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Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation. We discuss several computational tools, which enable deconvolutiondoi:10.1093/bib/bbu002 pmid:24562872 pmcid:PMC4794615 fatcat:mcg7fqdg65gbhnc7okknmpz774