Permutation-based adjustments for the significance of partial regression coefficients in microarray data analysis

Brandie D. Wagner, Gary O. Zerbe, Sharon Mexal, Sherry S. Leonard
2008 Genetic Epidemiology  
The aim of this paper is to generalize permutation methods for multiple testing adjustment of significant partial regression coefficients in a linear regression model used for microarray data. Using a permutation method outlined by Anderson and Legendre [1999] and the permutation P-value adjustment from Simon et al. [2004], the significance of disease related gene expression will be determined and adjusted after accounting for the effects of covariates, which are not restricted to be
more » ... . We apply these methods to a microarray dataset containing confounders and illustrate the comparisons between the permutation-based adjustments and the normal theory adjustments. The application of a linear model is emphasized for data containing confounders and the permutationbased approaches are shown to be better suited for microarray data.
doi:10.1002/gepi.20255 pmid:17630650 pmcid:PMC2592303 fatcat:linjquzirjcydlczpyvoyfywpq