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genomics_ancest_disease_dispar/
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library(stringr)
library(readxl)
library(dplyr)
library(stringi)
library(httr)
library(rentrez)
library(xml2)
library(jsonlite)
library(tokenizers)## Step 1:
# get only relevant disease studies
gwas_study_info <- data.table::fread(here::here("output/icd_map/gwas_study_gbd_causes.csv"))
gwas_study_info = gwas_study_info |>
dplyr::rename_with(~ gsub(" ", "_", .x))
# filter out infectious diseases
gwas_study_info <- gwas_study_info |>
dplyr::filter(!cause %in% c("HIV/AIDS",
"Tuberculosis",
"Malaria",
"Lower respiratory infections",
"Diarrhoeal diseases",
"Neonatal disorders",
"Tetanus",
"Diphtheria",
"Pertussis" ,
"Measles",
"Maternal disorders"))
# group conditions into broader categories to ensure we have enough papers in each category for sampling
lancet_cause_mapping <- readxl::read_xlsx(here::here("data/icd/lancet_conditions_icd10.xlsx"),
sheet = 1) |>
dplyr::rename_with(~ gsub(" ", "_", .x))
set.seed(500)
training_sample = gwas_study_info |>
left_join(lancet_cause_mapping |> select(cause = mapped_gbd_term,
lancet_condition),
by = "cause",
relationship = "many-to-many") |>
select(PUBMED_ID,
lancet_condition) |>
distinct() |>
dplyr::group_by(lancet_condition) |>
dplyr::slice_sample(prop = 0.25) |>
pull(PUBMED_ID) |>
unique()
pmids <- training_sample
gwas_study_info =
gwas_study_info |>
filter(PUBMED_ID %in% training_sample)
print("Number of unique pubmed ids for disease studies:")[1] "Number of unique pubmed ids for disease studies:"gwas_study_info$PUBMED_ID |> unique() |> length()[1] 241converted_ids <- data.table::fread(here::here("output/fulltexts/pmid_to_pmcid_mapping.csv"))
full_text_files <-
list.files(here::here("output/fulltexts"),
recursive = T,
pattern = "\\.html$|\\.xml$")
full_text_files <- basename(full_text_files) |>
stringr::str_remove_all("\\.html$|\\.xml$") |>
unique()
# convert pmcids to pmids
converted_fulltext_pmcids <-
converted_ids |>
filter(pmcids %in% full_text_files) |>
pull(PMID) |>
unique()
full_text_files <- c(full_text_files,
converted_fulltext_pmcids)
full_text_pmids <- grep("PMC",
full_text_files,
invert = T,
value = T)
print("Number of training sample papers with full text retrieved:")[1] "Number of training sample papers with full text retrieved:"sum(training_sample %in% full_text_pmids)[1] 231print("Number of unique pubmed ids for disease studies with full text retrieved:")[1] "Number of unique pubmed ids for disease studies with full text retrieved:"gwas_study_info |>
filter(PUBMED_ID %in% full_text_pmids) |>
select(PUBMED_ID, cause) |>
distinct() |>
group_by(cause) |>
summarise(n = n()) # A tibble: 21 × 2
cause n
<chr> <int>
1 Cervical cancer 22
2 Chronic kidney disease due to diabetes mellitus type 1 3
3 Chronic kidney disease due to diabetes mellitus type 2 5
4 Chronic obstructive pulmonary disease 17
5 Cirrhosis and other chronic liver diseases 80
6 Diabetes mellitus 87
7 Intracerebral hemorrhage 7
8 Ischemic heart disease 73
9 Ischemic stroke 31
10 Larynx cancer 18
# ℹ 11 more rowsGWAS ancestry data.frame contains: country of recruitment, ancestry label, population descriptors
gwas_ancest_info <- data.table::fread(here::here("data/gwas_catalog/gwas-catalog-v1.0.3.1-ancestries-r2025-07-21.tsv"),
sep = "\t",
quote = "")
gwas_ancest_info = gwas_ancest_info |>
dplyr::rename_with(~ gsub(" ", "_", .x))countries <-
gwas_ancest_info$COUNTRY_OF_RECRUITMENT |>
stringr::str_split("\\,") |>
