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Genotyping Copy Number Alterations from single-cell RNA sequencing

Cancers are constituted by heterogeneous populations of cells that show complex genotypes and phenotypes which we can read out by sequencing. Many attempts at deciphering the clonal process that drives these populations are focusing on single-cell technologies to resolve genetic and phenotypic intra-tumour heterogeneity. While the ideal technologies for these investigations are multi-omics assays, unfortunately these types of data are still too expensive and have limited scalability. We can resort to single-molecule assays, which are cheaper and scalable, and statistically emulate a joint assay, only if we can integrate measurements collected from independent cells of the same sample. In this work we follow this intuition and construct a new Bayesian method to genotype copy number alterations on single-cell RNA sequencing data, therefore integrating DNA and RNA measurements. Our method is unsupervised, and leverages on a segmentation of the input DNA to determine the sample subclonal composition at the copy number level, together with clone-specific phenotypes defined from RNA counts. By design our probabilistic method works without a reference RNA expression profile, and therefore can be applied in cases where this information may not be accessible. We implement the method on a probabilistic backend that allows easy running on both CPUs and GPUs, and test it on both simulated and real data. Our analysis shows its ability to determine copy number associated clones and their RNA phenotypes in tumour data from 10x and Smart-Seq assays, as well as in data from the Human Cell Atlas project.