Package 'ontoProc'

Title: processing of ontologies of anatomy, cell lines, and so on
Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.
Authors: Vincent Carey [ctb, cre] , Sara Stankiewicz [ctb]
Maintainer: Vincent Carey <[email protected]>
License: Artistic-2.0
Version: 1.27.0
Built: 2024-07-02 05:22:16 UTC
Source: https://github.com/bioc/ontoProc

Help Index

subset method

Description

subset method

Usage

## S3 method for class 'owlents'
x[i, j, drop = FALSE]

Arguments

x

owlents instance
i

character or numeric vector
j

not used
drop

not used

allGOterms: data.frame with ids and terms

Description

allGOterms: data.frame with ids and terms

Usage

allGOterms

Format

data.frame instance

Source

This is a snapshot of all the terms available from GO.db (3.4.2), August 2017, using keys(GO.db, keytype="TERM").

Examples

data(allGOterms)
head(allGOterms)

retrieve ancestor 'sets'

Description

retrieve ancestor 'sets'

Usage

ancestors(oe)

Arguments

oe

owlents instance

Value

a list of sets

Examples

pa = get_ordo_owl_path()
orde = setup_entities(pa)
orde
ancestors(orde[1:5])
labels(orde[1:5])

obtain list of names of a set of ancestors

Description

obtain list of names of a set of ancestors

Usage

ancestors_names(anclist)

Arguments

anclist

output of 'ancestors'

Value

list of vectors of character()

Note

non-entities are removed and names are extracted

Examples

pa = get_ordo_owl_path()
orde = setup_entities(pa)
al = ancestors(orde[1001:1002])
ancestors_names(al)

add mapping from informal to formal cell type tags to a SummarizedExperiment colData

Description

add mapping from informal to formal cell type tags to a SummarizedExperiment colData

Usage

bind_formal_tags(se, informal, tagmap, force = FALSE)

Arguments

se

SummarizedExperiment instance
informal

character(1) name of colData element with uncontrolled vocabulary
tagmap

data.frame with columns 'informal' and 'formal'
force

logical(1), defaults to FALSE; if TRUE, allows clobbering existing colData variable named "formal"

Value

SummarizedExperiment instance with a new colData column 'label.ont' giving the formal tags associated with each sample

Note

This function will fail if the value of 'informal' is not among the colData variable names, or if "formal" is among the colData variable names.

combine TermSet instances

Description

combine TermSet instances

Usage

## S4 method for signature 'TermSet'
c(x, ...)

Arguments

x

TermSet instance
...

additional instances

Value

TermSet instance

utilities for approximate matching of cell type terms to GO categories and annotations

Description

utilities for approximate matching of cell type terms to GO categories and annotations

Usage

cellTypeToGO(celltypeString, gotab, ...)

cellTypeToGenes(
  celltypeString,
  gotab,
  orgDb,
  cols = c("ENSEMBL", "SYMBOL"),
  ...
)

Arguments

celltypeString

character atom to be used to search GO terms using
gotab

a data.frame with columns GO (goids) and TERM (term strings) agrep
...

additional arguments to agrep
orgDb

instances of orgDb
cols

columns to be retrieved in select operation

Value

data.frame

data.frame

Note

Very primitive, uses agrep to try to find relevant terms.

Examples

library(org.Hs.eg.db)
data(allGOterms)
head(cellTypeToGO("serotonergic neuron", allGOterms))
head(cellTypeToGenes("serotonergic neuron", allGOterms, org.Hs.eg.db))

obtain list of names of a set of subclasses/children

Description

obtain list of names of a set of subclasses/children

Usage

children_names(sclist)

Arguments

sclist

output of 'subclasses'

Value

list of vectors of character()

Note

non-entities are removed and names are extracted

Examples

pa = get_ordo_owl_path()
orde = setup_entities(pa)
al = subclasses(orde[100:120])
children_names(al)

obtain named character vector of terms from Cell Line Ontology, omitting obsolete and trailing 'cell'

Description

obtain named character vector of terms from Cell Line Ontology, omitting obsolete and trailing 'cell'

Usage

cleanCLOnames()

Value

character()

Examples

cleanCLOnames()[1:10]

produce a data.frame of features relevant to a Cell Ontology class

Description

produce a data.frame of features relevant to a Cell Ontology class

Usage

CLfeats(ont, tag = "CL:0001054", pr, go)

Arguments

ont

instance of ontologyIndex ontology
tag

character(1) a CL: class tag
pr

instance of ontologyIndex PRO protein ontology
go

instance of ontologyIndex GO gene ontology

Value

a data.frame instance

Note

This function will look in the intersection_of and has_part, lacks_part components of the CL entry to find properties asserted of or inherited by the cell type identified in 'tag'. As of 1.19, this function does not look in global environment for ontologies. We use 2021 versions in the examples because some changes in ontologies omit important relationships; revisions to package code after 1.19.4 will attempt to address these.

