{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T16:47:05Z","timestamp":1775926025758,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["NHGRI R01 HG009937"],"award-info":[{"award-number":["NHGRI R01 HG009937"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["NIMH R01 MH118349"],"award-info":[{"award-number":["NIMH R01 MH118349"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The airpart package is available as an R\/Bioconductor package at https:\/\/bioconductor.org\/packages\/airpart.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac212","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T09:45:12Z","timestamp":1649238312000},"page":"2773-2780","source":"Crossref","is-referenced-by-count":19,"title":["Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets"],"prefix":"10.1093","volume":"38","author":[{"given":"Wancen","family":"Mu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of North Carolina-Chapel Hill , Chapel Hill, NC 27514, USA"}]},{"given":"Hirak","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9798-2079","authenticated-orcid":false,"given":"Avi","family":"Srivastava","sequence":"additional","affiliation":[{"name":"New York Genome Center , New York, NY 10013, USA"}]},{"given":"Kwangbom","family":"Choi","sequence":"additional","affiliation":[{"name":"The Jackson Laboratory , Bar Harbor, ME 04609, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8463-1675","authenticated-orcid":false,"given":"Rob","family":"Patro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Maryland , College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8401-0545","authenticated-orcid":false,"given":"Michael I","family":"Love","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina-Chapel Hill , Chapel Hill, NC 27514, USA"},{"name":"Department of Genetics, University of North Carolina-Chapel Hill , Chapel Hill, NC 27514, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"2023020109135401500_btac212-B1","doi-asserted-by":"crossref","first-page":"e25125","DOI":"10.7554\/eLife.25125","article-title":"Mapping the mouse allelome reveals tissue-specific regulation of allelic expression","volume":"6","author":"Andergassen","year":"2017","journal-title":"Elife"},{"key":"2023020109135401500_btac212-B2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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