Binary contingency table method for analysing gene mutation in cancer genome

Emi Ayada, Takanori Hasegawa, Atsushi Niida, Satoru Miyano, Seiya Imoto

Research output: Contribution to journalArticlepeer-review

Abstract

Somatic mutations are considered to initiate several disorders such as cancer and neurological disease. Hence, a number of computational methods have been developed to find loci subject to frequent mutations in cancer cells. Since normal cells turn into cancer cells through the accumulation of gene mutations, the elucidation of interactive relationships among loci has great potential to reveal the cause of cancer progression; however, only a few methods have been proposed for measuring statistical significance of pairs of loci that are co-mutated or exclusively mutated. In this study, we proposed a novel statistical method to find such significantly interactive pairs of loci by employing the framework of binary contingency tables. Using Markov chain Monte Carlo procedure, the statistical significance is evaluated by sampling null matrices whose marginal sums are equal to those of the input matrix. We applied the proposed method to mutation data of colon cancer patients and successfully obtained significant pairs of loci.

Original languageEnglish
Pages (from-to)211-226
Number of pages16
JournalInternational Journal of Bioinformatics Research and Applications
Volume12
Issue number3
DOIs
Publication statusPublished - 2016

Keywords

  • Binary contingency tables
  • Cancer
  • Gene mutation
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

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