GlobalAncova.closed: Closed testing procedure for testing several groups of genes using GlobalAncova

Description

Computation of a closed testing procedure for several groups of genes, e.g. pathways, as an alternative of correcting for multiple testing. Starting from the pathways of interest a family of null hypotheses is created that is closed under intersection. Each null hypothesis can be rejected at a given level if it is rejected along with all hypotheses included in it.

There are three possible ways of using GlobalAncova. Also GlobalAncova.closed can be invoked with these three alternatives.

Usage

"GlobalAncova.closed"(xx, test.genes,  formula.full, formula.red, model.dat, previous.test, level, method = c("permutation","approx"), perm = 10000,  max.group.size = 2500, eps = 1e-16, acc = 50)
"GlobalAncova.closed"(xx, test.genes,  formula.full, model.dat, test.terms, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)
"GlobalAncova.closed"(xx, test.genes,  group, covars = NULL, previous.test, level, method = c("permutation","approx"), perm = 10000,  max.group.size = 2500, eps = 1e-16, acc = 50)

Arguments

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.

test.genes

A list of named pathways that shall be tested, each containing vectors of gene names.

previous.test

The output of a call to GlobalAncova with specified option test.genes according to the pathways of interest (optional).

level

The global level of significance of the testing procedure.

formula.full

Model formula for the full model.

formula.red

Model formula for the reduced model (that does not contain the terms of interest).

model.dat

Data frame that contains all the variable information for each sample.

group

Vector with the group membership information.

covars

Vector or matrix which contains the covariate information for each sample.

test.terms

Character vector that contains names of the terms of interest.

method

Raw p-values can be calculated permutation-based ("permutation") or by means of an approximation ("approx").

perm

Number of permutations to be used for the permutation approach. The default is 10,000.

max.group.size

Maximum size of a gene set for which the asymptotic p-value is calculated. For bigger gene sets the permutation approach is used.

eps

Resolution of the asymptotic p-value.

acc

Accuracy parameter needed for the approximation. Higher values indicate higher accuracy.

Value

new.data: Family of null hypotheses (vectors of genes to be tested simultaneously with GlobalAncova).
test.results: Test results for each pathway of interest and all hypotheses included in it.
significant: Names of the significant pathways.
not.significant: Names of the non significant pathways.

Methods

xx = "matrix", test.genes="list", formula.full = "formula", formula.red = "formula", model.dat = "ANY", group = "missing", covars = "missing", test.terms = "missing": In this method, besides the expression matrix xx and the list of gene groups test.genes, model formulas for the full and reduced model and a data frame model.dat specifying corresponding model terms have to be given. Terms that are included in the full but not in the reduced model are those whose association with differential expression will be tested. The arguments group, covars and test.terms are '"missing"' since they are not needed for this method.
xx = "matrix", test.genes="list", formula.full = "formula", formula.red = "missing", model.dat = "ANY", group = "missing", covars = "missing", test.terms = "character": In this method, besides the expression matrix xx and the list of gene groups test.genes, a model formula for the full model and a data frame model.dat specifying corresponding model terms are required. The character argument test.terms names the terms of interest whose association with differential expression will be tested. The arguments formula.red, group and covars are '"missing"' since they are not needed for this method.
xx = "matrix", test.genes="list", formula.full = "missing", formula.red = "missing", model.dat = "missing", group = "ANY", covars = "ANY", test.terms = "missing": Besides the expression matrix xx and the list of gene groups test.genes a clinical variable group is required. Covariate adjustment is possible via the argument covars but more complex models have to be specified with the methods described above. This method emulates the function call in the first version of the package. The arguments formula.full, formula.red, model.dat and test.terms are '"missing"' since they are not needed for this method.

References

Marcus, R., Peritz, E. and Gabriel, K.R., 1976, On closed testing procedures with special reference to ordered analysis of variance, Biometrika 63 (3): 655--660.