Observation Weights Unlock Bulk RNA-seq Tools for Zero Inflation and Single-cell Applications

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2018 Feb 27

PMID 29478411

Citations 113

Authors

Koen Van den Berge

Fanny Perraudeau

Charlotte Soneson

Michael I Love

Davide Risso

Jean-Philippe Vert

Mark D Robinson

Sandrine Dudoit

Lieven Clement

Affiliations

Soon will be listed here.

Abstract

Dropout events in single-cell RNA 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 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.

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