R/chalmers_gem.R
In saezlab/OmnipathR: OmniPath web service client and more
Defines functions
chalmers_gem
Documented in
chalmers_gem
#!/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): Diego Mananes
# 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
#
#' Genome scale metabolic model by Wang et al. 2021
#'
#' Process the GEMs from Wang et al., 2021
#' (\url{https://github.com/SysBioChalmers}) into convenient tables.
#'
#' @param organism Character or integer: an organism (taxon) identifier.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicus), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#' @param orphans Logical: include orphan reactions (reactions without known
#' enzyme).
#'
#' @return List containing the following elements: \itemize{
#' \item{reactions: tibble of reaction data;}
#' \item{metabolites: tibble of metabolite data;}
#' \item{reaction_ids: translation table of reaction identifiers;}
#' \item{metabolite_ids: translation table of metabolite identifiers;}
#' \item{S: Stoichiometric matrix (sparse).}
#' }
#'
#' @examples
#' gem <- chalmers_gem()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>% extract extract2 equals
#' @importFrom dplyr pull across na_if first mutate slice rename n
#' @importFrom vctrs vec_cast
#' @importFrom tibble as_tibble
#' @importFrom purrr map map_chr map_lgl map2 pmap_int
#' @importFrom logger log_info
#' @importFrom tidyselect everything
#' @importFrom rlang are_na
#' @importFrom stringr str_split str_replace_all str_detect
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_metabolites}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem <- function(organism = 'Human', orphans = TRUE) {
.slow_doctest()
log_info('Processing Chalmers SysBio GEM.')
raw <- chalmers_gem_raw(organism = organism)
metabolite_ids <- chalmers_gem_metabolites(organism = organism)
reaction_ids <- chalmers_gem_reactions(organism = organism)
S <- raw %>% extract2(1L) %>% extract(,,1L) %>% extract2('S')
metabolites <-
raw %>%
chalmers_gem_matlab_tibble(mets, metNames, metFormulas, inchis) %>%
mutate(
across(
everything(),
~na_if(map_chr(unlist(.x, recursive = FALSE), first), '')
)
) %T>%
{log_info('Chalmers GEM: %i metabolites.', nrow(.))}
reactions <-
raw %>%
chalmers_gem_matlab_tibble(rxns, lb, ub, rev, grRules) %>%
rename(reversible = rev) %>%
mutate(
rxns = unlist(rxns),
direction = ifelse(ub + lb >= 0L, 1L, -1L),
reversible = vec_cast(reversible, logical()),
grRules = (
unlist(grRules, recursive = FALSE) %>%
map_chr(extract, 1L) %>%
str_replace_all('[\\(\\)]|_AT\\d+', '') %>%
str_split(' or ') %>%
map(str_split, ' and ') %>%
map(discard, is_empty_2)
),
orphan = are_na(map_lgl(
grRules,
~any(map_lgl(.x, ~any(str_detect(.x, '^\\d+$'))))
)),
reactants = map2(
1L:n(),
direction,
~.x %>% extract(S,,.) %>% equals(-.y) %>% which %>%
slice(metabolites, .) %>% pull(mets)
),
products = map2(
1L:n(),
direction,
~.x %>% extract(S,,.) %>% equals(.y) %>% which %>%
slice(metabolites, .) %>% pull(mets)
),
numof_interactions = pmap_int(
list(reactants, products),
~length(c(..1, ..2))
)
) %T>%
{log_info(
'Chalmers GEM: %i reactions, %i orphan.',
nrow(.),
pull(., orphan) %>% sum(na.rm = TRUE)
)} %>%
{`if`(orphans, ., filter(., !orphan))}
return(
list(
organism = organism,
reactions = reactions,
metabolites = metabolites,
reaction_ids = reaction_ids,
metabolite_ids = metabolite_ids,
S = S
)
)
}
#' Chalmers SysBio GEM in the form of gene-metabolite interactions
#'
#' Processing GEMs from Wang et al., 2021
#' (\url{https://github.com/SysBioChalmers}) to generate PKN for COSMOS
#'
#' @param organism_or_gem Character or integer or list or data frame: either
#' an organism (taxon) identifier or a list containing the ``reactions``
#' data frame as it is provided by \code{\link{chalmers_gem}}, or the
#' reactions data frame itself.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicus), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#' @param metab_max_degree Degree cutoff used to prune metabolites with high
#' degree assuming they are cofactors (400 by default).
