gs_summary_overview_pair: Plots a summary of enrichment results

In GeneTonic: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

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)

GeneTonic documentation built on Nov. 8, 2020, 5:27 p.m.