Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Academic Article uri icon

Overview

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.

publication date

  • February 26, 2018

Research

keywords

  • Gene Expression Profiling
  • Sequence Analysis, RNA

Identity

PubMed Central ID

  • PMC6251479

Scopus Document Identifier

  • 85042612161

Digital Object Identifier (DOI)

  • 10.1038/ncomms14049

PubMed ID

  • 29478411

Additional Document Info

volume

  • 19

issue

  • 1