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genomics_ancest_disease_dispar/
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| html | 8642872 | IJbeasley | 2025-10-27 | Build site. |
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library(httr)
library(xml2)
library(stringr)
library(here)
library(dplyr)gwas_study_info <- data.table::fread(here::here("output/gwas_cat/gwas_study_info_trait_group_l2.csv"))
gwas_study_info = gwas_study_info |>
dplyr::rename_with(~ gsub(" ", "_", .x))
gwas_study_info <- gwas_study_info |>
filter(DISEASE_STUDY == TRUE)
pmids <- unique(gwas_study_info$PUBMED_ID)
#pmids <- sample(pmids, size = 500)
length(pmids)[1] 4511# convert PMID to PMCID
convert_pmid_to_pmcid <- function(pmid_vec,
tool = "myTool",
email = "you@example.com",
format = "json",
batch_size = 100,
sleep_time = 1) {
base_url <- "https://pmc.ncbi.nlm.nih.gov/tools/idconv/api/v1/articles/"
batches <- split(pmid_vec,
ceiling(seq_along(pmid_vec) / batch_size))
#browser()
pmcid_list = purrr::map(batches,
function(pmid_vec) {
ids_param <- paste(pmid_vec,
collapse = ",")
query <- list(ids = ids_param,
idtype = "pmid",
tool = tool,
email = email,
format = format
)
resp <- httr::GET(base_url,
query = query)
httr::stop_for_status(resp)
content_text <- httr::content(resp,
as = "text",
encoding = "UTF-8")
# Handle cases where records might be empty or missing pmcid
parsed <- jsonlite::fromJSON(content_text,
flatten = TRUE)
parsed$records[is.na(parsed$records)] = ""
pmcid <- parsed$records |>
pull(pmcid)
Sys.sleep(sleep_time)
return(pmcid)
}
)
pmcid_list = unlist(pmcid_list)
names(pmcid_list) <- pmid_vec
return(pmcid_list)
}
pmcids <- convert_pmid_to_pmcid(pmids)
get_pmcid_europepmc <- function(pmid_vec) {
base_url <- "https://www.ebi.ac.uk/europepmc/webservices/rest/search"
purrr::map_dfr(pmid_vec, function(pmid) {
query <- list(
query = paste0("ext_id:", pmid),
format = "json"
)
resp <- httr::GET(base_url, query = query)
if (httr::status_code(resp) != 200) {
return(tibble(pmid = pmid,
pmcid = NA_character_))
}
dat <- jsonlite::fromJSON(httr::content(resp,
as = "text",
encoding = "UTF-8"))
if (length(dat$resultList$result) == 0) {
return(tibble(pmid = pmid, pmcid = NA_character_))
}
pmcid <- dat$resultList$result$pmcid
tibble(pmid = pmid, pmcid = pmcid)
})
}
pmids_missing = names(pmcids[pmcids == ""])
get_pmcid_europepmc(pmids_missing) -> pmcid_europepmc_df
converted_ids <-
data.frame(pmids = names(pmcids),
pmcids = pmcids
)
data.table::fwrite(converted_ids,
here::here("output/gwas_cat/gwas_pubmed_to_pmcid_mapping.csv")
)
