Last updated: 2025-10-27
Checks: 7 0
Knit directory:
genomics_ancest_disease_dispar/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20220216) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 0e28f49. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
Ignored: ancestry_dispar_env/
Ignored: data/.DS_Store
Ignored: data/cohort/
Ignored: data/gbd/.DS_Store
Ignored: data/gbd/IHME-GBD_2021_DATA-d8cf695e-1.csv
Ignored: data/gbd/ihme_gbd_2019_global_disease_burden_rate_all_ages.csv
Ignored: data/gbd/ihme_gbd_2019_global_paf_rate_percent_all_ages.csv
Ignored: data/gbd/ihme_gbd_2021_global_disease_burden_rate_all_ages.csv
Ignored: data/gbd/ihme_gbd_2021_global_paf_rate_percent_all_ages.csv
Ignored: data/gwas_catalog/
Ignored: data/icd/.DS_Store
Ignored: data/icd/IHME_GBD_2019_COD_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX
Ignored: data/icd/IHME_GBD_2019_NONFATAL_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX
Ignored: data/icd/IHME_GBD_2021_COD_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX
Ignored: data/icd/IHME_GBD_2021_NONFATAL_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX
Ignored: data/icd/UK_Biobank_master_file.tsv
Ignored: data/icd/cdc_valid_icd10_Sep_23_2025.xlsx
Ignored: data/icd/cdc_valid_icd9_Sep_23_2025.xlsx
Ignored: data/icd/manual_disease_icd10_mappings.xlsx
Ignored: data/icd/phecode_international_version_unrolled.csv
Ignored: data/icd/semiautomatic_ICD-pheno.txt
Ignored: data/who/
Ignored: output/.DS_Store
Ignored: output/abstracts/
Ignored: output/doccano/
Ignored: output/fulltexts/
Ignored: output/gwas_cat/
Ignored: output/gwas_cohorts/
Ignored: output/icd_map/
Ignored: output/trait_ontology/
Ignored: pubmedbert-cohort-ner-model/
Ignored: pubmedbert-cohort-ner/
Ignored: r-spacyr/
Ignored: renv/
Ignored: venv/
Unstaged changes:
Modified: .gitignore
Modified: analysis/correcting_cohort_names.Rmd
Modified: analysis/disease_inves_by_ancest.Rmd
Modified: analysis/gbd_data_plots.Rmd
Modified: analysis/group_cancer_diseases.Rmd
Modified: analysis/gwas_to_gbd.Rmd
Modified: analysis/level_1_disease_group_non_cancer.Rmd
Modified: analysis/level_2_disease_group.Rmd
Modified: analysis/map_trait_to_icd10.Rmd
Modified: analysis/trait_ontology_categorization.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/get_dbgap_ids.Rmd) and
HTML (docs/get_dbgap_ids.html) files. If you’ve configured
a remote Git repository (see ?wflow_git_remote), click on
the hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0e28f49 | IJbeasley | 2025-10-27 | Compare dbgap sources |
| html | dcc17fc | IJbeasley | 2025-10-27 | Build site. |
| 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 =
dbgap_text |>
group_by(pmcid) |>
summarise(dbgap_extracted = str_flatten(unique(dbgap_extracted),
collapse = "|",
na.rm = TRUE)
)
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")
)
# overlaps
comparison_df |>
filter(dbgap_annotation != "" &
dbgap_extracted != ""
) |>
nrow()[1] 20# only in annotation
comparison_df |>
filter(dbgap_annotation != "" &
(is.na(dbgap_extracted) | dbgap_extracted == "")
) |>
nrow()[1] 152# only in extracted
comparison_df |>
filter((is.na(dbgap_annotation) | dbgap_annotation == "") &
dbgap_extracted != ""
) |>
nrow()[1] 381
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