Detecting copy number variations from single-cell chromatin sequencing data by AtaCNV

Abstract

AbstractSinge-cell assay of transposase accessible chromatin sequencing (scATAC-seq) can unbiasedly profile genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often rely on copy number variations (CNVs). However, CNV detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNV, a computational algorithm that accurately detects high resolution CNVs from scATAC-seq data. We benchmark AtaCNV using simulation and real data and find AtaCNV’s superior performance. Analyses of 10 scATAC-seq datasets shows that AtaCNV could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial and ovarian cancer samples, AtaCNV identifies subclones at distinct cellular states, suggesting important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale CNVs, demonstrating the importance of high-resolution CNV detection. These data show that AtaCNV can aid the integrative analysis for understanding the complex heterogeneity in cancer.