التفاصيل البيبلوغرافية
| العنوان: |
Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition. |
| المؤلفون: |
Wu, Han-Ming1, Tien, Yin-Jing2, Ho, Meng-Ru, Hwu, Hai-Gwo3, Lin, Wen-Chang4, Tao, Mi-Hua4, Chen, Chun-Houh5 cchen@stat.sinica.edu.tw |
| المصدر: |
Bioinformatics. Oct2018, Vol. 34 Issue 20, p3529-3538. 10p. |
| مصطلحات موضوعية: |
*ANALYSIS of covariance, *DNA microarrays, *PEARSON correlation (Statistics), *STATISTICAL correlation, *PSYCHOSES |
| مستخلص: |
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/GAPTest. Supplementary information Supplementary Data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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