RT Journal Article
SR Electronic
T1 Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Tumor Subpopulations and Genotype-phenotype Linkage
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 095810
DO 10.1101/095810
A1 Olivier B. Poirion
A1 Xun Zhu
A1 Travers Ching
A1 Lana X. Garmire
YR 2017
UL http://biorxiv.org/content/early/2017/03/01/095810.abstract
AB Despite its popularity, characterization of subpopulations with transcript abundance is subject to 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 cancer datasets tested, eeSNVs achieve better accuracies and more complexity 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 power over the cutting-edge single-cell genomics techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. SSrGE method is available at https://github.com/lanagarmire/SSrGE.