ggs_graph {GeneTonic} | R Documentation

## Construct a gene-geneset-graph

### Description

Construct a gene-geneset-graph from the results of a functional enrichment analysis

### Usage

    ggs_graph(
      res_enrich,
      res_de,
      annotation_obj = NULL,
      gtl = NULL,
      n_gs = 15,
      gs_ids = NULL,
      prettify = TRUE,
      geneset_graph_color = "gold",
      genes_graph_colpal = NULL
    )

### Arguments

`res_enrich` | A `data.frame` object, storing the result of the functional enrichment analysis. See more in the main function, `GeneTonic()`, to check the formatting requirements (a minimal set of columns should be present).
`res_de` | A `DESeqResults` object.
`annotation_obj` | A `data.frame` object with the feature annotation information, with at least two columns, `gene_id` and `gene_name`.
`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 included
`gs_ids` | Character vector, containing a subset of `gs_id` as they are available in `res_enrich`. Lists the gene sets to be included in addition to the top ones (via `n_gs`)
`prettify` | Logical, controlling the aspect of the returned graph object. If TRUE (default value), different shapes of the nodes are returned, based on the node type
`geneset_graph_color` | Character value, specifying which color should be used for the fill of the shapes related to the gene sets.
`genes_graph_colpal` | A vector of colors, also provided with their hex string, to be used as a palette for coloring the gene nodes. If unspecified, defaults to a color ramp palette interpolating from blue through yellow to red.

### Value

An `igraph` object to be further manipulated or processed/plotted (e.g. via `igraph::plot.igraph()` or visNetwork::visIgraph())

### 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)

    ggs <- ggs_graph(
      res_enrich,
      res_de,
      anno_df
    )

    ggs

    #' # could be viewed interactively with
    # library(visNetwork)
    # library(magrittr)
    # ggs %>%
    #   visIgraph() %>%
    #   visOptions(highlightNearest = list(enabled = TRUE,
    #                                      degree = 1,
    #                                      hover = TRUE),
    #             nodesIdSelection = TRUE)

[Package GeneTonic version 1.6.4 Index]