Examples

cl = getOnto("cellOnto", year_added="2021")
pr = getOnto("Pronto", "2021")  # legacy tag, for 2022 would be PROnto
go = getOnto("goOnto", "2021")
CLfeats(cl, tag="CL:0001054", pr=pr, go=go)

list and count samples with common ontological annotation in two SEs

Description

list and count samples with common ontological annotation in two SEs

Usage

common_classes(ont, se1, se2)

Arguments

ont

instance of ontologyIndex ontology
se1

a SummarizedExperiment using 'label.ont' in colData to provide ontological tags (from 'ont') for samples
se2

a SummarizedExperiment using 'label.ont' in colData to provide ontological tags (from 'ont') for samples

Value

a data.frame with rownames given by the common tags, the class names as column 'clname', and counts of samples bearing the given tags in remaining columns.

Examples

if (requireNamespace("celldex")) {
  imm = celldex::ImmGenData()
  if ("label.ont" %in% names(SummarizedExperiment::colData(imm))) {
    cl = getOnto("cellOnto")
    blu = celldex::BlueprintEncodeData()
    common_classes( cl, imm, blu )
    }
  }

connect ontological categories between related, annotated SummarizedExperiments

Description

connect ontological categories between related, annotated SummarizedExperiments

Usage

connect_classes(ont, se1, se2)

Arguments

ont

an ontologyIndex ontology instance
se1

SummarizedExperiment instance with 'label.ont' among colData columns
se2

SummarizedExperiment instance with 'label.ont' among colData columns

Value

a list with two sublists mapping from terms in one SE to descendant terms in the other SE

app to review molecular properties of cell types via cell ontology

Description

app to review molecular properties of cell types via cell ontology

Usage

ctmarks(cl, pr, go)

Arguments

cl

an import of a Cell Ontology (or extended Cell Ontology) in ontology_index form
pr

an import of a Protein Ontology in ontology_index form
go

an import of a Gene Ontology in ontology_index form

Value

a data.frame with features for selected cell types

Note

Prototype of harvesting of cell ontology by searching has_part, has_plasma_membrane_part, intersection_of and allied ontology relationships. Uses shiny. Can perform better if getPROnto() and getGeneOnto() values are in .GlobalEnv as pr and go respectively.

Examples

if (interactive()) {
   co = getOnto("cellOnto", year_added="2023")  # has plasma membrane relations
   go = getOnto("goOnto", "2023")
   pr = getOnto("Pronto", "2021") # peculiar tag used in legacy, would be PROnto with 2022
   ctmarks(co, go, pr)
}

as in Bakken et al. (2017 PMID 29322913) create gene signatures for k cell types, each of which fails to express all but one gene in a set of k genes

Description

as in Bakken et al. (2017 PMID 29322913) create gene signatures for k cell types, each of which fails to express all but one gene in a set of k genes

Usage

cyclicSigset(
  idvec,
  conds = c("hasExp", "lacksExp"),
  tags = paste0("CL:X", 1:length(idvec))
)

Arguments

idvec

character vector of identifiers, must have names() set to identify cells bearing genes
conds

character(2) tokens used to indicate condition to which signature element contributes
tags

character vector of cell-type identifiers; for Cell Ontology use CL: as prefix, one element for each element of idvec

Value

a long data.frame

Examples

sigels = c("CL:X01"="GRIK3", "CL:X02"="NTNG1", "CL:X03"="BAGE2", 
        "CL:X04"="MC4R", "CL:X05"="PAX6", "CL:X06"="TSPAN12", "CL:X07"="hSHISA8", 
     "CL:X08"="SNCG", "CL:X09"="ARHGEF28", "CL:X10"="EGF")
sigdf = cyclicSigset(sigels)
head(sigdf)

demonstrate the use of makeSelectInput

Description

demonstrate the use of makeSelectInput

Usage

demoApp()

Value

Run only for side effect of starting a shiny app.

