Plots a summary of enrichment results

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

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)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)
#> using 'avgTxLength' from assays(dds), correcting for library size

# 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)
)
#> 'select()' returned 1:many mapping between keys and columns

# 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)
#> Found 500 gene sets in `topGOtableResult` object.
#> Converting for usage in GeneTonic...
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
)