GlobalAncova {GlobalAncova} | R Documentation |
Computation of the sum of squares decomposition of gene-expression values according to a two way layout with gene and group as factors. Gene specific covariate effects are allowed. A MC approximation to a permutation-test for group main effect and gene: group interaction is provided. If just one gene should be tested a squared t-statistic which is equivalent to a F-statistic is computed. Corresponding p-values and permutation p-values are provided.
GlobalAncova(xx, group, covars = NULL, perm = 10000, test.genes = NULL)
xx |
A matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of xx . |
group |
A vector with the group membership information. In the given version group must be coded as 0-1 . |
covars |
A vector or matrix which contains for each sample the covariate information. |
perm |
The number of permutations to be used. The default is 10,000. |
test.genes |
Vector of genes that shall be tested or list of pathways, each containing gene names. |
An ANOVA table
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Reinhard Meister meister@tfh-berlin.de
Ulrich Mansmann mansmann@ibe.med.uni-muenchen.de
Mansmann, U. and Meister, R., 2005, Testing differential gene expression in functional groups, Methods Inf Med 44 (3).
Plot.genes
, Plot.subjects
, GlobalAncova.closed
set.seed(123) data(p53.signalling) data(cov.info) data(group.info) table.1 <- GlobalAncova(xx=p53.signalling, group=group.info, covars=NULL, perm=10000, test.genes = NULL) table.2 <- GlobalAncova(xx=p53.signalling, group=group.info, covars=cov.info, perm=10000, test.genes = NULL) table.sex <- GlobalAncova(xx=p53.signalling, group=cov.info[,1], covars=NULL, perm=10000, test.genes = NULL) table.loc <- GlobalAncova(xx=p53.signalling, group=cov.info[,2], covars=NULL, perm=10000, test.genes = NULL)