gs_volcano {GeneTonic} | R Documentation

## Volcano plot for gene sets

### Description

Volcano plot for gene sets, to summarize visually the functional enrichment results

### Usage

    gs_volcano(
      res_enrich,
      p_threshold = 0.05,
      color_by = "aggr_score",
      volcano_labels = 10,
      scale_circles = 1,
      gs_ids = NULL,
      plot_title = 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). This object needs to be processed first by a function such as `get_aggrscores()` to compute the term-wise `z_score` or `aggr_score`, which will be used for plotting
`p_threshold` | Numeric, defines the threshold to be used for filtering the gene sets to display. Defaults to 0.05
`color_by` | Character specifying the column of `res_enrich` to be used for coloring the plotted gene sets. Defaults to `aggr_score`.
`volcano_labels` | Integer, maximum number of labels for the gene sets to be plotted as labels on the volcano scatter plot.
`scale_circles` | A numeric value, to define the scaling factor for the circle sizes. Defaults to 1.
`gs_ids` | Character vector, containing a subset of `gs_id` as they are available in `res_enrich`. Lists the gene sets to be labeled.
`plot_title` | Character string, used as title for the plot. If left `NULL`, it defaults to a general description of the plot and of the DE contrast

### Details

It is also possible to reduce the redundancy of the input `res_enrich` object, if it is passed in advance to the `gs_simplify()` function.

### Value

A `ggplot` object

### See Also

`gs_simplify()` can be applied in advance to `res_enrich` to reduce the redundancy of the displayed gene sets

### 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_volcano(res_enrich)

[Package GeneTonic version 1.0.1 Index]