RT Journal Article
SR Electronic
T1 Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 250126
DO 10.1101/250126
A1 Koen Van den Berge
A1 Fanny Perraudeau
A1 Charlotte Soneson
A1 Michael I. Love
A1 Davide Risso
A1 Jean-Philippe Vert
A1 Mark D. Robinson
A1 Sandrine Dudoit
A1 Lieven Clement
YR 2018
UL http://biorxiv.org/content/early/2018/01/18/250126.abstract
AB Dropout events in single-cell transcriptome sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial (ZINB) model, that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.