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Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition.

Han-Ming Wu1, Yin-Jing Tien2, Meng-Ru Ho

  • 1Department of Statistics, National Taipei University, New Taipei City, Taiwan, R.O.C.

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Summary

This study introduces covariate-adjusted heatmaps for exploring conditional associations in biological data. The new method enhances matrix visualization by revealing complex relationships beyond conventional techniques.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • Heatmaps are widely used for data visualization in biological sciences.
  • Matrix visualization (MV) effectively explores high-dimensional datasets without dimension reduction.
  • Conventional MV lacks the ability to explore conditional association structures.

Purpose of the Study:

  • To extend heatmaps within matrix visualization by incorporating covariate adjustment.
  • To enable the exploration of conditional association structures among subjects or variables.
  • To provide a novel approach for analyzing complex biological datasets.

Main Methods:

  • Estimation of conditional correlations for covariate adjustment.
  • Decomposition of correlation matrices into within- and between-component matrices for discrete covariates.
  • Utilizing partial correlation for continuous covariates under joint normal distribution assumption.
  • Development of a z-score significance map for visualizing results.

Main Results:

  • The proposed method allows for the assessment of covariate effects on correlation structures.
  • Identification of variable pairs with significant correlation differences before and after adjustment.
  • Demonstrated power and versatility through simulations and three biological datasets.

Conclusions:

  • Covariate-adjusted heatmaps offer enhanced exploration of conditional associations in biological data.
  • The method provides a powerful tool for uncovering complex relationships in high-dimensional datasets.
  • The developed technique complements existing matrix visualization approaches.