Package 'GOSemSim'

Title: GO-terms Semantic Similarity Measures
Description: Semantic similarity analysis of Gene Ontology (GO) annotations provides a quantitative framework for comparing GO terms, gene products, and gene clusters. GOSemSim implements widely used information content- and graph-based similarity measures, including the methods of Resnik, Schlicker, Jiang, Lin, Wang and TCSS. It also provides utilities for preparing annotation data and combining term-level similarities into gene- and cluster-level scores.
Authors: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb]
Maintainer: Guangchuang Yu <[email protected]>
License: Artistic-2.0
Version: 2.39.2
Built: 2026-06-29 14:19:01 UTC
Source: https://github.com/bioc/GOSemSim

Help Index

buildGOmap

Description

Addding indirect GO annotation

Usage

buildGOmap(TERM2GENE)

Arguments

TERM2GENE

data.frame with two or three columns of GO TERM, GENE and ONTOLOGY (optional)

Details

provided by a data.frame of GO TERM (column 1), GENE (column 2) and ONTOLOGY (optional) that describes GO direct annotation, this function will add indirect GO annotation of genes.

Value

data.frame, GO annotation with direct and indirect annotation

Author(s)

Yu Guangchuang

Semantic similarity between two gene clusters

Description

Semantic similarity between two gene clusters

Usage

clusterSim(
  cluster1,
  cluster2,
  semData,
  measure = "Wang",
  drop = "IEA",
  combine = "BMA"
)

Arguments

cluster1

A set of gene IDs
cluster2

Another set of gene IDs
semData

GOSemSimDATA object
measure

One of "Resnik", "Lin", "Rel", "Jiang", "TCSS" and "Wang" methods.
drop

Evidence codes to drop; use NULL to keep all GO annotations
combine

One of "max", "avg", "rcmax", "BMA" methods, used to combine multiple term scores.

Value

similarity

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

goSim() mgoSim() geneSim() mgeneSim() clusterSim() mclusterSim()

Examples

d <- godata('org.Hs.eg.db', ont = "MF", computeIC = FALSE)
cluster1 <- c("835", "5261", "241", "994")
cluster2 <- c("307", "308", "317", "321", "506", "540", "378", "388", "396")
clusterSim(cluster1, cluster2, semData = d, measure = "Wang")

combining similarity matrix to similarity score

Description

Functions for combining similarity matrix to similarity score

Usage

combineScores(SimScores, combine)

Arguments

SimScores

similarity matrix
combine

combine method

Value

similarity value

Author(s)

Guangchuang Yu https://yulab-smu.top

Semantic similarity between two genes

Description

Given two genes, calculate their semantic similarity and return the score with corresponding GO terms.

Usage

geneSim(gene1, gene2, semData, measure = "Wang", drop = "IEA", combine = "BMA")

Arguments

gene1

Entrez gene ID
gene2

Another Entrez gene ID
semData

GOSemSimDATA object
measure

One of "Resnik", "Lin", "Rel", "Jiang", "TCSS" and "Wang" methods.
drop

Evidence codes to drop; use NULL to keep all GO annotations
combine

One of "max", "avg", "rcmax", "BMA" methods, used to combine multiple term scores.

Value

A list containing similarity value and corresponding GO terms

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

goSim() mgoSim() mgeneSim() clusterSim() mclusterSim()

Examples

d <- godata('org.Hs.eg.db', ont = "MF", computeIC = FALSE)
geneSim("241", "251", semData = d, measure = "Wang")

Get organism name from OrgDb object

Description

Get organism name from OrgDb object

Usage

get_organism(object)

Arguments

object

OrgDb object or OrgDb package name

Value

Organism name

Author(s)

Guangchuang Yu

Information content of GO terms

Description

These datasets are the information contents of GOterms.

References

Yu et al. (2010) GOSemSim: an R package for measuring semantic similarity among GO terms and gene products Bioinformatics (Oxford, England), 26:7 976–978, April 2010. ISSN 1367-4803 http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/7/976 PMID: 20179076

godata

Description

prepare GO DATA for measuring semantic similarity

Usage

godata(
  OrgDb = NULL,
  annoDb = NULL,
  keytype = "ENTREZID",
  ont,
  computeIC = TRUE,
  processTCSS = FALSE,
  cutoff = NULL
)

Arguments

OrgDb

OrgDb object (will be removed in future, please use annoDb instead)
annoDb

GO annotation database, can be OrgDb or a data.frame contains three columns of 'GENE', 'GO' and 'ONTOLOGY'.
keytype

keytype
ont

one of 'BP', 'MF', 'CC'
computeIC

logical, whether computer IC
processTCSS

logical, whether to prepare TCSS data. TCSS requires computeIC = TRUE, which is enabled automatically when processTCSS = TRUE.
cutoff

topology cutoff for TCSS subgraph construction. If NULL, empirical defaults are used; users can estimate a cutoff with tcss_cutoff().

