pair.compare {GlobalAncova} | R Documentation

## Pairwise comparisons of factor levels within GlobalAncova

### Description

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.

### Usage

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

### Arguments

`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.

### Value

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

### Note

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

### Author(s)

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

### See Also

`GlobalAncova`, `GlobalAncova.decomp`

### Examples

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

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

[Package GlobalAncova version 4.6.0 Index]