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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a418670 | IJbeasley | 2025-10-27 | Testing overlap with annotation + text mining dbgap |
| html | 3feefe9 | IJbeasley | 2025-10-20 | Build site. |
| Rmd | bdb7fae | IJbeasley | 2025-10-20 | Extracting all dbgap id info |
| html | 47e3b12 | IJbeasley | 2025-10-20 | Build site. |
| Rmd | 5909160 | IJbeasley | 2025-10-20 | Extracting dbgap ids |
library(dplyr)
library(data.table)
library(rentrez)
library(purrr)
library(stringr)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 |>
dplyr::filter(DISEASE_STUDY == T)
all_pmids <-
unique(gwas_study_info$PUBMED_ID)
print(length(all_pmids))[1] 4511get_internal_dbgap_ids = function(pubmed_id) {
dbgap_links <- entrez_link(
dbfrom = "pubmed",
db = "gap",
id = pubmed_id
)
dbgap_ids <- unlist(dbgap_links$links$pubmed_gap)
return(data.frame(PUBMED_ID = pubmed_id,
DBGAP_ID = str_flatten(dbgap_ids,
collapse = ",",
na.rm = TRUE)
)
)
}
pubmed_dbgap_mapping <-
purrr::map(all_pmids,
get_internal_dbgap_ids
) |>
dplyr::bind_rows()data.table::fwrite(pubmed_dbgap_mapping,
here::here("output/gwas_cohorts/gwas_study_dbgap_ids.csv")
)# load previously saved mapping
pubmed_dbgap_mapping <- data.table::fread(
here::here("output/gwas_cohorts/gwas_study_dbgap_ids.csv")
)
n_with_dbgap = pubmed_dbgap_mapping |>
dplyr::filter(DBGAP_ID != "") |>
nrow()
percent_with_dbgap = n_with_dbgap / nrow(pubmed_dbgap_mapping) * 100
percent_with_dbgap[1] 8.778541# a lot of internal dbgap ids are not able be retrieve study accessions
# sad
# ? perhaps because they are old dbgap versions? dbgap_ids = pubmed_dbgap_mapping$DBGAP_ID
dbgap_ids = dbgap_ids[dbgap_ids != ""]
dbgap_ids = dbgap_ids |>
strsplit(",") |>
unlist() |>
unique()
get_dbgap_accession = function(internal_dbgap_id) {
summary = entrez_summary(db = "gap",
id = internal_dbgap_id)
accession = summary$d_study_results$d_study_id
return(data.frame(
INTERNAL_DBGAP_ID = internal_dbgap_id,
DBGAP_ACCESSION = str_flatten(accession,
collapse = ",",
na.rm = TRUE)
)
)
}
dbgap_accession_mapping <-
purrr::map(dbgap_ids,
get_dbgap_accession
) |>
dplyr::bind_rows()
pubmed_dbgap_mapping =
pubmed_dbgap_mapping |>
tidyr::separate_longer_delim(cols = "DBGAP_ID",
delim = ",")
dbgap_accession_mapping =
dbgap_accession_mapping |>
tidyr::separate_longer_delim(cols = "INTERNAL_DBGAP_ID",
delim = ",")
dbgap_accession_mapping =
left_join(pubmed_dbgap_mapping,
dbgap_accession_mapping,
by = c("DBGAP_ID" = "INTERNAL_DBGAP_ID")
) data.table::fwrite(
dbgap_accession_mapping,
here::here("output/gwas_cohorts/gwas_study_dbgap_accessions.csv")
)# load previously saved mapping
dbgap_accession_mapping <- data.table::fread(
here::here("output/gwas_cohorts/gwas_study_dbgap_accessions.csv")
)
# compress to one row per pubmed id
dbgap_accession_mapping =
dbgap_accession_mapping |>
group_by(PUBMED_ID) |>
summarise(DBGAP_ACCESSION = str_flatten(unique(DBGAP_ACCESSION),
collapse = ",",
na.rm = TRUE),
DBGAP_ID = str_flatten(unique(DBGAP_ID),
collapse = ",",
na.rm = TRUE)
)
n_with_dbgap_accession = dbgap_accession_mapping |>
dplyr::filter(DBGAP_ACCESSION != "") |>
nrow()
percent_with_dbgap_accession = n_with_dbgap_accession / nrow(pubmed_dbgap_mapping) * 100
percent_with_dbgap_accession[1] 4.832631dbgap_to_pubmed_id <- function(dbgap_accession) {
res <- entrez_search(db = "gap",
term = paste0(dbgap_accession, "[STID]"))
links <- entrez_link(dbfrom = "gap",
db = "pubmed",
id = res$ids
)
pmids <- unlist(links$links$gap_pubmed)
return(data.frame(
DBGAP_ACCESSION = dbgap_accession,
DBGAP_ID = str_flatten(res$ids,
collapse = ",",
na.rm = TRUE),
PUBMED_ID = str_flatten(pmids,
collapse = ",",
na.rm = TRUE)
)
)
}
safe_dbgap_to_pubmed_id <- purrr::possibly(dbgap_to_pubmed_id,
otherwise = data.frame(
DBGAP_ACCESSION = NA,
DBGAP_ID = NA,
PUBMED_ID = NA
))
# get known dbgap accessions from gwas study info
cohort_info <- readxl::read_xlsx(here::here("data/cohort/cohort_desc.xlsx"),
sheet = 1)
dbgap_accessions <-
