title: A new correlation clustering method for cancer mutation analysis.
abstract: 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.
  
  
  
  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 glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs.
  
  
  
  The source code for the C3method can be found at https://github.com/jackhou2/C3 CONTACTS: jianma@cs.cmu.edu or milenkov@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Medical Scholars Program, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
authors.forename: Jack P
authors.initials: JP
authors.lastname: Hou
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
authors.forename: Amin
authors.initials: A
authors.lastname: Emad
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Puleo
authors.affiliation: Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Jian
authors.initials: J
authors.lastname: Ma
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
authors.forename: Olgica
authors.initials: O
authors.lastname: Milenkovic
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D005260:Female; D053263:Gene Regulatory Networks; D005909:Glioblastoma / Q000235:genetics*; D006801:Humans; D009154:Mutation