Permutation-based adjustments for the significance of partial regression coefficients in microarray data analysis.
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.
Wagner
Brandie D
BD
Department of Preventative Medicine and Biometrics, University of Colorado Health Sciences Center, Denver, Colorado, USA.
Zerbe
Gary O
GO
Mexal
Sharon
S
Leonard
Sherry S
SS
D000465:Algorithms; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D020869:Gene Expression Profiling; D006306:Health Surveys; D006624:Hippocampus / Q000378:metabolism*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012559:Schizophrenia / Q000453:epidemiology* / Q000209:etiology