BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btu607 Data and text mining Advance Access publication September 10, 2014 UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples

Summary: We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular pro-portions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor–stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO’s ability to detect them blindly and correctly. Although the principal application here in-volves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data. Availability and implementation: UNDO is available a