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題名 Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition 作者 吳漢銘
Wu, Han-Ming
Tien, Yin-Jing
Ho, Meng-Ru
Hwu, Hai-Gwo
Lin, Wen-chang
Tao, Mi-Hua
Chen, Chun-houh貢獻者 統計系 日期 2018-10 上傳時間 2022-04-12 摘要 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. 關聯 Bioinformatics, Vol.34, No.20, pp.3529-3538 資料類型 article DOI https://doi.org/10.1093/bioinformatics/bty335 dc.contributor 統計系 dc.creator (作者) 吳漢銘 dc.creator (作者) Wu, Han-Ming dc.creator (作者) Tien, Yin-Jing dc.creator (作者) Ho, Meng-Ru dc.creator (作者) Hwu, Hai-Gwo dc.creator (作者) Lin, Wen-chang dc.creator (作者) Tao, Mi-Hua dc.creator (作者) Chen, Chun-houh dc.date (日期) 2018-10 dc.date.accessioned 2022-04-12 - dc.date.available 2022-04-12 - dc.date.issued (上傳時間) 2022-04-12 - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139842 - dc.description.abstract (摘要) 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. dc.format.extent 1256187 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) Bioinformatics, Vol.34, No.20, pp.3529-3538 dc.title (題名) Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition dc.type (資料類型) article dc.identifier.doi (DOI) 10.1093/bioinformatics/bty335 dc.doi.uri (DOI) https://doi.org/10.1093/bioinformatics/bty335
