gs_dendro {GeneTonic}R Documentation

Dendrogram of the gene set enrichment results

Description

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

Usage

gs_dendro(
  res_enrich,
  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.

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)
[Package GeneTonic version 0.99.6 Index]