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Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data.

Kosuke Yoshida1,2, Junichiro Yoshimoto3, Kenji Doya4

  • 1Graduate School of Informatics, Kyoto University, Kyoto, Japan. kosuke.yoshida@oist.jp.

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|February 16, 2017
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Summary

A new bioinformatics tool, two-stage kernel Canonical Correlation Analysis (TSKCCA), effectively identifies multiple, nonlinear associations within high-dimensional data. This method offers improved reliability for integrating complex biological datasets.

Keywords:
Hilbert-Schmidt independent criterionKernel canonical correlation analysisL1 regularization

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Area of Science:

  • Bioinformatics
  • Genomics
  • Transcriptomics
  • Metabolomics

Background:

  • High-throughput technologies generate complex, high-dimensional data requiring advanced bioinformatics tools.
  • Canonical Correlation Analysis (CCA) is a statistical method for identifying linear associations between datasets.
  • Existing nonlinear CCA methods lack feature selection and multi-component analysis capabilities.

Purpose of the Study:

  • To introduce a novel method, two-stage kernel CCA (TSKCCA), for integrating high-dimensional data.
  • To address limitations of previous nonlinear CCA methods by enabling feature selection and multi-component analysis.

Main Methods:

  • TSKCCA employs a two-stage approach within a multiple kernel learning framework.
  • Kernel selection is performed using the Hilbert-Schmidt Independence Criterion (HSIC).
  • Weights are determined via non-negative matrix decomposition with L1 regularization.

Main Results:

  • TSKCCA successfully extracts multiple nonlinear associations from high-dimensional data.
  • The method identifies multiplicative interactions among variables.
  • Performance was validated using artificial and nutrigenomic datasets.

Conclusions:

  • TSKCCA reliably identifies nonlinear associations in high-dimensional data.
  • This method surpasses previous nonlinear CCA techniques in accuracy and capability.