GlobalAncova.closed: Closed testing procedure for testing several groups of genes...
In hummelma/GlobalAncova: Global test for groups of variables via model comparisons

Description Usage Arguments Value Methods Note Author(s) References See Also

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.

## S4 method for signature 
## 'matrix,list,formula,formula,ANY,missing,missing,missing'
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)

## S4 method for signature 
## 'matrix,list,formula,missing,ANY,missing,missing,character'
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)

## S4 method for signature 
## 'matrix,list,missing,missing,missing,ANY,ANY,missing'
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)

`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`.
`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.

A list with components

`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.

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.

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

Reinhard Meister meister@beuth-hochschule.de
Ulrich Mansmann mansmann@ibe.med.uni-muenchen.de
Manuela Hummel m.hummel@dkfz.de

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.

GlobalAncova, Plot.genes, Plot.subjects

hummelma/GlobalAncova documentation built on Feb. 4, 2021, 8:25 a.m.