#' @param protein_ids Character: translate the protein identifiers to these ID
#' types. Each ID type results two extra columns in the output, for the "a"
#' and "b" sides of the interaction, respectively. The default ID type for
#' proteins is Esembl Gene ID, and by default UniProt IDs and Gene
#' Symbols are included.
#' @param metabolite_ids Character: translate the protein identifiers to these ID
#' types. Each ID type results two extra columns in the output, for the "a"
#' and "b" sides of the interaction, respectively. The default ID type for
#' metabolites is Metabolic Atlas ID, and HMDB IDs and KEGG IDs are included.
#'
#' @return Data frame (tibble) of gene-metabolite interactions.
#'
#' @examples
#' gem <- chalmers_gem_network()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %<>% %T>% extract2
#' @importFrom rlang is_list enquos
#' @importFrom purrr map map_chr
#' @importFrom dplyr mutate across select bind_rows filter
#' @importFrom dplyr left_join row_number arrange
#' @importFrom tidyr unnest_longer
#' @importFrom logger log_info log_error
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_metabolites}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_network <- function(
organism_or_gem = 'Human',
metab_max_degree = 400L,
protein_ids = c('uniprot', 'genesymbol'),
metabolite_ids = c('hmdb', 'kegg')
) {
.slow_doctest()
# NSE vs R CMD check workaround
orphan <- ri <- grRules <- reactants <- products <- reversible <-
met_to_gene <- target <- NULL
if (metab_max_degree < 1L) {
'`metab_max_degree` cannot be less than 1.' %T>% log_error %>% stop
}
organism_or_gem %<>% `if`(is_list(.), ., chalmers_gem(.))
organism <- organism_or_gem$organism
log_info(
paste0(
'Building Chalmers Sysbio GEM: organism=%s; metab_max_degree=%i; ',
'protein_ids=%s; metabolite_ids=%s'
),
organism,
metab_max_degree,
paste(protein_ids, collapse = ','),
paste(metabolite_ids, collapse = ',')
)
organism_or_gem %>%
extract2('reactions') %T>%
{log_info('Chalmers Sysbio GEM: compiling gene-metabolite interactions.')} %>%
filter(!orphan) %>%
# once I see better what's the purpose of this labeling,
# I might move it a bit further down the pipeline - denes
mutate(ri = row_number()) %>%
select(ri, grRules, reactants, products, reversible) %>%
unnest_longer(grRules, simplify = FALSE) %>%
# note: this represents the AND relationship between genes,
# i.e. both genes together are required for the reaction
mutate(ci = row_number()) %>%
unnest_longer(grRules, indices_include = FALSE) %>%
{bind_rows(
binary_from_reaction(., grRules, reactants) %T>%
{log_trace('Chalmers GEM: %i reactant-enzyme interactions.', nrow(.))},
binary_from_reaction(., grRules, products, met_to_gene = FALSE) %T>%
{log_trace('Chalmers GEM: %i enzyme-product interactions.', nrow(.))}
)} %T>%
{log_info(
'Chalmers GEM: %i records in gene-metabolite interactions table.',
nrow(.)
)} %>%
list(
lo_degree =
c(
pull(., source),
pull(., target)
) %>%
table %>%
keep(~.x < metab_max_degree) %>%
names
) %>%
{filter(
extract2(., 1L),
met_to_gene & source %in% .$lo_degree |
!met_to_gene & target %in% .$lo_degree
)} %>%
arrange(ri) %>%
left_join(
select(., ri, source = target, target = source, reverse) %>%
mutate(transporter = TRUE),
by = c('ri', 'source', 'target', 'reverse')
) %T>%
{log_info(
paste0(
'Chalmers GEM: %i gene-metabolite interactions after removing ',
'metabolites with more than %i interactions.'
