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Motivation: As cancer researchers have come to appreciate the importance ofintratumor heterogeneity, much attention has focused on the challenges ofaccurately profiling heterogeneity in individual patients. Experimentaltechnologies for directly profiling genomes of single cells are rapidlyimproving, but they are still impractical for large-scale sampling. Bulkgenomic assays remain the standard for population-scale studies, but conflatethe influences of mixtures of genetically distinct tumor, stromal, andinfiltrating immune cells. Many computational approaches have been developed todeconvolute these mixed samples and reconstruct the genomics of geneticallyhomogeneous clonal subpopulations. All such methods, however, are limited toreconstructing only coarse approximations to a few major subpopulations. Inprior work, we showed that one can improve deconvolution of genomic data byleveraging substructure in cellular mixtures through a strategy calledsimplicial complex inference. This strategy, however, is also limited by thedifficulty of inferring mixture structure from sparse, noisy assays. Results:We improve on past work by introducing enhancements to automate learning ofsubstructured genomic mixtures, with specific emphasis on genome-wide copynumber variation (CNV) data. We introduce methods for dimensionality estimationto better decompose mixture model substructure; fuzzy clustering to betteridentify substructure in sparse, noisy data; and automated model inferencemethods for other key model parameters. We show that these improvements lead tomore accurate inference of cell populations and mixture proportions insimulated scenarios. We further demonstrate their effectiveness in identifyingmixture substructure in real tumor CNV data. Availability: Source code isavailable at http://www.cs.cmu.edu/~russells/software/WSCUnmix.zip

Automated deconvolution of structured mixtures from bulk tumor genomic data