{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:56:52Z","timestamp":1780102612228,"version":"3.54.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T00:00:00Z","timestamp":1692489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM142702"],"award-info":[{"award-number":["R35GM142702"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R00MH117393"],"award-info":[{"award-number":["R00MH117393"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.<\/jats:p>","DOI":"10.1093\/bib\/bbad294","type":"journal-article","created":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T21:22:50Z","timestamp":1692566570000},"source":"Crossref","is-referenced-by-count":8,"title":["Differential variability analysis of single-cell gene expression data"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6432-0124","authenticated-orcid":false,"given":"Jiayi","family":"Liu","sequence":"first","affiliation":[{"name":"Graduate Programs in Molecular Biosciences, Rutgers, The State University of New Jersey , 604 Allison Rd, Piscataway, 08854, NJ , USA"},{"name":"Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey , 604 Allison Road, Piscataway, 08854, NJ , USA"},{"name":"Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey , 679 Hoes Lane West, Piscataway, Piscataway, 08854, NJ , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3066-5498","authenticated-orcid":false,"given":"Anat","family":"Kreimer","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey , 604 Allison Road, Piscataway, 08854, NJ , USA"},{"name":"Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey , 679 Hoes Lane West, Piscataway, Piscataway, 08854, NJ , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2087-2709","authenticated-orcid":false,"given":"Wei Vivian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California , Riverside, 900 University Ave, Riverside, 92521, CA , USA"},{"name":"Previous affiliation where part of the work was completed: Department of Biostatistics and Epidemiology, Rutgers, The State University of New Jersey , 683 Hoes Lane West, Piscataway, 08854, NJ , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,8,20]]},"reference":[{"issue":"4","key":"2023092216514469900_ref1","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.cell.2010.04.033","article-title":"Cellular heterogeneity: when do differences make a difference?","volume":"141","author":"Altschuler","year":"2010","journal-title":"Cell"},{"key":"2023092216514469900_ref2","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/B978-0-12-407722-5.00026-8","article-title":"Chapter 26 - 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