pair.compare: Pairwise comparisons of factor levels within GlobalAncova

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

View source: R/pair.compare.R

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

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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 [email protected]
Reinhard Meister [email protected]
Manuela Hummel [email protected]
Urlich Mansmann [email protected]

See Also

GlobalAncova, GlobalAncova.decomp

Examples

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

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

Bioconductor-mirror/GlobalAncova documentation built on May 29, 2017, 5:21 a.m.