xSocialiserR Documentation

Function to calculate pair-wise semantic similarity given the input data and the ontology and its annotation


xSocialiser is supposed to calculate pair-wise semantic similarity given the input data and the ontology direct acyclic graph (DAG) and its annotation. It returns an object of class "igraph", a network representation of socialized genes/SNPs. It first calculates semantic similarity between terms and then derives semantic similarity from term-term semantic similarity. Parallel computing is also supported.


measure = c("BM.average", "BM.max", "BM.complete", "average", "max"),
method.term = c("Resnik", "Lin", "Schlicker", "Jiang", "Pesquita"),
rescale = TRUE,
force = TRUE,
fast = TRUE,
parallel = TRUE,
multicores = NULL,
path.mode = c("all_paths", "shortest_paths", "all_shortest_paths"),
true.path.rule = TRUE,
verbose = T



an input vector containing a list of genes or SNPs of interest between which pair-wise semantic similarity is calculated/socialized


the vertices/nodes for which annotation data are provided. It can be a sparse Matrix of class "dgCMatrix" (with variants/genes as rows and terms as columns), or a list of nodes/terms each containing annotation data, or an object of class 'GS' (basically a list for each node/term with annotation data)


an object of class "igraph" to represent DAG. It must have node/vertice attributes: "name" (i.e. "Term ID"), "term_id" (i.e. "Term ID"), "term_name" (i.e "Term Name") and "term_distance" (i.e. Term Distance: the distance to the root; always 0 for the root itself)


the measure used to derive semantic similarity between genes/SNPs from semantic similarity between terms. Take the semantic similartity between SNPs as an example. It can be "average" for average similarity between any two terms (one from SNP 1, the other from SNP 2), "max" for the maximum similarity between any two terms, "BM.average" for best-matching (BM) based average similarity (i.e. for each term of either SNP, first calculate maximum similarity to any term in the other SNP, then take average of maximum similarity; the final BM-based average similiary is the pre-calculated average between two SNPs in pair), "BM.max" for BM based maximum similarity (i.e. the same as "BM.average", but the final BM-based maximum similiary is the maximum of the pre-calculated average between two SNPs in pair), "BM.complete" for BM-based complete-linkage similarity (inspired by complete-linkage concept: the least of any maximum similarity between a term of one SNP and a term of the other SNP). When comparing BM-based similarity between SNPs, "BM.average" and "BM.max" are sensitive to the number of terms involved; instead, "BM.complete" is much robust in this aspect. By default, it uses "BM.average"


the method used to measure semantic similarity between 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))


logical to indicate whether the resulting values are rescaled to the range [0,1]. By default, it sets to true


logical to indicate whether the only most specific terms (for each SNP) will be used. By default, it sets to true. It is always advisable to use this since it is computationally fast but without compromising accuracy (considering the fact that true-path-rule has been applied when running xDAGanno)


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


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


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


the mode of paths induced by vertices/nodes with input annotation data. It can be "all_paths" for all possible paths to the root, "shortest_paths" for only one path to the root (for each node in query), "all_shortest_paths" for all shortest paths to the root (i.e. for each node, find all shortest paths with the equal lengths)


logical to indicate whether the true-path rule should be applied to propagate annotations. By default, it sets to true


logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display


It returns an object of class "igraph", with nodes for input genes/SNPs and edges for pair-wise semantic similarity between them. Also added graph attribute is 'dag' storing the annotated ontology DAG used. If no similarity is calculuated, it returns NULL.


For the mode "shortest_paths", the induced subgraph is the most concise, and thus informative for visualisation when there are many nodes in query, while the mode "all_paths" results in the complete subgraph.

See Also

xDAGsim, xSocialiserGenes, xSocialiserSNPs


## Not run: 
# Load the library

# 1) SNP-based enrichment analysis using GWAS Catalog traits (mapped to EF)
# 1a) ig.EF (an object of class "igraph" storing as a directed graph)
g <- xRDataLoader('ig.EF')

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

# 1c) prepare the input SNPs of interest (eg 8 randomly chosen SNPs)
allSNPs <- rownames(anno)
data <- sample(allSNPs,8)

# 1d) perform calculate pair-wise semantic similarity between 8 randomly chosen SNPs
sim <- xSocialiser(data=data, annotation=anno, g=g, parallel=FALSE,

# 1e) save similarity results to the file called 'EF_similarity.txt'
output <- igraph::get.data.frame(sim, what="edges")
utils::write.table(output, file="EF_similarity.txt", sep="\t",

# 1f) visualise the SNP network
## extract edge weight (with 2-digit precision)
x <- signif(as.numeric(E(sim)$weight), digits=2)
## rescale into an interval [1,4] as edge width
edge.width <- 1 + (x-min(x))/(max(x)-min(x))*3
## do visualisation
xVisNet(g=sim, vertex.shape="sphere", edge.width=edge.width,
edge.label=x, edge.label.cex=0.7)

## End(Not run)