View source: R/hclustThreshold.R
hclustThreshold | R Documentation |
From a Sorensen-Dice threshold dissimilarity matrix, generate an object of class "hclust"
hclustThreshold(
x,
onTheFlyDev = NULL,
method = "complete",
jobName = paste("Equivalence cluster", method, sep = "_"),
ylab = "Sorensen equivalence threshold dissimilarity",
...
)
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 |
method |
character, one of the admissible methods in function |
jobName |
character, main plot name, defaults to
|
ylab |
character, label of the vertical axis of the plot, defaults to "Sorensen equivalence threshold dissimilarity" |
... |
additional arguments to |
An object of class equivClustSorensen
, descending from class hclust
# 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("allTabsBP.4")
dOncBP4 <- sorenThreshold(allTabsBP.4)
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")
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