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read_pgs_scoring_file.R
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read_pgs_scoring_file.R
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read_pgs_scoring_file_data <- function(file) {
col_types <- list(
# Variant description columns
rsID = vroom::col_character(),
chr_name = vroom::col_character(),
chr_position = vroom::col_integer(),
effect_allele = vroom::col_character(),
other_allele = vroom::col_character(),
locus_name = vroom::col_character(),
is_haplotype = vroom::col_logical(),
is_diplotype = vroom::col_logical(),
imputation_method = vroom::col_character(),
variant_description = vroom::col_character(),
inclusion_criteria = vroom::col_character(),
# Weight information columns
effect_weight = vroom::col_double(),
is_interaction = vroom::col_logical(),
is_dominant = vroom::col_logical(),
is_recessive = vroom::col_logical(),
dosage_0_weight = vroom::col_character(),
dosage_1_weight = vroom::col_character(),
dosage_2_weight = vroom::col_character(),
# Other information
OR = vroom::col_double(),
HR = vroom::col_double(),
allelefrequency_effect = vroom::col_double(),
# TODO: support the column allelefrequency_effect_<Ancestry>. Note that
# <Ancestry> is variable making the name of this column unpredictable.
# Harmonized columns
hm_source = vroom::col_character(),
hm_rsID = vroom::col_character(),
hm_chr = vroom::col_character(),
hm_pos = vroom::col_integer(),
hm_inferOtherAllele = vroom::col_character(),
hm_match_chr = vroom::col_logical(),
hm_match_pos = vroom::col_logical()
)
col_names <- read_file_column_names(file)
col_types2 <- vroom::cols(!!!col_types[col_names])
tbl <- vroom::vroom(file, comment = '#', col_types = col_types2)
return(tbl)
}
read_comment_block <- function(file, n_max = 200L) {
lines <- vroom::vroom_lines(file = file, n_max = n_max, progress = FALSE)
comment_block <- grep('^#', lines, perl = TRUE, value = TRUE)
return(comment_block)
}
extract_from_comment <- function(x, pattern) {
m <- stringr::str_match(x, pattern)
match <- m[first_non_na(m[, 1]), 2]
return(match)
}
read_pgs_scoring_file_metadata <- function(file) {
comment_block_lines <- read_comment_block(file)
format_version <- extract_from_comment(comment_block_lines, pattern = '^#format_version=(.+)')
pgs_id <- extract_from_comment(comment_block_lines, pattern = '^#pgs_id=(PGS\\d{6})')
pgs_name <- extract_from_comment(comment_block_lines, pattern = '^#pgs_name=(.+)')
reported_trait <- extract_from_comment(comment_block_lines, pattern = '^#trait_reported=(.+)')
mapped_trait <- extract_from_comment(comment_block_lines, pattern = '^#trait_mapped=(.+)')
efo_trait <- extract_from_comment(comment_block_lines, pattern = '^#trait_efo=(.+)')
genome_build <- extract_from_comment(comment_block_lines, pattern = '^#genome_build=(.+)')
weight_type <- extract_from_comment(comment_block_lines, pattern = '^#weight_type=(.+)')
number_of_variants <- extract_from_comment(comment_block_lines, pattern = '^#variants_number=(\\d+)')
pgp_id <- extract_from_comment(comment_block_lines, pattern = '^#pgp_id=(PGP\\d{6})')
citation <- extract_from_comment(comment_block_lines, pattern = '^#citation=(.+)')
license <- extract_from_comment(comment_block_lines, pattern = '^#license=(.+)')
# Harmonized-specific fields
HmPOS_build <- extract_from_comment(comment_block_lines, pattern = '^#HmPOS_build=(.+)')
HmPOS_date <- extract_from_comment(comment_block_lines, pattern = '^#HmPOS_date=(.+)')
HmPOS_match_chr <- extract_from_comment(comment_block_lines, pattern = '^#HmPOS_match_chr=(.+)')
HmPOS_match_pos <- extract_from_comment(comment_block_lines, pattern = '^#HmPOS_match_pos=(.+)')
metadata_tbl <- tibble::tibble(
format_version = nr_to_na(format_version),
pgs_id = nr_to_na(pgs_id),
pgs_name = nr_to_na(pgs_name),
reported_trait = nr_to_na(reported_trait),
mapped_trait = nr_to_na(mapped_trait),
efo_trait = nr_to_na(efo_trait),
genome_build = nr_to_na(genome_build),
weight_type = nr_to_na(weight_type),
number_of_variants = as.integer(nr_to_na(number_of_variants)),
pgp_id = nr_to_na(pgp_id),
citation = nr_to_na(citation),
license = nr_to_na(license),
HmPOS_build = nr_to_na(HmPOS_build),
HmPOS_date = lubridate::as_date(nr_to_na(HmPOS_date)),
HmPOS_match_chr = nr_to_na(HmPOS_match_chr),
HmPOS_match_pos = nr_to_na(HmPOS_match_pos)
)
return(metadata_tbl)
}
read_one_pgs_scoring_file <- function(file, metadata_only = FALSE) {
metadata_tbl <- read_pgs_scoring_file_metadata(file)
if (metadata_only) {
data_tbl <- NULL
} else {
data_tbl <- read_pgs_scoring_file_data(file)
}
return(list(metadata = metadata_tbl, data = data_tbl))
}
read_one_pgs_scoring_file_safe <- function(file, metadata_only = FALSE) {
tryCatch(expr = read_one_pgs_scoring_file(file = file, metadata_only = metadata_only),
error = function(cnd) {
message(glue::glue('Could not download {file} because of: {conditionMessage(cnd)}'))
list(metadata = tibble::tibble(), data = tibble::tibble())
}
)
}
#' Read a polygenic scoring file
#'
#' This function imports a PGS scoring file. For more information about the
#' scoring file schema check \code{vignette("pgs-scoring-file", package =
#' "quincunx")}.
