gs_radar {GeneTonic} | R Documentation

## Radar (spider) plot for gene sets

### Description

Radar (spider) plot for gene sets, either for one or more results from functional enrichment analysis.

### Usage

    gs_radar(
      res_enrich,
      res_enrich2 = NULL,
      n_gs = 20,
      p_value_column = "gs_pvalue"
    )

    gs_spider(
      res_enrich,
      res_enrich2 = NULL,
      n_gs = 20,
      p_value_column = "gs_pvalue"
    )

### 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_enrich2` | Analogous to `res_enrich1`, another `data.frame` object, storing the result of the functional enrichment analysis, but for a different setting (e.g. another contrast). Defaults to NULL (in this case, a single set of enrichment results is plotted).
`n_gs` | Integer value, corresponding to the maximal number of gene sets to be displayed
`p_value_column` | Character string, specifying the column of `res_enrich` where the p-value to be represented is specified. Defaults to `gs_pvalue` (it could have other values, in case more than one p-value - or an adjusted p-value - have been specified).

### Value

A `plotly` 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_radar(res_enrich = res_enrich)
    # or using the alias...
    gs_spider(res_enrich = res_enrich)

    # with more than one set
    res_enrich2 <- res_enrich[1:60, ]
    set.seed(42)
    shuffled_ones <- sample(seq_len(60)) # to generate permuted p-values
    res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones]
    # ideally, I would also permute the z scores and aggregated scores
    gs_radar(res_enrich = res_enrich,
             res_enrich2 = res_enrich2)

[Package GeneTonic version 1.2.0 Index]