R/intercell.R
In saezlab/OmnipathR: OmniPath web service client and more
Defines functions
intercell_consensus_filter
import_omnipath_intercell
Documented in
import_omnipath_intercell
intercell_consensus_filter
#!/usr/bin/env Rscript
#
# This file is part of the `OmnipathR` R package
#
# Copyright
# 2018-2024
# Saez Lab, Uniklinik RWTH Aachen, Heidelberg University
#
# File author(s): Alberto Valdeolivas
# Dénes Türei (turei.denes@gmail.com)
# Attila Gábor
#
# Distributed under the MIT (Expat) License.
# See accompanying file `LICENSE` or find a copy at
# https://directory.fsf.org/wiki/License:Expat
#
# Website: https://r.omnipathdb.org/
# Git repo: https://github.com/saezlab/OmnipathR
#
TOPOLOGIES <-
c(
'secreted',
'plasma_membrane_transmembrane',
'plasma_membrane_peripheral'
)
TOPOLOGIES_SHORT <-
rlang::set_names(TOPOLOGIES, c('sec', 'pmtm', 'pmp'))
#' Imports OmniPath intercell annotations
#'
#' Imports the OmniPath intercellular communication role annotation database
#' from \url{https://omnipathdb.org/intercell}. It provides information
#' on the roles in inter-cellular signaling. E.g. if a protein is
#' a ligand, a receptor, an extracellular matrix (ECM) component, etc.
#'
#' @return A dataframe cotaining information about roles in intercellular
#' signaling.
#'
#' @param categories vector containing the categories to be retrieved.
#' All the genes belonging to those categories will be returned. For
#' further information about the categories see
#' \code{\link{get_intercell_categories}}.
#' @param parent vector containing the parent classes to be retrieved.
#' All the genes belonging to those classes will be returned. For
#' furter information about the main classes see
#' \code{\link{get_intercell_categories}}.
#' @param resources limit the query to certain resources; see the available
#' resources by \code{\link{get_intercell_resources}}.
#' @param scope either `specific` or `generic`
#' @param aspect either `locational` or `functional`
#' @param source either `resource_specific` or `composite`
#' @param transmitter logical, include only transmitters i.e. proteins
#' delivering signal from a cell to its environment.
#' @param receiver logical, include only receivers i.e. proteins delivering
#' signal to the cell from its environment.
#' @param plasma_membrane_peripheral logical, include only plasma membrane
#' peripheral membrane proteins.
#' @param plasma_membrane_transmembrane logical, include only plasma membrane
#' transmembrane proteins.
#' @param secreted logical, include only secreted proteins
#' @param proteins limit the query to certain proteins
#' @param topology topology categories: one or more of `secreted` (sec),
#' `plasma_membrane_peripheral` (pmp), `plasma_membrane_transmembrane`
#' (pmtm) (both short or long notation can be used).
#' @param causality `transmitter` (trans), `receiver` (rec) or `both` (both
#' short or long notation can be used).
#' @param consensus_percentile Numeric: a percentile cut off for the
#' consensus score of generic categories. The consensus score is the
#' number of resources supporting the classification of an entity into a
#' category based on combined information of many resources. Here you can
#' apply a cut-off, keeping only the annotations supported by a higher
#' number of resources than a certain percentile of each category. If
#' \code{NULL} no filtering will be performed. The value is either in the
#' 0-1 range, or will be divided by 100 if greater than 1. The
#' percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_consensus_percentile Numeric: similar to
#' \code{consensus_percentile} for major localizations. For example, with
#' a value of 50, the secreted, plasma membrane transmembrane or
#' peripheral attributes will be true only where at least 50 percent
#' of the resources support these.
#' @param ... Additional optional arguments, ignored.
#'
#' @examples
#' intercell <- import_omnipath_intercell(categories = 'ecm')
#'
#' @importFrom magrittr %<>% %>%
#' @importFrom purrr reduce
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{get_intercell_categories}}}
#' \item{\code{\link{get_intercell_generic_categories}}}
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{intercell_consensus_filter}}}
#' }
#'
#' @aliases import_Omnipath_intercell import_OmniPath_intercell
import_omnipath_intercell <- function(
categories = NULL,
resources = NULL,
parent = NULL,
scope = NULL,
aspect = NULL,
source = NULL,
transmitter = NULL,
receiver = NULL,
secreted = NULL,
plasma_membrane_peripheral = NULL,
plasma_membrane_transmembrane = NULL,
proteins = NULL,
topology = NULL,
causality = NULL,
consensus_percentile = NULL,
loc_consensus_percentile = NULL,
...
){
topology %<>%
topology_long %>%
{reduce(
TOPOLOGIES,
function(topos, topo){
arg_value <- get(topo)
`if`(
is.null(arg_value),
topos,
`if`(
arg_value,
union(topos, topo),
setdiff(topos, topo)
)
)
},
.init = .
)}
args <- c(as.list(environment()), list(...))
args$query_type <- 'intercell'
args$logicals <- c(
'transmitter',
'receiver',
'secreted',
'plasma_membrane_peripheral',
'plasma_membrane_transmembrane'
)
args$consensus_percentile <- NULL
args$loc_consensus_percentile <- NULL
result <-
do.call(import_omnipath, args) %>%
intercell_consensus_filter(
percentile = consensus_percentile,
loc_percentile = loc_consensus_percentile,
topology = topology
)
return(result)
}
#' Quality filter for intercell annotations
#'
#' @param data A data frame with intercell annotations, as provided by
#' \code{\link{import_omnipath_intercell}}.
#' @param percentile Numeric: a percentile cut off for the consensus score
#' of composite categories. The consensus score is the number of
#' resources supporting the classification of an entity into a category
#' based on combined information of many resources. Here you can apply
#' a cut-off, keeping only the annotations supported by a higher number
#' of resources than a certain percentile of each category. If
#' \code{NULL} no filtering will be performed. The value is either in the
#' 0-1 range, or will be divided by 100 if greater than 1. The
#' percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_percentile Numeric: similar to \code{percentile} for major
#' localizations. For example, with a value of 50, the secreted, plasma
#' membrane transmembrane or peripheral attributes will be \code{TRUE}
#' only where at least 50 percent of the resources support these.
#' @param topology Character vector: list of allowed topologies, possible
#' values are *"secreted"*, *"plasma_membrane_peripheral"* and
#' *"plasma_membrane_transmembrane"*.
#'
#' @return The data frame in \code{data} filtered by the consensus scores.
#'
#' @examples
#' intercell <- import_omnipath_intercell(parent = c('ligand', 'receptor'))
#' nrow(intercell)
#' # [1] 50174
#' intercell_q50 <- intercell_consensus_filter(intercell, 50)
#' nrow(intercell_q50)
#' # [1] 42863
#'
#' @importFrom magrittr %>% %<>%
#' @importFrom dplyr group_by filter ungroup bind_rows
#' @importFrom dplyr select distinct inner_join pull
#' @importFrom stats quantile
#' @importFrom rlang !! := sym eval_tidy parse_expr
#' @importFrom purrr reduce
#' @export
intercell_consensus_filter <- function(
data,
percentile = NULL,
loc_percentile = NULL,
topology = NULL
){
# NSE vs. R CMD check workaround
scope <- source <- parent <- consensus_score <- NULL
percentile %<>%
if_null(0L) %>%
{`if`(. > 1, . / 100, .)}
thresholds <-
data %>%
filter(scope == 'generic' & source == 'composite') %>%
group_by(parent) %>%
filter(
consensus_score >= quantile(consensus_score, percentile)
) %>%
ungroup %>%
select(parent, uniprot) %>%
distinct
composite_parents <-
data %>%
filter(source == 'composite') %>%
pull(parent) %>%
unique
data %<>%
inner_join(thresholds, by = c('parent', 'uniprot')) %>%
bind_rows(
data %>%
filter(!parent %in% composite_parents)
)
if(!is.null(loc_percentile)){
locations <- import_omnipath_intercell(
aspect = 'locational',
parent = TOPOLOGIES,
consensus_percentile = loc_percentile
)
data %<>%
{reduce(
TOPOLOGIES,
function(data, loc){
in_location <-
locations %>%
filter(!!sym(loc)) %>%
pull(uniprot) %>%
unique
data %>%
mutate(!!sym(loc) := uniprot %in% in_location)
},
.init = .
)} %>%
{`if`(
is.null(topology),
.,
filter(
.,
eval_tidy(parse_expr(paste(topology, collapse = ' | ')))
)
)}
}
return(data)
}
# Aliases (old names) to be deprecated
#' @rdname import_omnipath_intercell
#' @param ... Passed to \code{import_omnipath_intercell}.
#' @export
#'
#' @noRd
import_Omnipath_intercell <- function(...){
.Deprecated("import_omnipath_intercell")
import_omnipath_intercell(...)
}
#' @rdname import_omnipath_intercell
#' @param ... Passed to \code{import_omnipath_intercell}.
#' @export
#'
#' @noRd
import_OmniPath_intercell <- function(...){
.Deprecated("import_omnipath_intercell")
import_omnipath_intercell(...)
}
#' Retrieves a list of intercellular communication resources available in
#' OmniPath
#'
#' Retrieves a list of the databases from
#' \url{https://omnipath.org/intercell}.
