Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage

Olivier Poirion, Xun Zhu, Travers Ching and Lana X. Garmire ()
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Olivier Poirion: Epidemiology Program, University of Hawaii Cancer Center
Xun Zhu: Epidemiology Program, University of Hawaii Cancer Center
Travers Ching: Epidemiology Program, University of Hawaii Cancer Center
Lana X. Garmire: Building 520

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract Despite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We develop a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship.

Date: 2018
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DOI: 10.1038/s41467-018-07170-5

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