xDAGsim | R Documentation

## Function to calculate pair-wise semantic similarity between input terms based on a direct acyclic graph (DAG) with annotated data

### Description

`xDAGsim` is supposed to calculate pair-wise semantic similarity between input terms based on a direct acyclic graph (DAG) with annotated data. It returns an object of class "igraph", a network representation of input terms. Parallel computing is also supported for Linux or Mac operating systems.

### Usage

    xDAGsim(g, terms = NULL, method.term = c("Resnik", "Lin", "Schlicker",
    "Jiang", "Pesquita"), fast = T, parallel = TRUE, multicores = NULL,
    verbose = T)

### Arguments

`g` | an object of class "igraph". It must contain a vertex attribute called 'anno' for storing annotation data (see example for howto)
`terms` | the terms/nodes between which pair-wise semantic similarity is calculated. If NULL, all terms in the input DAG will be used for calcluation, which is very prohibitively expensive!

`method.term`

the method used to measure semantic similarity between input terms. It can be "Resnik" for information content (IC) of most informative common ancestor (MICA) (see http://dl.acm.org/citation.cfm?id=1625914), "Lin" for 2*IC at MICA divided by the sum of IC at pairs of terms, "Schlicker" for weighted version of 'Lin' by the 1-prob(MICA) (see http://www.ncbi.nlm.nih.gov/pubmed/16776819), "Jiang" for 1 - difference between the sum of IC at pairs of terms and 2*IC at MICA (see https://arxiv.org/pdf/cmp-lg/9709008.pdf), "Pesquita" for graph information content similarity related to Tanimoto-Jacard index (ie. summed information content of common ancestors divided by summed information content of all ancestors of term1 and term2 (see http://www.ncbi.nlm.nih.gov/pubmed/18460186)). By default, it uses "Schlicker" method

`fast` | logical to indicate whether a vectorised fast computation is used. By default, it sets to true. It is always advisable to use this vectorised fast computation; since the conventional computation is just used for understanding scripts
`parallel` | logical to indicate whether parallel computation with multicores is used. By default, it sets to true, but not necessarily does so. It will depend on whether these two packages "foreach" and "doParallel" have been installed. It can be installed via: `source("http://bioconductor.org/biocLite.R"); biocLite(c("foreach","doParallel"))`. If not yet installed, this option will be disabled
`multicores` | an integer to specify how many cores will be registered as the multicore parallel backend to the 'foreach' package. If NULL, it will use a half of cores available in a user's computer. This option only works when parallel computation is enabled
`verbose` | logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display

### Value

It returns an object of class "igraph", with nodes for input terms and edges for pair-wise semantic similarity between terms.

### Note

none

### See Also

`xDAGanno`, `xConverter`

### Examples

    ## Not run:
    # 1) SNP-based ontology
    # 1a) ig.EF (an object of class "igraph" storing as a directed graph)
    g <- xRDataLoader('ig.EF')
    g

    # 1b) load GWAS SNPs annotated by EF (an object of class "dgCMatrix" storing a spare matrix)
    anno <- xRDataLoader(RData='GWAS2EF')

    # 1c) prepare for ontology and its annotation information
    dag <- xDAGanno(g=g, annotation=anno, path.mode="all_paths",
    true.path.rule=TRUE, verbose=TRUE)

    # 1d) calculate pair-wise semantic similarity between 5 randomly chosen terms
    terms <- sample(V(dag)$name, 5)
    sim <- xDAGsim(g=dag, terms=terms, method.term="Schlicker",
    parallel=FALSE)
    sim

    ###########################################################
    # 2) Gene-based ontology
    # 2a) ig.MP (an object of class "igraph" storing as a directed graph)
    g <- xRDataLoader('ig.MP')

    # 2b) load human genes annotated by MP (an object of class "GS" containing the 'gs' component)
    GS <- xRDataLoader(RData='org.Hs.egMP')
    anno <- GS$gs # notes: This is a list

    # 2c) prepare for annotation data
    dag <- xDAGanno(g=g, annotation=anno, path.mode="all_paths",
    true.path.rule=TRUE, verbose=TRUE)

    # 2d) calculate pair-wise semantic similarity between 5 randomly chosen terms
    terms <- sample(V(dag)$name, 5)
    sim <- xDAGsim(g=dag, terms=terms, method.term="Schlicker",
    parallel=FALSE)
    sim

    ## End(Not run)