ggs_backbone {GeneTonic} | R Documentation

## Extract the backbone for the gene-geneset graph

### Description

Extract the backbone for the gene-geneset graph, either for the genes or for the genesets

### Usage

    ggs_backbone(
      res_enrich,
      res_de,
      annotation_obj = NULL,
      gtl = NULL,
      n_gs = 15,
      gs_ids = NULL,
      bb_on = c("genesets", "features"),
      bb_method = c("sdsm", "fdsm", "hyperg"),
      bb_extract_alpha = 0.05,
      bb_extract_fwer = c("none", "bonferroni", "holm"),
      bb_fullinfo = FALSE,
      bb_remove_singletons = TRUE,
      color_graph = TRUE,
      color_by_geneset = "z_score",
      color_by_feature = "log2FoldChange",
      ...
    )

### 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`)
`bb_on` | A character string, either "genesets" or "features", to specify which entity should be based the backbone graph on.
`bb_method` | A character string, referring to the function to be called ( from the `backbone` package) for computing the backbone of the specified bipartite graph. Defaults to "sdsm", as recommended in the `backbone` package.
`bb_extract_alpha` | A numeric value, specifying the significance level to use when detecting the backbone of the network
`bb_extract_fwer` | A character string, defaulting to "none", specifying which method to use for the multiple testing correction for controlling the family-wise error rate
`bb_fullinfo` | Logical value, determining what will be returned as output: either a simple `ìgraph` object with the graph backbone (if set to `FALSE`), or a list object containing also the `backbone` object, and the gene-geneset graph used for the computation (if `TRUE`)
`bb_remove_singletons` | Logical value, defines whether to remove or leave in the returned graph the nodes that are not connected to other vertices
`color_graph` | Logical value, specifies whether to use information about genesets or features to colorize the nodes, e.g. for this info to be used in interactive versions of the graph
`color_by_geneset` | Character string, corresponding to the column in `res_enrich` to be used for coloring the nodes if `bb_on` is set to "genesets". Defaults to the "z_score", which can be obtained via `get_aggrscores()`
`color_by_feature` | Character string, corresponding to the column in `res_de` to be used for coloring the nodes if `bb_on` is set to "features". Defaults to the "log2FoldChange", which should be normally included in a DESeqResults object.
`...` | Additional parameters to be passed internally

### Value

According to the `bb_fullinfo`, either a simple `ìgraph` object with the graph backbone, or a named list object containing:

- the `igraph` of the extracted backbone

- the `backbone` object itself

- the gene-geneset graph used for the computation

### 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_bbg <- ggs_backbone(res_enrich,
      res_de,
      anno_df,
      n_gs = 50,
      bb_on = "genesets",
      color_graph = TRUE,
      color_by_geneset = "z_score"
    )
    plot(ggs_bbg)

    # if desired, one can also plot the interactive version
    visNetwork::visIgraph(ggs_bbg)

[Package GeneTonic version 1.4.1 Index]