M3Drop: dropout-based feature selection for scRNASeq

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. We pre...

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Published inBioinformatics (Oxford, England) Vol. 35; no. 16; pp. 2865 - 2867
Main Authors Andrews, Tallulah S, Hemberg, Martin
Format Journal Article
LanguageEnglish
Published England Oxford University Press 15.08.2019
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Summary: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. 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. M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https://github.com/tallulandrews/M3Drop. Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/bty1044