TY - JOUR T1 - Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Tumor Subpopulations and Genotype-phenotype Linkage JF - bioRxiv DO - 10.1101/095810 SP - 095810 AU - Olivier B. Poirion AU - Xun Zhu AU - Travers Ching AU - Lana X. Garmire Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/03/01/095810.abstract N2 - 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. ER -