\/bbac063\/43745710\/bbac063.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/3\/bbac063\/43745710\/bbac063.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T09:21:38Z","timestamp":1652865698000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac063\/6546262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,10]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5,13]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac063","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.08.10.455821","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,5]]},"published":{"date-parts":[[2022,3,10]]},"article-number":"bbac063"}}