Biclustering with heterogeneous variance.

TitleBiclustering with heterogeneous variance.
Publication TypeJournal Article
Year of Publication2013
AuthorsChen, Guanhua, Patrick F. Sullivan, and Michael R. Kosorok
JournalProc Natl Acad Sci U S A
Date Published2013 Jul 23
KeywordsCluster Analysis, DNA, Gene Expression Profiling, Humans, Lung Neoplasms

In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, and methods that do not consider heterogeneity of variance can lead to inaccurate subgroup prediction. Research has shown that hypervariability is a common feature among cancer subtypes. In this paper, we present a statistical approach that can capture both mean and variance structure in genetic data. We demonstrate the strength of our method in both synthetic data and in two cancer data sets. In particular, our method confirms the hypervariability of methylation level in cancer patients, and it detects clearer subgroup patterns in lung cancer data.

Alternate JournalProc Natl Acad Sci U S A
Original PublicationBiclustering with heterogeneous variance.
PubMed ID23836637
PubMed Central IDPMC3725096
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States