),
nrow(.),
metab_max_degree
)} %>%
translate_ids_multi(
source = ensg,
target,
!!!syms(protein_ids),
ensembl = TRUE,
organism = organism
) %>%
translate_ids_multi(
source = metabolicatlas,
target,
!!!syms(metabolite_ids),
chalmers = TRUE,
organism = organism
) %T>%
{log_info(
'Chalmers GEM: built gene-metabolite network of %i interactions.',
nrow(.)
)}
}
#' Binary interactions from reactions
#'
#' These interactions point from reactants to enzymes or from enzymes to
#' products, according to the parameters.
#'
#' @importFrom rlang enquo !! sym set_names
#' @importFrom magrittr %>% inset2
#' @importFrom dplyr filter select bind_rows mutate rename
#' @importFrom tidyr unnest_longer separate_wider_regex
#' @importFrom stringr str_replace str_c
#' @noRd
binary_from_reaction <- function(
reactions,
gene_col,
met_col,
met_to_gene = TRUE
) {
# NSE vs R CMD check workaround
source_comp <- target_comp <- target <- ri <- ci <-
source_id <- target_id <- NULL
list(
s = .nse_ensure_str(!!enquo(gene_col)),
t = .nse_ensure_str(!!enquo(met_col))
) %>%
{`if`(met_to_gene, rev(.), .)} %>%
c(list(unnest_longer(reactions, !!sym(.$t), indices_include = FALSE))) %>%
set_names(c('s', 't', 'r')) %>%
{bind_rows(
select(.$r, ri, ci, source = !!sym(.$s), target = !!sym(.$t)) %>%
mutate(reverse = FALSE, met_to_gene = met_to_gene),
inset2(., 'r', filter(.$r, reversible)) %>%
{select(.$r, ri, ci, source = !!sym(.$t), target = !!sym(.$s))} %>%
mutate(reverse = TRUE, met_to_gene = !met_to_gene)
)} %>%
mutate(
across(
c(source, target),
~str_replace(.x, '^\\(?(ENS[A-Z]+\\d+)\\)?$', '\\1')
)
) %>%
filter(!is.na(source), !is.na(target)) %>%
separate_wider_regex(
c(source, target),
patterns = c(id = '(?:MAM|ENSG)\\d+', comp = '[cmxrelgni]?'),
names_sep = '_'
) %>%
mutate(comp = as.factor(str_c(source_comp, target_comp))) %>%
select(-source_comp, -target_comp) %>%
rename(source = source_id, target = target_id)
}
#' Reactions from the Chalmers SysBio GEM (Wang et al., 2021)
#'
#' @param organism Character or integer: an organism (taxon) identifier.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#'
#' @return Data frame of reaction identifiers.
#'
#' @examples
#' chalmers_gem_reactions()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>%
#' @importFrom readr read_tsv
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_metabolites}}}
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_reactions <- function(organism = 'Human') {
.slow_doctest()
organism %<>% organism_for('chalmers-gem')
'chalmers_gem' %>%
generic_downloader(
reader = read_tsv,
url_key_param = list(),
url_param = organism %>% list('reactions.tsv'),
reader_param = list(),
resource = 'Chalmers GEM',
post = NULL,
use_httr = FALSE
) %T>%
load_success()
}
#' Metabolites from the Chalmers SysBio GEM (Wang et al., 2021)
#'
#' @param organism Character or integer: an organism (taxon) identifier.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#'
#' @return Data frame of metabolite identifiers.
#'
#' @examples
#' chalmers_gem_metabolites()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>% %<>%
#' @importFrom readr read_tsv
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_metabolites <- function(organism = 'Human') {
.slow_doctest()
organism %<>% organism_for('chalmers-gem')
suppressWarnings(generic_downloader(
'chalmers_gem',
reader = read_tsv,
url_key_param = list(),
url_param = organism %>% list('metabolites.tsv'),
reader_param = list(),
resource = 'Chalmers GEM',
post = NULL,
use_httr = FALSE
)) %T>%
load_success()
}
#' GEM matlab file from Chalmers Sysbio (Wang et al., 2021)
#'
#' Downloads and imports the matlab file containing the genome scale metabolic
#' models created by Chalmers SysBio.
#'
#' @param organism Character or integer: name or identifier of the organism.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#'
#' @return Matlab object containing the GEM.
