Gene Ontology is the most widely used bio-ontologies. On Bioconductor, there are standard packages for GO (GO.db) and organism-specific GO annotation packages (org.*.db). In simona, there is a helper function create_ontology_DAG_from_GO_db() which makes use of the Biocoductor standard GO packages and constructs a DAG object automatically.
GO has three namespaces (or ontologies): biological process (BP), molecular function (MF) and celullar component (CC). The three GO namespaces are mutually exclusive, so the first argument of create_ontology_DAG_from_GO_db() is the GO namespace.
library(simona)
dag = create_ontology_DAG_from_GO_db("BP")
dag## An ontology_DAG object:
## Source: GO BP / GO.db package 3.17.0
## 27942 terms / 55956 relations
## Root: GO:0008150
## Terms: GO:0000001, GO:0000002, GO:0000003, GO:0000011, ...
## Max depth: 18
## Avg number of parents: 2.00
## Avg number of children: 1.88
## Aspect ratio: 363.92:1 (based on the longest distance from root)
## 782.78:1 (based on the shortest distance from root)
## Relations: is_a, part_of
##
## With the following columns in the metadata data frame:
## id, name, definitionThere are three main GO relations: “is_a”, “part_of” and “regulates”. “regulates” has two child relation types in GO: “negatively_regulates” and “positively_regulates”. So when “regulates” is selected, the two child relation types are automatically selected. By default only “is_a” and “part_of” are selected.
You can set a subset of relation types with the argument relations.
create_ontology_DAG_from_GO_db("BP", relations = c("part of", "regulates")) # "part_of" is also OK## An ontology_DAG object:
## Source: GO BP / GO.db package 3.17.0
## 27942 terms / 64560 relations
## Root: GO:0008150
## Terms: GO:0000001, GO:0000002, GO:0000003, GO:0000011, ...
## Max depth: 18
## Avg number of parents: 2.31
## Avg number of children: 2.20
## Aspect ratio: 285:1 (based on the longest distance from root)
## 1015:1 (based on the shortest distance from root)
## Relations: is_a, negatively_regulates, part_of, positively_regulates,
## regulates
## Relation types may have hierarchical relations.
##
## With the following columns in the metadata data frame:
## id, name, definition“is_a” is always selected because this is primary semantic relation type. So if you only want to include “is_a” relation, you can assign an empty vector to relations:
create_ontology_DAG_from_GO_db("BP", relations = character(0)) # or NULL, NA
Or you can apply dag_filter() after DAG is generated.
dag = create_ontology_DAG_from_GO_db("BP")
dag_filter(dag, relations = "is_a")
Gene annotation can be set with the argument org_db. The value is an OrgDb object of the corresponding organism. The primary gene ID type in the __org.*.db__ package is internally used (which is normally the EntreZ ID type).
library(org.Hs.eg.db)
dag = create_ontology_DAG_from_GO_db("BP", org_db = org.Hs.eg.db)
dag## An ontology_DAG object:
## Source: GO BP / GO.db package 3.17.0
## 27942 terms / 55956 relations
## Root: GO:0008150
## Terms: GO:0000001, GO:0000002, GO:0000003, GO:0000011, ...
## Max depth: 18
## Avg number of parents: 2.00
## Avg number of children: 1.88
## Aspect ratio: 363.92:1 (based on the longest distance from root)
## 782.78:1 (based on the shortest distance from root)
## Relations: is_a, part_of
## Annotations: 18614 items
## 291, 1890, 4205, 4358, ...
##
## With the following columns in the metadata data frame:
## id, name, definitionFor standard organism packages on Biocoductor, the OrgDb object always has the same name as the package, so the name of the organism package can also be set to org_db:
create_ontology_DAG_from_GO_db("BP", org_db = "org.Hs.eg.db")
Similarly, if the analysis is applied on mouse, the mouse organism package can be set to org_db. If the mouse organism package is not installed yet, it will be installed automatically.
create_ontology_DAG_from_GO_db("BP", org_db = "org.Mm.eg.db")
Genes that are annotated to GO terms can be obtained by term_annotations(). Note the genes are automatically merged from offspring terms.
term_annotations(dag, c("GO:0000002", "GO:0000012"))## $`GO:0000002`
## [1] "291" "1890" "4205" "4358" "4976" "9361" "10000" "55186"
## [9] "80119" "84275" "92667" "1763" "142" "7157" "9093" "7156"
## [17] "6240" "50484" "2021" "11232" "83667" "5428" "6742" "56652"
## [25] "201973"
##
## $`GO:0000012`
## [1] "1161" "2074" "3981" "7141" "7515" "23411"
## [7] "54840" "55775" "200558" "100133315"There are additional meta columns attached to the DAG object. They can be accessed by mcols().
head(mcols(dag))## DataFrame with 6 rows and 3 columns
## id name definition
## <character> <character> <character>
## GO:0000001 GO:0000001 mitochondrion inheri.. The distribution of ..
## GO:0000002 GO:0000002 mitochondrial genome.. The maintenance of t..
## GO:0000003 GO:0000003 reproduction The production of ne..
## GO:0000011 GO:0000011 vacuole inheritance The distribution of ..
## GO:0000012 GO:0000012 single strand break .. The repair of single..
## GO:0000017 GO:0000017 alpha-glucoside tran.. The directed movemen..The additional information of GO terms is from the GO.db package. The row order of the meta data frame is the same as in dag_all_terms(dag).
## R version 4.3.3 (2024-02-29)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.6.1
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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## time zone: Europe/Berlin
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] org.Hs.eg.db_3.17.0 AnnotationDbi_1.62.2 IRanges_2.36.0
## [4] S4Vectors_0.40.2 Biobase_2.60.0 BiocGenerics_0.48.1
## [7] simona_1.3.12 knitr_1.45
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## loaded via a namespace (and not attached):
## [1] KEGGREST_1.40.1 circlize_0.4.16 shape_1.4.6.1
## [4] rjson_0.2.21 xfun_0.43 bslib_0.7.0
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