#'
#' @param dataset ignored at this query type
#'
#' @return character vector with the names of the databases
#'
#' @examples
#' get_intercell_resources()
#'
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{get_resources}}}
#' \item{\code{\link{import_omnipath_intercell}}}
#' }
get_intercell_resources <- function(dataset = NULL){
return(get_resources(query_type = 'intercell', datasets = dataset))
}
#' Intercellular communication network
#'
#' Imports an intercellular network by combining intercellular annotations
#' and protein interactions. First imports a network of protein-protein
#' interactions. Then, it retrieves annotations about the proteins
#' intercellular communication roles, once for the transmitter (delivering
#' information from the expressing cell) and second, the receiver (receiving
#' signal and relaying it towards the expressing cell) side. These 3 queries
#' can be customized by providing parameters in lists which will be passed to
#' the respective methods (\code{\link{import_omnipath_interactions}} for
#' the network and \code{\link{import_omnipath_intercell}} for the
#' annotations). Finally the 3 data frames combined in a way that the source
#' proteins in each interaction annotated by the transmitter, and the target
#' proteins by the receiver categories. If undirected interactions present
#' (these are disabled by default) they will be duplicated, i.e. both
#' partners can be both receiver and transmitter.
#'
#' @return A dataframe containing information about protein-protein
#' interactions and the inter-cellular roles of the protiens involved in those
#' interactions.
#'
#' @param interactions_param a list with arguments for an interactions query:
#' \code{\link{import_omnipath_interactions}},
#' \code{\link{import_pathwayextra_interactions}},
#' \code{\link{import_kinaseextra_interactions}},
#' \code{\link{import_ligrecextra_interactions}}
#' @param transmitter_param a list with arguments for
#' \code{\link{import_omnipath_intercell}}, to define the transmitter side
#' of intercellular connections
#' @param receiver_param a list with arguments for
#' \code{\link{import_omnipath_intercell}}, to define the receiver side
#' of intercellular connections
#' @param resources A character vector of resources to be applied to
#' both the interactions and the annotations. For example, \code{resources
#' = 'CellChatDB'} will download the transmitters and receivers defined by
#' CellChatDB, connected by connections from CellChatDB.
#' @param entity_types Character, possible values are "protein", "complex" or
#' both.
#' @param ligand_receptor Logical. If \code{TRUE}, only \emph{ligand} and
#' \emph{receptor} annotations will be used instead of the more generic
#' \emph{transmitter} and \emph{receiver} categories.
#' @param high_confidence Logical: shortcut to do some filtering in order to
#' include only higher confidence interactions. The intercell database
#' of OmniPath covers a very broad range of possible ways of cell to cell
#' communication, and the pieces of information, such as localization,
#' topology, function and interaction, are combined from many, often
#' independent sources. This unavoidably result some weird and unexpected
#' combinations which are false positives in the context of intercellular
#' communication. This option sets some minimum criteria to remove most
#' (but definitely not all!) of the wrong connections. These criteria
#' are the followings: 1) the receiver must be plasma membrane
#' transmembrane; 2) the curation effort for interactions must be larger
#' than one; 3) the consensus score for annotations must be larger than
#' the 50 percentile within the generic category (you can override this
#' by \code{consensus_percentile}). 4) the transmitter must be secreted
#' or exposed on the plasma membrane. 5) The major localizations have
#' to be supported by at least 30 percent of the relevant resources (
#' you can override this by \code{loc_consensus_percentile}). 6) The
#' datasets with lower level of curation (\emph{kinaseextra} and \emph{
#' pathwayextra}) will be disabled. These criteria are of medium
#' stringency, you can always tune them to be more relaxed or stringent
#' by filtering manually, using \code{\link{filter_intercell_network}}.
#' @param simplify Logical: keep only the most often used columns. This
#' function combines a network data frame with two copies of the
#' intercell annotation data frames, all of them already having quite
#' some columns. With this option we keep only the names of the
#' interacting pair, their intercellular communication roles, and the
#' minimal information of the origin of both the interaction and
#' the annotations.
#' @param unique_pairs Logical: instead of having separate rows for each
#' pair of annotations, drop the annotations and reduce the data frame to
#' unique interacting pairs. See \code{\link{unique_intercell_network}}
#' for details.
#' @param consensus_percentile Numeric: a percentile cut off for the consensus
#' score of generic categories in intercell annotations. The consensus
#' score is the number of resources supporting the classification of an
#' entity into a category based on combined information of many resources.
#' Here you can apply a cut-off, keeping only the annotations supported
#' by a higher number of resources than a certain percentile of each
#' category. If \code{NULL} no filtering will be performed. The value is
#' either in the 0-1 range, or will be divided by 100 if greater than 1.
#' The percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_consensus_percentile Numeric: similar to
#' \code{consensus_percentile} for major localizations. For example, with
#' a value of 50, the secreted, plasma membrane transmembrane or
#' peripheral attributes will be \code{TRUE} only where at least 50
#' percent of the resources support these.
#' @param omnipath Logical: shortcut to include the \emph{omnipath} dataset
#' in the interactions query.
#' @param ligrecextra Logical: shortcut to include the \emph{ligrecextra}
#' dataset in the interactions query.
#' @param kinaseextra Logical: shortcut to include the \emph{kinaseextra}
#' dataset in the interactions query.
#' @param pathwayextra Logical: shortcut to include the \emph{pathwayextra}
#' dataset in the interactions query.
#' @param ... If \code{simplify} or \code{unique_pairs} is \code{TRUE},
#' additional column names can be passed here to \code{dplyr::select}
#' on the final data frame. Otherwise ignored.
#'
#' @details
#' By default this function creates almost the largest possible network of
#' intercellular interactions. However, this might contain a large number
#' of false positives. Please refer to the documentation of the arguments,
#' especially \code{high_confidence}, and the \code{
#' \link{filter_intercell_network}} function. Note: if you restrict the query
#' to certain intercell annotation resources or small categories, it's not
#' recommended to use the \code{consensus_percentile} or
#' \code{high_confidence} options, instead filter the network with \code{
#' \link{filter_intercell_network}} for more consistent results.
#'
#' @examples
#' intercell_network <- import_intercell_network(
#' interactions_param = list(datasets = 'ligrecextra'),
#' receiver_param = list(categories = c('receptor', 'transporter')),
#' transmitter_param = list(categories = c('ligand', 'secreted_enzyme'))
#' )
#'
#' @importFrom dplyr rename bind_rows filter inner_join distinct group_by
#' @importFrom dplyr summarize_all first
#' @importFrom rlang %||%
#' @importFrom magrittr %>% %<>%
#' @importFrom utils modifyList
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{get_intercell_categories}}}
#' \item{\code{\link{get_intercell_generic_categories}}}
#' \item{\code{\link{import_omnipath_intercell}}}
#' \item{\code{\link{import_omnipath_interactions}}}
#' \item{\code{\link{import_pathwayextra_interactions}}}
#' \item{\code{\link{import_kinaseextra_interactions}}}
#' \item{\code{\link{import_ligrecextra_interactions}}}
#' \item{\code{\link{unique_intercell_network}}}
#' \item{\code{\link{simplify_intercell_network}}}
#' \item{\code{\link{filter_intercell_network}}}
#' }
import_intercell_network <- function(
interactions_param = list(),
transmitter_param = list(),
receiver_param = list(),
resources = NULL,
entity_types = NULL,
ligand_receptor = FALSE,
high_confidence = FALSE,
simplify = FALSE,
unique_pairs = FALSE,
consensus_percentile = NULL,
loc_consensus_percentile = NULL,
omnipath = TRUE,
ligrecextra = TRUE,
kinaseextra = !high_confidence,
pathwayextra = !high_confidence,
...
){
.slow_doctest()
# NSE vs. R CMD check workaround
parent <- secreted <- plasma_membrane_transmembrane <-
plasma_membrane_peripheral <- NULL
datasets <-
environment() %>%
select_interaction_datasets
# retrieving interactions
interactions_param <- list(
query_type = 'interactions',
datasets = datasets,
fields = 'datasets'
) %>%
insert_if_not_null(
resources = resources,
entity_types = entity_types
) %>%
modifyList(interactions_param)
interactions <- do.call(
import_omnipath,
interactions_param
)
interactions <- swap_undirected(interactions)
# retrieving intercell annotations
consensus_percentile %<>%
{`if`(high_confidence, . %||% 50, .)}
loc_consensus_percentile %<>%
{`if`(high_confidence, . %||% 30, .)}
transmitter_param <- list(
causality = 'trans',
scope = 'generic'
) %>%
insert_if_not_null(
resources = resources,
entity_types = entity_types,
consensus_percentile = consensus_percentile,
loc_consensus_percentile = loc_consensus_percentile
) %>%
{`if`(
ligand_receptor,
`[[<-`(., 'parent', 'ligand'),
.
)} %>%
modifyList(transmitter_param)
receiver_param <- list(
causality = 'rec',
scope = 'generic'
) %>%
insert_if_not_null(
resources = resources,
entity_types = entity_types,
consensus_percentile = consensus_percentile
) %>%
{`if`(
ligand_receptor,
`[[<-`(., 'parent', 'receptor'),
.
)} %>%
modifyList(receiver_param)
intracell <- c('intracellular_intercellular_related', 'intracellular')
transmitters <-
do.call(import_omnipath_intercell, transmitter_param) %>%
filter(!parent %in% intracell) %>%
rename(category_source = source) %>%
{`if`(
high_confidence,
filter(
.,
secreted |
plasma_membrane_transmembrane |
plasma_membrane_peripheral
),
.
)}
receivers <-
do.call(import_omnipath_intercell, receiver_param) %>%
filter(!parent %in% intracell) %>%
rename(category_source = source) %>%
{`if`(
high_confidence,
filter(., plasma_membrane_transmembrane),
.
