Influence function of multiple kernel canonical analysis to identify outliers in imaging genetics data

Md Ashad Alam, Vince Calhoun, Yu Ping Wang

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

Abstract

Imaging genetic research has essentially focused on discovering unique and co-association effects, but typically ignoring to identify outliers or atypical objects in genetic as well as non-genetics variables. Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple sources data analysis. Therefore, we need to examine for transcription errors of identified outliers. First, we address the influence function (IF) of kernel mean element, kernel covariance operator, kernel cross-covariance operator, kernel canonical correlation analysis (kernel CCA) and multiple kernel CCA. Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets. Third, we propose a visualization method to detect influential observations of multiple sources of data based on the IF of kernel CCA and multiple kernel CCA. Finally, the proposed methods are capable of analyzing outliers of subjects usually found in biomedical applications, in which the number of dimension is large. To examine the outliers, we use the stem-and-leaf display. Experiments on both synthesized and imaging genetics data (e.g., SNP, fMRI, and DNA methylation) demonstrate that the proposed visualization can be applied effectively.

Original languageEnglish (US)
Title of host publicationACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages210-219
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

  • Data integration
  • Influence function
  • Kernel CCA
  • Multiple kernel CCA
  • Outlier detection in imaging genetics

ASJC Scopus subject areas

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

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