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Cross-Modal Multivariate Pattern Analysis
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Joint association and classification analysis of multi-view data.

Yunfeng Zhang1, Irina Gaynanova1

  • 1Department of Statistics, Texas A&M University, College Station, Texas, USA.

Biometrics
|August 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Joint Association and Classification Analysis (JACA), a novel framework for multi-view data. JACA effectively integrates class information and between-view associations for improved subtype discrimination and data representation.

Keywords:
canonical correlation analysisdata integrationdiscriminant analysissemi-supervised learningsparsityvariable selection

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Multi-view data from multi-omics technologies are increasingly common.
  • Integrating class information with between-view associations is challenging for existing methods.

Purpose of the Study:

  • Propose a framework for Joint Association and Classification Analysis (JACA) of multi-view data.
  • Develop a latent representation relevant for subtype discrimination and coherent across views.
  • Address multi-view data with block-missing structures.

Main Methods:

  • Connect canonical correlation analysis with discriminant analysis.
  • Establish estimation consistency for JACA in high-dimensional settings.
  • Apply JACA to RNAseq and miRNA data for colorectal cancer subtype analysis.

Main Results:

  • JACA improves misclassification rates compared to existing methods.
  • JACA identifies stronger associations between RNAseq and miRNA views.
  • Demonstrates effectiveness on The Cancer Genome Atlas (TCGA) colorectal cancer data.

Conclusions:

  • JACA provides a robust framework for analyzing multi-view data with integrated association and classification.
  • The method is applicable to datasets with missing views or class labels.
  • JACA enhances understanding of molecular subtypes in complex diseases like colorectal cancer.