#'
#' @param source PGS scoring file. This can be specified in three forms: (i) a
#' PGS identifier, e.g. \code{"PGS000001"}, (ii) a path to a local file, e.g.
#' \code{"~/PGS000001.txt"} or \code{"~/PGS000001.txt.gz"} or (iii) a direct
#' URL to the PGS Catalog FTP server, e.g.
#' \code{"http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000001/ScoringFiles/PGS000001.txt.gz"}.
#' @param harmonized Whether to read an alternative, harmonized version of the
#' PGS scoring file. This version contains harmonized variant information.
#' This information is provided in extra columns whose names are prefixed with
#' `"hm_"`.
#' @param assembly If `harmonized` is `TRUE`, `assembly` indicates which the
#' genome assembly to choose for the harmonized variant data. `assembly` must
#' be either `"GRCh38"` (default) or `"GRCh37"`.
#' @param protocol Network protocol for communication with the PGS Catalog FTP
#' server: either \code{"http"} or \code{"ftp"}.
#' @param metadata_only Whether to read only the comment block (header) from the
#' scoring file.
#'
#' @return The returned value is a named list. The names are copied from the
#' arguments passed in \code{source}. Each element of the list contains
#' another list of two elements: \code{"metadata"} and \code{"data"}. The
#' "metadata" element contains data parsed from the header of the PGS scoring
#' file. The "data" element contains a data frame with as many rows as
#' variants that constitute the PGS score. The columns can vary. There are
#' mandatory and optional columns. The mandatory columns are those that
#' identify the variant, effect allele (\code{effect_allele}), and its
#' respective weight (\code{effect_weight}) in the score. The columns that
#' identify the variant can either be the \code{rsID} or the combination of
#' \code{chr_name} and \code{chr_position}. The "data" element will be
#' \code{NULL} is argument \code{metadata_only} is \code{TRUE}. For more
#' information about the scoring file schema check
#' \code{vignette("pgs-scoring-file", package = "quincunx")}.
#'
#' @examples
#' \dontrun{
#' # Read a PGS scoring file by PGS ID
#' # (internally, it translates the PGS ID
#' # to the corresponding FTP URL)
#' try(read_scoring_file("PGS000655"))
#'
#' # Equivalent to `read_scoring_file("PGS000655")`
#' url <- paste0(
#' "http://ftp.ebi.ac.uk/",
#' "pub/databases/spot/pgs/scores/",
#' "PGS000655/ScoringFiles/",
#' "PGS000655.txt.gz"
#' )
#' read_scoring_file(url)
#'
#'
#' # Reading from a local file
#' try(read_scoring_file("~/PGS000655.txt.gz"))
#' }
#' @export
read_scoring_file <-
function(source,
harmonized = FALSE,
assembly = c("GRCh38", "GRCh37"),
protocol = 'http',
metadata_only = FALSE) {
pgs_ids <- source[is_pgs_id(source)]
if (!harmonized) {
ftp_resources <- glue::glue(
'{protocol}://ftp.ebi.ac.uk',
'/pub/databases/spot/pgs/scores/',
'{pgs_ids}/ScoringFiles/{pgs_ids}.txt.gz'
)
} else {
assembly <- match.arg(assembly)
ftp_resources <- glue::glue(
'{protocol}://ftp.ebi.ac.uk',
'/pub/databases/spot/pgs/scores/',
'{pgs_ids}/ScoringFiles/Harmonized/{pgs_ids}_hmPOS_{assembly}.txt.gz'
)
}
# Replace PGS identifiers with their corresponding ftp resource URLs
# All other sources, e.g., local files or other sources at left untouched.
source2 <- source
source2[is_pgs_id(source2)] <- ftp_resources
source2 <- stats::setNames(object = source2, nm = source)
purrr::map(source2, read_one_pgs_scoring_file_safe, metadata_only = metadata_only)
}