Embracing the dropouts in single-cell RNA-seq data

BioRxiv : the Preprint Server for Biology
Peng Qiu

Abstract

One primary reason that makes the analysis of single-cell RNA-seq data challenging is the dropouts, where the data only captures a small fraction of the transcriptome of each cell. Many computational algorithms developed for single-cell RNA-seq adopted gene selection and dimension reduction strategies to address dropout. Here, an opposite view is explored. Instead of treating dropout as a problem to be fixed, we embrace it as a useful signal for defining cell types. We present an iterative co-occurrence clustering algorithm that works with binarized single-cell RNA-seq count data. Surprisingly, although all the quantitative information is removed after the data is binarized, co-occurrence clustering of the binarized data is able to effectively identify cell populations, as well as cell-type specific pathways and signatures. We demonstrate that the binary dropout patterns of the data provides not only overlapping but also complementary information compared to the quantitative gene expression counts in single-cell RNA-seq data.

Related Concepts

Cell Count
Gene Expression
Genes
Analysis
Population Group
Gene Feature
Opposite
Biochemical Pathway
Transcriptome

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