# How many missing?
sum(pmcids == "")[1] 1005converted_ids <-
data.table::fread(here::here("output/gwas_cat/gwas_pubmed_to_pmcid_mapping.csv")
)
convert_xml_text <- function(xml_content,
text #output text file
){
for(section in 1:xml_length(xml_content)){
section_node = xml_child(xml_content, section)
if(length(xml_path(xml_find_all(section_node,
".//*[.//title and .//p]"
)
)
) == 0
)
{
# Get section name:
section_name = xml_text(xml_find_all(section_node,
".//title"))
section_name = str_squish(section_name)
if(!rlang::is_empty(section_name)) {
text = c(text, paste0("\n\n", section_name, "\n"))
}
# Get paragraphs
para_nodes = xml_find_all(section_node, ".//p")
para_texts = xml_text(para_nodes)
para_texts = str_squish(para_texts)
if(!rlang::is_empty(para_texts)) {
text = c(text, paste0("\n",
para_texts,
"\n")
)
}
if(rlang::is_empty(section_name) &&
rlang::is_empty(para_texts)) {
all_node_text <- xml_text(section_node)
if(!rlang::is_empty(all_node_text)){
text = c(text, paste0("\n",
all_node_text,
"\n")
)
}
}
label <- xml_text(xml_find_all(section_node,
".//label"))
href <- xml_attr(xml_find_all(section_node,
".//media"),
"href")
if(!rlang::is_empty(label) && !rlang::is_empty(href)) {
text = c(text, paste0("\n", label, ". ", href, "\n"))
}
} else {
for(subsection in 1:xml_length(section_node)){
subsection_node = xml_child(section_node,
subsection
)
if(length(xml_children(subsection_node)) == 0){
if(xml_name(subsection_node) == "title"){
text = c(text,
paste0("\n\n",
xml_text(subsection_node),
"\n")
)
} else {
text = c(text,
paste0("\n",
xml_text(subsection_node),
"\n")
)
}
next
}
# Get section name:
subsection_name = xml_text(xml_find_all(subsection_node,
".//title")
)
subsection_name = str_squish(subsection_name)
if(!rlang::is_empty(subsection_name)) {
# Add spaces around section titles
text = c(text, paste0("\n\n",
subsection_name,
"\n")
)
}
# Get paragraphs
para_nodes = xml_find_all(subsection_node,
".//p")
para_texts = xml_text(para_nodes)
para_texts = str_squish(para_texts)
if(!rlang::is_empty(para_texts)) {
# Add spaces around paragraphs
text = c(text, paste0("\n",
para_texts,
"\n")
)
}
if(rlang::is_empty(subsection_name) &&
rlang::is_empty(para_texts)) {
all_node_text <- xml_text(subsection_node)
if(!rlang::is_empty(all_node_text)){
text = c(text, paste0("\n",
all_node_text,
"\n")
)
}
}
}
}
}
return(text)
}
extract_app_text <- function(xml_back,
text){
# Add separator for appendices section
text <- c(text, "\n\n=== APPENDICES ===\n")
#browser()
for(node_id in 1:xml_length(xml_back)){
node = xml_child(xml_back,
node_id)
#print(node)
if(xml_name(node) == "app-group" &
length(xml_find_all(node, ".//sec")) > 0) {
app_node = xml_find_all(node, ".//sec")
text = convert_xml_text(app_node, text)
} else if(xml_name(node) == "ref-list") {
next
} else {
text = convert_xml_text(node,
text)
}
}
return(text)
}
download_pmc_text <- function(pmcid,
out_dir = here::here("output/fulltexts/europepmc")
) {
url_xml <- paste0("https://www.ebi.ac.uk/europepmc/webservices/rest/",
pmcid,
"/fullTextXML"
)
resp <- GET(url_xml)
if (status_code(resp) != 200) stop("Failed to fetch XML for ", pmcid)
xml_content <- read_xml(content(resp,
as = "text",
encoding = "UTF-8")
)
# Get text body xml content
#browser()
xml_body = xml_child(xml_content, "body")
xml_back = xml_child(xml_content, "back")
#browser()
# Build text file
# By converting xml structure into sections and subsections
text = c()
text = convert_xml_text(xml_body,
text
)
text = c(text, "\n\n")
text = extract_app_text(xml_back,
text
)
# if Nat Genet article
if(grepl("Nature genetics",
xml_text(xml_find_all(xml_content,
".//journal-title")),
ignore.case = TRUE
)
){
# Find all figure nodes
xml_figures <- xml_find_all(xml_content,
".//fig")
if (length(xml_figures) != 0){
text = c(text, "\n\nFigures:\n")
}
for(nodes in 1:length(xml_figures)){
figure_node = xml_figures[nodes]
# Extract label:
label <- xml_text(xml_find_all(figure_node,
".//label"))
# Extract title
title = xml_text(xml_find_all(figure_node, ".//title"))
if(!rlang::is_empty(label) | !rlang::is_empty(title)){
text = c(text,
paste0("\n", label, ". ", title, "\n")
)
}
# Extract caption
caption = xml_text(xml_find_all(figure_node,
".//caption//p"))
if(!rlang::is_empty(caption)){
text = c(text,
paste0("\n", caption, "\n")
)
}
}
}
# --- Save ---
text_full <- paste(text, collapse = " ")
txt_file <- file.path(out_dir,
paste0(pmcid, ".txt")
)
writeLines(text_full,
txt_file,
useBytes = TRUE)
#message("✅ Cleaned text saved for ", pmcid)
invisible(text_full)
}
safe_download_pmc_text <- purrr::safely(download_pmc_text)
# Download full texts for all PMCIDs
for(pmcid in pmcids){
if(pmcid != ""){
result <- safe_download_pmc_text(pmcid)
# if(!is.null(result$error)){
# message("❌ Failed to download text for ", pmcid,
# ": ", result$error)
# }
}
}Warning in 1:xml_length(xml_content): numerical expression has 4 elements: only
the first used
Warning in 1:xml_length(xml_content): numerical expression has 4 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 10 elements:
only the first usedWarning in 1:xml_length(xml_content): numerical expression has 2 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 10 elements:
only the first usedWarning in 1:xml_length(xml_content): numerical expression has 13 elements:
only the first usedWarning in 1:xml_length(xml_content): numerical expression has 5 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 9 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 7 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 2 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 5 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 12 elements:
only the first usedWarning in 1:xml_length(xml_content): numerical expression has 5 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 9 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 6 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 2 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 4 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 3 elements: only
the first used
Warning in 1:xml_length(xml_content): numerical expression has 3 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 43 elements:
only the first usedWarning in 1:xml_length(xml_content): numerical expression has 3 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 2 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 10 elements:
only the first usedWarning in 1:xml_length(xml_content): numerical expression has 5 elements: only
the first usedWarning in 1:xml_length(xml_content): numerical expression has 4 elements: only
the first used# How many texts saved?