Value

GOSemSimDATA object

Author(s)

Guangchuang Yu

Class "GOSemSimDATA" This class stores IC and gene to go mapping for semantic similarity measurement

Description

Class "GOSemSimDATA" This class stores IC and gene to go mapping for semantic similarity measurement

Slots

keys

gene ID

ont

ontology

IC

IC data

geneAnno

gene to GO mapping

tcssdata

tcssdata

metadata

metadata

Semantic similarity between two GO terms

Description

Given two GO IDs, calculate their semantic similarity.

Usage

goSim(GOID1, GOID2, semData, measure = "Wang")

Arguments

GOID1

GO ID 1
GOID2

GO ID 2
semData

GOSemSimDATA object
measure

One of "Resnik", "Lin", "Rel", "Jiang", "TCSS" and "Wang" methods.

Value

similarity

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

goSim() mgoSim() geneSim() mgeneSim() clusterSim() mclusterSim()

Examples

d <- godata('org.Hs.eg.db', ont = "MF", computeIC = FALSE)
goSim("GO:0004022", "GO:0005515", semData = d, measure = "Wang")

information content based methods

Description

Information Content Based Methods for semantic similarity measuring

Usage

infoContentMethod(ID1, ID2, method, godata)

Arguments

ID1

Ontology Term
ID2

Ontology Term
method

one of "Resnik", "Jiang", "Lin" and "Rel", "TCSS".
godata

GOSemSimDATA object

Details

implemented for methods proposed by Resnik, Jiang, Lin and Schlicker.

Value

semantic similarity score

Author(s)

Guangchuang Yu https://yulab-smu.top

Pairwise semantic similarities for a list of gene clusters

Description

Calculate pairwise semantic similarities for a list of gene clusters.

Usage

mclusterSim(
  clusters,
  semData,
  measure = "Wang",
  drop = "IEA",
  combine = "BMA",
  BPPARAM = NULL
)

Arguments

clusters

A list of gene clusters
semData

GOSemSimDATA object
measure

One of "Resnik", "Lin", "Rel", "Jiang", "TCSS" and "Wang" methods.
drop

Evidence codes to drop; use NULL to keep all GO annotations
combine

One of "max", "avg", "rcmax", "BMA" methods, used to combine multiple term scores.
BPPARAM

optional BiocParallel::BiocParallelParam object for parallel pairwise similarity calculation. The default NULL uses serial calculation.

Value

similarity matrix

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

goSim() mgoSim() geneSim() mgeneSim() clusterSim() mclusterSim()

Examples

d <- godata('org.Hs.eg.db', ont = "MF", computeIC = FALSE)
cluster1 <- c("835", "5261", "241")
cluster2 <- c("578", "582")
cluster3 <- c("307", "308", "317")
clusters <- list(a = cluster1, b = cluster2, c = cluster3)
mclusterSim(clusters, semData = d, measure = "Wang")

Pairwise semantic similarity for a list of genes

Description

Calculate pairwise semantic similarities for a given list of genes.

Usage

mgeneSim(
  genes,
  semData,
  measure = "Wang",
  drop = "IEA",
  combine = "BMA",
  verbose = TRUE,
  BPPARAM = NULL
)

Arguments

genes

A list of Entrez gene IDs
semData

GOSemSimDATA object
measure

One of "Resnik", "Lin", "Rel", "Jiang", "TCSS" and "Wang" methods.
drop

Evidence codes to drop; use NULL to keep all GO annotations
combine

One of "max", "avg", "rcmax", "BMA" methods, used to combine multiple term scores.
verbose

Whether to show a progress bar
BPPARAM

optional BiocParallel::BiocParallelParam object for parallel pairwise similarity calculation. The default NULL uses the original serial implementation.

Details

Parallel calculation is opt-in. With the default BPPARAM = NULL, mgeneSim() keeps the original serial behavior and can show a progress bar when verbose = TRUE. When a BPPARAM object is supplied, pairwise similarities are calculated through BiocParallel::bplapply() and the progress bar is not shown.

Value

similarity matrix

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

goSim() mgoSim() geneSim() mgeneSim() clusterSim() mclusterSim()

Examples

d <- godata('org.Hs.eg.db', ont = "MF", computeIC = FALSE)
mgeneSim(c("835", "5261", "241"), semData = d, measure = "Wang")

Semantic similarity between two GO term sets

Description

Given two sets of GO terms, calculate their semantic similarity.

Usage

mgoSim(GO1, GO2, semData, measure = "Wang", combine = "BMA")

Arguments

GO1

A set of GO terms
GO2

Another set of GO terms
semData

GOSemSimDATA object
measure

One of "Resnik", "Lin", "Rel", "Jiang", "TCSS" and "Wang" methods.
combine

One of "max", "avg", "rcmax", "BMA" methods, used to combine multiple term scores.

