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

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

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

See Also

GeneTonic() embeds an interactive visualization for the enrichment map

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)

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