M3Drop: dropout-based feature selection for scRNASeq

被引：163

作者：

Andrews, Tallulah S. ^{[1
]}

Hemberg, Martin ^{[1
]}

机构：

[1] Wellcome Trust Sanger Inst, Dept Cellular Genet, Hinxton, Cambs, England

来源：

BIOINFORMATICS | 2019年 / 35卷 / 16期

基金：

英国惠康基金;

关键词：

SINGLE; NOISE;

D O I：

10.1093/bioinformatics/bty1044

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Motivation: Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise. Results: We present M3Drop, an R package that implements popular existing feature selection methods and two novel methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show these new methods outperform existing methods on simulated and real datasets.

引用

页码：2865 / 2867

页数：3

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