Abstract:
: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 categorical. 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 permutation-based approaches are shown to be better suited for microarray data.
journal_name
Genet Epidemioljournal_title
Genetic epidemiologyauthors
Wagner BD,Zerbe GO,Mexal S,Leonard SSdoi
10.1002/gepi.20255subject
Has Abstractpub_date
2008-01-01 00:00:00pages
1-8issue
1eissn
0741-0395issn
1098-2272journal_volume
32pub_type
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