Mapping single-cell transcriptomes to copy number evolutionary trees

Abstract

Cancer arises and evolves by the accumulation of somatic mutations that provide a selective advantage. The interplay of mutations and their functional consequences shape the evolutionary dynamics of tumors and contribute to different clinical outcomes. In the absence of scalable methods to jointly assay genomic and transcriptomic profiles of the same individual cell, the two data modalities are usually measured separately and need to be integrated computationally. Here, we introduce SCATrEx, a statistical model to map single-cell gene expression data onto the evolutionary history of copy number alterations of the tumor. SCATrEx jointly assigns cancer cells assayed with scRNA-seq to copy number profiles arranged in a copy number aberration tree and augments the tree with clone-specific clusters. Our simulations show that SCATrEx improves over both state-of-the-art unsupervised clustering methods and cell-to-clone assignment methods. In an application to real data, we observe that SCATrEx finds inter-clone and intra-clone gene expression heterogeneity not detectable using other integration methods. SCATrEx will allow for a better understanding of tumor evolution by jointly analysing the genomic and transcriptomic changes that drive it.