gs_dendro {GeneTonic} | R Documentation

## Dendrogram of the gene set enrichment results

### Description

Calculate (and plot) the dendrogram of the gene set enrichment results

### Usage

    gs_dendro(
      res_enrich,
      n_gs = nrow(res_enrich),
      gs_ids = NULL,
      gs_dist_type = "kappa",
      clust_method = "ward.D2",
      color_leaves_by = "z_score",
      size_leaves_by = "gs_pvalue",
      color_branches_by = "clusters",
      create_plot = TRUE
    )

### Arguments

`res_enrich` | A `data.frame` object, storing the result of the functional enrichment analysis. See more in the main function, `GeneTonic()`, to see the formatting requirements.
`n_gs` | Integer value, corresponding to the maximal number of gene sets to be included (from the top ranked ones). Defaults to the number of rows of `res_enrich`
`gs_ids` | Character vector, containing a subset of `gs_id` as they are available in `res_enrich`. Lists the gene sets to be included, additionally to the ones specified via `n_gs`. Defaults to NULL.
`gs_dist_type` | Character string, specifying which type of similarity (and therefore distance measure) will be used. Defaults to `kappa`, which uses `create_kappa_matrix()`
`clust_method` | Character string defining the agglomeration method to be used for the hierarchical clustering. See `stats::hclust()` for details, defaults to `ward.D2`
`color_leaves_by` | Character string, which columns of `res_enrich` will define the color of the leaves. Defaults to `z_score`
`size_leaves_by` | Character string, which columns of `res_enrich` will define the size of the leaves. Defaults to the `gs_pvalue`
`color_branches_by` | Character string, which columns of `res_enrich` will define the color of the branches. Defaults to `clusters`, which calls `dynamicTreeCut::cutreeDynamic()` to define the clusters
`create_plot` | Logical, whether to create the plot as well.

### Value

A dendrogram object is returned invisibly, and a plot can be generated as well on that object.

### 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)

    gs_dendro(res_enrich,
              n_gs = 100)

[Package GeneTonic version 1.0.1 Index]