unlist() |>
str_trim() |>
unique()
countries <- countries[countries != "NR"]
countries <- c(countries,
"United States",
"United Kingdom",
"Korea",
"America",
"USA",
"United States of America",
"Latin America")population_descriptors <-
c(gwas_ancest_info$BROAD_ANCESTRAL_CATEGORY, gwas_ancest_info$ADDITIONAL_ANCESTRY_DESCRIPTION) |>
str_replace_all("Recruitment in North, Central, and South America, Asia, and Europe",
"North America, Central America, Asia, Europe") |>
#stringr::str_split("\\,|;|heritage:\\|") |>
stringr::str_split("\\,|;|heritage:|") |>
unlist() |>
str_trim()
# processing population descriptors to remove extra information and get more general terms to match cohort descriptions in abstracts (e.g. "European" instead of "European (non-Finnish)")
population_descriptors <- population_descriptors |>
str_remove_all("\\.$") |> # Escape the dot to make it literal
str_remove_all("^and ") |>
str_remove_all(" \\(founder/genetic isolate\\)$|\\(founder/genetic isolate$") |>
#str_remove_all("^[0-9+]% ") |>
str_remove_all("^\\d+% ") |> # \d is shorthand for [0-9]
str_remove_all("^[‡†*]+") |> # Remove dagger symbols at start
str_remove_all(" - Korea Association Resource \\(KARE\\)$") |>
str_remove_all("or unknown ancestry$|\\(middle eastern$") |>
str_remove_all("\\(see Springelkamp 2017\\)") |>
str_remove_all("parents & grandparents born in ") |>
str_remove_all(" cell lines$| cell line$") |>
str_remove_all(" cohort$") |>
str_remove_all(" population$") |>
str_remove_all("^Erasmus Rucphen in |^Erasmus Rucphen") |>
str_remove_all("^including ") |>
str_remove_all("\\(Middle Eastern$") |>
unique()
# removing population descriptors that are not valid to match cohort descriptions in abstracts (e.g. "cases", "controls", "Bipolar disorder")
not_valid_pop_descriptors <- c("NR", "N.R", "Other", "other", "", "unspecified",
"East", "Euopre",
"UKB", "UKBB", "DECODE", "Controls from UKBiobank", "UKBB and deCODE",
"NHAPC", "GeneID-I", " Family",
"non-Hispaniic white", "Zimbabweian", "Portugese", "Europen American",
"(see Moffatt et al 2010)", "See Wu J. H",
"et al. 2013. PMID: 23362303", "See Wu et al 23362303", "23362303",
"See Locke (PubMed 25673413) for BMI and Shungin (PubMed 25673412) for WHR")
disease_descriptors <- grep("cases|Bipolar|controls|Schizophrenia|disorder",
population_descriptors,
value = T)
population_descriptors <- population_descriptors[!(population_descriptors %in%
c(not_valid_pop_descriptors,
disease_descriptors
)
)]# Get all terms in one go (if total < 500)
response <- GET("https://www.ebi.ac.uk/ols4/api/ontologies/hancestro/terms?size=1000")
data <- fromJSON(content(response, "text"), flatten = TRUE)
hancestro_terms <- data$`_embedded`$terms$label
# Remove terms with specific biobanks / datasets in brackets, as these are unlikely to be used in cohort descriptions in abstracts / texts
hancestro_terms <- str_remove_all(hancestro_terms,
" \\(SGDP\\)$| \\(1KGP\\)$| \\(HGDP\\)$| \\(GGVP\\)$")
# Remove obsolete terms
hancestro_terms <- grep("obsolete|obsolescence",
hancestro_terms,
value = TRUE,
invert = TRUE)
# remove terms with specific regions in brackets, as these are unlikely to be used in cohort descriptions in abstracts / texts
hancestro_terms <- str_remove_all(hancestro_terms, pattern = "\\(Carmel\\)$|\\(pre1989\\)$|\\(Bergamo\\)$|\\(Negev\\)$|\\(Central\\)|\\(Caucasus\\)")
# replace underscores with spaces
hancestro_terms <- str_replace_all(hancestro_terms,
pattern = "_",
replacement = " ")
hancestro_terms <- unique(hancestro_terms)
