Robust kernel canonical correlation analysis to detect gene-gene interaction for imaging genetics data

Md Ashad Alam, Vince Calhoun, Osamu Komori, Yu Ping Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, the kernel canonical correlation analysis (Classical kernel CCA) based U statistic (KCCU) has been proposed to detect the nonlinear relationship between genes. To estimate the variance in KCCU, they have used resampling based methods which are highly computationally intensive. In addition, classical kernel CCA is not robust to contaminated data. We, therefore, first discuss robust kernel mean element, the robust kernel covariance, and cross-covariance operators. Second, we propose a method based on influence function to estimate the variance of the KCCU. Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA. Finally, we investigate the proposed methods to synthesized data and imaging genetic data set. Based on gene ontology and pathway analysis, the synthesized and genetics analysis demonstrate that the proposed robust method shows the superior performance of the state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages279-288
Number of pages10
ISBN (Electronic)9781450342254
DOIs
StatePublished - Oct 2 2016
Event7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016 - Seattle, United States
Duration: Oct 2 2016Oct 5 2016

Publication series

NameACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016
CountryUnited States
CitySeattle
Period10/2/1610/5/16

Keywords

  • Gene-gene interaction
  • Imaging genetic data
  • Kernel CCA
  • Robust kernel CCA
  • Robustness

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Biomedical Engineering
  • Computer Science Applications

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  • Cite this

    Alam, M. A., Calhoun, V., Komori, O., & Wang, Y. P. (2016). Robust kernel canonical correlation analysis to detect gene-gene interaction for imaging genetics data. In ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 279-288). (ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/2975167.2975196