Detection of differentially abundant cell subpopulations in scRNA-seq data

被引:63
作者
Zhao, Jun [1 ]
Jaffe, Ariel [2 ]
Li, Henry [2 ]
Lindenbaum, Ofir [2 ]
Sefik, Esen [3 ]
Jackson, Ruaidhri [4 ]
Cheng, Xiuyuan [5 ]
Flavell, Richard A. [3 ,6 ]
Kluger, Yuval [1 ,2 ]
机构
[1] Yale Univ, Dept Pathol, New Haven, CT 06511 USA
[2] Yale Univ, Program Appl Math, New Haven, CT 06511 USA
[3] Yale Univ, Dept Immunobiol, New Haven, CT 06511 USA
[4] Harvard Med Sch, Blavatnik Inst, Dept Immunol, Boston, MA 02115 USA
[5] Duke Univ, Dept Math, Durham, NC 27708 USA
[6] Yale Univ, HHMI, New Haven, CT 06520 USA
关键词
single cell; RNA-seq; local differential abundance; SINGLE-CELL; ADHESION MOLECULE-1; EXPRESSION; MOUSE; SOCS3;
D O I
10.1073/pnas.2100293118
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
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) sub populations 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.
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页数:12
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