distill_enrichment: Distill enrichment results

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

Distill enrichment results

Description

Distill the main topics from the enrichment results, based on the graph derived from constructing an enrichment map

Usage

distill_enrichment(
  res_enrich,
  res_de,
  annotation_obj,
  gtl = NULL,
  n_gs = nrow(res_enrich),
  cluster_fun = "cluster_markov"
)

Arguments

res_enrich	A data.frame object, storing the result of the functional enrichment analysis.
res_de	A DESeqResults object. As for the dds parameter, this is also commonly used in the DESeq2 framework.
annotation_obj	A data.frame object, containing two columns, gene_id with a set of unambiguous identifiers (e.g. ENSEMBL ids) and gene_name, containing e.g. HGNC-based gene symbols.
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 used.
cluster_fun	Character, referring to the name of the function used for the community detection in the enrichment map graph. Could be one of "cluster_markov", "cluster_louvain", or "cluster_walktrap", as they all return a communities object.

Value

A list containing three objects:

• the distilled table of enrichment, distilled_table, where the new meta-genesets are identified and defined, specifying e.g. the names of each component, and the genes associated to these.

• the distilled graph for the enrichment map, distilled_em, with the information on the membership

• the original res_enrich, augmented with the information of the membership related to the meta-genesets

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)

distilled <- distill_enrichment(res_enrich,
  res_de,
  annotation_obj,
  n_gs = 100,
  cluster_fun = "cluster_markov"
)
colnames(distilled$distilled_table)
distilled$distilled_em

federicomarini/GeneTonic documentation built on March 27, 2024, 4:19 p.m.