runEnrich {MineICA} | R Documentation

## Enrichment analysis through GOstats

### Description

This function tests the enrichment of the components of an `IcaSet` object using package `GOstats` through function `hyperGTest`.

### Usage

      runEnrich(icaSet, params, dbs = c("KEGG", "GO"),
        ontos = c("BP", "CC", "MF"), cond = TRUE,
        hgCutoff = params["pvalCutoff"])

### Arguments

`icaSet` | An object of class `IcaSet`
`params` | An object of class `MineICAParams` providing the parameters of the analysis
`dbs` | The database to use, default is `c("GO","KEGG")`
`ontos` | A string specifying the GO ontology to use. Must be one of `"BP"`, `"CC"`, or `"MF"`, see `GOHyperGParams-class`. Only used when argument `dbs` includes `"GO"`.
`cond` | A logical indicating whether the calculation should condition on the GO structure, see `GOHyperGParams-class`. Only used when argument `dbs` includes `"GO"`.
`hgCutoff` | The threshold p-value for statistical significance, default is `pvalCutoff(params)`

### Details

An annotation package should be available in `annotation(icaSet)` to provide the contents of the gene sets. If none corresponds to the technology you deal with, please choose the org.*.eg.db package according to the organism (for example org.Hs.eg.db for Homo sapiens). By default, if `annotation(icaSet)` is empty and organism is one of `c("Human","HomoSapiens","Mouse","Mus Musculus")`, then either `org.Hs.eg.db` or `org.Mm.eg.db` is used.

Use of `GOstats` requires the input IDs to be Entrez Gene, this function will therefore annotate either the feature names or the gene names into Entrez Gene ID using either the annotation package (`annotation(icaSet)`) or `biomaRt`.

Three types of enrichment tests are computed for each component: the threshold is first used to select gene based on their absolute projections, then positive and negative projections are treated individually.

For each database `db` (each ontology if `db` is "GO"), this function writes an HTML file containing the outputs of the enrichment tests computed through the function `hyperGTest`. The corresponding files are located in `resPath(icaSet)`/GOstatsEnrichAnalysis/byDb/. The results obtained for each database/ontology are then merged into an array for each component, this array is written as an HTML file in the directory `resPath(icaSet)`/GOstatsEnrichmentAnalysis/ (this directory is first deleted if it already exists). This file is the one the user should look at.

The outputs of `hyperGTest` that are given in each table are:

DB, ID, Term:

  the database, the gene set ID, and the gene Set name

P-value:

  probability of observing the number of genes annotated for the gene set among the selected gene list, knowing the total number of annotated genes among the universe

,

Expected counts:

  expected number of genes in the selected gene list to be found at each tested category term/gene set,

Odds ratio:

  odds ratio for each category term tested which is an indicator of the level of enrichment of genes within the list as against the universe,

Counts:

  number of genes in the selected gene list that are annotated for the gene set,

Size:

  number of genes from the universe annotated for the gene set.

### Value

NULL

### Author(s)

Anne Biton

### See Also

`buildIcaSet`, `useMart`, `hyperGTest`, `GOHyperGParams`, `hypergeoAn`, `mergeGostatsResults`

### Examples

    ## Not run:
    # Load examples of IcaSet object
    data(icaSetCarbayo)

    ## Define parameters
    # Use threshold 3 to select contributing genes on which enrichment analysis will be applied
    # Results of enrichment analysis will be written in path 'resPath(params)/GOstatsEnrichAnalysis'
    params <- buildMineICAParams(resPath="carbayo/", selCutoff=3)

    ## Run enrichment analysis on the first two components contained in the icaSet object 'icaSetCarbayo'
    runEnrich(params=params,icaSet=icaSetCarbayo[,,1:2],dbs="GO", ontos="BP")

    ## End(Not run)

[Package MineICA version 1.26.0 Index]