Cancer arises through the accumulation of genetic and epigenetic alterations in the cells of a tissue. This tutorial will describe models and algorithms to address some of the challenges that arise in inferring cancer evolution from high-throughput DNA/RNA sequencing data. These challenges include: deconvolving mixtures of mutations from sequencing of bulk tumor sampling, addressing high rates of error and missing data in single-cell sequencing, modeling copy number aberrations that alter large genomic regions, and leveraging information from regional, spatial and longitudinal sampling.
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