)}
interactions %>%
{`if`(
high_confidence,
filter(., curation_effort > 1),
.
)} %>%
inner_join(
transmitters,
by = c('source' = 'uniprot')
) %>%
group_by(
category, parent, source, target
) %>%
mutate(
database = paste(database, collapse = ';')
) %>%
summarize_all(first) %>%
inner_join(
receivers,
by = c('target' = 'uniprot'),
suffix = c('_intercell_source', '_intercell_target')
) %>%
group_by(
category_intercell_source,
parent_intercell_source,
source,
target,
category_intercell_target,
parent_intercell_target
) %>%
mutate(
database_intercell_target = paste(
database_intercell_target,
collapse = ';'
)
) %>%
summarize_all(first) %>%
ungroup() %>%
{`if`(
simplify,
simplify_intercell_network(., ...),
.
)} %>%
{`if`(
unique_pairs,
unique_intercell_network(., ...),
.
)}
}
#' Filter intercell annotations
#'
#' Filters a data frame retrieved by \code{\link{import_omnipath_intercell}}.
#'
#' @param data An intercell annotation data frame as provided by
#' \code{\link{import_omnipath_intercell}}.
#' @param categories Character: allow only these values in the \code{category}
#' column.
#' @param resources Character: allow records only from these resources.
#' @param parent Character: filter for records with these parent categories.
#' @param scope Character: filter for records with these annotation scopes.
#' Possible values are \code{generic} and \code{specific}.
#' @param aspect Character: filter for records with these annotation aspects.
#' Possible values are \code{functional} and \code{locational}.
#' @param source Character: filter for records with these annotation sources.
#' Possible values are \code{composite} and \code{resource_specific}.
#' @param transmitter Logical: if \code{TRUE} only transmitters, if
#' \code{FALSE} only non-transmitters will be selected, if \code{NULL}
#' it has no effect.
#' @param receiver Logical: works the same way as \code{transmitters}.
#' @param secreted Logical: works the same way as \code{transmitters}.
#' @param plasma_membrane_peripheral Logical: works the same way as
#' \code{transmitters}.
#' @param plasma_membrane_transmembrane Logical: works the same way as
#' \code{transmitters}.
#' @param proteins Character: filter for annotations of these proteins.
#' Gene symbols or UniProt IDs can be used.
#' @param causality Character: filter for records with these causal roles.
#' Possible values are \code{transmitter} and \code{receiver}. The filter
#' applied simultaneously to the \code{transmitter} and \code{receiver}
#' arguments, it's just a different notation for the same thing.
#' @param topology Character: filter for records with these localization
#' topologies. Possible values are \code{secreced},
#' \code{plasma_membrane_peripheral} and
#' \code{plasma_membrane_transmembrane}; the shorter notations \code{sec},
#' \code{pmp} and \code{pmtm} can be used. Has the same effect as the
#' logical type arguments, just uses a different notation.
#' @param ... Ignored.
#'
#' @return The intercell annotation data frame filtered according to the
#' specified conditions.
#'
#' @examples
#' ic <- import_omnipath_intercell()
#' ic <- filter_intercell(
#' ic,
#' transmitter = TRUE,
#' secreted = TRUE,
#' scope = "specific"
#' )
#'
#' @importFrom dplyr recode rename_all
#' @importFrom magrittr %>% %<>%
#' @importFrom rlang .data set_names
#' @importFrom stringr str_detect
#' @export
filter_intercell <- function(
data,
categories = NULL,
resources = NULL,
parent = NULL,
scope = NULL,
aspect = NULL,
source = NULL,
transmitter = NULL,
receiver = NULL,
secreted = NULL,
plasma_membrane_peripheral = NULL,
plasma_membrane_transmembrane = NULL,
proteins = NULL,
causality = NULL,
topology = NULL,
...
){
args <- environment() %>% as.list()
args$cats <- args$categories
before <- nrow(data)
topology %<>%
{data.frame(set_names(
as.list(.),
rep(TRUE, length(.))
))} %>%
rename_all(
recode,
secreted = 'sec',
plasma_membrane_peripheral = 'pmp',
plasma_membrane_transmembrane = 'pmtm'
)
causality %<>%
{`if`('both' %in% ., c('transmitter', 'receiver'), .)} %>%
{data.frame(set_names(
as.list(.),
rep(TRUE, length(.))
))} %>%
rename_all(
recode,
transmitter = 'trans',
receiver = 'rec'
)
data <-
data %>%
filter(
(is.null(args$cats) | category %in% args$cats) &
(is.null(args$parent) | .data$parent %in% args$parent) &
(is.null(args$scope) | .data$scope %in% args$scope) &
(is.null(args$aspect) | .data$aspect %in% aspect) &
(is.null(args$source) | .data$source %in% source) &
(is.null(args$transmitter) | .data$transmitter) &
(is.null(args$receiver) | .data$receiver) &
(is.null(args$secreted) | .data$secreted) &
(
is.null(args$plasma_membrane_peripheral) |
.data$plasma_membrane_peripheral
) &
(
is.null(args$plasma_membrane_transmembrane) |
.data$plasma_membrane_transmembrane
) &
(
is.null(proteins) |
uniprot %in% proteins |
genesymbol %in% proteins
)
) %>%
{`if`(
ncol(topology) > 0,
inner_join(., topology, by = names(topology)),
.
)} %>%
{`if`(
ncol(causality) > 0,
inner_join(., causality, by = names(causality)),
.
)}
after <- nrow(data)
message(
sprintf(
'Removed %d and kept %d records of intercell data.',
before - after,
after
)
)
return(data)
}
#' Quality filter an intercell network
#'
#' The intercell database of OmniPath covers a very broad range of possible
#' ways of cell to cell communication, and the pieces of information, such as
#' localization, topology, function and interaction, are combined from many,
#' often independent sources. This unavoidably result some weird and
#' unexpected combinations which are false positives in the context of
#' intercellular communication. \code{\link{import_intercell_network}}
#' provides a shortcut (\code{high_confidence}) to do basic quality filtering.
#' For custom filtering or experimentation with the parameters we offer this
#' function.
#'
#' @param network An intercell network data frame, as provided by
#' \code{\link{import_intercell_network}}, without \code{simplify}.
#' @param transmitter_topology Character vector: topologies allowed for the
#' entities in transmitter role. Abbreviations allowed: "sec", "pmtm"
#' and "pmp".
#' @param receiver_topology Same as \code{transmitter_topology} for the
#' entities in the receiver role.
#' @param min_curation_effort Numeric: a minimum value of curation effort
#' (resource-reference pairs) for network interactions. Use zero to
#' disable filtering.
#' @param min_resources Numeric: minimum number of resources for
#' interactions. The value 1 means no filtering.
#' @param min_references Numeric: minimum number of references for
#' interactions. Use zero to disable filtering.
#' @param min_provenances Numeric: minimum number of provenances (either
#' resources or references) for interactions. Use zero or one to
#' disable filtering.
#' @param consensus_percentile Numeric: percentile threshold for the consensus
#' score of generic categories in intercell annotations. The consensus
#' score is the number of resources supporting the classification of an
#' entity into a category based on combined information of many resources.
#' Here you can apply a cut-off, keeping only the annotations supported
#' by a higher number of resources than a certain percentile of each
#' category. If \code{NULL} no filtering will be performed. The value is
#' either in the 0-1 range, or will be divided by 100 if greater than 1.
#' The percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_consensus_percentile Numeric: similar to
#' \code{consensus_percentile} for major localizations. For example, with
#' a value of 50, the secreted, plasma membrane transmembrane or
#' peripheral attributes will be \code{TRUE} only where at least 50
#' percent of the resources support these.
#' @param ligand_receptor Logical. If \code{TRUE}, only \emph{ligand} and
#' \emph{receptor} annotations will be used instead of the more generic
#' \emph{transmitter} and \emph{receiver} categories.
#' @param simplify Logical: keep only the most often used columns. This
#' function combines a network data frame with two copies of the
#' intercell annotation data frames, all of them already having quite
#' some columns. With this option we keep only the names of the
#' interacting pair, their intercellular communication roles, and the
#' minimal information of the origin of both the interaction and
#' the annotations.
#' @param unique_pairs Logical: instead of having separate rows for each
#' pair of annotations, drop the annotations and reduce the data frame to
#' unique interacting pairs. See \code{\link{unique_intercell_network}}
#' for details.
#' @param omnipath Logical: shortcut to include the \emph{omnipath} dataset
#' in the interactions query.
#' @param ligrecextra Logical: shortcut to include the \emph{ligrecextra}
#' dataset in the interactions query.
#' @param kinaseextra Logical: shortcut to include the \emph{kinaseextra}
#' dataset in the interactions query.
#' @param pathwayextra Logical: shortcut to include the \emph{pathwayextra}
#' dataset in the interactions query.
#' @param ... If \code{simplify} or \code{unique_pairs} is \code{TRUE},
#' additional column names can be passed here to \code{dplyr::select}
#' on the final data frame. Otherwise ignored.
#'
#' @return An intercell network data frame filtered.