# specific hancestro terms to exclude:
terms_to_exclude <- c("ancestry category",
"ancestry status",
"BFO 0000006",
"Country",
"continent",
"continuant",
"curation status specification",
"denotator type",
"entity",
"ethnicity category",
"ethnicity descriptor",
"geographic location",
"geographic descriptor",
"geography-based population category",
"immaterial entity",
"independent continuant",
"!Kung",
"material entity",
"quality",
"reference population",
"region",
"organization",
"population",
"specifically dependent continuant",
"Thing",
"uncategorised population",
"undefined ancestry population")
hancestro_terms <- hancestro_terms[!c(hancestro_terms %in% terms_to_exclude)]Country R packages Population descriptors in: https://pmc.ncbi.nlm.nih.gov/articles/PMC8715140/#sec2
# combine terms from different sources, and add some extra terms based on looking at cohort descriptions in abstracts
cohort_context_terms <-
c(countries, hancestro_terms, population_descriptors) |>
unique() |>
sort()
# terms to add:
cohort_context_terms <- c(cohort_context_terms,
"Scandinavians",
"Native Hawaiians")
# terms to remove ... "Qatar", "UK", "Taiwan", "Korean"
cohort_context_terms <- cohort_context_terms[!cohort_context_terms %in% c("Qatar",
"UK",
"Taiwan",
"Korean",
"population")]
# replace brackets with \\( & and \\)
cohort_context_terms <- cohort_context_terms |>
str_replace_all(pattern = "\\(", replacement = "\\\\(") |>
str_replace_all(pattern = "\\)", replacement = "\\\\)")
cohort_context_terms <- c(cohort_context_terms,
"UK\\b(?! Biobank)",
"UK\\b\\(?! Biobank\\)",
"Qatar\\b\\(?! Biobank\\)",
"Taiwan\\b\\(?! Biobank\\)",
"Korean\\b\\(?! Biobank\\)"
)
# sort by length of term (longest first) to match longest names first
cohort_context_terms <- cohort_context_terms[order(-nchar(cohort_context_terms))]
writeLines(cohort_context_terms,
here::here("output/gwas_cat/ancestry_population_terms.txt"))cohort_context_terms <- readLines(here::here("output/gwas_cat/ancestry_population_terms.txt"))
print("Number of cohort context terms:")[1] "Number of cohort context terms:"length(cohort_context_terms)[1] 924gwas_study_info_cohort =
data.table::fread(here::here("output/gwas_cohorts/gwas_cohort_name_corrected.csv"))
gwas_study_info_cohort =
gwas_study_info_cohort |>
dplyr::rename_with(~ gsub(" ", "_", .x))
gwas_study_info_cohort =
gwas_study_info_cohort |>
select(PUBMED_ID,
COHORT) |>
distinct()
print("Check adding cohort information has only added columns, not rows:")[1] "Check adding cohort information has only added columns, not rows:"print("Before adding:")[1] "Before adding:"dim(gwas_study_info)[1] 8241 13gwas_study_info =
left_join(gwas_study_info,
gwas_study_info_cohort,
by = "PUBMED_ID"
)
print("After adding:")[1] "After adding:"dim(gwas_study_info)[1] 8241 14print("Check adding cohort information has not increaed number of unique pubmed ids:")[1] "Check adding cohort information has not increaed number of unique pubmed ids:"gwas_study_info |>
pull(PUBMED_ID) |>
unique() |>
length()[1] 241print("Number of unique pubmed ids for disease studies with cohort info:")[1] "Number of unique pubmed ids for disease studies with cohort info:"gwas_study_info |>
filter(COHORT != "") |>
pull(PUBMED_ID) |>
unique() |>
length()[1] 52gwas_ancest_info =
gwas_ancest_info |>
select(PUBMED_ID,
BROAD_ANCESTRAL_CATEGORY,
COUNTRY_OF_RECRUITMENT) |>
distinct() |>
group_by(PUBMED_ID) |>
summarise(
BROAD_ANCESTRAL_CATEGORY = paste(
unique(
unlist(strsplit(BROAD_ANCESTRAL_CATEGORY, split = "\\|"))
),
