LAUSR

Tumor purity is an intrinsic property of tumor samples and potentially has severe impact on many types of data analysis. We have previously developed a statistical method, InfiniumPurify, which could infer purity of a tumor sample given its tumor type (available in TCGA) or a set of informative CpG (iDMC) sites. However, in many clinical practices, researchers may focus on a specific type of tumor samples that is not included in TCGA, and samples which are too few to identify reliable iDMCs. This greatly restricts the application of InfiniumPurify in cancer research. In this paper, we proposed an updated version of InfiniumPurify (termed as uiInfiniumPurify) through identifying a universal set of iDMCs (uiDMCs) and redesigning the algorithm to determine hyper- and hypo-methylation status of each uiDMC. Through the application, we estimated tumor purities of 8830 tumor samples from TCGA. Result shows that our estimates are highly consistent with those by other available methods. Consequently, the updated uiInfiniumPurify, can be applied to a single sample (or a few samples) of interest whose tumor type is not included in TCGA. This characteristic will greatly broaden the application of uiInfiniumPurify in cancer research.