gs_dendro: Dendrogram of the gene set enrichment results

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

Dendrogram of the gene set enrichment results

Description

Calculate (and plot) the dendrogram of the gene set enrichment results

Usage

gs_dendro(
  res_enrich,
  gtl = NULL,
  n_gs = nrow(res_enrich),
  gs_ids = NULL,
  gs_dist_type = "kappa",
  clust_method = "ward.D2",
  color_leaves_by = "z_score",
  size_leaves_by = "gs_pvalue",
  color_branches_by = "clusters",
  create_plot = TRUE
)

Arguments

res_enrich	A data.frame object, storing the result of the functional enrichment analysis. See more in the main function, GeneTonic(), to see the formatting requirements.
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 (from the top ranked ones). Defaults to the number of rows of res_enrich
gs_ids	Character vector, containing a subset of gs_id as they are available in res_enrich. Lists the gene sets to be included, additionally to the ones specified via n_gs. Defaults to NULL.
gs_dist_type	Character string, specifying which type of similarity (and therefore distance measure) will be used. Defaults to kappa, which uses create_kappa_matrix()
clust_method	Character string defining the agglomeration method to be used for the hierarchical clustering. See stats::hclust() for details, defaults to ward.D2
color_leaves_by	Character string, which columns of res_enrich will define the color of the leaves. Defaults to z_score
size_leaves_by	Character string, which columns of res_enrich will define the size of the leaves. Defaults to the gs_pvalue
color_branches_by	Character string, which columns of res_enrich will define the color of the branches. Defaults to clusters, which calls dynamicTreeCut::cutreeDynamic() to define the clusters
create_plot	Logical, whether to create the plot as well.

Value

A dendrogram object is returned invisibly, and a plot can be generated as well on that 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_dendro(res_enrich,
  n_gs = 100
)

federicomarini/GeneTonic documentation built on Oct. 10, 2024, 8:49 p.m.