A new correlation clustering method for cancer mutation analysis.

ABSTRACT:

Motivation

Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer.

Results

To expand our capability to analyze combinatorial patterns of cancer alterations, we developed a rigorous methodology for cancer mutation pattern discovery based on a new, constrained form of correlation clustering. Our new algorithm, named C³ (Cancer Correlation Clustering), leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C³, we performed a detailed analysis on TCGA breast cancer and glioblas

SUBMITTER: Hou JP 

PROVIDER: S-EPMC6078169 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature