M3Drop: dropout-based feature selection for scRNASeq.
Ontology highlight
ABSTRACT: 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.Availability and implementation
M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https://github.com/tallulandrews/M3Drop.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Andrews TS
PROVIDER: S-EPMC6691329 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
M3Drop: dropout-based feature selection for scRNASeq.
Andrews Tallulah S TS Hemberg Martin M
Bioinformatics (Oxford, England) 20190801 16
<h4>Motivation</h4>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.<h4>Results</h4>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) ...[more]
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