#'
#' @examples
#' icn <- import_intercell_network()
#' icn_f <- filter_intercell_network(
#' icn,
#' consensus_percentile = 75,
#' min_provenances = 3,
#' simplify = TRUE
#' )
#'
#' @importFrom magrittr %>%
#' @importFrom dplyr select distinct filter inner_join left_join
#' @importFrom rlang !!! parse_expr exprs syms
#' @importFrom logger log_warn
#' @importFrom purrr walk
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{unique_intercell_network}}}
#' \item{\code{\link{simplify_intercell_network}}}
#' }
filter_intercell_network <- function(
network,
transmitter_topology = c(
'secreted',
'plasma_membrane_transmembrane',
'plasma_membrane_peripheral'
),
receiver_topology = 'plasma_membrane_transmembrane',
min_curation_effort = 2,
min_resources = 1,
min_references = 0,
min_provenances = 1,
consensus_percentile = 50,
loc_consensus_percentile = 30,
ligand_receptor = FALSE,
simplify = FALSE,
unique_pairs = FALSE,
omnipath = TRUE,
ligrecextra = TRUE,
kinaseextra = FALSE,
pathwayextra = FALSE,
...
){
.slow_doctest()
# NSE vs. R CMD check workaround
parent <- curation_effort <- n_resources <- n_references <- NULL
consensus <-
import_omnipath_intercell(
consensus_percentile = consensus_percentile,
loc_consensus_percentile = loc_consensus_percentile
)
consensus_annot <-
consensus %>%
select(uniprot, parent) %>%
distinct
consensus_loc <-
consensus %>%
select(uniprot, !!!syms(TOPOLOGIES)) %>%
distinct
topologies <- unlist(TOPOLOGIES_SHORT)
check_topo <- function(x){
if(!x %in% topologies){
log_warn('Unknown topology: %s', x)
}
}
datasets <-
environment() %>%
select_interaction_datasets
missing_datasets <- datasets %>% setdiff(colnames(network))
if(length(missing_datasets)){
msg <- sprintf(
paste0(
'filter_intercell_network: cannot select %s %s, ',
'apparently %s %s not included in the original ',
'download.'
),
missing_datasets %>%
plural('dataset'),
missing_datasets %>%
pretty_list,
missing_datasets %>%
plural('this', 'these'),
missing_datasets %>%
plural('was', 'were')
)
log_warn(msg)
warning(msg)
}
transmitter_topology %<>%
topology_long %>%
walk(check_topo) %>%
intersect(topologies) %>%
sprintf('%s_intercell_source', .) %>%
paste(collapse = ' | ')
receiver_topology %<>%
topology_long %>%
walk(check_topo) %>%
intersect(topologies) %>%
sprintf('%s_intercell_target', .) %>%
paste(collapse = ' | ')
datasets %<>%
intersect(colnames(network)) %>%
paste(collapse = ' | ')
network %>%
{`if`(
is.null(loc_consensus_percentile),
.,
select(
.,
-(!!!exprs(sprintf('%s_intercell_source', TOPOLOGIES))),
-(!!!exprs(sprintf('%s_intercell_target', TOPOLOGIES)))
) %>%
left_join(consensus_loc, by = c('source' = 'uniprot')) %>%
left_join(consensus_loc, by = c('target' = 'uniprot'),
suffix = c('_intercell_source', '_intercell_target')
)
)} %>%
filter(eval(parse_expr(receiver_topology))) %>%
filter(eval(parse_expr(transmitter_topology))) %>%
filter(eval(parse_expr(datasets))) %>%
filter(
curation_effort >= min_curation_effort &
n_resources >= min_resources &
n_references >= min_references &
(
n_resources >= min_provenances |
n_references >= min_provenances
)
) %>%
inner_join(
consensus_annot,
by = c(
'parent_intercell_source' = 'parent',
'source' = 'uniprot'
)
) %>%
inner_join(
consensus_annot,
by = c(
'parent_intercell_target' = 'parent',
'target' = 'uniprot'
)
) %>%
{`if`(
ligand_receptor,
filter(
.,
parent_intercell_source == 'ligand' &
parent_intercell_target == 'receptor'
),
.
)} %>%
{`if`(
simplify,
simplify_intercell_network(., ...),
.
)} %>%
{`if`(
unique_pairs,
unique_intercell_network(., ...),
.
)}
}
#' Simplify an intercell network
#'
#' The intercellular communication network data frames, created by
#' \code{\link{import_intercell_network}}, are combinations of a network data
#' frame with two copies of the intercell annotation data frames, all of them
#' already having quite some columns. Here we keep only the names of the
#' interacting pair, their intercellular communication roles, and the minimal
#' information of the origin of both the interaction and the annotations.
#' Optionally further columns can be selected.
#'
#' @param network An intercell network data frame, as provided by
#' \code{\link{import_intercell_network}}.
#' @param ... Optional, further columns to select.
#'
#' @return An intercell network data frame with some columns removed.
#'
#' @examples
#' icn <- import_intercell_network()
#' icn_s <- simplify_intercell_network(icn)
#'
#' @importFrom magrittr %>%
#' @importFrom dplyr select
#' @importFrom rlang ensyms !!!
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{unique_intercell_network}}}
#' \item{\code{\link{filter_intercell_network}}}
#' }
simplify_intercell_network <- function(network, ...){
.slow_doctest()
# NSE vs. R CMD check workaround
source <- target <- source_genesymbol <- target_genesymbol <-
category_intercell_source <- database_intercell_source <-
category_intercell_target <- database_intercell_target <-
is_directed <- is_stimulation <- is_inhibition <-
sources <- references <- NULL
simplify_cols <-
alist(
source,
target,
source_genesymbol,
target_genesymbol,
category_intercell_source,
database_intercell_source,
category_intercell_target,
database_intercell_target,
is_directed,
is_stimulation,
is_inhibition,
sources,
references
) %>%
c(ensyms(...)) %>%
unique
network %>%
select(!!!simplify_cols)
}
#' Unique intercellular interactions
#'
#' In the intercellular network data frames produced by \code{
#' \link{import_intercell_network}}, by default each pair of annotations for
#' an interaction is represented in a separate row. This function drops the
#' annotations and keeps only the distinct interacting pairs.
#'
#' @param network An intercellular network data frame as produced by
#' \code{\link{import_intercell_network}}.
#' @param ... Additional columns to keep. Note: if these have multiple
#' values for an interacting pair, only the first row will be
#' preserved.
#'
#' @return A data frame with interacting pairs and interaction attributes.
#'
#' @examples
#' icn <- import_intercell_network()
#' icn_unique <- unique_intercell_network(icn)
#'
#' @importFrom magrittr %>%
#' @importFrom dplyr select distinct
#' @importFrom rlang !!!
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{simplify_intercell_network}}}
#' \item{\code{\link{filter_intercell_network}}}
#' }
unique_intercell_network <- function(network, ...){
.slow_doctest()
# NSE vs. R CMD check workaround
source <- target <- source_genesymbol <- target_genesymbol <-
is_directed <- is_stimulation <- is_inhibition <-
sources <- references <- NULL
cols <-
alist(
source,
target,
source_genesymbol,
target_genesymbol,
is_directed,
is_stimulation,
is_inhibition,
sources,
references
) %>%
c(ensyms(...)) %>%
unique
network %>%
select(!!!cols) %>%
distinct(source, target)
}
#' Categories in the intercell database of OmniPath
#'
#' Retrieves a list of categories from \url{https://omnipath.org/intercell}.
#'
#' @return character vector with the different intercell categories
#' @export
#'
#' @examples
#' get_intercell_categories()
#'
#' @seealso \itemize{
#' \item{\code{\link{import_omnipath_intercell}}}
#' \item{\code{\link{get_intercell_generic_categories}}}
#' }
get_intercell_categories <- function(){
return(
unique(
import_omnipath('intercell_summary', license = NA)$category
)
)
}
#' Full list of intercell categories and resources
#'
#' @return A data frame of categories and resources.
#'
#' @examples
#' ic_cat <- intercell_categories()
#' ic_cat
#' # # A tibble: 1,125 x 3
#' # category parent database
#' # <chr> <chr> <chr>
#' # 1 transmembrane transmembrane UniProt_location
#' # 2 transmembrane transmembrane UniProt_topology
#' # 3 transmembrane transmembrane UniProt_keyword
#' # 4 transmembrane transmembrane_predicted Phobius
#' # 5 transmembrane_phobius transmembrane_predicted Almen2009
#' # # . with 1,120 more rows
#'
#' @export
intercell_categories <- function(){
import_omnipath('intercell_summary', license = NA)
}
#' Retrieves a list of the generic categories in the intercell database
#' of OmniPath
#'
#' Retrieves a list of the generic categories from
#' \url{https://omnipath.org/intercell}.
#'
#' @return character vector with the different intercell main classes
#' @export
#'
#' @examples
#' get_intercell_generic_categories()
#'
#' @seealso \itemize{
#' \item{\code{\link{import_omnipath_intercell}}}
#' \item{\code{\link{get_intercell_categories}}}
#' }
#'
#' @aliases get_intercell_classes
get_intercell_generic_categories <- function(){
return(
unique(
import_omnipath('intercell_summary', license = NA)$parent
)
)
}
# Aliases (old names) to be deprecated
#' @rdname get_intercell_generic_categories
#' @param ... Passed to \code{get_intercell_generic_categories}.
#' @export
#'
#' @noRd
get_intercell_classes <- function(...){
.Deprecated("get_intercell_generic_categories")
get_intercell_generic_categories(...)
}
#' Short to long topology names
#'
#' @param topologies Character vector of short topology names. Long names and
#' any other strings will be left intact.
#'
#' @importFrom rlang !!!
#' @importFrom dplyr recode
#' @importFrom magrittr %>%
#' @noRd
topology_long <- function(topologies){
topologies %>%
{`if`(is.null(.), ., recode(., !!!TOPOLOGIES_SHORT))}
}
saezlab/OmnipathR documentation built on May 3, 2024, 5:32 a.m.
