modelCalcME {Envisage} | R Documentation |
This is the main calculation for the package Envisage. An optimised model, containing terms shown to have practical importance for explaining variation, is produced using a step-wise procedure. Terms may be main effect terms only. The significnace of each term is computed by using a type II sum of squares.
modelCalcME(paramData, exprData, startingModel, progress)
paramData |
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 . |
startingModel |
An object of class formula
containing the minimal model thta should be used in the step-wise
model fitting process. |
progress |
Object of class tclVar indicating progress
through the genes in the gene list. Used only if widget=TRUE
for envisage . |
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.
The function modelCalcME
performs the main
modelling step for the package Envisage. A stepwise procedure is
used on a per-gene basis to calculate the optimum model for each
gene. In comparison to modelCalcINT
, terms
used in the model may be main effect terms only. The step-wise
procedure begins by fitting the maximal
model
to the data, then removes terms in a leave-one-out approach based on
the Akaike information criterion (AIC). Specification of a minimal
model allows terms to be 'forced' into the final formula.
The significance of each term in the final model is estimated by using a Type II sum of squares to compare the model with and without each term. Also, if fitting the model results in aliased terms (either due to experimental design, insufficient data, or non-independence of model terms), this information is also returned.
modelCalcME
is an internal function and is not
designed to be accessed by users.
A list is returned with the following slots:
pValues |
An object of class data.frame containing the
p-value from the Type II sum of squares analysis for each model term
for each gene. |
failedAnova |
A vector of genes for which the Anova Type II sum of squares failed, indicating model aliasing. |
errorGenes |
A list of the genes that failed and the model terms for which aliasing was detected. |
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 interaction version of this function, see
modelCalcINT
. For the main Envisage
method, see envisage
.