Examples

if (interactive()) {
require(shiny)
print(demoApp())
}

dropStop is a utility for removing certain words from text data

Description

dropStop is a utility for removing certain words from text data

Usage

dropStop(x, drop, lower = TRUE, splitby = " ")

Arguments

x

character vector of strings to be cleaned
drop

character vector of words to scrub
lower

logical, if TRUE, x converted with tolower
splitby

character, used with strsplit to tokenize x

Value

a list with one element per input string, split by " ", with elements in drop removed

Examples

data(minicorpus)
minicorpus[1:3]
dropStop(minicorpus)[1:3]

some fields of interest are lists, and grep per se should not be used – this function checks and uses grep within vapply when appropriate

Description

some fields of interest are lists, and grep per se should not be used – this function checks and uses grep within vapply when appropriate

Usage

fastGrep(patt, onto, field, ...)

Arguments

patt

a regular expression whose presence in field should be checked
onto

an ontologyIndex instance
field

the ontologyIndex component to be searched
...

passed to grep

Value

logical vector indicating vector or list elements where a match is found

Examples

cheb = getOnto("chebi_lite")
ind = fastGrep("tanespimycin", cheb, "name")
cheb$name[ind]

Find common ancestors

Description

Given a set of ontology terms, find their latest common ancestors based on the term hierarchy.

Usage

findCommonAncestors(..., g, remove.self = TRUE, descriptions = NULL)

Arguments

...

One or more (possibly named) character vectors containing ontology terms.
g

A graph object containing the hierarchy of all ontology terms.
remove.self

Logical scalar indicating whether to ignore ancestors containing only a single term (themselves).
descriptions

Named character vector containing plain-English descriptions for each term. Names should be the term identifier while the values are the descriptions.

Details

This function identifies all terms in g that are the latest common ancestor (LCA) of any subset of terms in .... An LCA is one that has no children that have the exact same set of descendent terms in ..., i.e., it is the most specific term for that set of observed descendents. Knowing the LCA is useful for deciding how terms should be rolled up to broader definitions in downstream applications, usually when the exact terms in ... are too specific for practical use.

The descendents DataFrame in each row of the output describes the descendents for each LCA, stratified by their presence or absence in each entry of .... This is particularly useful for seeing how different sets of terms would be aggregated into broader terms, e.g., when harmonizing annotation from different datasets or studies. Note that any names for ... will be reflected in the columns of the DataFrame for each LCA.

Value

A DataFrame where each row corresponds to a common ancestor term. This contains the columns number, the number of descendent terms across all vectors in ...; and descendents, a List of DataFrames containing the identities of the descendents. It may also contain the column description, containing the description for each term.

Author(s)

Aaron Lun

Examples

co <- getOnto("cellOnto")

# TODO: wrap in utility function.
parents <- co$parents
self <- rep(names(parents), lengths(parents))
library(igraph)
g <- make_graph(rbind(unlist(parents), self))

# Selecting random terms:
LCA <- ontoProc:::findCommonAncestors(A=sample(names(V(g)), 20),
   B=sample(names(V(g)), 20), g=g)

LCA[1,]
LCA[1,"descendents"][[1]]

return a generator with ontology classes

Description

return a generator with ontology classes

Usage

get_classes(owlfile)

Arguments

owlfile

reference to OWL file, can be URL, will be processed by owlready2.get_ontology

Value

generator with output of classes() on the loaded ontology

decompress ordo owl file

Description

decompress ordo owl file

Usage

get_ordo_owl_path(target = tempdir())

Arguments

target

character(1) path to where decompressed owl will live

basic getters in old style, retained 2023 for deprecation interval

Description

basic getters in old style, retained 2023 for deprecation interval

Usage

getChebiLite()

getCellosaurusOnto()

getUBERON_NE()

getChebiOnto()

getOncotreeOnto()

getDiseaseOnto()

getGeneOnto()

getHCAOnto()

getPROnto()

getPATOnto()

getMondoOnto()

getSIOOnto()