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Defines functions intercell_consensus_filter import_omnipath_intercell
Documented in import_omnipath_intercell intercell_consensus_filter
#!/usr/bin/env Rscript
#
# This file is part of the `OmnipathR` R package
#
# Copyright
# 2018-2024
# Saez Lab, Uniklinik RWTH Aachen, Heidelberg University
#
# File author(s): Alberto Valdeolivas
# Dénes Türei (turei.denes@gmail.com)
# Attila Gábor
#
# Distributed under the MIT (Expat) License.
# See accompanying file `LICENSE` or find a copy at
# https://directory.fsf.org/wiki/License:Expat
#
# Website: https://r.omnipathdb.org/
# Git repo: https://github.com/saezlab/OmnipathR
#
TOPOLOGIES <-
c(
'secreted',
'plasma_membrane_transmembrane',
'plasma_membrane_peripheral'
)
TOPOLOGIES_SHORT <-
rlang::set_names(TOPOLOGIES, c('sec', 'pmtm', 'pmp'))
#' Imports OmniPath intercell annotations
#'
#' Imports the OmniPath intercellular communication role annotation database
#' from \url{https://omnipathdb.org/intercell}. It provides information
#' on the roles in inter-cellular signaling. E.g. if a protein is
#' a ligand, a receptor, an extracellular matrix (ECM) component, etc.
#'
#' @return A dataframe cotaining information about roles in intercellular
#' signaling.
#'
#' @param categories vector containing the categories to be retrieved.
#' All the genes belonging to those categories will be returned. For
#' further information about the categories see
#' \code{\link{get_intercell_categories}}.
#' @param parent vector containing the parent classes to be retrieved.
#' All the genes belonging to those classes will be returned. For
#' furter information about the main classes see
#' \code{\link{get_intercell_categories}}.
#' @param resources limit the query to certain resources; see the available
#' resources by \code{\link{get_intercell_resources}}.
#' @param scope either `specific` or `generic`
#' @param aspect either `locational` or `functional`
#' @param source either `resource_specific` or `composite`
#' @param transmitter logical, include only transmitters i.e. proteins
#' delivering signal from a cell to its environment.
#' @param receiver logical, include only receivers i.e. proteins delivering
#' signal to the cell from its environment.
#' @param plasma_membrane_peripheral logical, include only plasma membrane
#' peripheral membrane proteins.
#' @param plasma_membrane_transmembrane logical, include only plasma membrane
#' transmembrane proteins.
#' @param secreted logical, include only secreted proteins
#' @param proteins limit the query to certain proteins
#' @param topology topology categories: one or more of `secreted` (sec),
#' `plasma_membrane_peripheral` (pmp), `plasma_membrane_transmembrane`
#' (pmtm) (both short or long notation can be used).
#' @param causality `transmitter` (trans), `receiver` (rec) or `both` (both
#' short or long notation can be used).
#' @param consensus_percentile Numeric: a percentile cut off for the
#' consensus score of generic categories. The consensus score is the
#' number of resources supporting the classification of an entity into a
#' category based on combined information of many resources. Here you can
#' apply a cut-off, keeping only the annotations supported by a higher
#' number of resources than a certain percentile of each category. If
#' \code{NULL} no filtering will be performed. The value is either in the
#' 0-1 range, or will be divided by 100 if greater than 1. The
#' percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_consensus_percentile Numeric: similar to
#' \code{consensus_percentile} for major localizations. For example, with
#' a value of 50, the secreted, plasma membrane transmembrane or
#' peripheral attributes will be true only where at least 50 percent
#' of the resources support these.
#' @param ... Additional optional arguments, ignored.
#'
#' @examples
#' intercell <- import_omnipath_intercell(categories = 'ecm')
#'
#' @importFrom magrittr %<>% %>%
#' @importFrom purrr reduce
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{get_intercell_categories}}}
#' \item{\code{\link{get_intercell_generic_categories}}}
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{intercell_consensus_filter}}}
#' }
#'
#' @aliases import_Omnipath_intercell import_OmniPath_intercell
import_omnipath_intercell <- function(
categories = NULL,
resources = NULL,
parent = NULL,
scope = NULL,
aspect = NULL,
source = NULL,
transmitter = NULL,
receiver = NULL,
secreted = NULL,
plasma_membrane_peripheral = NULL,
plasma_membrane_transmembrane = NULL,
proteins = NULL,
topology = NULL,
causality = NULL,
consensus_percentile = NULL,
loc_consensus_percentile = NULL,
...
){
topology %<>%
topology_long %>%
{reduce(
TOPOLOGIES,
function(topos, topo){
arg_value <- get(topo)
`if`(
is.null(arg_value),
topos,
`if`(
arg_value,
union(topos, topo),
setdiff(topos, topo)
)
)
},
.init = .
)}
args <- c(as.list(environment()), list(...))
args$query_type <- 'intercell'
args$logicals <- c(
'transmitter',
'receiver',
'secreted',
'plasma_membrane_peripheral',
'plasma_membrane_transmembrane'
)
args$consensus_percentile <- NULL
args$loc_consensus_percentile <- NULL
result <-
do.call(import_omnipath, args) %>%
intercell_consensus_filter(
percentile = consensus_percentile,
loc_percentile = loc_consensus_percentile,
topology = topology
)
return(result)
}
#' Quality filter for intercell annotations
#'
#' @param data A data frame with intercell annotations, as provided by
#' \code{\link{import_omnipath_intercell}}.
#' @param percentile Numeric: a percentile cut off for the consensus score
#' of composite categories. The consensus score is the number of
#' resources supporting the classification of an entity into a category
#' based on combined information of many resources. Here you can apply
#' a cut-off, keeping only the annotations supported by a higher number
#' of resources than a certain percentile of each category. If
#' \code{NULL} no filtering will be performed. The value is either in the
#' 0-1 range, or will be divided by 100 if greater than 1. The
#' percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_percentile Numeric: similar to \code{percentile} for major
#' localizations. For example, with a value of 50, the secreted, plasma
#' membrane transmembrane or peripheral attributes will be \code{TRUE}
#' only where at least 50 percent of the resources support these.
#' @param topology Character vector: list of allowed topologies, possible
#' values are *"secreted"*, *"plasma_membrane_peripheral"* and
#' *"plasma_membrane_transmembrane"*.
#'
#' @return The data frame in \code{data} filtered by the consensus scores.
#'
#' @examples
#' intercell <- import_omnipath_intercell(parent = c('ligand', 'receptor'))
#' nrow(intercell)
#' # [1] 50174
#' intercell_q50 <- intercell_consensus_filter(intercell, 50)
#' nrow(intercell_q50)
#' # [1] 42863
#'
#' @importFrom magrittr %>% %<>%
#' @importFrom dplyr group_by filter ungroup bind_rows
#' @importFrom dplyr select distinct inner_join pull
#' @importFrom stats quantile
#' @importFrom rlang !! := sym eval_tidy parse_expr
#' @importFrom purrr reduce
#' @export
intercell_consensus_filter <- function(
data,
percentile = NULL,
loc_percentile = NULL,
topology = NULL
){
# NSE vs. R CMD check workaround
scope <- source <- parent <- consensus_score <- NULL
percentile %<>%
if_null(0L) %>%
{`if`(. > 1, . / 100, .)}
thresholds <-
data %>%
filter(scope == 'generic' & source == 'composite') %>%
group_by(parent) %>%
filter(
consensus_score >= quantile(consensus_score, percentile)
) %>%
ungroup %>%
select(parent, uniprot) %>%
distinct
composite_parents <-
data %>%
filter(source == 'composite') %>%
pull(parent) %>%
unique
data %<>%
inner_join(thresholds, by = c('parent', 'uniprot')) %>%
bind_rows(
data %>%
filter(!parent %in% composite_parents)
)
if(!is.null(loc_percentile)){
locations <- import_omnipath_intercell(
aspect = 'locational',
parent = TOPOLOGIES,
consensus_percentile = loc_percentile
)
data %<>%
{reduce(
TOPOLOGIES,
function(data, loc){
in_location <-
locations %>%
filter(!!sym(loc)) %>%
pull(uniprot) %>%
unique
data %>%
mutate(!!sym(loc) := uniprot %in% in_location)
},
.init = .
)} %>%
{`if`(
is.null(topology),
.,
filter(
.,
eval_tidy(parse_expr(paste(topology, collapse = ' | ')))
)
)}
}
return(data)
}
# Aliases (old names) to be deprecated
#' @rdname import_omnipath_intercell
#' @param ... Passed to \code{import_omnipath_intercell}.
#' @export
#'
#' @noRd
import_Omnipath_intercell <- function(...){
.Deprecated("import_omnipath_intercell")
import_omnipath_intercell(...)
}
#' @rdname import_omnipath_intercell
#' @param ... Passed to \code{import_omnipath_intercell}.
#' @export
#'
#' @noRd
import_OmniPath_intercell <- function(...){
.Deprecated("import_omnipath_intercell")
import_omnipath_intercell(...)
}
#' Retrieves a list of intercellular communication resources available in
#' OmniPath
#'
#' Retrieves a list of the databases from
#' \url{https://omnipath.org/intercell}.
