Embracing the dropouts in single-cell RNA-seq analysis

By Peng Qiu. Nature Communications, 2020

341 citations recorded by the source corpus.

One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction of the transcriptome of each cell. Almost all computational algorithms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation to address the dropouts. Here, an opposite view is explored. Instead of treating dropouts as a problem to be fixed, we embrace it as a useful signal. We represent the dropout pattern by binarizing single-cell RNA-seq count data, and present a co-occurrence clustering algorithm to cluster cells based on the dropout pattern. We demonstrate in multiple published datasets that the binary dropout pattern is as informative as the quantitative expression of highly variable genes for the purpose of identifying cell types. We expect that recognizing the utility of dropouts provides an alternative direction for developing computational algorithms for single-cell RNA-seq analysis. The analysis of RNA-seq data is complicated by dropouts, and these are usually treated as a problem to be addressed. Here, Peng Qiu uses dropouts as a source of information and presents a co-occurrence clustering algorithm to cluster cells based on the dropout pattern; this could be a complementary approach to existing methods.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6a5b4eb52804c8721bf568f44aaf8500d55300fd