Value

instance of ontology_index (S3) from ontologyIndex

Note

getChebiOnto loads ontoRda/chebi_full.rda

getOncotreeOnto loads ontoRda/oncotree.rda

getDiseaseOnto loads ontoRda/diseaseOnto.rda

getHCAOnto loads ontoRda/hcaOnto.rda produced from hcao.owl at https://github.com/HumanCellAtlas/ontology/releases/tag/1.0.6 2/11/2019, python pronto was used to convert OWL to OBO.

getPROnto loads ontoRda/PRonto.rda, produced from http://purl.obolibrary.org/obo/pr.obo 'reasoned' ontology from OBO foundry, 02-08-2019. In contrast to other ontologies, this is imported via get_OBO with ‘extract_tags=’minimal''.

getPATOnto loads ontoRda/patoOnto.rda, produced from https://raw.githubusercontent.com/pato-ontology/pato/master/pato.obo from OBO foundry, 02-08-2019.

obtain childless descendents of a term (including query)

Description

obtain childless descendents of a term (including query)

Usage

getLeavesFromTerm(x, ont)

Arguments

x

a character(1) id element for ontology_index instance
ont

an ontology_index instance as defined in ontologyIndex package

Value

character vector of 'leaves' of ontology tree

Examples

ch = getOnto("chebi_lite")
alldr = getLeavesFromTerm("CHEBI:23888", ch)
head(ch$name[alldr[1:15]])

get the ontology based on a short tag and year

Description

get the ontology based on a short tag and year

Usage

getOnto(ontoname = "cellOnto", year_added = "2023")

Arguments

ontoname

character(1) must be an element in 'valid_ontonames()'
year_added

character(1) refers to 'rdatadateadded' in AnnotationHub metadata

Note

This queries AnnotationHub for "ontoProcData" and then filters to find the AnnotationHub accession number and retrieves the ontologyIndex serialization of the associated OBO representation of the ontology.

Examples

co = getOnto()
tail(co$name[1000:1500])

humrna: a data.frame of SRA metadata related to RNA-seq in humans

Description

humrna: a data.frame of SRA metadata related to RNA-seq in humans

Usage

humrna

Format

data.frame

Note

arbitrarily chosen from RNA-seq studies for taxon 9606

Source

NCBI SRA

Examples

data(humrna)
names(humrna)
head(humrna[,1:5])

inject linefeeds for node names for graph, with textual annotation from ontology

Description

inject linefeeds for node names for graph, with textual annotation from ontology

Usage

improveNodes(g, ont)

Arguments

g

graphNEL instance
ont

instance of ontology from ontologyIndex

retrieve labels with names

Description

retrieve labels with names

Usage

## S3 method for class 'owlents'
labels(object, ...)

Arguments

object

owlents instance
...

not used

Note

When multiple labels are present, only first is silently returned. Note that reticulate 1.35.0 made a change that appears to imply that '[0]' can be used to retrieve the desired components. To get ontology tags, use 'names(labels(...))'.

use output of cyclicSigset to generate a series of character vectors constituting OBO terms

Description

use output of cyclicSigset to generate a series of character vectors constituting OBO terms

Usage

ldfToTerms(
  ldf,
  propmap,
  sigels,
  prologMaker = function(id, ...) sprintf("id: %s", id)
)

Arguments

ldf

a 'long format' data.frame as created by cyclicSigset
propmap

a character vector with names of elements corresponding to 'abbreviated' relationship tokens and element values corresponding to full relationship-naming strings
sigels

a named character vector associating cell types (names) to genes expressed in a cyclic set, one element per type
prologMaker

a function with arguments (id, ...), in which id is character(1), that generates a vector of strings that will be used for each cell type-specific term.

Value

a character vector, strings can be concatenated to OBO

Note

ldfToTerms is not sufficiently general to produce terms for any reasonably populated long data frame/propmap combination, but it is a working example for the cyclic set context.

