gs_horizon: Plots a summary of enrichment results

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

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"
)

federicomarini/GeneTonic documentation built on Oct. 10, 2024, 8:49 p.m.