Open AccessM3Drop: dropout-based feature selection for scRNASeq
Author(s) -
Tallulah Andrews,
Martin Hemberg
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty1044
Subject(s) - dropout (neural networks) , computer science , selection (genetic algorithm) , feature (linguistics) , feature selection , artificial intelligence , machine learning , philosophy , linguistics
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.
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