Examples

# a set of cell types -- names are cell type token, values are genes expressed in a
# cyclic set -- each cell type expresses exactly one gene in the set and fails to
# express all the other genes in the set.  See Figs 3 and 4 of Bakken et al [PMID 29322913].
sigels = c("CL:X01"="GRIK3", "CL:X02"="NTNG1", "CL:X03"="BAGE2", 
        "CL:X04"="MC4R", "CL:X05"="PAX6", "CL:X06"="TSPAN12", "CL:X07"="hSHISA8", 
        "CL:X08"="SNCG", "CL:X09"="ARHGEF28", "CL:X10"="EGF")
# create the associated long data frame
ldf = cyclicSigset(sigels)
# describe the abbreviations
pmap = c("hasExp"="has_expression_of", lacksExp="lacks_expression_of")

# now define the prolog for each cell type
makeIntnProlog = function(id, ...) {
# make type-specific prologs as key-value pairs
    c(
      sprintf("id: %s", id),
      sprintf("name: %s-expressing cortical layer 1 interneuron, human", ...),
      sprintf("def: '%s-expressing cortical layer 1 interneuron, human described via RNA-seq observations' [PMID 29322913]", ...),
      "is_a: CL:0000099 ! interneuron",
      "intersection_of: CL:0000099 ! interneuron")
}
tms = ldfToTerms(ldf, pmap, sigels, makeIntnProlog)
cat(tms[[1]], sep="\n")

Produce a data.frame with a set of naive terms mapped to all matching ontology ids and their formal terms

Description

Produce a data.frame with a set of naive terms mapped to all matching ontology ids and their formal terms

Usage

liberalMap(terms, onto, useAgrep = FALSE, ...)

Arguments

terms

character() vector, can use grep-compatible regular expressions
onto

an instance of ontologyIndex::ontology_index
useAgrep

logical(1) if TRUE, agrep will be used
...

passed to agrep if used

Value

a data.frame

Examples

cands = c("astrocyte$", "oligodendrocyte", "oligodendrocyte precursor",
   "neoplastic", "^neuron$", "^vascular", "badterm")
#co = ontoProc::getCellOnto()
co = getOnto("cellOnto", year_added="2023")
liberalMap(cands, co)

obtain graphNEL from ontology_plot instance of ontologyPlot

Description

obtain graphNEL from ontology_plot instance of ontologyPlot

Usage

make_graphNEL_from_ontology_plot(x)

Arguments

x

instance of S3 class ontology_plot

Value

instance of S4 graphNEL class

Examples

requireNamespace("Rgraphviz")
requireNamespace("graph")
cl = getOnto("cellOnto")
cl3k = c("CL:0000492", "CL:0001054", "CL:0000236", "CL:0000625",
   "CL:0000576", "CL:0000623", "CL:0000451", "CL:0000556")
p3k = ontologyPlot::onto_plot(cl, cl3k)
gnel = make_graphNEL_from_ontology_plot(p3k)
gnel = improveNodes(gnel, cl)
graph::graph.par(list(nodes=list(shape="plaintext", cex=.8)))
gnel = Rgraphviz::layoutGraph(gnel)
Rgraphviz::renderGraph(gnel)

generate a selectInput control for an ontologyIndex slice

Description

generate a selectInput control for an ontologyIndex slice

Usage

makeSelectInput(
  onto,
  term,
  type = "siblings",
  inputId,
  label,
  multiple = TRUE,
  ...
)

Arguments

onto

ontologyIndex instance
term

character(1) term used as basis for term list option set in the control
type

character(1) 'siblings' or 'children', relationship to 'term' that the options will satisfy
inputId

character(1) for use in server
label

character(1) for labeling in ui
multiple

logical(1) passed to selectInput
...

additional parameters passed to selectInput

Value

a selectInput control

Examples

makeSelectInput

use prose terminology with output of connect_classes

Description

use prose terminology with output of connect_classes

Usage

map2prose(x, cl)

Arguments

x

a component of connect_classes output
cl

an ontologyIndex ontology instance

Value

a decorated list

use grep or agrep to find a match for a naive token into ontology

Description

use grep or agrep to find a match for a naive token into ontology

Usage

mapOneNaive(naive, onto, useAgrep = FALSE, ...)

Arguments

naive

character(1)
onto

an instance of ontologyIndex::ontology_index
useAgrep

logical(1) if TRUE, agrep will be used
...

passed to agrep if used

Value

if a match is found, the result of grep/agrep with value=TRUE is returned; otherwise a named NA_character_ is returned

named vector, names are ontology identifiers, values are matched strings

Examples

#co = ontoProc::getCellOnto()
co = getOnto("cellOnto", year_added="2023")
mapOneNaive("astrocyte", co)

minicorpus: a vector of annotation strings found in 'study title' of SRA metadata.

