M3Drop: dropout-based feature selection for scRNASeq

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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Veröffentlicht in:Bioinformatics (Oxford, England) England), 2019-08, Vol.35 (16), p.2865-2867
Hauptverfasser: Andrews, Tallulah S, Hemberg, Martin
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Applications Notes
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Hemberg, Martin
description 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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subjects Applications Notes
title M3Drop: dropout-based feature selection for scRNASeq
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