length(list.files(here::here("output/fulltexts/europepmc"),
pattern = "\\.txt$")
)[1] 2198
# get list of pmcids with full text - author_manuscript available
# available in XML and plain text for text mining purposes.
aws s3 cp s3://pmc-oa-opendata/author_manuscript/txt/metadata/txt/author_manuscript.filelist.txt output/fulltexts/aws_locations/ --no-sign-request
# get list of pmcids with full text - non-commerical use
# oa_noncomm
aws s3 cp s3://pmc-oa-opendata/oa_noncomm/txt/metadata/txt/oa_noncomm.filelist.txt output/fulltexts/aws_locations/ --no-sign-request
europeanpmc_full_texts <-
list.files(here::here("output/fulltexts/europepmc"),
pattern = "\\.txt$"
)
# get pmcids of these files
europeanpmc_full_texts <-
gsub("\\.txt$",
"",
europeanpmc_full_texts
) left_over_pmcids = pmcids[!pmcids %in% europeanpmc_full_texts]
length(left_over_pmcids)[1] 2313author_manu = data.table::fread(here::here("output/fulltexts/aws_locations/author_manuscript.filelist.txt"))
oa_noncomm = data.table::fread(here::here("output/fulltexts/aws_locations/oa_noncomm.filelist.txt"))
author_manu_to_get <-
author_manu |>
filter(PMID %in% names(left_over_pmcids))
nrow(author_manu_to_get)[1] 829oa_noncomm_to_get =
oa_noncomm |>
filter(PMID %in% names(left_over_pmcids))
nrow(oa_noncomm_to_get)[1] 4not_available = names(left_over_pmcids)[!names(left_over_pmcids) %in%
c(oa_noncomm_to_get$PMID,
author_manu_to_get$PMID)]
length(not_available)[1] 1480file_paths =
c(oa_noncomm_to_get$Key,
author_manu_to_get$Key)
# percentage not avaliable, from all:
100 * length(not_available) / length(pmcids)[1] 32.80869# download all the available ones:
writeLines(file_paths, here::here("output/fulltexts/aws_locations/selected_paths.txt"))
system("xargs -a output/fulltexts/aws_locations/selected_paths.txt -I {} aws s3 cp s3://pmc-oa-opendata/{} output/fulltexts/ncbi_cloud/ --no-sign-request")
# for (i in seq_along(file_paths)) {
#
# system(
# paste(
# "aws s3 cp",
# paste0("s3://pmc-oa-opendata/", file_paths[i]),
# here::here("output/fulltexts/ncbi_cloud/"),
# "--no-sign-request"
# )
# )
# }library(tokenizers)
all_full_texts =
c(
list.files(here::here("output/fulltexts/europepmc"),
pattern = "\\.txt$",
full.names = TRUE
),
list.files(here::here("output/fulltexts/ncbi_cloud"),
pattern = "\\.txt$",
full.names = TRUE)
)
# get pmcids of these files
all_pmcids <-
all_full_texts |>
gsub(pattern = ".*/", replacement = "") |>
gsub(pattern = "\\.txt$", replacement = "")
get_grep_sentences <- function(file_path,
grep_pattern) {
txt_in <- readLines(file_path,
warn = FALSE,
encoding = "UTF-8")
sentences <- unlist(tokenize_sentences(txt_in))
dbgap_sentences = sentences[grepl(grep_pattern, sentences)]
return(dbgap_sentences)
}
# get dbgap ids
# get EGA ids
# get JGAS
# get PRJEB / PRJNA ids
grep_pattern <- "phs\\d+|EGAC\\d+|EGAD\\d+|EGAF\\d+|JGAS\\d+|JGAD\\d+|PRJEB\\d+|PRJNA\\d+"
