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zingeR: unlocking RNA-seq tools for zero-inflation and single cell applications

By Koen Van den Berge, Charlotte Soneson, Michael I Love, Mark D Robinson, Lieven Clement

Posted 30 Jun 2017
bioRxiv DOI: 10.1101/157982

Dropout in single cell RNA-seq (scRNA-seq) applications causes many transcripts to go undetected. It induces excess zero counts, which leads to power issues in differential expression (DE) analysis and has triggered the development of bespoke scRNA-seq DE tools that 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 zingeR, a zero-inflated negative binomial model that identifies excess zero counts and generates observation weights to unlock bulk RNA-seq pipelines for zero-inflation, boosting performance in scRNA-seq differential expression analysis.

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