title: An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.
abstract: 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 deconvolution of genomic and transcriptomic data from heterogeneous samples. We also performed a systematic comparative assessment of these tools. If properly used, these tools have potentials to complement single-cell genomics and immunoFISH analyses, and provide novel insights into tumor heterogeneity.
authors.forename: Vinod Kumar
authors.initials: VK
authors.lastname: Yadav
authors.forename: Subhajyoti
authors.initials: S
authors.lastname: De
mesh_terms: D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D015894:Genome, Human; D023281:Genomics / Q000706:statistics & numerical data; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology*; D012984:Software
keywords: deconvolution; mixed cell population; software; tumor purity and heterogeneity