#'
#' @details The Matlab object is parsed into a nested list containing a number
#' of vectors and two sparse matrices. The top level contains a single
#' element under the name "ihuman" for human; under this key there is an
#' array of 31 elements. These elements are labeled by the row names of the
#' array.
#'
#' @examples
#' chalmers_gem_raw()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>% %<>%
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_raw <- function(organism = 'Human') {
.slow_doctest()
organism %<>%
organism_for('chalmers-gem') %T>%
log_info('Downloading GEM from Chalmers Sysbio for organism `%s`.', .)
'chalmers_gem' %>%
generic_downloader(
reader = R.matlab::readMat,
url_key_param = list(),
url_param = organism %>% list(sprintf('%s-GEM.mat', .)),
reader_param = list(),
resource = 'Chalmers GEM',
post = NULL,
use_httr = FALSE
) %T>%
load_success()
}
#' Tibble from Chalmers GEM Matlab object
#'
#' @param matlab Chalmers GEM in an R object loaded from the Matlab
#' dump.
#' @param ... Variable names: should contain either only reaction or
#' metabolite variables, otherwise num of rows won't be uniform.
#'
#' @return Tibble with the requested variables.
#'
#' @importFrom purrr map
#' @importFrom tibble as_tibble
#' @importFrom dplyr pull
#' @importFrom magrittr %>% extract extract2 equals
#' @importFrom rlang enquos
#'
#' @noRd
chalmers_gem_matlab_tibble <- function(matlab, ...) {
cols <-
enquos(...) %>%
map_chr(.nse_ensure_str)
matlab %>%
as_tibble %>%
pull(1L) %>%
extract(,,1L) %>%
map(
function(x) {
if (x %>% dim %>% equals(1L) %>% all) x %>% extract(1L,1L) else x
}
) %>%
extract(cols) %>%
map(extract,,1L) %>%
as_tibble
}
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Defines functions chalmers_gem
Documented in chalmers_gem
#!/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): Diego Mananes
# 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
#
#' Genome scale metabolic model by Wang et al. 2021
#'
#' Process the GEMs from Wang et al., 2021
#' (\url{https://github.com/SysBioChalmers}) into convenient tables.
#'
#' @param organism Character or integer: an organism (taxon) identifier.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicus), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#' @param orphans Logical: include orphan reactions (reactions without known
#' enzyme).
#'
#' @return List containing the following elements: \itemize{
#' \item{reactions: tibble of reaction data;}
#' \item{metabolites: tibble of metabolite data;}
#' \item{reaction_ids: translation table of reaction identifiers;}
#' \item{metabolite_ids: translation table of metabolite identifiers;}
#' \item{S: Stoichiometric matrix (sparse).}
#' }
#'
#' @examples
#' gem <- chalmers_gem()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>% extract extract2 equals
#' @importFrom dplyr pull across na_if first mutate slice rename n
#' @importFrom vctrs vec_cast
#' @importFrom tibble as_tibble
#' @importFrom purrr map map_chr map_lgl map2 pmap_int
#' @importFrom logger log_info
#' @importFrom tidyselect everything
#' @importFrom rlang are_na
#' @importFrom stringr str_split str_replace_all str_detect
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_metabolites}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem <- function(organism = 'Human', orphans = TRUE) {
.slow_doctest()
log_info('Processing Chalmers SysBio GEM.')