Description

minicorpus: a vector of annotation strings found in 'study title' of SRA metadata.

Usage

minicorpus

Format

character vector

Note

arbitrarily chosen from titles of RNA-seq studies for taxon 9606

Source

NCBI SRA

Examples

data(minicorpus)
head(minicorpus)

repair nomenclature mismatches (to curated term set) in a vector of terms

Description

repair nomenclature mismatches (to curated term set) in a vector of terms

Usage

nomenCheckup(cand, namedOffic, n = 1, tagcolname = "tag", ...)

Arguments

cand

character vector of candidate terms
namedOffic

named character vector of curated terms, the names are regarded as tags, intended to be identifiers in curated ontologies
n

numeric(1) number of nearest neighbors to return
tagcolname

character(1) prefix used to name columns for tags in output
...

passed to adist

Value

a data.frame instance with 2n+1 columns (column 1 is candidate, remaining n pairs of columns are (term, tag) for n nearest neighbors as measured by adist.

Examples

candidates = c("JHH7", "HUT102", "HS739T", "NCIH716")
# the candidates are cell line names returned in the text dump from
# https://portals.broadinstitute.org/ccle/page?gene=AHR
# note that one must travel to the third nearest neighbor
# to find the match (and tag) for Hs 739.T
# in this example, we compare to cell line names in Cell Line Ontology
nomenCheckup(candidates, cleanCLOnames(), n=3, tagcolname="clo")

high-level use of graph/Rgraphviz for rendering ontology relations

Description

high-level use of graph/Rgraphviz for rendering ontology relations

Usage

onto_plot2(ont, terms2use, cex = 0.8, ...)

Arguments

ont

instance of ontology from ontologyIndex
terms2use

character vector
cex

numeric(1) defaults to .8, supplied to Rgraphviz::graph.par
...

passed to onto_plot of ontologyPlot

Value

graphNEL instance (invisibly)

Examples

cl = getOnto("cellOnto")
cl3k = c("CL:0000492", "CL:0001054", "CL:0000236", "CL:0000625",
   "CL:0000576", "CL:0000623", "CL:0000451", "CL:0000556")
onto_plot2(cl, cl3k)

list parentless nodes in ontology_index instance

Description

list parentless nodes in ontology_index instance

Usage

onto_roots(x)

Arguments

x

an ontology_index instance

Value

a report (produced by cat()) of root ids and associated names

Examples

onto_roots

cache an owl file accessible via URL

Description

cache an owl file accessible via URL

Usage

owl2cache(cache = BiocFileCache::BiocFileCache(), url)

Arguments

cache

BiocFileCache instance or equivalent
url

character(1)

Note

This function will check for presence of url in cache using bfcquery; if a hit is found, returns the rpath associated with the last matching record. etags can be available for use with bfcneedsupdate.

Examples

ca = BiocFileCache::BiocFileCache()
hppa = owl2cache(ca, 
   url="http://purl.obolibrary.org/obo/hp/releases/2023-10-09/hp-base.owl")
setup_entities(hppa)

packDesc2019: overview of ontoProc resources

Description

packDesc2019: overview of ontoProc resources

Usage

packDesc2019

Format

data.frame instance

Note

Brief survey of functions available to load serialized ontology_index instances imported from OBO.

Examples

data(packDesc2019)
head(packDesc2019)

packDesc2021: overview of ontoProc resources

Description

packDesc2021: overview of ontoProc resources

Usage

packDesc2021

Format

data.frame instance

Note

Brief survey of functions available to load serialized ontology_index instances imported from OBO. Focus is on versions added in 2021.

Examples

data(packDesc2021)
head(packDesc2021)

packDesc2022: overview of ontoProc resources

Description

packDesc2022: overview of ontoProc resources

Usage

packDesc2022

Format

data.frame instance

Note

Brief survey of functions available to load serialized ontology_index instances imported from OBO. Focus is on versions added in 2022.

Examples

data(packDesc2022)
head(packDesc2022)

packDesc2023: overview of ontoProc resources

Description

packDesc2023: overview of ontoProc resources

Usage

packDesc2023

Format

data.frame instance

Note

Brief survey of functions available to load serialized ontology_index instances imported from OBO. Focus is on versions added in 2023. Several manual interventions were needed – cellosaurus was too large to use the script in inst/scripts/desc.R, and a number of ontologies do not have 2023 versions.

