removeFlatGenes: Flags a set of genes which demonstrates little variation...

In attract: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape

Description

This function uses a linear model set up in limma to assess the degree of association between celltype and a gene's expression profile. In this way, we can flag those genes whose profiles show very little change across different celltype groups, or in other words are "flat".

Usage

removeFlatGenes(eSet, cellTypeTag, contrasts = NULL, limma.cutoff = 0.05, ...)

Arguments

eSet	ExpressionSet object.
cellTypeTag	character string of the variable name which stores the cell-lineages or experimental groups of interest for the samples in the data set (this string should be one of the column names of pData(myEset)).
contrasts	optional vector of contrasts that specify the comparisons of interest. By default, all comparisons between the differnt groups are generated.
limma.cutoff	numeric specifying the P-value cutoff. Genes with P-values greater than this value are considered "flat" and will be included in the set of flat genes.
...	additional arguments.

Details

Flat genes are removed from the analysis after the core attractor pathway modules are first inferred (i.e. the findAttractors step).

Value

A vector with gene names (as defined in the eset) of those genes with expression profiles that hardly vary across different celltype or experimental groups.

Author(s)

Jessica Mar

References

limma package.

Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3.

Examples

data(subset.loring.eset)
remove.these.genes <- removeFlatGenes(subset.loring.eset, "celltype", contrasts=NULL, limma.cutoff=0.05)

attract documentation built on Nov. 8, 2020, 8:04 p.m.