+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

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

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

Genetic Epidemiology 32(1): 1-8

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.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 021539517

Download citation: RISBibTeXText

PMID: 17630650

DOI: 10.1002/gepi.20255

Related references

A note on permutation tests of significance for multiple regression coefficients. Psychological Reports 100(2): 339-345, 2007

A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics 21(23): 4280-4288, 2005

Gene association networks from microarray data using a regularized estimation of partial correlation based on PLS regression. Ieee/Acm Transactions on Computational Biology and Bioinformatics 7(2): 251-262, 2010

Partial Cox regression analysis for high-dimensional microarray gene expression data. Bioinformatics 20 Suppl 1: I208-I215, 2004

Permutation-validated principal components analysis of microarray data. Genome Biology 3(4): Research0019, 2002

Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression. Journal of Computational Biology 11(2-3): 215-226, 2004

A web-based tool for principal component and significance analysis of microarray data. Bioinformatics 21(10): 2548-2549, 2005

Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection Onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing. International Journal of Data Mining and Bioinformatics 5(2): 143-157, 2011

Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics 18(12): 1625-1632, 2002

Statistical studies in agronomy. 1. Significance of the difference between partial regression coefficients between protein and gluten in wheats grown in different districts. Agronom. lusitana. 8: 73-88, 1946

Validation data-based adjustments for outcome misclassification in logistic regression: an illustration. Epidemiology 22(4): 589-597, 2011

Statistical studies. I. Test of significance of the differences between partial coefficients of regression in the variables protein and gluten of wheats grown in different places. Agron. lusit, 8: 73-88, 1946

A comparison of permutation and mixed-model regression methods for the analysis of simulated data in the context of a group-randomized trial. Statistics in Medicine 25(3): 375-388, 2005

A partial least squares-based consensus regression method for the analysis of near-infrared complex spectral data of plant samples. Analytical Letters 39(9): 2073-2083, 2006

Application of reliability coefficients in cDNA microarray data analysis. Statistics in Medicine 25(6): 1051-1066, 2005