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. 2019 Aug 15;35(16):2865-2867.
doi: 10.1093/bioinformatics/bty1044.

M3Drop: dropout-based feature selection for scRNASeq

Tallulah S Andrews  1 , Martin Hemberg  1
Affiliations

M3Drop: dropout-based feature selection for scRNASeq

Tallulah S Andrews et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
Comparison of feature selection methods. (A and B) Accuracy in identifying DE genes in simulated data. (C and D) Reproducibility of features across five mouse embryo and four human pancreas datasets. (E and F) Average fold-change in expression of reproducible features. (C–F) Each point represents a pair of datasets and the horizontal lines indicate the mean across all pairs. PCA scored genes by their loadings for the top components, Gini is the method used by GiniClust (Jiang et al., 2016), Cons is the consensus across all other methods
See this image and copyright information in PMC

References

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