DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data
- Details
- Publication Year 2019-12-15,Volume 35,Issue #24,Page 5155-5162
- Journal Title
- Bioinformatics
- Publication Type
- Journal Article
- Abstract
- 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.
- Publisher
- Oxford Academic
- Research Division(s)
- Bioinformatics
- PubMed ID
- 31197307
- Link To PubMed Central Version
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954660/
- Publisher's Version
- https://doi.org/10.1093/bioinformatics/btz453
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2020-01-21 11:05:22
Last Modified: 2020-02-10 04:10:07