Abstract
We here develop and implement a Clonal Fraction Hidden Markov Model (CFHMM), to leverage positional information in classifying Tumor CNVs and their corresponding clonal fraction from log-ratio-normalized Tumor/Normal sequencing data. In simulated data, this approach shows accurate calling of CNVs for high-fraction mutations, and improvement in calling over a naïve clustering benchmark across the board, as well as useful purity estimation for dominant clones.