Population Power Curves in ASCA with Permutation Testing
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917737836576768 |
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| author | Camacho, Jose Armstrong, Michael Sorochan |
| author_facet | Camacho, Jose Armstrong, Michael Sorochan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Population Power Curves in ASCA with Permutation Testing Camacho, Jose Armstrong, Michael Sorochan Methodology Computation 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. |
| title | Population Power Curves in ASCA with Permutation Testing |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2403.00429 |