removeFlatGenes {attract} | R Documentation

## Flags a set of genes which demonstrates little variation across the celltypes or experimental groups of interest.

### 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)

[Package attract version 1.42.0 Index]