XGR (version 1.1.5)

xSocialiserGenes: Function to calculate pair-wise semantic similarity given a list of genes and the ontology in query

Description

xSocialiserGenes is supposed to calculate pair-wise semantic similarity between a list of input genes and the ontology in query. It returns an object of class "igraph", a network representation of socialized genes. Now it supports enrichment analysis using a wide variety of ontologies such as Gene Ontology and Phenotype Ontologies. It first calculates semantic similarity between terms and then derives semantic similarity from term-term semantic similarity. Parallel computing is also supported.

Usage

xSocialiserGenes(data, check.symbol.identity = F, ontology = c("GOBP",
"GOMF", "GOCC", "DO", "HPPA", "HPMI", "HPCM", "HPMA", "MP"),
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 = T, verbose = T,
RData.location = "http://galahad.well.ox.ac.uk/bigdata")

Arguments

data

an input vector containing gene symbols

check.symbol.identity

logical to indicate whether to match the input data via Synonyms for those unmatchable by official gene symbols. By default, it sets to false

ontology

the ontology supported currently. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPCM" for Human Phenotype Clinical Modifier, "HPMA" for Human Phenotype Mortality Aging, "MP" for Mammalian Phenotype

measure

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"

method.term

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))

rescale

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

force

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)

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

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

path.mode

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)

true.path.rule

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

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display

RData.location

the characters to tell the location of built-in RData files. See xRDataLoader for details

Value

It returns an object of class "igraph", with nodes for input genes 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.

See Also

xSocialiser

Examples

# NOT RUN {
# Load the XGR package and specify the location of built-in data
library(XGR)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata/"

# Gene-based similarity analysis using Mammalian Phenotype Ontology (MP)
# a) provide the input Genes of interest (eg 100 randomly chosen human genes)
## load human genes
org.Hs.eg <- xRDataLoader(RData='org.Hs.eg',
RData.location=RData.location)
data <- as.character(sample(org.Hs.eg$gene_info$Symbol, 100))
data

# b) perform similarity analysis
sim <- xSocialiserGenes(data=data, ontology="MP",
RData.location=RData.location)

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

# d) visualise the gene 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)
# }