#'
#' @param dataset ignored at this query type
#'
#' @return character vector with the names of the databases
#'
#' @examples
#' get_intercell_resources()
#'
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{get_resources}}}
#' \item{\code{\link{import_omnipath_intercell}}}
#' }
get_intercell_resources <- function(dataset = NULL){
return(get_resources(query_type = 'intercell', datasets = dataset))
}
#' Intercellular communication network
#'
#' Imports an intercellular network by combining intercellular annotations
#' and protein interactions. First imports a network of protein-protein
#' interactions. Then, it retrieves annotations about the proteins
#' intercellular communication roles, once for the transmitter (delivering
#' information from the expressing cell) and second, the receiver (receiving
#' signal and relaying it towards the expressing cell) side. These 3 queries
#' can be customized by providing parameters in lists which will be passed to
#' the respective methods (\code{\link{import_omnipath_interactions}} for
#' the network and \code{\link{import_omnipath_intercell}} for the
#' annotations). Finally the 3 data frames combined in a way that the source
#' proteins in each interaction annotated by the transmitter, and the target
#' proteins by the receiver categories. If undirected interactions present
#' (these are disabled by default) they will be duplicated, i.e. both
#' partners can be both receiver and transmitter.
#'
#' @return A dataframe containing information about protein-protein
#' interactions and the inter-cellular roles of the protiens involved in those
#' interactions.
#'
#' @param interactions_param a list with arguments for an interactions query:
#' \code{\link{import_omnipath_interactions}},
#' \code{\link{import_pathwayextra_interactions}},
#' \code{\link{import_kinaseextra_interactions}},
#' \code{\link{import_ligrecextra_interactions}}
#' @param transmitter_param a list with arguments for
#' \code{\link{import_omnipath_intercell}}, to define the transmitter side
#' of intercellular connections
#' @param receiver_param a list with arguments for
#' \code{\link{import_omnipath_intercell}}, to define the receiver side
#' of intercellular connections
#' @param resources A character vector of resources to be applied to
#' both the interactions and the annotations. For example, \code{resources
#' = 'CellChatDB'} will download the transmitters and receivers defined by
#' CellChatDB, connected by connections from CellChatDB.
#' @param entity_types Character, possible values are "protein", "complex" or
#' both.
#' @param ligand_receptor Logical. If \code{TRUE}, only \emph{ligand} and
#' \emph{receptor} annotations will be used instead of the more generic
#' \emph{transmitter} and \emph{receiver} categories.
#' @param high_confidence Logical: shortcut to do some filtering in order to
#' include only higher confidence interactions. The intercell database
#' of OmniPath covers a very broad range of possible ways of cell to cell
#' communication, and the pieces of information, such as localization,
#' topology, function and interaction, are combined from many, often
#' independent sources. This unavoidably result some weird and unexpected
#' combinations which are false positives in the context of intercellular
#' communication. This option sets some minimum criteria to remove most
#' (but definitely not all!) of the wrong connections. These criteria
#' are the followings: 1) the receiver must be plasma membrane
#' transmembrane; 2) the curation effort for interactions must be larger
#' than one; 3) the consensus score for annotations must be larger than
#' the 50 percentile within the generic category (you can override this
#' by \code{consensus_percentile}). 4) the transmitter must be secreted
#' or exposed on the plasma membrane. 5) The major localizations have
#' to be supported by at least 30 percent of the relevant resources (
#' you can override this by \code{loc_consensus_percentile}). 6) The
#' datasets with lower level of curation (\emph{kinaseextra} and \emph{
#' pathwayextra}) will be disabled. These criteria are of medium
#' stringency, you can always tune them to be more relaxed or stringent
#' by filtering manually, using \code{\link{filter_intercell_network}}.
#' @param simplify Logical: keep only the most often used columns. This
#' function combines a network data frame with two copies of the
#' intercell annotation data frames, all of them already having quite
#' some columns. With this option we keep only the names of the
#' interacting pair, their intercellular communication roles, and the
#' minimal information of the origin of both the interaction and
#' the annotations.
#' @param unique_pairs Logical: instead of having separate rows for each
#' pair of annotations, drop the annotations and reduce the data frame to
#' unique interacting pairs. See \code{\link{unique_intercell_network}}
#' for details.
#' @param consensus_percentile Numeric: a percentile cut off for the consensus
#' score of generic categories in intercell annotations. The consensus
#' score is the number of resources supporting the classification of an
#' entity into a category based on combined information of many resources.
#' Here you can apply a cut-off, keeping only the annotations supported
#' by a higher number of resources than a certain percentile of each
#' category. If \code{NULL} no filtering will be performed. The value is
#' either in the 0-1 range, or will be divided by 100 if greater than 1.
#' The percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_consensus_percentile Numeric: similar to
#' \code{consensus_percentile} for major localizations. For example, with
#' a value of 50, the secreted, plasma membrane transmembrane or
#' peripheral attributes will be \code{TRUE} only where at least 50
#' percent of the resources support these.
#' @param omnipath Logical: shortcut to include the \emph{omnipath} dataset
#' in the interactions query.
#' @param ligrecextra Logical: shortcut to include the \emph{ligrecextra}
#' dataset in the interactions query.
#' @param kinaseextra Logical: shortcut to include the \emph{kinaseextra}
#' dataset in the interactions query.
#' @param pathwayextra Logical: shortcut to include the \emph{pathwayextra}
#' dataset in the interactions query.
#' @param ... If \code{simplify} or \code{unique_pairs} is \code{TRUE},
#' additional column names can be passed here to \code{dplyr::select}
#' on the final data frame. Otherwise ignored.
#'
#' @details
#' By default this function creates almost the largest possible network of
#' intercellular interactions. However, this might contain a large number
#' of false positives. Please refer to the documentation of the arguments,
#' especially \code{high_confidence}, and the \code{
#' \link{filter_intercell_network}} function. Note: if you restrict the query
#' to certain intercell annotation resources or small categories, it's not
#' recommended to use the \code{consensus_percentile} or
#' \code{high_confidence} options, instead filter the network with \code{
#' \link{filter_intercell_network}} for more consistent results.
#'
#' @examples
#' intercell_network <- import_intercell_network(
#' interactions_param = list(datasets = 'ligrecextra'),
#' receiver_param = list(categories = c('receptor', 'transporter')),
#' transmitter_param = list(categories = c('ligand', 'secreted_enzyme'))
#' )
#'
#' @importFrom dplyr rename bind_rows filter inner_join distinct group_by
#' @importFrom dplyr summarize_all first
#' @importFrom rlang %||%
#' @importFrom magrittr %>% %<>%
#' @importFrom utils modifyList
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{get_intercell_categories}}}
#' \item{\code{\link{get_intercell_generic_categories}}}
#' \item{\code{\link{import_omnipath_intercell}}}
#' \item{\code{\link{import_omnipath_interactions}}}
#' \item{\code{\link{import_pathwayextra_interactions}}}
#' \item{\code{\link{import_kinaseextra_interactions}}}
#' \item{\code{\link{import_ligrecextra_interactions}}}
#' \item{\code{\link{unique_intercell_network}}}
#' \item{\code{\link{simplify_intercell_network}}}
#' \item{\code{\link{filter_intercell_network}}}
#' }
import_intercell_network <- function(
interactions_param = list(),
transmitter_param = list(),
receiver_param = list(),
resources = NULL,
entity_types = NULL,
ligand_receptor = FALSE,
high_confidence = FALSE,
simplify = FALSE,
unique_pairs = FALSE,
consensus_percentile = NULL,
loc_consensus_percentile = NULL,
omnipath = TRUE,
ligrecextra = TRUE,
kinaseextra = !high_confidence,
pathwayextra = !high_confidence,
...
){
.slow_doctest()
# NSE vs. R CMD check workaround
parent <- secreted <- plasma_membrane_transmembrane <-
plasma_membrane_peripheral <- NULL
datasets <-
environment() %>%
select_interaction_datasets
# retrieving interactions
interactions_param <- list(
query_type = 'interactions',
datasets = datasets,
fields = 'datasets'
) %>%
insert_if_not_null(
resources = resources,
entity_types = entity_types
) %>%
modifyList(interactions_param)
interactions <- do.call(
import_omnipath,
interactions_param
)
interactions <- swap_undirected(interactions)
# retrieving intercell annotations
consensus_percentile %<>%
{`if`(high_confidence, . %||% 50, .)}
loc_consensus_percentile %<>%
{`if`(high_confidence, . %||% 30, .)}
transmitter_param <- list(
causality = 'trans',
scope = 'generic'
) %>%
insert_if_not_null(
resources = resources,
entity_types = entity_types,
consensus_percentile = consensus_percentile,
loc_consensus_percentile = loc_consensus_percentile
) %>%
{`if`(
ligand_receptor,
`[[<-`(., 'parent', 'ligand'),
.
)} %>%
modifyList(transmitter_param)
receiver_param <- list(
causality = 'rec',
scope = 'generic'
) %>%
insert_if_not_null(
resources = resources,
entity_types = entity_types,
consensus_percentile = consensus_percentile
) %>%
{`if`(
ligand_receptor,
`[[<-`(., 'parent', 'receptor'),
.
)} %>%
modifyList(receiver_param)
intracell <- c('intracellular_intercellular_related', 'intracellular')
transmitters <-
do.call(import_omnipath_intercell, transmitter_param) %>%
filter(!parent %in% intracell) %>%
rename(category_source = source) %>%
{`if`(
high_confidence,
filter(
.,
secreted |
plasma_membrane_transmembrane |
plasma_membrane_peripheral
),
.
)}
receivers <-
do.call(import_omnipath_intercell, receiver_param) %>%
filter(!parent %in% intracell) %>%
rename(category_source = source) %>%
{`if`(
high_confidence,
filter(., plasma_membrane_transmembrane),
.
)}
interactions %>%
{`if`(
high_confidence,
filter(., curation_effort > 1),
.
