This function calculates the Gene Ontology (GO) similarity between two groups of GO terms or two Entrez gene IDs.
twoGOSim(id1, id2, type = c("go", "gene"), ont = "MF", organism = "human",
measure = "Resnik", combine = "BMA")
A character vector. length > 1: each element is a GO term; length = 1: the Entrez Gene ID.
A character vector. length > 1: each element is a GO term; length = 1: the Entrez Gene ID.
Input type of id1 and id2, 'go'
for GO Terms,
'gene'
for gene ID.
Default is 'MF'
, can be one of 'MF'
,
'BP'
, or 'CC'
subontologies.
Default is 'human'
, can be one of
'anopheles'
, 'arabidopsis'
, 'bovine'
, 'canine'
,
'chicken'
, 'chimp'
, 'coelicolor'
, 'ecolik12'
,
'ecsakai'
, 'fly'
, 'human'
, 'malaria'
,
'mouse'
, 'pig'
, 'rat'
, 'rhesus'
,
'worm'
, 'xenopus'
, 'yeast'
or 'zebrafish'
.
Default is 'Resnik'
, can be one of
'Resnik'
, 'Lin'
, 'Rel'
, 'Jiang'
or 'Wang'
.
Default is 'BMA'
, can be one of
'max'
, 'average'
, 'rcmax'
or 'BMA'
for combining semantic similarity scores of multiple GO terms
associated with protein.
A n x n matrix.
See parGOSim
for protein similarity calculation
based on Gene Ontology (GO) semantic similarity.
See parSeqSim
for paralleled protein similarity
calculation based on Smith-Waterman local alignment.
# NOT RUN {
# Be careful when testing this since it involves GO similarity computation
# and might produce unpredictable results in some environments
library("GOSemSim")
library("org.Hs.eg.db")
# by GO terms
go1 = c("GO:0004022", "GO:0004024", "GO:0004023")
go2 = c("GO:0009055", "GO:0020037")
gsim1 = twoGOSim(go1, go2, type = "go", ont = "MF", measure = "Wang")
print(gsim1)
# by Entrez gene id
gene1 = "241"
gene2 = "251"
gsim2 = twoGOSim(gene1, gene2, type = "gene", ont = "BP", measure = "Lin")
print(gsim2)
# }
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