Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-12-24
pubmed:abstractText
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.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0741-0395
pubmed:author
pubmed:issnType
Print
pubmed:volume
32
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-8
pubmed:dateRevised
2011-8-1
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Permutation-based adjustments for the significance of partial regression coefficients in microarray data analysis.
pubmed:affiliation
Department of Preventative Medicine and Biometrics, University of Colorado Health Sciences Center, Denver, Colorado, USA.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, N.I.H., Extramural