ontologySimliarity comes with data objects encapsulating the GO (Gene Ontology) annotation of genes :
gene_GO_terms, a list of character vectors of term IDs of GO terms annotating each gene, named by gene,
GO_IC, a numeric vector containing the information content of Gene Ontology terms based on frequencies of annotation in
These data objects can be loaded in an R session using
data(GO_IC) respectively. To process these objects, one can load the
ontologyIndex package and a data object encapsulating the Gene Ontology.
library(ontologyIndex) data(go) library(ontologySimilarity) data(gene_GO_terms) data(GO_IC)
Users can simply subset the
gene_GO_terms object to obtain GO annotation for their genes of interest, using a
character vector of gene names. In this example, we'll use the BEACH domain containing gene family .
beach <- gene_GO_terms[c("LRBA", "LYST", "NBEA", "NBEAL1", "NBEAL2", "NSMAF", "WDFY3", "WDFY4", "WDR81")]
To see the names of the terms annotating a particular gene, the
ontology_index object can be used, using the term IDs to subset the
name slot. For example, for
gene_GO_terms object contains annotation relating to all branches of the Gene Ontology, i.e.
"molecular_function". If you are only interested in one branch - for example
"cellular_component", you can use the
ontologyIndex package's function
intersection_with_descendants to subset the annotation.
cc <- go$id[go$name == "cellular_component"] beach_cc <- lapply(beach, function(x) intersection_with_descendants(go, roots=cc, x)) data.frame(check.names=FALSE, `#terms`=sapply(beach, length), `#CC terms`=sapply(beach_cc, length))
A pairwise gene semantic similarity matrix can be computed simply using the function
get_sim_grid, and passing an
ontology_index object, information content and annotation list as parameters (see
?get_sim_grid for more details). Here we plot the resulting similarity matrix using the
sim_matrix <- get_sim_grid( ontology=go, information_content=GO_IC, term_sets=beach) library(paintmap) paintmap(colour_matrix(sim_matrix))
One can test whether a subset of genes is significantly similar as a group in the context of a larger collection by using the function
get_sim_p_from_ontology to compute a p-value of similarity. For example here, we will compare the significance of the mean pairwise gene similarity within the BEACH group against randomly selected subsets of genes of the same size chosen from the
get_sim_p_from_ontology( ontology=go, information_content=GO_IC, term_sets=gene_GO_terms, group=names(beach) )
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