RobustClone: A robust PCA method of tumor clone and evolution inference from single-cell sequencing data

Abstract

Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and build phylogenetic relationships of tumor cells/clones. However, high technical errors bring much noise into the genetic data, thus limiting the application of evolutionary tools in the large reservoir. To recover the low-dimensional subspace of tumor subpopulations from error-prone SCS data in the presence of corrupted and/or missing elements, we developed an efficient computational framework, termed RobustClone, to recover the true genotypes of subclones based on the low-rank matrix factorization method of extended robust principal component analysis (RPCA) and reconstruct the subclonal evolutionary tree. RobustClone is a model-free method, fast and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods, both in accuracy and efficiency. We further validated RobustClone on 2 single-cell SNV and 2 single-cell CNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. RobustClone software is available at https://github.com/ucasdp/RobustClone.