Construct a gene-geneset-graph from the results of a functional enrichment analysis

ggs_graph(
  res_enrich,
  res_de,
  annotation_obj = NULL,
  gtl = NULL,
  n_gs = 15,
  gs_ids = NULL,
  prettify = TRUE,
  geneset_graph_color = "gold",
  genes_graph_colpal = NULL
)

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.

gtl

A GeneTonic-list object, containing in its slots the arguments specified above: dds, res_de, res_enrich, and annotation_obj - the names of the list must be specified following the content they are expecting

n_gs

Integer value, corresponding to the maximal number of gene sets to be included

gs_ids

Character vector, containing a subset of gs_id as they are available in res_enrich. Lists the gene sets to be included in addition to the top ones (via n_gs)

prettify

Logical, controlling the aspect of the returned graph object. If TRUE (default value), different shapes of the nodes are returned, based on the node type

geneset_graph_color

Character value, specifying which color should be used for the fill of the shapes related to the gene sets.

genes_graph_colpal

A vector of colors, also provided with their hex string, to be used as a palette for coloring the gene nodes. If unspecified, defaults to a color ramp palette interpolating from blue through yellow to red.

Value

An igraph object to be further manipulated or processed/plotted (e.g. via igraph::plot.igraph() or visNetwork::visIgraph())

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)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)
#> using 'avgTxLength' from assays(dds), correcting for library size

# 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)
)
#> 'select()' returned 1:many mapping between keys and columns

# 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)
#> Found 500 gene sets in `topGOtableResult` object.
#> Converting for usage in GeneTonic...
res_enrich <- get_aggrscores(res_enrich, res_de, anno_df)

ggs <- ggs_graph(
  res_enrich,
  res_de,
  anno_df
)

ggs
#> IGRAPH 3156d5e UN-- 253 569 -- 
#> + attr: name (v/c), nodetype (v/c), shape (v/c), color (v/c), title
#> | (v/c)
#> + edges from 3156d5e (vertex names):
#>  [1] adaptive immune response--B2M    adaptive immune response--BCL6  
#>  [3] adaptive immune response--BTN3A1 adaptive immune response--BTN3A2
#>  [5] adaptive immune response--BTN3A3 adaptive immune response--C1QB  
#>  [7] adaptive immune response--C1R    adaptive immune response--C1RL  
#>  [9] adaptive immune response--C1S    adaptive immune response--C2    
#> [11] adaptive immune response--C3     adaptive immune response--C4A   
#> [13] adaptive immune response--C4B    adaptive immune response--CD1A  
#> + ... omitted several edges

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