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

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 bespokescRNA-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-seqtools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell- specific weightsto unlock bulk RNA-seqDE pipelines for zero-inflated data, boosting performance for scRNA-seq.