essGene {gage} | R Documentation

## Essential member genes in a gene set

### Description

This function extracts data for essential member genes in a gene set. Essential genes are genes that have changes over noise level.

### Usage

    essGene(gs, exprs, ref = NULL, samp = NULL, gsets = NULL, compare
    = "paired", use.fold = TRUE, rank.abs = FALSE, use.chi = FALSE, chi.p =
    0.05, ...)

### Arguments

`gs` | character, either the name of an interesting gene set in a gene set collection passed by `gsets` argument, or a vector of gene IDs. Make sure that the same gene ID system is used for both `gs` and `exprs`.
`exprs` | an expression matrix or matrix-like data structure, with genes as rows and samples as columns.
`ref` | a numeric vector of column numbers for the reference condition or phenotype (i.e. the control group) in the exprs data matrix. Default ref = NULL, all columns are considered as target experiments.
`samp` | a numeric vector of column numbers for the target condition or phenotype (i.e. the experiment group) in the exprs data matrix. Default samp = NULL, all columns other than ref are considered as target experiments.
`gsets` | a named list, each element contains a gene set that is a character vector of gene IDs or symbols. For example, type `head(kegg.gs)`. A gene set can also be a "smc" object defined in PGSEA package. Make sure that the same gene ID system is used for both `gsets` and `exprs`. Default to be NULL, then argument `gs` needs to be a vector of gene IDs.
`compare` | character, which comparison scheme to be used: 'paired', 'unpaired', '1ongroup', 'as.group'. 'paired' is the default, ref and samp are of equal length and one-on-one paired by the original experimental design; 'as.group', group-on-group comparison between ref and samp; 'unpaired' (used to be '1on1'), one-on-one comparison between all possible ref and samp combinations, although the original experimental design may not be one-on-one paired; '1ongroup', comparison between one samp column at a time vs the average of all ref columns.
`use.fold` | Boolean, whether the input `gage` results used fold changes or t-test statistics as per gene statistics. Default use.fold= TRUE.
`rank.abs` | boolean, whether to sort the essential gene data based on absoluate changes. Default to be FALSE.
`use.chi` | boolean, whether to use chi-square test to select the essential genes. Default to be FALSE, use the mean plus standard deviation of all gene changes instead. Check details for more information.
`chi.p` | numeric value between 0 and 1, cutoff p-value for the chi-square test to select the essential genes. Default to 0.05.
`...` | other arguments to be passed into the inside `gagePrep` function.

### Details

There are two different criteria for essential gene selection. One uses a chi-square test to determin whether the change of a gene is more than noise. A second considers any changes beyond 1 standard deviation from mean of all genes as real.

Note that essential genes are different from core genes considered in `esset.grp` function. Essential genes may change in a different direction than the overall change of a gene set. But core genes need to change in the in the interesting direction(s) of the gene set test.

### Value

A expression data matrix extracted for the essential genes, with similar structure as `exprs`.

### Author(s)

Weijun Luo <luo_weijun@yahoo.com>

### References

Luo, W., Friedman, M., Shedden K., Hankenson, K. and Woolf, P GAGE: Generally Applicable Gene Set Enrichment for Pathways Analysis. BMC Bioinformatics 2009, 10:161

### See Also

`gage` the main function for GAGE analysis; `geneData` output and visualization of expression data for selected genes; `esset.grp` non-redundant signcant gene set list;

### Examples

    data(gse16873)
    cn=colnames(gse16873)
    hn=grep('HN',cn, ignore.case =TRUE)
    dcis=grep('DCIS',cn, ignore.case =TRUE)

    #kegg test for 1-directional changes
    data(kegg.gs)
    gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs,
        ref = hn, samp = dcis)
    rownames(gse16873.kegg.p$greater)[1:3]
    gs=unique(unlist(kegg.gs[rownames(gse16873.kegg.p$greater)[1:3]]))
    essData=essGene(gs, gse16873, ref =hn, samp =dcis)
    head(essData)
    ref1=1:6
    samp1=7:12
    #generated text file for data table, pdf files for heatmap and scatterplot
    for (gs in rownames(gse16873.kegg.p$greater)[1:3]) {
        outname = gsub(" |:|/", "_", substr(gs, 10, 100))
        geneData(genes = kegg.gs[[gs]], exprs = essData, ref = ref1,
            samp = samp1, outname = outname, txt = TRUE, heatmap = TRUE,
            Colv = FALSE, Rowv = FALSE, dendrogram = "none", limit = 3, scatterplot = TRUE)
    }

[Package gage version 2.30.0 Index]