Abstract 2477: CopyCat: estimating genomic copy number profiles from high-throughput single cell RNA seq data

Abstract

Abstract CopyCat is a computational approach for estimate genomic-copy number profiles from high-throughput single cell RNAseq data that is generated by technologies, such as Drop-Seq, 10X genomics or Nanowells. CopyCat involves first normalizing and transforming unique molecular index (UMI) count data matrices to calculate single cell copy number states from low-coverage datasets using gene-bin windows across the human genome, and does not require a reference normal cell control. The resulting binned data is normalized and used for segmentation to reduce noise from individual genes that can contribute to gene dosage effects along chromosomes. We show that this methods is highly effective at distinguishing diploid and aneuploid cells in human tumors or cancer cell lines, and can efficiently detect major (>10mb) chromosomal amplifications and deletions compared to copy number data generated by DNA sequencing of match samples. We further show that this method can resolve some clonal substructure within individual tumors, when subpopulations are distinguished by large chromosomal events. Our method outputs segmented copy number profiles and predicted classifications of normal (diploid) and tumor (aneuploid) cell states for individual single cells. It is implemented in R and is available on Github. Citation Format: Ruli Gao, Nicholas Navin. CopyCat: estimating genomic copy number profiles from high-throughput single cell RNA seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2477.