Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

BioRxiv : the Preprint Server for Biology
J. BoenNoemi Di Nanni

Abstract

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods' results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.

Datasets Mentioned

BETA
GSE57872

Methods Mentioned

BETA
RNA-seq
scRNA-seq

Software Mentioned

CaSpER
InferCNV
CopyKAT

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