)} %>%
inner_join(
transmitters,
by = c('source' = 'uniprot')
) %>%
group_by(
category, parent, source, target
) %>%
mutate(
database = paste(database, collapse = ';')
) %>%
summarize_all(first) %>%
inner_join(
receivers,
by = c('target' = 'uniprot'),
suffix = c('_intercell_source', '_intercell_target')
) %>%
group_by(
category_intercell_source,
parent_intercell_source,
source,
target,
category_intercell_target,
parent_intercell_target
) %>%
mutate(
database_intercell_target = paste(
database_intercell_target,
collapse = ';'
)
) %>%
summarize_all(first) %>%
ungroup() %>%
{`if`(
simplify,
simplify_intercell_network(., ...),
.
)} %>%
{`if`(
unique_pairs,
unique_intercell_network(., ...),
.
)}
}
#' Filter intercell annotations
#'
#' Filters a data frame retrieved by \code{\link{import_omnipath_intercell}}.
#'
#' @param data An intercell annotation data frame as provided by
#' \code{\link{import_omnipath_intercell}}.
#' @param categories Character: allow only these values in the \code{category}
#' column.
#' @param resources Character: allow records only from these resources.
#' @param parent Character: filter for records with these parent categories.
#' @param scope Character: filter for records with these annotation scopes.
#' Possible values are \code{generic} and \code{specific}.
#' @param aspect Character: filter for records with these annotation aspects.
#' Possible values are \code{functional} and \code{locational}.
#' @param source Character: filter for records with these annotation sources.
#' Possible values are \code{composite} and \code{resource_specific}.
#' @param transmitter Logical: if \code{TRUE} only transmitters, if
#' \code{FALSE} only non-transmitters will be selected, if \code{NULL}
#' it has no effect.
#' @param receiver Logical: works the same way as \code{transmitters}.
#' @param secreted Logical: works the same way as \code{transmitters}.
#' @param plasma_membrane_peripheral Logical: works the same way as
#' \code{transmitters}.
#' @param plasma_membrane_transmembrane Logical: works the same way as
#' \code{transmitters}.
#' @param proteins Character: filter for annotations of these proteins.
#' Gene symbols or UniProt IDs can be used.
#' @param causality Character: filter for records with these causal roles.
#' Possible values are \code{transmitter} and \code{receiver}. The filter
#' applied simultaneously to the \code{transmitter} and \code{receiver}
#' arguments, it's just a different notation for the same thing.
#' @param topology Character: filter for records with these localization
#' topologies. Possible values are \code{secreced},
#' \code{plasma_membrane_peripheral} and
#' \code{plasma_membrane_transmembrane}; the shorter notations \code{sec},
#' \code{pmp} and \code{pmtm} can be used. Has the same effect as the
#' logical type arguments, just uses a different notation.
#' @param ... Ignored.
#'
#' @return The intercell annotation data frame filtered according to the
#' specified conditions.
#'
#' @examples
#' ic <- import_omnipath_intercell()
#' ic <- filter_intercell(
#' ic,
#' transmitter = TRUE,
#' secreted = TRUE,
#' scope = "specific"
#' )
#'
#' @importFrom dplyr recode rename_all
#' @importFrom magrittr %>% %<>%
#' @importFrom rlang .data set_names
#' @importFrom stringr str_detect
#' @export
filter_intercell <- function(
data,
categories = NULL,
resources = NULL,
parent = NULL,
scope = NULL,
aspect = NULL,
source = NULL,
transmitter = NULL,
receiver = NULL,
secreted = NULL,
plasma_membrane_peripheral = NULL,
plasma_membrane_transmembrane = NULL,
proteins = NULL,
causality = NULL,
topology = NULL,
...
){
args <- environment() %>% as.list()
args$cats <- args$categories
before <- nrow(data)
topology %<>%
{data.frame(set_names(
as.list(.),
rep(TRUE, length(.))
))} %>%
rename_all(
recode,
secreted = 'sec',
plasma_membrane_peripheral = 'pmp',
plasma_membrane_transmembrane = 'pmtm'
)
causality %<>%
{`if`('both' %in% ., c('transmitter', 'receiver'), .)} %>%
{data.frame(set_names(
as.list(.),
rep(TRUE, length(.))
))} %>%
rename_all(
recode,
transmitter = 'trans',
receiver = 'rec'
)
data <-
data %>%
filter(
(is.null(args$cats) | category %in% args$cats) &
(is.null(args$parent) | .data$parent %in% args$parent) &
(is.null(args$scope) | .data$scope %in% args$scope) &
(is.null(args$aspect) | .data$aspect %in% aspect) &
(is.null(args$source) | .data$source %in% source) &
(is.null(args$transmitter) | .data$transmitter) &
(is.null(args$receiver) | .data$receiver) &
(is.null(args$secreted) | .data$secreted) &
(
is.null(args$plasma_membrane_peripheral) |
.data$plasma_membrane_peripheral
) &
(
is.null(args$plasma_membrane_transmembrane) |
.data$plasma_membrane_transmembrane
) &
(
is.null(proteins) |
uniprot %in% proteins |
genesymbol %in% proteins
)
) %>%
{`if`(
ncol(topology) > 0,
inner_join(., topology, by = names(topology)),
.
)} %>%
{`if`(
ncol(causality) > 0,
inner_join(., causality, by = names(causality)),
.
)}
after <- nrow(data)
message(
sprintf(
'Removed %d and kept %d records of intercell data.',
before - after,
after
)
)
return(data)
}
#' Quality filter an intercell network
#'
#' The intercell database of OmniPath covers a very broad range of possible
#' ways of cell to cell communication, and the pieces of information, such as
#' localization, topology, function and interaction, are combined from many,
#' often independent sources. This unavoidably result some weird and
#' unexpected combinations which are false positives in the context of
#' intercellular communication. \code{\link{import_intercell_network}}
#' provides a shortcut (\code{high_confidence}) to do basic quality filtering.
#' For custom filtering or experimentation with the parameters we offer this
#' function.
#'
#' @param network An intercell network data frame, as provided by
#' \code{\link{import_intercell_network}}, without \code{simplify}.
#' @param transmitter_topology Character vector: topologies allowed for the
#' entities in transmitter role. Abbreviations allowed: "sec", "pmtm"
#' and "pmp".
#' @param receiver_topology Same as \code{transmitter_topology} for the
#' entities in the receiver role.
#' @param min_curation_effort Numeric: a minimum value of curation effort
#' (resource-reference pairs) for network interactions. Use zero to
#' disable filtering.
#' @param min_resources Numeric: minimum number of resources for
#' interactions. The value 1 means no filtering.
#' @param min_references Numeric: minimum number of references for
#' interactions. Use zero to disable filtering.
#' @param min_provenances Numeric: minimum number of provenances (either
#' resources or references) for interactions. Use zero or one to
#' disable filtering.
#' @param consensus_percentile Numeric: percentile threshold for the consensus
#' score of generic categories in intercell annotations. The consensus
#' score is the number of resources supporting the classification of an
#' entity into a category based on combined information of many resources.
#' Here you can apply a cut-off, keeping only the annotations supported
#' by a higher number of resources than a certain percentile of each
#' category. If \code{NULL} no filtering will be performed. The value is
#' either in the 0-1 range, or will be divided by 100 if greater than 1.
#' The percentiles will be calculated against the generic composite
#' categories and then will be applied to their resource specific
#' annotations and specific child categories.
#' @param loc_consensus_percentile Numeric: similar to
#' \code{consensus_percentile} for major localizations. For example, with
#' a value of 50, the secreted, plasma membrane transmembrane or
#' peripheral attributes will be \code{TRUE} only where at least 50
#' percent of the resources support these.
#' @param ligand_receptor Logical. If \code{TRUE}, only \emph{ligand} and
#' \emph{receptor} annotations will be used instead of the more generic
#' \emph{transmitter} and \emph{receiver} categories.
#' @param simplify Logical: keep only the most often used columns. This
#' function combines a network data frame with two copies of the
#' intercell annotation data frames, all of them already having quite
#' some columns. With this option we keep only the names of the
#' interacting pair, their intercellular communication roles, and the
#' minimal information of the origin of both the interaction and
#' the annotations.
#' @param unique_pairs Logical: instead of having separate rows for each
#' pair of annotations, drop the annotations and reduce the data frame to
#' unique interacting pairs. See \code{\link{unique_intercell_network}}
#' for details.
#' @param omnipath Logical: shortcut to include the \emph{omnipath} dataset
#' in the interactions query.
#' @param ligrecextra Logical: shortcut to include the \emph{ligrecextra}
#' dataset in the interactions query.
#' @param kinaseextra Logical: shortcut to include the \emph{kinaseextra}
#' dataset in the interactions query.
#' @param pathwayextra Logical: shortcut to include the \emph{pathwayextra}
#' dataset in the interactions query.
#' @param ... If \code{simplify} or \code{unique_pairs} is \code{TRUE},
#' additional column names can be passed here to \code{dplyr::select}
#' on the final data frame. Otherwise ignored.
#'
#' @return An intercell network data frame filtered.
