Erqiang Hu
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University.
if(!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("YuLab-SMU/MPO.db")We have developed the human disease ontology R package HDO.db, which provides the semantic relationship between human diseases. Relying on the DOSE and GOSemSim packages we developed, we can carry out disease enrichment and semantic similarity analyses. Many biological studies are achieved through mouse models, and a large number of data indicate the association between genotypes and phenotypes or diseases.
The study of model organisms can be transformed into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Organism-specific genotype-phenotypic associations can be applied to cross-species phenotypic studies to clarify previously unknown phenotypic connections in other species. Using the same principle to diseases can identify genetic associations and even help to identify disease associations that are not obvious. Therefore, as a supplement to HDO.db and DOSE, we developed mouse phenotypic ontology R package MPO.db.
MPO.db mainly contains four kinds of annotation information, which come from:
(1) Mammalian Phenotype Ontology data The ontology data contains the id, name, def, and synonym of the ontology, as also as the parent-child relationship between the ontology. The data comes from: MPheno_OBO.ontology file downloaded from http://www.informatics.jax.org/downloads/reports/index.html#pheno.
(2) Gene-phenotype association data These data demonstrate the effect of each genotype on the phenotype. The data come from: MGI database(http://www.informatics.jax.org/downloads/reports/index.html#pheno) and IMPC database(http://ftp.ebi.ac.uk/pub/databases/impc/all-data-releases/release-18.0/results/).
(3) Gene-disease association data These data demonstrate the effect of each genotype on the disease. The data come from: MGI database(http://www.informatics.jax.org/downloads/reports/index.html#pheno), IMPC database(http://ftp.ebi.ac.uk/pub/databases/impc/all-data-releases/release-18.0/results/), and alliance of genome resources(https://www.alliancegenome.org/downloads).
(4) Phenotype-disease association data These data demonstrate the effect of each phenotype on the disease. The data come from: https://github.com/DiseaseOntology/HumanDiseaseOntology, and https://github.com/mapping-commons/mh_mapping_initiative.
library(MPO.db)
library(AnnotationDbi)
MPO.db provide these AnnDbBimap object:
ls("package:MPO.db")
packageVersion("MPO.db")
You can use help function to get their documents: help(MPOFFSPRING)
toTable(MPOmetadata)
MPOMAPCOUNTS
In MPO.db, MPOTERM represet the whole MP terms and their names. The users can
also get their aliases and synonyms from MPOALIAS and MPOSYNONYM,
respectively.
convert MPOTERM to table
doterm <- toTable(MPOTERM)
head(doterm)
convert MPOTERM to list
dotermlist <- as.list(MPOTERM)
head(dotermlist)
get alias of MP:0000013
doalias <- as.list(MPOALIAS)
doalias[['MP:0000013']]
get synonym of MP:0000013
dosynonym <- as.list(MPOSYNONYM)
dosynonym[['MP:0000013']]
Similar to HDO.db, we provide four Bimap objects to represent relationship
between MP terms: MPOANCESTOR,MPOPARENTS,MPOOFFSPRING, and MPOCHILDREN.
MPOANCESTOR describes the association between MP terms and their ancestral
terms based on a directed acyclic graph (DAG) defined by the Mouse Phenotype Ontology.
We can use toTable function in AnnotationDbi package to get a two-column
data.frame: the first column means the MP term ids, and the second column means
their ancestor terms.
anc_table <- toTable(MPOANCESTOR)
head(anc_table)
get ancestor of "MP:0000013"
anc_list <- AnnotationDbi::as.list(MPOANCESTOR)
anc_list[["MP:0000013"]]
MPOPARENTS describes the association between MP terms and their direct parent
terms based on DAG. We can use toTable function in AnnotationDbi package to
get a two-column data.frame: the first column means the MP term ids, and the
second column means their parent terms.
parent_table <- toTable(MPOPARENTS)
head(parent_table)
get parent term of "MP:0000013"
parent_list <- AnnotationDbi::as.list(MPOPARENTS)
parent_list[["MP:0000013"]]
MPOPARENTS describes the association between MP terms and their offspring
terms based on DAG. it's the exact opposite of MPOANCESTOR,
whose usage is similar to it.
get offspring of "MP:0000013"
off_list <- AnnotationDbi::as.list(MPO.db::MPOOFFSPRING)
off_list[["MP:0000013"]]
MPOCHILDREN describes the association between MP terms and their direct
children terms based on DAG. it's the exact opposite of MPOPARENTS,
whose usage is similar to it.
get children of "MP:0000013"
child_list <- AnnotationDbi::as.list(MPO.db::MPOCHILDREN)
child_list[["MP:0000013"]]
The MPO.db support the select(), keys(), keytypes(),
and columns interface.
columns(MPO.db)
## use mpid keys
dokeys <- keys(MPO.db)[1:100]
res <- select(x = MPO.db, keys = dokeys, keytype = "mpid",
columns = c("offspring", "term", "doid", "mgi"))
head(na.omit(res))
key <- keys(MPO.db, "mpid")[1:100]
res <- select(x = MPO.db, keys = key, keytype = "mpid",
columns = c("mpid", "term", "children"))
head(na.omit(res))
## use term keys
# dokeys <- head(keys(MPO.db, keytype = "term"))
# res <- select(x = MPO.db, keys = dokeys, keytype = "term",
# columns = c("offspring", "mpid", "parent"))
# head(res)
dokeys <- keys(MPO.db, keytype = "term")[1:100]
res <- select(x = MPO.db, keys = dokeys, keytype = "term",
columns = c("offspring", "mpid", "doid", "mgi"))
head(na.omit(res))
## use mgi keys
key <- keys(MPO.db, "mgi")[1:100]
res <- select(x = MPO.db, keys = key, keytype = "mgi",
columns = c("mgi", "mpid", "children"))
head(na.omit(res))
res <- select(x = MPO.db, keys = key, keytype = "mgi",
columns = c("doid", "mgi"))
head(na.omit(res))
Please go to https://yulab-smu.top/biomedical-knowledge-mining-book/ for the vignette.
Please go to https://yulab-smu.top/biomedical-knowledge-mining-book/dose-enrichment.html for the vignette.