pair.compare: Pairwise comparisons of factor levels within GlobalAncova
In hummelma/GlobalAncova: Global test for groups of variables via model comparisons

Description Usage Arguments Value Note Author(s) See Also Examples

Pairwise comparisons of gene expression in different levels of a factor by GlobalAncova tests. The method uses the reduction in residual sum of squares obtained when two respective factor levels are set to the same level. Holm-adjusted permutation-based p-values are given.

1	pair.compare(xx, formula, group, model.dat = NULL, test.genes = NULL, perm = 10000)

`xx`	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`.
`formula`	Model formula for the linear model.
`group`	Factor for which pairwise comparisons shall be calculated.
`model.dat`	Data frame that contains all the variable information for each sample.
`test.genes`	Vector of gene names or a list where each element is a vector of gene names.
`perm`	Number of permutations to be used for the permutation approach. The default is 10,000.

An ANOVA table, or list of ANOVA tables for each gene set, for the pairwise comparisons.

This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.

Ramona Scheufele ramona.scheufele@charite.de
Reinhard Meister meister@tfh-berlin.de
Manuela Hummel m.hummel@dkfz.de
Urlich Mansmann mansmann@ibe.med.uni-muenchen.de

GlobalAncova, GlobalAncova.decomp

data(vantVeer)
data(phenodata)
data(pathways)

pair.compare(xx = vantVeer, formula = ~ grade, group = "grade", model.dat = phenodata, test.genes = pathways[1:3], perm = 100)