XClone: detection of allele-specific subclonal copy number variations from single-cell transcriptomic data

Abstract Somatic copy number variations (CNVs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNVs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNVs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNVs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNVs, enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.