c(cohort_info$dbGaP[cohort_info$dbGaP != "" & !is.na(cohort_info$dbGaP)],
dbgap_accession_mapping$DBGAP_ACCESSION[dbgap_accession_mapping$DBGAP_ACCESSION != ""]
)
dbgap_accessions <- unlist(strsplit(dbgap_accessions, ","))
dbgap_accessions <- unique(dbgap_accessions)
dbgap_to_pubmed_mapping <-
purrr::map(dbgap_accessions,
safe_dbgap_to_pubmed_id
) |>
dplyr::bind_rows()
dbgap_to_pubmed_mapping =
dbgap_to_pubmed_mapping |>
filter(!is.na(PUBMED_ID)) |>
tidyr::separate_longer_delim(cols = "PUBMED_ID", delim = ",")
dbgap_accession_mapping =
dbgap_accession_mapping |>
mutate(DBGAP_ACCESSION = as.character(DBGAP_ACCESSION))
dbgap_to_pubmed_mapping =
dbgap_to_pubmed_mapping |>
mutate(PUBMED_ID = as.numeric(PUBMED_ID))
combined_dbgap_pubmed_mapping =
bind_rows(
dbgap_accession_mapping,
dbgap_to_pubmed_mapping)
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
distinct()
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
filter(!(DBGAP_ID == "" & is.na(DBGAP_ACCESSION))
)
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
filter(!(is.na(PUBMED_ID)))
# filter for pmids in gwas study info
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
filter(PUBMED_ID %in% all_pmids)
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
arrange(PUBMED_ID)
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
mutate(DBGAP_ACCESSION = stringr::str_remove_all(pattern = "^,|,$",
DBGAP_ACCESSION),
DBGAP_ID = stringr::str_remove_all(pattern = "^,|,$",
DBGAP_ID)
)
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
group_by(PUBMED_ID) |>
summarise(
DBGAP_ACCESSION = str_flatten(unique(
unlist(strsplit(DBGAP_ACCESSION, ","))
),
collapse = ",",
na.rm = TRUE),
DBGAP_ID = str_flatten(unique(
unlist(strsplit(DBGAP_ID, ","))
),
collapse = ",",
na.rm = TRUE)
)data.table::fwrite(
combined_dbgap_pubmed_mapping,
here::here("output/gwas_cohorts/gwas_study_combined_dbgap_pubmed_mapping.csv")
)# load previously saved mapping
combined_dbgap_pubmed_mapping <- data.table::fread(
here::here("output/gwas_cohorts/gwas_study_combined_dbgap_pubmed_mapping.csv")
)
n_studies_with_dbgap =
combined_dbgap_pubmed_mapping |>
filter(!(DBGAP_ACCESSION == "" & DBGAP_ID == "")) |>
nrow()
n_studies_with_dbgap / nrow(pubmed_dbgap_mapping) * 100[1] 12.65795combined_dbgap_pubmed_mapping <- data.table::fread(
here::here("output/gwas_cohorts/gwas_study_combined_dbgap_pubmed_mapping.csv")
)
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
select(pmid = PUBMED_ID,
dbgap_annotation = DBGAP_ACCESSION
) |>
distinct()
combined_dbgap_pubmed_mapping =
combined_dbgap_pubmed_mapping |>
filter(!is.na(dbgap_annotation) & dbgap_annotation != "") |>
summarise(dbgap_annotation = str_flatten(unique(dbgap_annotation),
collapse = "|",
na.rm = TRUE),
.by = pmid
)
dbgap_text <- data.table::fread(
here::here("output/gwas_cat/gwas_study_dbgap_ega_sentences.csv")
)
pmid_to_pmcid = data.table::fread(
here::here("output/gwas_cat/gwas_pubmed_to_pmcid_mapping.csv")
)
dbgap_text <- data.table::fread(
here::here("output/gwas_cat/gwas_study_dbgap_ega_sentences.csv")
)
dbgap_text <-
dbgap_text |>
select(dbgap_extracted = dbgap_id,
pmcid
) |>
filter(dbgap_extracted != "")
dbgap_text =
left_join(dbgap_text,
pmid_to_pmcid |> rename(pmcid = pmcids,
pmid = pmids),
by = "pmcid"
)
comparison_df =
full_join(
combined_dbgap_pubmed_mapping,
dbgap_text,
by = c("pmid")
)
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] stringr_1.5.2 purrr_1.1.0 rentrez_1.2.4 data.table_1.17.8
[5] dplyr_1.1.4 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 yaml_2.3.10 fastmap_1.2.0
[13] here_1.0.1 R6_2.6.1 generics_0.1.4 knitr_1.50
[17] XML_3.99-0.19 tibble_3.3.0 rprojroot_2.1.0 bslib_0.9.0
[21] pillar_1.11.1 rlang_1.1.6 cachem_1.1.0 stringi_1.8.7
[25] httpuv_1.6.16 xfun_0.53 getPass_0.2-4 fs_1.6.6
[29] sass_0.4.10 cli_3.6.5 withr_3.0.2 magrittr_2.0.4
[33] ps_1.9.1 digest_0.6.37 processx_3.8.6 rstudioapi_0.17.1
[37] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.5 glue_1.8.0
[41] whisker_0.4.1 rmarkdown_2.30 httr_1.4.7 tools_4.3.1
[45] pkgconfig_2.0.3 htmltools_0.5.8.1