semsim {ontoTools} | R Documentation

## Compute semantic similarity measure for terms in an object-ontology complex

### Description

Compute semantic similarity measure for terms in an object-ontology complex

### Usage

    semsim(c1, c2, ooc, acc=NULL, pc=NULL)
    conceptProbs(ooc,acc=NULL,inds=NULL)
    subsumers(c1, c2, ont, acc=NULL)
    pms(c1, c2, ooc, acc=NULL, pc=NULL)
    usageCount(map,acc,inds)

### Arguments

`c1` | c1, c2: "character" terms to be compared
`c2` | c1, c2: "character" terms to be compared
`ooc` | ooc: an object of class "OOC": object-ontology complex
`ont` | ont: an object of class "ontology": annotated rooted DAG
`acc` | acc: optional (sparse) accessibility matrix for the ontology
`pc` | pc: optional vector of concept probabilities, if pre-computed
`map` | map: OOmap component of an ooc
`inds` | inds: vector of numeric indices, row indices of object-ontology map to be processed

### Details

For large ontologies, computation of the term accessibility relationships and term probabilities can be costly. Once these are computed to support one semsim calculation, they should be saved. The acc and pc parameters allow use of this saved information.

### Value

semsim returns the measure of semantic similarity cited by Lord et al (2003).

### Author(s)

Vince Carey <stvjc@channing.harvard.edu>

### References

PW Lord et al, Bioinformatics, 19(10)2003:1275

### Examples

    #
    # we are given a graph of GOMF and the OOmap between LL and GOMF
    # derived from humanLLMappings and stored as data resources in
    # ontoTools -- these will have to be updated regularly
    #
    data(goMFgraph.1.5)
    data(LL2GOMFooMap.1.5)
    #
    # build the rooted DAG, the ontology, and the OOC objects
    #
    gomfrDAG <- new("rootedDAG", root="GO:0003674", DAG=goMFgraph.1.5)
    GOMFonto <- new("ontology", name="GOMF", version="bioc GO 1.5", rDAG=gomfrDAG)
    LLGOMFOOC <- makeOOC(GOMFonto, LL2GOMFooMap.1.5)
    #
    # we are given the accessibility matrix for the GO MF graph as a
    # data resource, and we can compute some term probabilities
    #
    data(goMFamat.1.5)
    pc <- conceptProbs(LLGOMFOOC, goMFamat.1.5, inds=1:20)
    #
    # now we will get a sample of GO MF terms and compute the
    # semantic similarities of pairs of terms in the sample
    #
    data(LL2GOMFcp.1.5) # full set of precomputed concept probabilities
    library(GO)
    library(Biobase)
    library(combinat)
    GO() # get the GO environments
    GOtags <- ls(env=GOTERM)
    GOlabs <- multiget(GOtags, env=GOTERM)
    GOMFtags <- GOtags[sapply(GOlabs,names)=="MF"]
    GOMFterms <- unlist(multiget(GOMFtags,env=GOTERM))
    ntags <- length(GOMFtags)
    if (any(duplicated(GOMFterms)))
     {
     dups <- (1:ntags)[duplicated(GOMFterms)]
     GOMFterms[dups] <- paste(GOMFterms[dups],".2",sep="")
     }
    names(GOMFterms) <- GOMFtags
    set.seed(1234)
    st <- sample(names(GOMFterms),size=50) # take the sample
    st <- intersect(st, names(LL2GOMFcp.1.5))[1:10] # use only those terms available in GO 1.5
    pst <- combn(st,2)   # get a matrix with the pairs of terms in columns
    npst <- ncol(pst)
    ss <- rep(NA,npst)
    for (i in 1:npst)  # compute semantic similarities
      {
      cat(i)
      ss[i] <- semsim( pst[1,i], pst[2,i], ooc=LLGOMFOOC, acc=goMFamat.1.5, pc=LL2GOMFcp.1.5 )
      }
    print(summary(ss))
    top <- (1:npst)[ss==max(ss,na.rm=TRUE)][1]  # index of the most similar pair
                 # note -- must come to an understanding of the NAs
    print( GOMFterms[ as.character(pst[,top]) ] )
    pen <- (1:npst)[ss==max(ss[-top],na.rm=TRUE)][1] # second most similar
    print( GOMFterms[ as.character(pst[,pen]) ] )

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