hclustThreshold: From a Sorensen-Dice threshold dissimilarity matrix, generate...

In pablof1988/goSorensen: Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO)

From a Sorensen-Dice threshold dissimilarity matrix, generate an object of class "hclust"

Description

From a Sorensen-Dice threshold dissimilarity matrix, generate an object of class "hclust"

Usage

hclustThreshold(
  x,
  onTheFlyDev = NULL,
  method = "complete",
  jobName = paste("Equivalence cluster", method, sep = "_"),
  ylab = "Sorensen equivalence threshold dissimilarity",
  ...
)

Arguments

x	an object of class "dist" with the Sorensen-Dice equivalence threshold dissimilarities matrix
onTheFlyDev	character, name of the graphical device where to immediately display the resulting diagram. The appropriate names depend on the operating system. Defaults to NULL and then nothing is displayed
method	character, one of the admissible methods in function hclust. Defaults to "complete"
jobName	character, main plot name, defaults to paste("Equivalence cluster", onto, ontoLevel, method, sep = "_")
ylab	character, label of the vertical axis of the plot, defaults to "Sorensen equivalence threshold dissimilarity"
...	additional arguments to hclust

Value

An object of class equivClustSorensen, descending from class hclust

Examples

# Gene lists to analyse:
data("allOncoGeneLists")

# Obtaining ENTREZ identifiers for the gene universe of humans:
library(org.Hs.eg.db)
humanEntrezIDs <- keys(org.Hs.eg.db, keytype = "ENTREZID")

# First, compute the Sorensen-Dice threshold equivalence dissimilarity
# for ontology BP at level 4:
# # Very time consuming, it requires building all joint enrichment contingency tables
# dOncBP4 <- sorenThreshold(allOncoGeneLists, onto = "BP", GOLevel = 4,
#                         geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
# Better (much faster), using the previously tabulated contingency tables:
data("cont_all_BP4")
dOncBP4 <- sorenThreshold(cont_all_BP4)
clust.threshold <- hclustThreshold(dOncBP4)
plot(clust.threshold, main = "AllOnco genelists, BP ontology at level 4",
     ylab = "Sorensen equivalence threshold")
# With the same data, an UPGMA dendrogram:
clust.threshold <- hclustThreshold(dOncBP4, method = "average")
plot(clust.threshold, main = "AllOnco genelists, BP ontology at level 4",
     ylab = "Sorensen equivalence threshold")

pablof1988/goSorensen documentation built on April 17, 2025, 10:44 p.m.