gs_summary_heat: Plots a heatmap for genes and genesets

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

Description

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

Usage

gs_summary_heat(res_enrich, res_de, annotation_obj, 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.
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)
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)

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

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