ATAClone: Cancer Clone Identification and Copy Number Estimation from Single-cell ATAC-seq

Abstract

Single-cell analyses of cancer typically begin by identifying distinct populations of cancer cells by unsupervised clustering. However, in many cases this clustering is explained simply by differences in DNA copy number, which affects the interpretation of differential expression results and tumour heterogeneity studies. To detect and estimate these differences in copy number, we have developed ATAClone. Applicable to both standalone and multiome scATAC-seq assays, ATAClone first identifies cancer cells with shared DNA copy number profiles (i.e. clones), then estimates their copy number jointly. Importantly, ATAClone can determine an optimal clustering resolution automatically using simulations. By utilising only stably accessible regions, ATAClone maximises copy number signal while minimising unrelated biological and technical noise. Additionally, by leveraging differences in total DNA between cells, ATAClone can infer absolute copy number, even in the presence of polyploidy. Using cancer cell mixture experiments, we validate ATAClones accuracy in separating clones based on copy number differences. Moreover, using matched scATAC-seq and bulk whole genome sequencing, we show that copy number estimates from ATAClone are more accurate than those derived with existing methods, achieving Pearson correlations between 0.80-0.98 with their bulk-derived estimates and outperforming four other tools that derive copy number from scATAC-seq data. Finally, we show that ATAClone accurately estimates ploidy, including several cases of whole genome doubling which were missed in the matched whole genome sequencing analysis. ATAClone represents an important tool for disentangling the genetic and non-genetic contributions to gene expression in cancer, providing deeper insight into the evolutionary history and adaptive forces driving a tumour.