gs_horizon {GeneTonic} | R Documentation

## Plots a summary of enrichment results

### Description

Plots a summary of enrichment results - horizon plot to compare one or more sets of results

### Usage

    gs_horizon(
      res_enrich,
      compared_res_enrich_list,
      n_gs = 20,
      p_value_column = "gs_pvalue",
      color_by = "z_score",
      ref_name = "ref_scenario",
      sort_by = c("clustered", "first_set")
    )

### 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).
`compared_res_enrich_list` | A named list, where each element is a `data.frame` formatted like the standard `res_enrich` objects used by `GeneTonic`. The names of the list are the names of the scenarios.
`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`.
`ref_name` | Character, defining the name of the scenario to compare against (the one in `res_enrich`) - defaults to "ref_scenario".
`sort_by` | Character string, either "clustered", or "first_set". This controls the sorting order of the included terms in the final plot. "clustered" presents the terms grouped by the scenario where they assume the highest values. "first_set" sorts the terms by the significance value in the reference scenario.

### Details

It makes sense to have the results in `res_enrich` sorted by increasing `gs_pvalue`, to make sure the top results are first sorted by the significance (when selecting the common gene sets across the `res_enrich` elements provided in `compared_res_enrich_list`)

The gene sets included are a subset of the ones in common to all different scenarios included in `res_enrich` and the elements of `compared_res_enrich_list`.

### Value

A `ggplot` object

### See Also

`gs_summary_overview()`, `gs_summary_overview_pair()`

### 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, ]
    res_enrich3 <- res_enrich[1:42, ]
    res_enrich4 <- res_enrich[1:42, ]

    set.seed(2*42)
    shuffled_ones_2 <- sample(seq_len(42)) # to generate permuted p-values
    res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones_2]
    res_enrich2$z_score <- res_enrich2$z_score[shuffled_ones_2]
    res_enrich2$aggr_score <- res_enrich2$aggr_score[shuffled_ones_2]

    set.seed(3*42)
    shuffled_ones_3 <- sample(seq_len(42)) # to generate permuted p-values
    res_enrich3$gs_pvalue <- res_enrich3$gs_pvalue[shuffled_ones_3]
    res_enrich3$z_score <- res_enrich3$z_score[shuffled_ones_3]
    res_enrich3$aggr_score <- res_enrich3$aggr_score[shuffled_ones_3]

    set.seed(4*42)
    shuffled_ones_4 <- sample(seq_len(42)) # to generate permuted p-values
    res_enrich4$gs_pvalue <- res_enrich4$gs_pvalue[shuffled_ones_4]
    res_enrich4$z_score <- res_enrich4$z_score[shuffled_ones_4]
    res_enrich4$aggr_score <- res_enrich4$aggr_score[shuffled_ones_4]

    compa_list <- list(
      scenario2 = res_enrich2,
      scenario3 = res_enrich3,
      scenario4 = res_enrich4
    )

    gs_horizon(res_enrich,
               compared_res_enrich_list = compa_list,
               n_gs = 50,
               sort_by = "clustered")
    gs_horizon(res_enrich,
               compared_res_enrich_list = compa_list,
               n_gs = 20,
               sort_by = "first_set")

[Package GeneTonic version 1.0.1 Index]