R: Creates an enrichment map for the results of functional...

enrichment_map {GeneTonic}

R Documentation

Creates an enrichment map for the results of functional enrichment

Description

Generates a graph for the enrichment map, combining information from res_enrich and res_de. This object can be further plotted, e.g. statically via igraph::plot.igraph(), or dynamically via visNetwork::visIgraph()

Usage

enrichment_map(
  res_enrich,
  res_de,
  annotation_obj,
  n_gs = 50,
  gs_ids = NULL,
  overlap_threshold = 0.1,
  scale_edges_width = 200,
  scale_nodes_size = 5,
  color_by = "gs_pvalue"
)

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_de`	A `DESeqResults` object.
`annotation_obj`	A `data.frame` object with the feature annotation information, with at least two columns, `gene_id` and `gene_name`.
`n_gs`	Integer value, corresponding to the maximal number of gene sets to be displayed
`gs_ids`	Character vector, containing a subset of `gs_id` as they are available in `res_enrich`. Lists the gene sets to be displayed.
`overlap_threshold`	Numeric value, between 0 and 1. Defines the threshold to be used for removing edges in the enrichment map - edges below this value will be excluded from the final graph. Defaults to 0.1.
`scale_edges_width`	A numeric value, to define the scaling factor for the edges between nodes. Defaults to 200 (works well chained to `visNetwork` functions).
`scale_nodes_size`	A numeric value, to define the scaling factor for the node sizes. Defaults to 5 - works well chained to `visNetwork` functions.
`color_by`	Character, specifying the column of `res_enrich` to be used for coloring the plotted gene sets. Defaults to `gs_pvalue`.

Value

An igraph object to be further manipulated or processed/plotted

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)

em <- enrichment_map(res_enrich,
                     res_de,
                     anno_df,
                     n_gs = 20
)

em

# could be viewed interactively with
# library(visNetwork)
# library(magrittr)
# em %>%
#   visIgraph() %>%
#   visOptions(highlightNearest = list(enabled = TRUE,
#                                      degree = 1,
#                                      hover = TRUE),
#             nodesIdSelection = TRUE)