Examples

data(packDesc2023)
head(packDesc2023)

retrieve is_a

Description

retrieve is_a

Usage

parents(oe)

Arguments

oe

owlents instance

Value

list of vectors of tags of parents

Examples

pa = get_ordo_owl_path()
orde = setup_entities(pa)
orde
parents(orde[1000:1001])
labels(orde[1000:1001])

visualize ontology selection via onto_plot2, based on owlents

Description

visualize ontology selection via onto_plot2, based on owlents

Usage

plot.owlents(x, y, ..., dropThing = TRUE)

Arguments

x

owlents instance
y

character() vector of entries in x$clnames
...

passed to onto_plot2
dropThing

logical(1) defaults to TRUE; if "Thing" is present in terms to display, it is removed

Examples

cl3k = c("CL:0000492", "CL:0001054", "CL:0000236", 
  "CL:0000625", "CL:0000576", 
  "CL:0000623", "CL:0000451", "CL:0000556")
cl3k = gsub(":", "_", cl3k)
clont_path = owl2cache(url="http://purl.obolibrary.org/obo/cl.owl")
clont = setup_entities(clont_path)
plot(clont,cl3k)

short printer

Description

short printer

Usage

## S3 method for class 'owlents'
print(x, ...)

Arguments

x

owlents instance
...

not used

PROSYM: HGNC symbol synonyms for PR (protein ontology) entries identified in Cell Ontology

Description

PROSYM: HGNC symbol synonyms for PR (protein ontology) entries identified in Cell Ontology

Usage

PROSYM

Format

data.frame instance

Note

This is a snapshot of the synonyms component of an extract_tags='everything' import of PR. The 'EXACT.*PRO-short.*:DNx' pattern is used to retrieve HGNC symbols. See ?getPROnto for more provenance information.

Source

OBO Foundry

Examples

data(PROSYM)
head(PROSYM)

enumerate ontological relationships used in ontoProc utilities

Description

enumerate ontological relationships used in ontoProc utilities

Usage

recognizedPredicates()

Value

character vector, names of elements are abbreviated tokens that may be used in code

Examples

head(recognizedPredicates())

simple generation of children of 'choices' given as terms, returned as TermSet

Description

simple generation of children of 'choices' given as terms, returned as TermSet

Usage

secLevGen(choices, ont)

Arguments

choices

vector of terms
ont

instance of ontology_index (S3) from ontologyIndex package

Value

TermSet instance

Examples

efoOnto = getOnto("efoOnto")
secLevGen( "disease", efoOnto )

select a set of elements from a term 'map' and return a contribution to a data.frame

Description

select a set of elements from a term 'map' and return a contribution to a data.frame

Usage

selectFromMap(namedvec, index)

Arguments

namedvec

named character vector, as returned from mapOneNaive
index

numeric() or integer(), typically of length one

Value

a data.frame; if index does not inherit from numeric, a data.frame of one row with columns 'ontoid' and 'term' populated with NA_character_ is returned, otherwise a similarly named data.frame is returned with contents from the selected elements of namedvec

Examples

#co = ontoProc::getCellOnto()
co = getOnto("cellOnto", year_added="2023")
mast = mapOneNaive("astrocyte", co)
selectFromMap(mast, 1)

construct owlents instance from an owl file

Description

construct owlents instance from an owl file

Usage

setup_entities(owlfn)

Arguments

owlfn

character(1) path to valid owl ontology

Value

instance of owlents, which is a list with clnames ( a vector of term names in form '[namespace]_[tag]'), allents (a list with python references to owlready2 entities, that can be operated on using owlready2.EntityClass methods), owlfn (filename), iri (IRI), call (record of call producing the entity.)

