enrichment_map {GeneTonic} | R Documentation |
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()
enrichment_map( res_enrich, res_de, annotation_obj, gtl = NULL, n_gs = 50, gs_ids = NULL, overlap_threshold = 0.1, scale_edges_width = 200, scale_nodes_size = 5, color_by = "gs_pvalue" )
res_enrich |
A |
res_de |
A |
annotation_obj |
A |
gtl |
A |
n_gs |
Integer value, corresponding to the maximal number of gene sets to be displayed |
gs_ids |
Character vector, containing a subset of |
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 |
scale_nodes_size |
A numeric value, to define the scaling factor for the
node sizes. Defaults to 5 - works well chained to |
color_by |
Character, specifying the column of |
An igraph
object to be further manipulated or processed/plotted
GeneTonic()
embeds an interactive visualization for the enrichment map
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)