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DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data


Ye, C; Speed, TP; Salim, A
2019-12-15
Bioinformatics
Journal Article
35
24
5155-5162
MOTIVATION: Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. RESULTS: We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. AVAILABILITY AND IMPLEMENTATION: The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Oxford Academic
Bioinformatics
10.1093/bioinformatics/btz453
31197307
Refer to copyright notice on published article.

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Creation Date 2020-01-21 11:05:22 Last Modified 2020-02-10 04:10:07