We describe identification of candidate tumor purity and ploidy values and calculation of their SCNA-fit log-likelihood scores using a probabilistic model. This is accomplished by fitting the input HSCR estimates with a Gaussian mixture model, with components centered at the discrete concentration-ratios implied by eq. 1. The model also supports a small fraction of subclonal events which are not restricted to the discrete levels. Candidate solutions are identified by searching for local optima of this likelihood over a large range of purity and ploidy values. This results in a discrete set of candidate solutions with corresponding SCNA-fit likelihoods (eq. 1, Supplementary Fig. 1d,h).
