Classification of low quality cells from single-cell RNA-seq data

被引：421

作者：

Ilicic, Tomislav ^{[1
,2
]}

Kim, Jong Kyoung ^{[1
]}

Kolodziejczyk, Aleksandra A. ^{[1
,2
]}

Bagger, Frederik Otzen ^{[1
,5
,6
]}

McCarthy, Davis James ^{[1
,7
]}

Marioni, John C. ^{[1
,2
,4
]}

Teichmann, Sarah A. ^{[1
,2
,3
]}

机构：

[1] EBI, EMBL, Wellcome Trust Genome Campus, Cambridge CB10 1SD, England

[2] Wellcome Trust Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England

[3] Univ Cambridge, Dept Phys, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England

[4] Univ Cambridge, Canc Res UK Cambridge Inst, Robinson Way, Cambridge CB2 0RE, England

[5] Univ Cambridge, Dept Haematol, Cambridge Biomed Campus, Cambridge CB2 0PT, England

[6] Natl Hlth Serv NHS Blood & Transplant, Cambridge Biomed Campus, Cambridge CB2 0PT, England

[7] St Vincents Inst Med Res, Fitzroy, Vic 3065, Australia

来源：

GENOME BIOLOGY | 2016年 / 17卷

基金：

英国生物技术与生命科学研究理事会;

关键词：

BURROWS-WHEELER TRANSFORM; GENE-EXPRESSION; READ ALIGNMENT; GENOME-WIDE; TRANSCRIPTOMICS; HETEROGENEITY; REGULATORS; ULTRAFAST; DYNAMICS; NOISE;

D O I：

10.1186/s13059-016-0888-1

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.

引用

页数：15

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