Value

similarity

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

goSim() mgoSim() geneSim() mgeneSim() clusterSim() mclusterSim()

Examples

d <- godata('org.Hs.eg.db', ont = "MF", computeIC = FALSE)
go1 <- c("GO:0004022", "GO:0004024", "GO:0004023")
go2 <- c("GO:0009055", "GO:0020037")
mgoSim("GO:0003824", go2, semData = d, measure = "Wang")
mgoSim(go1, go2, semData = d, measure = "Wang")

read.blast2go

Description

given a BLAST2GO file, this function extracts the information from it and make it use for TERM2GENE.

Usage

read.blast2go(file, add_indirect_GO = FALSE)

Arguments

file

BLAST2GO file
add_indirect_GO

whether add indirect GO annotation

Value

a data frame with three columns: GENE, GO and ONTOLOGY

read.gaf

Description

parse GAF files

Usage

read.gaf(file, asis = FALSE, add_indirect_GO = FALSE)

parse_gff(file, asis = FALSE, add_indirect_GO = FALSE)

Arguments

file

GAF file
asis

logical, whether output the original contains of the file and only works if 'add_indirect_GO = FALSE'
add_indirect_GO

whether to add indirect GO annotation

Details

given a GAF file, this function extracts the information from it

Value

A data.frame. Original table if 'asis' works, otherwise contains 3 conlumns of 'GENE', 'GO' and 'ONTOLOGY'

Control auto-update of ontology databases

Description

When enabled, outdated local ontology SQLite databases will be automatically updated from remote mirrors.

Usage

set_auto_update(x = TRUE)

Arguments

x

logical, whether to enable auto-update (default: TRUE)

determine the topological cutoff for TCSS method

Description

determine the topological cutoff for TCSS method

Usage

tcss_cutoff(
  OrgDb = NULL,
  keytype = "ENTREZID",
  ont,
  combine_method = "max",
  ppidata
)

Arguments

OrgDb

OrgDb object
keytype

keytype
ont

ontology : "BP", "MF", "CC"
combine_method

one of "max", "BMA", "avg", "rcmax", "rcmax.avg"
ppidata

A data.frame contains positive set and negative set. Positive set is PPI pairs that already verified. ppidata has three columns, column 1 and 2 are character, column 3 must be logical value:TRUE/FALSE.

Value

numeric, topological cutoff for TCSS subgraph construction. The returned value can be passed to godata() via the cutoff argument together with processTCSS = TRUE.

Examples

## Not run: 
    library(org.Hs.eg.db)
    library(STRINGdb)

    string_db <- STRINGdb$new(version = "11.0", species = 9606,
    score_threshold = 700)
    string_proteins <- string_db$get_proteins()

    #get relationship
    ppi <- string_db$get_interactions(string_proteins$protein_external_id)

    ppi$from <- vapply(ppi$from, function(e)
                       strsplit(e, "9606.")[[1]][2], character(1))
    ppi$to <- vapply(ppi$to, function(e)
                       strsplit(e, "9606.")[[1]][2], character(1))
    len <- nrow(ppi)

    #select length
    s_len <- 100
    pos_1 <- sample(len, s_len, replace = T)
    #negative set
    pos_2 <- sample(len, s_len, replace = T)
    pos_3 <- sample(len, s_len, replace = T)
    #union as ppidata
    ppidata <- data.frame(pro1 = c(ppi$from[pos_1], ppi$from[pos_2]),
     pro2 = c(ppi$to[pos_1], ppi$to[pos_3]),
     label = c(rep(TRUE, s_len), rep(FALSE, s_len)),
     stringsAsFactors = FALSE)

    cutoff <- tcss_cutoff(OrgDb = org.Hs.eg.db, keytype = "ENSEMBLPROT",
    ont = "BP", combine_method = "max", ppidata)

    semData <- godata(annoDb = org.Hs.eg.db, keytype = "ENSEMBLPROT",
    ont = "BP", computeIC = TRUE, processTCSS = TRUE, cutoff = cutoff)

## End(Not run)

termSim

Description

Measure similarities between two term vectors.

Usage

termSim(
  t1,
  t2,
  semData,
  method = c("Wang", "Resnik", "Rel", "Jiang", "Lin", "TCSS")
)

Arguments

t1

Term vector
t2

Term vector
semData

GOSemSimDATA object
method

One of "Wang", "Resnik", "Rel", "Jiang", "Lin", "TCSS"

Details

Provide two term vectors, this function calculates their similarities.

The TCSS method requires semData generated by godata() with computeIC = TRUE and processTCSS = TRUE.

Value

score matrix

Author(s)

Guangchuang Yu https://yulab-smu.top

wangMethod

Description

Method Wang for semantic similarity measuring

Usage

wangMethod_internal(ID1, ID2, ont = "BP")

Arguments

ID1

Ontology Term
ID2

Ontology Term
ont

Ontology

Value

semantic similarity score

Author(s)

Guangchuang Yu https://yulab-smu.top