raw <- chalmers_gem_raw(organism = organism)
metabolite_ids <- chalmers_gem_metabolites(organism = organism)
reaction_ids <- chalmers_gem_reactions(organism = organism)
S <- raw %>% extract2(1L) %>% extract(,,1L) %>% extract2('S')
metabolites <-
raw %>%
chalmers_gem_matlab_tibble(mets, metNames, metFormulas, inchis) %>%
mutate(
across(
everything(),
~na_if(map_chr(unlist(.x, recursive = FALSE), first), '')
)
) %T>%
{log_info('Chalmers GEM: %i metabolites.', nrow(.))}
reactions <-
raw %>%
chalmers_gem_matlab_tibble(rxns, lb, ub, rev, grRules) %>%
rename(reversible = rev) %>%
mutate(
rxns = unlist(rxns),
direction = ifelse(ub + lb >= 0L, 1L, -1L),
reversible = vec_cast(reversible, logical()),
grRules = (
unlist(grRules, recursive = FALSE) %>%
map_chr(extract, 1L) %>%
str_replace_all('[\\(\\)]|_AT\\d+', '') %>%
str_split(' or ') %>%
map(str_split, ' and ') %>%
map(discard, is_empty_2)
),
orphan = are_na(map_lgl(
grRules,
~any(map_lgl(.x, ~any(str_detect(.x, '^\\d+$'))))
)),
reactants = map2(
1L:n(),
direction,
~.x %>% extract(S,,.) %>% equals(-.y) %>% which %>%
slice(metabolites, .) %>% pull(mets)
),
products = map2(
1L:n(),
direction,
~.x %>% extract(S,,.) %>% equals(.y) %>% which %>%
slice(metabolites, .) %>% pull(mets)
),
numof_interactions = pmap_int(
list(reactants, products),
~length(c(..1, ..2))
)
) %T>%
{log_info(
'Chalmers GEM: %i reactions, %i orphan.',
nrow(.),
pull(., orphan) %>% sum(na.rm = TRUE)
)} %>%
{`if`(orphans, ., filter(., !orphan))}
return(
list(
organism = organism,
reactions = reactions,
metabolites = metabolites,
reaction_ids = reaction_ids,
metabolite_ids = metabolite_ids,
S = S
)
)
}
#' Chalmers SysBio GEM in the form of gene-metabolite interactions
#'
#' Processing GEMs from Wang et al., 2021
#' (\url{https://github.com/SysBioChalmers}) to generate PKN for COSMOS
#'
#' @param organism_or_gem Character or integer or list or data frame: either
#' an organism (taxon) identifier or a list containing the ``reactions``
#' data frame as it is provided by \code{\link{chalmers_gem}}, or the
#' reactions data frame itself.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicus), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#' @param metab_max_degree Degree cutoff used to prune metabolites with high
#' degree assuming they are cofactors (400 by default).
#' @param protein_ids Character: translate the protein identifiers to these ID
#' types. Each ID type results two extra columns in the output, for the "a"
#' and "b" sides of the interaction, respectively. The default ID type for
#' proteins is Esembl Gene ID, and by default UniProt IDs and Gene
#' Symbols are included.
#' @param metabolite_ids Character: translate the protein identifiers to these ID
#' types. Each ID type results two extra columns in the output, for the "a"
#' and "b" sides of the interaction, respectively. The default ID type for
#' metabolites is Metabolic Atlas ID, and HMDB IDs and KEGG IDs are included.
#'
#' @return Data frame (tibble) of gene-metabolite interactions.
#'
#' @examples
#' gem <- chalmers_gem_network()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %<>% %T>% extract2
#' @importFrom rlang is_list enquos
#' @importFrom purrr map map_chr
#' @importFrom dplyr mutate across select bind_rows filter
#' @importFrom dplyr left_join row_number arrange
#' @importFrom tidyr unnest_longer
#' @importFrom logger log_info log_error
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_metabolites}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_network <- function(
organism_or_gem = 'Human',
metab_max_degree = 400L,
protein_ids = c('uniprot', 'genesymbol'),
metabolite_ids = c('hmdb', 'kegg')
) {
.slow_doctest()
# NSE vs R CMD check workaround
orphan <- ri <- grRules <- reactants <- products <- reversible <-
met_to_gene <- target <- NULL
if (metab_max_degree < 1L) {
'`metab_max_degree` cannot be less than 1.' %T>% log_error %>% stop
}
organism_or_gem %<>% `if`(is_list(.), ., chalmers_gem(.))
