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Bioinformatics. 2017 Sep 1;33(17):2651-2657. doi: 10.1093/bioinformatics/btx303.

Accounting for tumor purity improves cancer subtype classification from DNA methylation data.

Author information

Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.
School of Science, East China University of Technology, Nanchang, Jiangxi 330013, China.
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.



Tumor sample classification has long been an important task in cancer research. Classifying tumors into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. Thus, the data obtained from these samples are mixed signals. The 'tumor purity', or the percentage of cancer cells in cancer tissue sample, will bias the clustering results if not properly accounted for.


In this article, we developed a model-based clustering method and an R function which uses DNA methylation microarray data to infer tumor subtypes with the consideration of tumor purity. Simulation studies and the analyses of The Cancer Genome Atlas data demonstrate improved results compared with existing methods.

Availability and implementation:

InfiniumClust is part of R package InfiniumPurify , which is freely available from CRAN ( ).

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Supplementary information:

Supplementary data are available at Bioinformatics online.

[Indexed for MEDLINE]

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