parameters for the model assuming expected distributions for normal allelic ratios, and apply post-processing heuristics to reduce false positives. In our application, we are interested in cancer genomes and transcriptomes, both of which may not follow expected distributions due to tumor-normal admixtures in the sample, within sample tumor heterogeneity, copy number changes and other factors. We use the expectation maximization (EM) algorithm to find a maximum a posteriori (MAP) estimate of the parameters given some training data, allowing the model to adapt to genomes and transcriptomes that may deviate from the assumed distributions for normal genomes and thus model the data more accurately.
