clusterSim: Semantic Similarity Between Two Gene Clusters

In YuLab-SMU/GOSemSim: GO-terms Semantic Similarity Measures

Semantic Similarity Between Two Gene Clusters

Description

Given two gene clusters, this function calculates semantic similarity between them.

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	A set of evidence codes based on which certain annotations are dropped. Use NULL to keep all GO annotations.
combine	One of "max", "avg", "rcmax", "BMA" methods, for combining semantic similarity scores of multiple GO terms associated with protein or multiple proteins assiciated with protein cluster.

Value

similarity

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

See Also

goSim mgoSim geneSim mgeneSim 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")

YuLab-SMU/GOSemSim documentation built on Nov. 1, 2024, 4:43 a.m.