Detecting Subclones from Spatially Resolved RNA-Seq Data.

Recently developed technologies allow us to view the transcriptome at high resolution while preserving the spatial location of samples. These advances are particularly relevant to cancer research, since clonal theory predicts that nearby cells are likely to belong to the same expanding subclone. Using this evolutionary hypothesis, we develop a statistical procedure which uses a test of local spatial association along with a graph-based approach to infer subclones from spatially resolved RNA-seq data. Our method is robust, scalable, and can be applied to data from any of the existing spatial transcriptomics technologies. On data from spatially resolved RNA-seq of breast cancer tissue, our method infers seven distinct subclones and identifies potential driver genes.