Displaying Variation in Large Datasets: Plotting a Visual Summary of Effect Sizes

Displaying the component-wise between-group differences high-dimensional datasets is problematic because widely used plots such as Bland–Altman and Volcano plots do not show what they are colloquially <i>believed</i> to show. Thus, it is difficult for the experimentalist to grasp why the between-group difference of one component is “significant” while that of another component is not. Here, we propose a type of “Effect Plot” that displays between-group differences in relation to respective underlying variability for every component of a high-dimensional dataset. We use synthetic data to show that such a plot captures the essence of what determines “significance” for between-group differences in each component, and provide guidance in the interpretation of the plot. Supplementary online materials contain the code and data for this article and include simple R functions to produce an effect plot from suitable datasets.

针对高维数据集逐组分展示组间差异时存在固有局限，因常用的Bland–Altman图（Bland–Altman plot）、火山图（Volcano plot）等可视化工具，无法呈现其被通俗认为应当展示的内容。因此实验研究者难以理解：为何某一组分的组间差异具有统计学显著性，而另一组分却无此显著性。为此，我们提出一种「效应图（Effect Plot）」，可针对高维数据集的每一组分，展示其组间差异与对应内在变异性的关联关系。我们通过合成数据验证，该可视化方法能够揭示决定各组分组间差异「显著性」的核心本质，并为该图的解读提供指导。本文的补充在线材料包含相关代码与数据集，并提供了可从适配数据集生成效应图的简易R函数。