souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes

By Haynes Heaton, A. Talman, Andrew J. Knights, M. Imaz, Daniel J. Gaffney, Richard Durbin, Martin Hemberg, M. Lawniczak. Nature Methods, 2020

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Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios. Souporcell clusters single-cell RNA-seq data using genotype information without the use of a genotype reference.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4df0eab2c8ac71963850a7c4d2e1c71bdcb3752a

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