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bioRxiv · 10.1101/095810

Using Single Nucleotide Variations in Cancer Single-Cell RNA-Seq Data for Subpopulation Identification and Genotype-phenotype Linkage Analysis

Poirion, O. B.·Zhu, X.·Ching, T.·Garmire, L. X.
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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 developed 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. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

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Poirion, O. B., Zhu, X., Ching, T., Garmire, L. X.. 2016-12-21. Using Single Nucleotide Variations in Cancer Single-Cell RNA-Seq Data for Subpopulation Identification and Genotype-phenotype Linkage Analysis. https://doi.org/10.1101/095810

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