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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/139842


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139842


    Title: Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition
    Authors: 吳漢銘
    Wu, Han-Ming
    Tien, Yin-Jing
    Ho, Meng-Ru
    Hwu, Hai-Gwo
    Lin, Wen-chang
    Tao, Mi-Hua
    Chen, Chun-houh
    Contributors: 統計系
    Date: 2018-10
    Issue Date: 2022-04-12
    Abstract: Motivation: Heatmap is a popular visualization technique in biology and related fields. In this study, we extend heatmaps within the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. MV can explore the embedded information structure of high-dimensional large-scale datasets effectively without dimension reduction. The benefit of the proposed covariate-adjusted heatmap is in the exploration of conditional association structures among the subjects or variables that cannot be done with conventional MV.

    Results: For adjustment of a discrete covariate, the conditional correlation is estimated by the within and between analysis. This procedure decomposes a correlation matrix into the within- and between-component matrices. The contribution of the covariate effects can then be assessed through the relative structure of the between-component to the original correlation matrix while the within-component acts as a residual. When a covariate is of continuous nature, the conditional correlation is equivalent to the partial correlation under the assumption of a joint normal distribution. A test is then employed to identify the variable pairs which possess the most significant differences at varying levels of correlation before and after a covariate adjustment. In addition, a z-score significance map is constructed to visualize these results. A simulation and three biological datasets are employed to illustrate the power and versatility of our proposed method.

    Availability and implementation: GAP is available to readers and is free to non-commercial applications. The installation instructions, the user`s manual, and the detailed tutorials can be found at http://gap.stat.sinica.edu.tw/Software/GAP.

    Supplementary information: Supplementary Data are available at Bioinformatics online.
    Relation: Bioinformatics, Vol.34, No.20, pp.3529-3538
    Data Type: article
    DOI 連結: https://doi.org/10.1093/bioinformatics/bty335
    DOI: 10.1093/bioinformatics/bty335
    Appears in Collections:[統計學系] 期刊論文

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