Plots a heatmap for genes and genesets — gs_summary

Plots a heatmap for genes and genesets, useful to spot out intersections across genesets and an overview of them

gs_summary_heat(res_enrich, res_de, annotation_obj, gtl = NULL, n_gs = 80)

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 displayed

Value

A ggplot object

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)

gs_summary_heat(
  res_enrich = res_enrich,
  res_de = res_de,
  annotation_obj = anno_df,
  n_gs = 20
)