Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Genome Biol. 2018 Feb 26;19(1):24. doi: 10.1186/s13059-018-1406-4.


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.

Keywords: Differential expression; Single-cell RNA sequencing; Weights; Zero-inflated negative binomial.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Gene Expression Profiling / methods*
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis
  • Software