mulTestCor {Envisage} | R Documentation |
This function corrects the p-values obtained from package Envisage to account for problems with multiple testing. These corrected p-values give a measure of the significance of model terms on the effects on gene expression changes for individual genes.
mulTestCor(pValues, MTC, pData, exprData, param4INT)
pValues |
An object of class data.frame containing the
p-values from the Type II sum of squares analysis for each model
term for each gene. |
MTC |
A character string that defines which multiple testing
correction method to use (if any). Available methods are
None , BH , BY , Bonferroni , Holm ,
Hochberg , SidakSS , SidakSD . More information on
these can be found in the package vignette. BH is selected by
default. |
pData |
An object of class
phenoData containing the
phenotypic information for the experiment. |
exprData |
A matrix of expression values for each gene for each
sample. Usually calculated using exprs on an
object of class
ExpressionSet . |
param4INT |
Character vector of variables for which interaction terms should be calculated. |
The Envisage package contains methods allowing the use of linear models (LMs) for analysing significantly changing genes in experiments with a variety of sources of variation, be they experimentally controlled variables such as drug treatment or time, or non-controlled sources of confounding variation such as phenotypic or environmental differences. This allows all sources of variation to be considered when analysing for significant differential expression, ensuring resulting genes are biologically relevent to the experimental question.
Due to the large number of genes analysed, gene expression analyses
can suffer from problems due to multiple hypothesis testing. The
function mulTestCor
performs the selected
multiple testing correction on the p-values indicating significance of
each model term for each gene in the analysis.
mulTestCor
is an internal function and is not
designed to be accessed by users.
An object of class data.frame
is returned containing the
adjusted p-values for each model term for each gene.
Sam Robson S.C.Robson@warwick.ac.uk
Robson, S. C., Hunter, E., Bird, H., Turner, H. (2008) Envisage: model-based significance analysis of microarray gene expression data, manuscript in preparation
For the main Envisage method, see
envisage
.