Limitation of permutation-based differential correlation analysis

The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genom...

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Bibliographic Details
Published in:Genetic epidemiology 2023-12, Vol.47 (8), p.637-641
Main Authors: Song, Hoseung, Wu, Michael C
Format: Article
Language:English
Subjects:
Correlation analysis
Genomics
Humans
Statistical analysis
Statistics as Topic
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Summary:The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genomic features differ across conditions or outcomes. Because ascertaining the null distribution of test statistics to capture variations in correlation is challenging, several DCA methods utilize permutation which can loosen parametric (e.g., normality) assumptions. However, permutation is often problematic for DCA due to violating the assumption that samples are exchangeable under the null. Here, we examine the limitations of permutation-based DCA and investigate instances where the permutation-based DCA exhibits poor performance. Experimental results show that the permutation-based DCA often fails to control the type I error under the null hypothesis of equal correlation structures.
ISSN:0741-0395
1098-2272
1098-2272
DOI:10.1002/gepi.22540