In this paper, we revisit the Power Curves in ANOVA Simultaneous Component Analysis (ASCA) based on permutation testing, and introduce the Population Curves derived from population parameters describing the relative effect among factors and interactions. We distinguish Relative from Absolute Population Curves, where the former represent statistical power in terms of the normalized effect size between structure and noise, and the latter in terms of the sample size. Relative Population Curves are useful to find the optimal ASCA model (e.g., fixed/random factors, crossed/nested relationships, interactions, the test statistic, transformations, etc.) for the analysis of an experimental design at hand. Absolute Population Curves are useful to determine the sample size and the optimal number of levels for each factor during the planning phase on an experiment. We illustrate both types of curves through simulation. We expect Population Curves to become the go-to approach to plan the optimal analysis pipeline and the required sample size in an omics study analyzed with ASCA.

翻译：本文重新审视了基于置换检验的方差分析同步成分分析（ASCA）中的功效曲线，并引入了从描述因子及交互效应相对关系的总体参数推导出的总体曲线。我们区分了相对总体曲线与绝对总体曲线：前者通过结构与噪声间的归一化效应量表征统计功效，后者则通过样本量进行表征。相对总体曲线有助于为特定实验设计分析寻找最优ASCA模型（例如固定/随机因子、交叉/嵌套关系、交互效应、检验统计量、数据变换等）。绝对总体曲线可在实验规划阶段用于确定样本量及各因子的最优水平数。我们通过仿真实验对两类曲线进行了演示。预期总体曲线将成为规划ASCA组学研究中最优分析流程及所需样本量的首选方法。