organism <- organism_or_gem$organism
log_info(
paste0(
'Building Chalmers Sysbio GEM: organism=%s; metab_max_degree=%i; ',
'protein_ids=%s; metabolite_ids=%s'
),
organism,
metab_max_degree,
paste(protein_ids, collapse = ','),
paste(metabolite_ids, collapse = ',')
)
organism_or_gem %>%
extract2('reactions') %T>%
{log_info('Chalmers Sysbio GEM: compiling gene-metabolite interactions.')} %>%
filter(!orphan) %>%
# once I see better what's the purpose of this labeling,
# I might move it a bit further down the pipeline - denes
mutate(ri = row_number()) %>%
select(ri, grRules, reactants, products, reversible) %>%
unnest_longer(grRules, simplify = FALSE) %>%
# note: this represents the AND relationship between genes,
# i.e. both genes together are required for the reaction
mutate(ci = row_number()) %>%
unnest_longer(grRules, indices_include = FALSE) %>%
{bind_rows(
binary_from_reaction(., grRules, reactants) %T>%
{log_trace('Chalmers GEM: %i reactant-enzyme interactions.', nrow(.))},
binary_from_reaction(., grRules, products, met_to_gene = FALSE) %T>%
{log_trace('Chalmers GEM: %i enzyme-product interactions.', nrow(.))}
)} %T>%
{log_info(
'Chalmers GEM: %i records in gene-metabolite interactions table.',
nrow(.)
)} %>%
list(
lo_degree =
c(
pull(., source),
pull(., target)
) %>%
table %>%
keep(~.x < metab_max_degree) %>%
names
) %>%
{filter(
extract2(., 1L),
met_to_gene & source %in% .$lo_degree |
!met_to_gene & target %in% .$lo_degree
)} %>%
arrange(ri) %>%
left_join(
select(., ri, source = target, target = source, reverse) %>%
mutate(transporter = TRUE),
by = c('ri', 'source', 'target', 'reverse')
) %T>%
{log_info(
paste0(
'Chalmers GEM: %i gene-metabolite interactions after removing ',
'metabolites with more than %i interactions.'
),
nrow(.),
metab_max_degree
)} %>%
translate_ids_multi(
source = ensg,
target,
!!!syms(protein_ids),
ensembl = TRUE,
organism = organism
) %>%
translate_ids_multi(
source = metabolicatlas,
target,
!!!syms(metabolite_ids),
chalmers = TRUE,
organism = organism
) %T>%
{log_info(
'Chalmers GEM: built gene-metabolite network of %i interactions.',
nrow(.)
)}
}
#' Binary interactions from reactions
#'
#' These interactions point from reactants to enzymes or from enzymes to
#' products, according to the parameters.
#'
#' @importFrom rlang enquo !! sym set_names
#' @importFrom magrittr %>% inset2
#' @importFrom dplyr filter select bind_rows mutate rename
#' @importFrom tidyr unnest_longer separate_wider_regex
#' @importFrom stringr str_replace str_c
#' @noRd
binary_from_reaction <- function(
reactions,
gene_col,
met_col,
met_to_gene = TRUE
) {
# NSE vs R CMD check workaround
source_comp <- target_comp <- target <- ri <- ci <-
source_id <- target_id <- NULL
list(
s = .nse_ensure_str(!!enquo(gene_col)),
t = .nse_ensure_str(!!enquo(met_col))
) %>%
{`if`(met_to_gene, rev(.), .)} %>%
c(list(unnest_longer(reactions, !!sym(.$t), indices_include = FALSE))) %>%
set_names(c('s', 't', 'r')) %>%
{bind_rows(
select(.$r, ri, ci, source = !!sym(.$s), target = !!sym(.$t)) %>%
mutate(reverse = FALSE, met_to_gene = met_to_gene),
inset2(., 'r', filter(.$r, reversible)) %>%
{select(.$r, ri, ci, source = !!sym(.$t), target = !!sym(.$s))} %>%
mutate(reverse = TRUE, met_to_gene = !met_to_gene)
)} %>%
mutate(
across(
c(source, target),
~str_replace(.x, '^\\(?(ENS[A-Z]+\\d+)\\)?$', '\\1')
)
) %>%
filter(!is.na(source), !is.na(target)) %>%
separate_wider_regex(
c(source, target),
patterns = c(id = '(?:MAM|ENSG)\\d+', comp = '[cmxrelgni]?'),
names_sep = '_'
) %>%
mutate(comp = as.factor(str_c(source_comp, target_comp))) %>%
select(-source_comp, -target_comp) %>%
rename(source = source_id, target = target_id)
}
#' Reactions from the Chalmers SysBio GEM (Wang et al., 2021)
#'
#' @param organism Character or integer: an organism (taxon) identifier.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#'
#' @return Data frame of reaction identifiers.
