Haarman Bartholomeus C M Benno, Riemersma-Van der Lek Rixt F, Nolen Willem A, Mendes R, Drexhage Hemmo A, Burger Huibert
University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands.
Health E-Solutions, Rotterdam, The Netherlands.
J Biomed Inform. 2015 Feb;53:156-61. doi: 10.1016/j.jbi.2014.10.003. Epub 2014 Oct 14.
Existing methods such as correlation plots and cluster heat maps are insufficient in the visual exploration of multiple associations between genetics and phenotype, which is of importance to achieve a better understanding of the pathophysiology of psychiatric and other illnesses. The implementation of a combined presentation of effect size and statistical significance in a graphical method, added to the ordering of the variables based on the effect-ordered data display principle was deemed useful by the authors to facilitate in the process of recognizing meaningful patterns in these associations.
The requirements, analyses and graphical presentation of the feature-expression heat map are described. The graphs display associations of two sets of ordered variables where a one-way direction is assumed. The associations are depicted as circles representing a combination of effect size (color) and statistical significance (radius).
An example dataset is presented and relation to other methods, limitations, areas of application and possible future enhancements are discussed.
The feature-expression heat map is a useful graphical instrument to explore associations in complex biological systems where one-way direction is assumed, such as genotype-phenotype pathophysiological models.
诸如相关性图和聚类热图等现有方法在对遗传学与表型之间的多种关联进行可视化探索时存在不足，而理解这些关联对于更好地了解精神疾病和其他疾病的病理生理学至关重要。作者认为，以图形方法将效应大小和统计显著性进行联合呈现，并基于效应排序的数据显示原则对变量进行排序，有助于在这些关联中识别有意义的模式。
描述了特征表达热图的要求、分析和图形呈现。这些图展示了两组有序变量之间的关联，假设存在单向关系。这些关联被描绘为代表效应大小（颜色）和统计显著性（半径）组合的圆圈。
给出了一个示例数据集，并讨论了与其他方法的关系、局限性、应用领域以及未来可能的改进。
特征表达热图是一种有用的图形工具，可用于探索假设存在单向关系的复杂生物系统中的关联，如基因型 - 表型病理生理模型。
