Phertilizer: growing a clonal tree from ultra-low coverage single-cell DNA sequencing data of tumors
Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present PO_SCPLOWHERTILIZERC_SCPLOW, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of PO_SCPLOWHERTILIZERC_SCPLOW on simulated data as well as on two real datasets, finding that PO_SCPLOWHERTILIZERC_SCPLOW effectively utilizes the copynumber signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods. Availabilityhttps://github.com/elkebir-group/phertilizer