Embracing the dropouts in single-cell RNA-seq analysis

Abstract

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 cooccurrence 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.

Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis

“…The term "dropout" has become commonly used in connection with the zeros in scRNAseq data [5][6][7][8][9] . Historically, dropout referred to a particular failure mode of PCR, allelic dropout, in which specific primers would fail to amplify sequences containing a particular allele of a polymorphism, leading to genotyping errors for heterozygous individuals 10,11 .…”

Measuring and Modelling the Epithelial Mesenchymal Hybrid State in Cancer: Clinical Implications

“…The marker genes used in these "reference-free" methods are found on a correlative basis, where the functional relevance of the genes in specific phenotypes is not guaranteed has been performed. There is work being done to identify cell-type specific drop out patterns [Qiu, 2020], suggesting that drop out perhaps cannot be modelled during cell type discovery, and that we need an approach for characterising phenotype that is independent of drop out patterns.…”

Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states

“…We next assessed the performance of MUSE as data quality in one modality degrades (requirement 2 above). Two persistent problems in single-cell data are sequencing dropouts and noise in feature measurements 28,29 . First, we varied dropout level for the transcript modality while leaving the simulation parameters for the morphology modality unchanged (Methods); as before, 10 ground-truth clusters were used.…”