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PMID: 24562872 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Review

An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.

Briefings in bioinformatics ·Vol. 16 ·No. 2 ·2015-03-00 ·Pages 232-41

Yadav VK, De S

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.

Keywords
deconvolution mixed cell population software tumor purity and heterogeneity
MeSH Terms
Computational Biology/methods Gene Expression Profiling/statistics & numerical data Genome, Human Genomics/statistics & numerical data High-Throughput Nucleotide Sequencing/statistics & numerical data Humans Neoplasms/genetics,pathology Software
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Yadav Vinod Kumar
De Subhajyoti
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Article Info
Journal
Briefings in bioinformatics
Abbr.
Brief Bioinform
ISSN
1477-4054
DOI
10.1093/bib/bbu002
Published
2015-03-00
Epub
2014-00-20
Pages
232-41
Language
English
Region
England
NLM ID
100912837
PMCID
PMC4794615
Subset
IM
Grants
NCI NIH HHS · P50 CA058187 · United States
External Links
PubMed source DOI: 10.1093/bib/bbu002 PMC Full Text View journal details
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