Examples

pa = get_ordo_owl_path()
orde = setup_entities(pa)
orde
ancestors(orde[1000:1001])
labels(orde[1000:1001])

tabulate the basic outcome of PBMC 3K tutorial of Seurat

Description

tabulate the basic outcome of PBMC 3K tutorial of Seurat

Usage

seur3kTab()

Value

a data.frame

Examples

seur3kTab()

generate a TermSet with siblings of a given term, excluding that term by default

Description

generate a TermSet with siblings of a given term, excluding that term by default

acquire the label of an ontology subject tag

acquire the labels of children of an ontology subject tag

Usage

siblings_TAG(Tagstring = "EFO:1001209", ontology, justSibs = TRUE)

label_TAG(Tagstring = "EFO:0000311", ontology)

children_TAG(Tagstring = "EFO:1001209", ontology)

Arguments

Tagstring

a character(1) that identifies a term
ontology

instance of ontology_index (S3) from ontologyIndex
justSibs

character(1)

Value

TermSet instance

character(1)

TermSet instance

Note

for label_TAG, Tagstring may be a vector

Examples

efoOnto = getOnto("efoOnto")
siblings_TAG( "EFO:1001209", efoOnto )
efoOnto = getOnto("efoOnto")
label_TAG( "EFO:0000311", efoOnto )
efoOnto = getOnto("efoOnto")
children_TAG( ontology = efoOnto )

stopWords: vector of stop words from xpo6.com

Description

stopWords: vector of stop words from xpo6.com

Usage

stopWords

Format

character vector

Note

"Stop words" are english words that are assumed to contribute limited semantic value in the analysis of free text.

Source

http://xpo6.com/list-of-english-stop-words/

Examples

data(stopWords)
head(stopWords)

retrieve subclass entities

Description

retrieve subclass entities

Usage

subclasses(oe)

Arguments

oe

owlents instance

Examples

pa = get_ordo_owl_path()
orde = setup_entities(pa)
orde
sc <- subclasses(orde[1:5])
labels(orde[3])
o3 = reticulate::iterate(sc[[3]])
print(length(o3))
o3[[2]]
labels(orde["Orphanet_100011"])

subset a SummarizedExperiment to which ontology tags have been bound using 'bind_formal_tags', obtaining the 'descendants' of the class of interest

Description

subset a SummarizedExperiment to which ontology tags have been bound using 'bind_formal_tags', obtaining the 'descendants' of the class of interest

Usage

subset_descendants(
  se,
  onto,
  class_name,
  class_tag,
  formal_cd_name = "label.ont"
)

Arguments

se

SummarizedExperiment instance
onto

representation of an ontology using representation from ontologyIndex package
class_name

character(1) if 'class_tag' is missing, this will be grepped in onto[["name"]] to find class and its descendants
class_tag

character(1) used if given to identify "ontological descendants" of this term in se
formal_cd_name

character(1) tells name used for ontology tag column in 'colData(se)'

Value

instance of SummarizedExperiment

use Cell Ontology and Protein Ontology to identify cell-type defining conditions in which a given gene is named

Description

use Cell Ontology and Protein Ontology to identify cell-type defining conditions in which a given gene is named

Usage

sym2CellOnto(sym, cl, pr)

Arguments

sym

gene symbol, must be used in protein ontology as a PRO:DNx exact match token
cl

result of getOnto("cellOnto")
pr

result of getOnto("PROnto")

Value

DataFrame if any hits are found. A field 'cond' abbreviates the identified conditions: (has/lacks)PMP (plasma membrane part) (hi/lo)PMAmt (plasma membrane amount), (has/lacks)Part.

Note

Currently just checks for *plasma_membrane_part, *plasma_membrane_amount, and *Part conditions.

Examples

if (!exists("cl")) cl = getOnto("cellOnto")
if (!exists("pr")) pr = getOnto("PROnto")
sym2CellOnto("ITGAM", cl, pr)
sym2CellOnto("FOXP3", cl, pr)

manage ontological data with tags and a DataFrame instance

Description

manage ontological data with tags and a DataFrame instance

abbreviated display for TermSet instances

Usage

## S4 method for signature 'TermSet'
show(object)

Arguments

object

instance of TermSet class

Value

instance of TermSet

Examples

efoOnto = getOnto("efoOnto")
defsibs = siblings_TAG("EFO:1001209", efoOnto)
class(defsibs)
defsibs

check that a URL can get a 200 for a HEAD request

Description

check that a URL can get a 200 for a HEAD request

Usage

url_ok(url)

Arguments

url

character(1)

Value

logical(1)

give a vector of valid 'names' of ontoProc ontologies

Description

give a vector of valid 'names' of ontoProc ontologies

Usage

valid_ontonames()

Examples

head(valid_ontonames())