|
Abstract:
|
Tumor sample classification has long been an important task in cancer research. Classifying tumor into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue sample obtained from clinical settings is always a mixture 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 work, we developed a model-based clustering method that 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 (TCGA) data demonstrate improved results compared with existing methods. The method is implemented in an R package InfiniumPurify, which is available from CRAN.
|
