Single-cell sequencing provided an unprecedented resolution to reveal genomic alterations driving oncogenesis in individual patients. Identifying these driver alterations through genome wide association analysis requires accurate partitioning of cell lineages based on single-cell genomic profiles, a computationally challenging problem when copy number profiles are considered. Most existing phylogenetics approaches cannot be accurately applied, as copy number alterations (CNAs) evolve under special genomic constraints and in overlapping intervals. Here, we propose a directed minimal spanning tree approach that is accurate and scalable to current single-cell datasets consisting of thousands of cells. By applying our approach on cohorts of cancer patients, we revealed novel, phenotype-associated evolutionary patterns and CNAs within and across patients.
Dr. Ken Chen obtained B. Eng. from Tsinghua University (Beijing) and Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2004. He is currently an associate professor in the department of Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer center. His primary interest is to develop computational methods to analyze and interpret high-throughput human genetic and phenotype data towards understanding the evolution of cancer as a consequence of genetics and environment and identifying biomarkers useful for cancer diagnosis and prognosis. Among the tools he developed, BreakDancer, VarScan and Monovar have been widely used for characterizing the genomes and transcriptomes of tumor tissues and single-cells.