gs_summary_overview_pair {GeneTonic} | R Documentation

## Plots a summary of enrichment results

### Description

Plots a summary of enrichment results - for two sets of results

### Usage

    gs_summary_overview_pair(
      res_enrich,
      res_enrich2,
      n_gs = 20,
      p_value_column = "gs_pvalue",
      color_by = "z_score",
      alpha_set2 = 1
    )

### 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` | As `res_enrich`, the result of functional enrichment analysis, in a scenario/contrast different than the first set.
`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).
`color_by` | Character, specifying the column of `res_enrich` to be used for coloring the plotted gene sets. Defaults sensibly to `z_score`.
`alpha_set2` | Numeric value, between 0 and 1, which specified the alpha transparency used for plotting the points for gene set 2.

### Value

A `ggplot` object

### See Also

`gs_summary_overview()`, `gs_horizon()`

### 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)

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

[Package GeneTonic version 1.6.4 Index]