气候变化领域集成服务门户(CCPortal): Detection of differentially abundant cell subpopulations in scrna-seq data

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DOI	10.1073/PNAS.2100293118
	Detection of differentially abundant cell subpopulations in scrna-seq data
	Zhao J.; Jaffe A.; Li H.; Lindenbaum O.; Sefik E.; Jackson R.; Cheng X.; Flavell R.A.; Kluger Y.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:22
英文摘要	Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Local differential abundance; RNA-seq; Single cell
语种	英语
scopus关键词	aging; algorithm; animal cell; Article; brain tissue; cancer tissue; cell subpopulation; controlled study; coronavirus disease 2019; disease severity; embryo development; epithelium cell; human; human cell; human tissue; immunocompetent cell; macrophage; melanoma; mouse; nonhuman; phenotype; RNA sequencing; skin cell; transcriptomics; B lymphocyte; brain; cell lineage; cytology; dendritic cell; gene expression profiling; gene expression regulation; genetics; high throughput sequencing; immunology; information processing; metabolism; monocyte; pathogenicity; pathology; procedures; severity of illness index; single cell analysis; skin tumor; T lymphocyte; virology; cytokine; small cytoplasmic RNA; transcriptome; Aging; B-Lymphocytes; Brain; Cell Lineage; COVID-19; Cytokines; Datasets as Topic; Dendritic Cells; Gene Expression Profiling; Gene Expression Regulation; High-Throughput Nucleotide Sequencing; Humans; Melanoma; Monocytes; Phenotype; RNA, Small Cytoplasmic; SARS-CoV-2; Severity of Illness Index; Single-Cell Analysis; Skin Neoplasms; T-Lymphocytes; Transcriptome
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/251167
作者单位	Department of Pathology, Yale University, New Haven, CT 06511, United States; Program in Applied Mathematics, Yale University, New Haven, CT 06511, United States; Department of Immunobiology, Yale University, New Haven, CT 06511, United States; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, United States; Department of Mathematics, Duke University, Durham, NC 27708, United States; HHMI, Yale University, New Haven, CT 06520, United States
推荐引用方式 GB/T 7714	Zhao J.,Jaffe A.,Li H.,et al. Detection of differentially abundant cell subpopulations in scrna-seq data[J],2021,118(22).
APA	Zhao J..,Jaffe A..,Li H..,Lindenbaum O..,Sefik E..,...&Kluger Y..(2021).Detection of differentially abundant cell subpopulations in scrna-seq data.Proceedings of the National Academy of Sciences of the United States of America,118(22).
MLA	Zhao J.,et al."Detection of differentially abundant cell subpopulations in scrna-seq data".Proceedings of the National Academy of Sciences of the United States of America 118.22(2021).