distill_enrichment {GeneTonic} | R Documentation

## Distill enrichment results

### Description

Distill the main topics from the enrichment results, based on the graph derived from constructing an enrichment map

### Usage

    distill_enrichment(
      res_enrich,
      res_de,
      annotation_obj,
      gtl = NULL,
      n_gs = nrow(res_enrich),
      cluster_fun = "cluster_markov"
    )

### Arguments

`res_enrich` | A `data.frame` object, storing the result of the functional enrichment analysis.
`res_de` | A `DESeqResults` object. As for the `dds` parameter, this is also commonly used in the `DESeq2` framework.
`annotation_obj` | A `data.frame` object, containing two columns, `gene_id` with a set of unambiguous identifiers (e.g. ENSEMBL ids) and `gene_name`, containing e.g. HGNC-based gene symbols.
`gtl` | A `GeneTonic`-list object, containing in its slots the arguments specified above: `dds`, `res_de`, `res_enrich`, and `annotation_obj` - the names of the list must be specified following the content they are expecting
`n_gs` | Integer value, corresponding to the maximal number of gene sets to be used.
`cluster_fun` | Character, referring to the name of the function used for the community detection in the enrichment map graph. Could be one of "cluster_markov", "cluster_louvain", or "cluster_walktrap", as they all return a `communities` object.

### Value

A list containing three objects:

- the distilled table of enrichment, `distilled_table`, where the new meta-genesets are identified and defined, specifying e.g. the names of each component, and the genes associated to these.

- the distilled graph for the enrichment map, `distilled_em`, with the information on the membership

- the original `res_enrich`, augmented with the information of the membership related to the meta-genesets

### Examples

    library("macrophage")
    library("DESeq2")
    library("org.Hs.eg.db")
    library("AnnotationDbi")

    # dds object
    data("gse", package = "macrophage")
    dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
    rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
    dds_macrophage <- estimateSizeFactors(dds_macrophage)

    # annotation object
    anno_df <- data.frame(
      gene_id = rownames(dds_macrophage),
      gene_name = mapIds(org.Hs.eg.db,
        keys = rownames(dds_macrophage),
        column = "SYMBOL",
        keytype = "ENSEMBL"
      ),
      stringsAsFactors = FALSE,
      row.names = rownames(dds_macrophage)
    )

    # res object
    data(res_de_macrophage, package = "GeneTonic")
    res_de <- res_macrophage_IFNg_vs_naive

    # res_enrich object
    data(res_enrich_macrophage, package = "GeneTonic")
    res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive)
    res_enrich <- get_aggrscores(res_enrich, res_de, anno_df)

    distilled <- distill_enrichment(res_enrich,
      res_de,
      annotation_obj,
      n_gs = 100,
      cluster_fun = "cluster_markov"
    )
    colnames(distilled$distilled_table)
    distilled$distilled_em

[Package GeneTonic version 1.6.4 Index]