iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects

Background Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA...

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Veröffentlicht in:	BMC bioinformatics Jg. 24; H. 1; S. 318 - 20
Hauptverfasser:	Liu, Yunqing, Zhao, Jiayi, Adams, Taylor S., Wang, Ningya, Schupp, Jonas C., Wu, Weimiao, McDonough, John E., Chupp, Geoffrey L., Kaminski, Naftali, Wang, Zuoheng, Yan, Xiting
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 22.08.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Binomial distribution Bioinformatics Biomedical and Life Sciences Cells Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data analysis Datasets Differential expression analysis Disease Dropouts Gene expression Gene sequencing Genes Genetic research Heterogeneity Humans Life Sciences Management Microarrays Models, Statistical Research Article Ribonucleic acid RNA RNA sequencing School dropout programs School dropouts Sequence Analysis, RNA Single-cell RNA sequencing Subject effect Transcriptome Transcriptomes Zero-inflated negative binomial mixed model United States Subject effect Zero-inflated negative binomial mixed model Single-cell RNA sequencing Differential expression analysis
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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