#'
#' @examples
#' icn <- import_intercell_network()
#' icn_f <- filter_intercell_network(
#' icn,
#' consensus_percentile = 75,
#' min_provenances = 3,
#' simplify = TRUE
#' )
#'
#' @importFrom magrittr %>%
#' @importFrom dplyr select distinct filter inner_join left_join
#' @importFrom rlang !!! parse_expr exprs syms
#' @importFrom logger log_warn
#' @importFrom purrr walk
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{unique_intercell_network}}}
#' \item{\code{\link{simplify_intercell_network}}}
#' }
filter_intercell_network <- function(
network,
transmitter_topology = c(
'secreted',
'plasma_membrane_transmembrane',
'plasma_membrane_peripheral'
),
receiver_topology = 'plasma_membrane_transmembrane',
min_curation_effort = 2,
min_resources = 1,
min_references = 0,
min_provenances = 1,
consensus_percentile = 50,
loc_consensus_percentile = 30,
ligand_receptor = FALSE,
simplify = FALSE,
unique_pairs = FALSE,
omnipath = TRUE,
ligrecextra = TRUE,
kinaseextra = FALSE,
pathwayextra = FALSE,
...
){
.slow_doctest()
# NSE vs. R CMD check workaround
parent <- curation_effort <- n_resources <- n_references <- NULL
consensus <-
import_omnipath_intercell(
consensus_percentile = consensus_percentile,
loc_consensus_percentile = loc_consensus_percentile
)
consensus_annot <-
consensus %>%
select(uniprot, parent) %>%
distinct
consensus_loc <-
consensus %>%
select(uniprot, !!!syms(TOPOLOGIES)) %>%
distinct
topologies <- unlist(TOPOLOGIES_SHORT)
check_topo <- function(x){
if(!x %in% topologies){
log_warn('Unknown topology: %s', x)
}
}
datasets <-
environment() %>%
select_interaction_datasets
missing_datasets <- datasets %>% setdiff(colnames(network))
if(length(missing_datasets)){
msg <- sprintf(
paste0(
'filter_intercell_network: cannot select %s %s, ',
'apparently %s %s not included in the original ',
'download.'
),
missing_datasets %>%
plural('dataset'),
missing_datasets %>%
pretty_list,
missing_datasets %>%
plural('this', 'these'),
missing_datasets %>%
plural('was', 'were')
)
log_warn(msg)
warning(msg)
}
transmitter_topology %<>%
topology_long %>%
walk(check_topo) %>%
intersect(topologies) %>%
sprintf('%s_intercell_source', .) %>%
paste(collapse = ' | ')
receiver_topology %<>%
topology_long %>%
walk(check_topo) %>%
intersect(topologies) %>%
sprintf('%s_intercell_target', .) %>%
paste(collapse = ' | ')
datasets %<>%
intersect(colnames(network)) %>%
paste(collapse = ' | ')
network %>%
{`if`(
is.null(loc_consensus_percentile),
.,
select(
.,
-(!!!exprs(sprintf('%s_intercell_source', TOPOLOGIES))),
-(!!!exprs(sprintf('%s_intercell_target', TOPOLOGIES)))
) %>%
left_join(consensus_loc, by = c('source' = 'uniprot')) %>%
left_join(consensus_loc, by = c('target' = 'uniprot'),
suffix = c('_intercell_source', '_intercell_target')
)
)} %>%
filter(eval(parse_expr(receiver_topology))) %>%
filter(eval(parse_expr(transmitter_topology))) %>%
filter(eval(parse_expr(datasets))) %>%
filter(
curation_effort >= min_curation_effort &
n_resources >= min_resources &
n_references >= min_references &
(
n_resources >= min_provenances |
n_references >= min_provenances
)
) %>%
inner_join(
consensus_annot,
by = c(
'parent_intercell_source' = 'parent',
'source' = 'uniprot'
)
) %>%
inner_join(
consensus_annot,
by = c(
'parent_intercell_target' = 'parent',
'target' = 'uniprot'
)
) %>%
{`if`(
ligand_receptor,
filter(
.,
parent_intercell_source == 'ligand' &
parent_intercell_target == 'receptor'
),
.
)} %>%
{`if`(
simplify,
simplify_intercell_network(., ...),
.
)} %>%
{`if`(
unique_pairs,
unique_intercell_network(., ...),
.
)}
}
#' Simplify an intercell network
#'
#' The intercellular communication network data frames, created by
#' \code{\link{import_intercell_network}}, are combinations of a network data
#' frame with two copies of the intercell annotation data frames, all of them
#' already having quite some columns. Here we keep only the names of the
#' interacting pair, their intercellular communication roles, and the minimal
#' information of the origin of both the interaction and the annotations.
#' Optionally further columns can be selected.
#'
#' @param network An intercell network data frame, as provided by
#' \code{\link{import_intercell_network}}.
#' @param ... Optional, further columns to select.
#'
#' @return An intercell network data frame with some columns removed.
#'
#' @examples
#' icn <- import_intercell_network()
#' icn_s <- simplify_intercell_network(icn)
#'
#' @importFrom magrittr %>%
#' @importFrom dplyr select
#' @importFrom rlang ensyms !!!
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{unique_intercell_network}}}
#' \item{\code{\link{filter_intercell_network}}}
#' }
simplify_intercell_network <- function(network, ...){
.slow_doctest()
# NSE vs. R CMD check workaround
source <- target <- source_genesymbol <- target_genesymbol <-
category_intercell_source <- database_intercell_source <-
category_intercell_target <- database_intercell_target <-
is_directed <- is_stimulation <- is_inhibition <-
sources <- references <- NULL
simplify_cols <-
alist(
source,
target,
source_genesymbol,
target_genesymbol,
category_intercell_source,
database_intercell_source,
category_intercell_target,
database_intercell_target,
is_directed,
is_stimulation,
is_inhibition,
sources,
references
) %>%
c(ensyms(...)) %>%
unique
network %>%
select(!!!simplify_cols)
}
#' Unique intercellular interactions
#'
#' In the intercellular network data frames produced by \code{
#' \link{import_intercell_network}}, by default each pair of annotations for
#' an interaction is represented in a separate row. This function drops the
#' annotations and keeps only the distinct interacting pairs.
#'
#' @param network An intercellular network data frame as produced by
#' \code{\link{import_intercell_network}}.
#' @param ... Additional columns to keep. Note: if these have multiple
#' values for an interacting pair, only the first row will be
#' preserved.
#'
#' @return A data frame with interacting pairs and interaction attributes.
#'
#' @examples
#' icn <- import_intercell_network()
#' icn_unique <- unique_intercell_network(icn)
#'
#' @importFrom magrittr %>%
#' @importFrom dplyr select distinct
#' @importFrom rlang !!!
#' @export
#'
#' @seealso \itemize{
#' \item{\code{\link{import_intercell_network}}}
#' \item{\code{\link{simplify_intercell_network}}}
#' \item{\code{\link{filter_intercell_network}}}
#' }
unique_intercell_network <- function(network, ...){
.slow_doctest()
# NSE vs. R CMD check workaround
source <- target <- source_genesymbol <- target_genesymbol <-
is_directed <- is_stimulation <- is_inhibition <-
sources <- references <- NULL
cols <-
alist(
source,
target,
source_genesymbol,
target_genesymbol,
is_directed,
is_stimulation,
is_inhibition,
sources,
references
) %>%
c(ensyms(...)) %>%
unique
network %>%
select(!!!cols) %>%
distinct(source, target)
}
#' Categories in the intercell database of OmniPath
#'
#' Retrieves a list of categories from \url{https://omnipath.org/intercell}.
#'
#' @return character vector with the different intercell categories
#' @export
#'
#' @examples
#' get_intercell_categories()
#'
#' @seealso \itemize{
#' \item{\code{\link{import_omnipath_intercell}}}
#' \item{\code{\link{get_intercell_generic_categories}}}
#' }
get_intercell_categories <- function(){
return(
unique(
import_omnipath('intercell_summary', license = NA)$category
)
)
}
#' Full list of intercell categories and resources
#'
#' @return A data frame of categories and resources.
#'
#' @examples
#' ic_cat <- intercell_categories()
#' ic_cat
#' # # A tibble: 1,125 x 3
#' # category parent database
#' # <chr> <chr> <chr>
#' # 1 transmembrane transmembrane UniProt_location
#' # 2 transmembrane transmembrane UniProt_topology
#' # 3 transmembrane transmembrane UniProt_keyword
#' # 4 transmembrane transmembrane_predicted Phobius
#' # 5 transmembrane_phobius transmembrane_predicted Almen2009
#' # # . with 1,120 more rows
#'
#' @export
intercell_categories <- function(){
import_omnipath('intercell_summary', license = NA)
}
#' Retrieves a list of the generic categories in the intercell database
#' of OmniPath
#'
#' Retrieves a list of the generic categories from
#' \url{https://omnipath.org/intercell}.
#'
#' @return character vector with the different intercell main classes
#' @export
#'
#' @examples
#' get_intercell_generic_categories()
#'
#' @seealso \itemize{
#' \item{\code{\link{import_omnipath_intercell}}}
#' \item{\code{\link{get_intercell_categories}}}
#' }
#'
#' @aliases get_intercell_classes
get_intercell_generic_categories <- function(){
return(
unique(
import_omnipath('intercell_summary', license = NA)$parent
)
)
}
# Aliases (old names) to be deprecated
#' @rdname get_intercell_generic_categories
#' @param ... Passed to \code{get_intercell_generic_categories}.
#' @export
#'
#' @noRd
get_intercell_classes <- function(...){
.Deprecated("get_intercell_generic_categories")
get_intercell_generic_categories(...)
}
#' Short to long topology names
#'
#' @param topologies Character vector of short topology names. Long names and
#' any other strings will be left intact.
#'
#' @importFrom rlang !!!
#' @importFrom dplyr recode
#' @importFrom magrittr %>%
#' @noRd
topology_long <- function(topologies){
topologies %>%
{`if`(is.null(.), ., recode(., !!!TOPOLOGIES_SHORT))}
}
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