pmcid_dbgap_sentences <-
purrr::map(all_full_texts,
~get_grep_sentences(.x,
grep_pattern = grep_pattern
)
)
names(pmcid_dbgap_sentences) <- all_pmcids
keep_pmcid_dbgap_sentences <-
purrr::discard(
pmcid_dbgap_sentences,
~rlang::is_empty(.x)
)
# Convert to data frame
sentences_df <-
purrr::imap(keep_pmcid_dbgap_sentences,
~data.frame(pmcid = .y,
sentence = unique(.x),
stringsAsFactors = FALSE)
)|>
dplyr::bind_rows()
# Extract all dbGaP IDs from the sentences
sentences_df <- sentences_df |>
mutate(
dbgap_id = str_extract_all(sentence, "phs\\d+(\\.v\\d+)?(\\.p\\d+)?"),
ega_id = str_extract_all(sentence, "EGA[CDFS]\\d+"),
jgas_id = str_extract_all(sentence, "JGAS\\d+|JGAD"),
prj_id = str_extract_all(sentence, "PRJEB\\d+|PRJNA\\d+")
)
sentences_df =
sentences_df |>
distinct()
# number of pubmed ids with dbgap / ega ids found
sentences_df$pmcid |> unique() |> length()[1] 464data.table::fwrite(sentences_df,
here::here("output/gwas_cat/gwas_study_dbgap_ega_sentences.csv")
)sentences_df <- data.table::fread(
here::here("output/gwas_cat/gwas_study_dbgap_ega_sentences.csv")
)
cohort_dbgap_mapping <- readxl::read_xlsx(here::here("data/cohort/cohort_desc.xlsx"),
sheet = 1)New names:
• `` -> `...14`current_dbgap_ids =
cohort_dbgap_mapping |>
pull(dbGaP) |>
strsplit(",") |>
unlist() |>
str_trim() |>
unique()
old_dbgap_ids =
cohort_dbgap_mapping |>
pull(old_dbGaP) |>
strsplit(",") |>
unlist() |>
str_trim() |>
unique()
mapped_dbgap_ids = c(current_dbgap_ids,
old_dbgap_ids
)
found_dbgap_ids =
sentences_df$dbgap_id |>
strsplit("\\|") |>
unlist() |>
unique()
not_found_dbgap_ids <-
sort(found_dbgap_ids[!found_dbgap_ids %in% mapped_dbgap_ids])
not_found_dbgap_ids |> length()[1] 201not_found_dbgap_ids[1:5][1] "phs000" "phs000001" "phs000007.v22.p10"
[4] "phs000007.v31" "phs000020.v1.p1"
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Los_Angeles
tzcode source: internal
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] tokenizers_0.3.0 dplyr_1.1.4 here_1.0.1 stringr_1.5.2
[5] xml2_1.4.0 httr_1.4.7 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] jsonlite_2.0.0 compiler_4.3.1 renv_1.0.3 promises_1.3.3
[5] tidyselect_1.2.1 Rcpp_1.1.0 git2r_0.36.2 callr_3.7.6
[9] later_1.4.4 jquerylib_0.1.4 readxl_1.4.5 yaml_2.3.10
[13] fastmap_1.2.0 R6_2.6.1 SnowballC_0.7.1 generics_0.1.4
[17] curl_7.0.0 knitr_1.50 tibble_3.3.0 rprojroot_2.1.0
[21] bslib_0.9.0 pillar_1.11.1 rlang_1.1.6 cachem_1.1.0
[25] stringi_1.8.7 httpuv_1.6.16 xfun_0.53 getPass_0.2-4
[29] fs_1.6.6 sass_0.4.10 cli_3.6.5 withr_3.0.2
[33] magrittr_2.0.4 ps_1.9.1 digest_0.6.37 processx_3.8.6
[37] rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5 data.table_1.17.8
[41] evaluate_1.0.5 glue_1.8.0 cellranger_1.1.0 whisker_0.4.1
[45] purrr_1.1.0 rmarkdown_2.30 tools_4.3.1 pkgconfig_2.0.3
[49] htmltools_0.5.8.1