#'
#' @examples
#' chalmers_gem_reactions()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>%
#' @importFrom readr read_tsv
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_metabolites}}}
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_reactions <- function(organism = 'Human') {
.slow_doctest()
organism %<>% organism_for('chalmers-gem')
'chalmers_gem' %>%
generic_downloader(
reader = read_tsv,
url_key_param = list(),
url_param = organism %>% list('reactions.tsv'),
reader_param = list(),
resource = 'Chalmers GEM',
post = NULL,
use_httr = FALSE
) %T>%
load_success()
}
#' Metabolites from the Chalmers SysBio GEM (Wang et al., 2021)
#'
#' @param organism Character or integer: an organism (taxon) identifier.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#'
#' @return Data frame of metabolite identifiers.
#'
#' @examples
#' chalmers_gem_metabolites()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>% %<>%
#' @importFrom readr read_tsv
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_raw}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_metabolites <- function(organism = 'Human') {
.slow_doctest()
organism %<>% organism_for('chalmers-gem')
suppressWarnings(generic_downloader(
'chalmers_gem',
reader = read_tsv,
url_key_param = list(),
url_param = organism %>% list('metabolites.tsv'),
reader_param = list(),
resource = 'Chalmers GEM',
post = NULL,
use_httr = FALSE
)) %T>%
load_success()
}
#' GEM matlab file from Chalmers Sysbio (Wang et al., 2021)
#'
#' Downloads and imports the matlab file containing the genome scale metabolic
#' models created by Chalmers SysBio.
#'
#' @param organism Character or integer: name or identifier of the organism.
#' Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus),
#' 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila
#' melanogaster) and 6239 (Caenorhabditis elegans).
#'
#' @return Matlab object containing the GEM.
#'
#' @details The Matlab object is parsed into a nested list containing a number
#' of vectors and two sparse matrices. The top level contains a single
#' element under the name "ihuman" for human; under this key there is an
#' array of 31 elements. These elements are labeled by the row names of the
#' array.
#'
#' @examples
#' chalmers_gem_raw()
#'
#' @references Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley
#' PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J.
#' Genome-scale metabolic network reconstruction of model animals as a
#' platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul
#' 27;118(30):e2102344118. doi: \doi{10.1073/pnas.2102344118}.
#'
#' @importFrom magrittr %>% %T>% %<>%
#'
#' @export
#' @seealso \itemize{
#' \item{\code{\link{chalmers_gem_network}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem}}}
#' \item{\code{\link{chalmers_gem_reactions}}}
#' \item{\code{\link{chalmers_gem_id_mapping_table}}}
#' \item{\code{\link{cosmos_pkn}}}
#' }
chalmers_gem_raw <- function(organism = 'Human') {
.slow_doctest()
organism %<>%
organism_for('chalmers-gem') %T>%
log_info('Downloading GEM from Chalmers Sysbio for organism `%s`.', .)
'chalmers_gem' %>%
generic_downloader(
reader = R.matlab::readMat,
url_key_param = list(),
url_param = organism %>% list(sprintf('%s-GEM.mat', .)),
reader_param = list(),
resource = 'Chalmers GEM',
post = NULL,
use_httr = FALSE
) %T>%
load_success()
}
#' Tibble from Chalmers GEM Matlab object
#'
#' @param matlab Chalmers GEM in an R object loaded from the Matlab
#' dump.
#' @param ... Variable names: should contain either only reaction or
#' metabolite variables, otherwise num of rows won't be uniform.
#'
#' @return Tibble with the requested variables.
#'
#' @importFrom purrr map
#' @importFrom tibble as_tibble
#' @importFrom dplyr pull
#' @importFrom magrittr %>% extract extract2 equals
#' @importFrom rlang enquos
#'
#' @noRd
chalmers_gem_matlab_tibble <- function(matlab, ...) {
cols <-
enquos(...) %>%
map_chr(.nse_ensure_str)
matlab %>%
as_tibble %>%
pull(1L) %>%
extract(,,1L) %>%
map(
function(x) {
if (x %>% dim %>% equals(1L) %>% all) x %>% extract(1L,1L) else x
}
) %>%
extract(cols) %>%
map(extract,,1L) %>%
as_tibble
}
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