collapse = "|"
),
COUNTRY_OF_RECRUITMENT = paste(
unique(
unlist(strsplit(COUNTRY_OF_RECRUITMENT, split = "\\|"))
),
collapse = "|"
)
)
print("Check adding ancestry information has only added columns, not rows:")[1] "Check adding ancestry information has only added columns, not rows:"print("Before adding:")[1] "Before adding:"dim(gwas_study_info)[1] 8241 14gwas_study_info =
left_join(gwas_study_info,
gwas_ancest_info,
by = "PUBMED_ID"
)
print("After adding:")[1] "After adding:"dim(gwas_study_info)[1] 8241 16gwas_study_info <-
gwas_study_info |>
#filter(COHORT != "") |>
select(PUBMED_ID,
COHORT,
YEAR,
BROAD_ANCESTRAL_CATEGORY,
COUNTRY_OF_RECRUITMENT) |>
distinct() |>
group_by(PUBMED_ID,
YEAR,
BROAD_ANCESTRAL_CATEGORY,
COUNTRY_OF_RECRUITMENT) |>
summarise(
COHORT = paste(
unique(
unlist(strsplit(COHORT, split = "\\|"))
),
collapse = "|"
)
)`summarise()` has grouped output by 'PUBMED_ID', 'YEAR',
'BROAD_ANCESTRAL_CATEGORY'. You can override using the `.groups` argument.gwas_study_info =
gwas_study_info |>
ungroup() |>
arrange(PUBMED_ID)
pmids = gwas_study_info$PUBMED_ID
cohort = gwas_study_info$COHORT
names(cohort) = pmids
date = gwas_study_info$YEAR
names(date) = pmids
country = gwas_study_info$COUNTRY_OF_RECRUITMENT
names(country) = pmids
ancestry = gwas_study_info$BROAD_ANCESTRAL_CATEGORY
names(ancestry) = pmids
print("Number of papers without cohort information:")[1] "Number of papers without cohort information:"gwas_study_info |>
filter(COHORT == "") |>
nrow()[1] 189print("Number of papers with cohort information:")[1] "Number of papers with cohort information:"gwas_study_info |>
filter(COHORT != "") |>
nrow()[1] 52# this xlsx was built from looking at acrynyms / cohort names in the gwas catalog
# and finding the corresponding full names / details of cohorts
cohort_names <- readxl::read_xlsx(here::here("data/cohort/cohort_desc.xlsx"),
sheet = 1) |>
mutate(across(everything(),
~stringr::str_replace_all(.x,
pattern = "\u00A0",
replacement = " "))
) New names:
• `` -> `...15`cohort_full_names = cohort_names$full_name[!is.na(cohort_names$full_name)]
cohort_full_names <- str_trim(cohort_full_names)
cohort_full_names <- iconv(cohort_full_names, to = "UTF-8")
cohort_full_names <- gsub("[\u00A0\r\n]", " ", cohort_full_names) # replace non-breaking spaces, CR, LF with space
cohort_full_names <- str_squish(cohort_full_names) # trims and removes extra spaces
# sort by length of name (longest first) to match longest names first
cohort_full_names <- cohort_full_names[order(-nchar(cohort_full_names))]
cohort_full_names <- cohort_full_names[cohort_full_names != "Not Reported"]
cohort_full_names <- unique(cohort_full_names)
print("Number of unique cohort full names:")[1] "Number of unique cohort full names:"length(cohort_full_names)[1] 767cohort_abbr_names = cohort_names$cohort[!is.na(cohort_names$cohort)]
cohort_abbr_names <- str_trim(cohort_abbr_names)
cohort_abbr_names <- iconv(cohort_abbr_names, to = "UTF-8")
cohort_abbr_names <- gsub("[\u00A0\r\n]", " ", cohort_abbr_names) # replace non-breaking spaces, CR, LF with space
cohort_abbr_names <- str_squish(cohort_abbr_names) # trims and removes extra spaces
# remove abbreviations that are too short
# cohort_abbr_names <- cohort_abbr_names[nchar(cohort_abbr_names) >= 4]
# small_abbr_to_keep <- c("C4D",
# "BBJ",
# "UKB",
# "MVP",
# "TWB",
# "QBB",
# "MEC",
# "WHI"
# )
# cohort_abbr_names <- unique(c(cohort_abbr_names,
# small_abbr_to_keep
# ))
cohort_abbr_names <- cohort_abbr_names[!str_detect(pattern = "\\?",
cohort_abbr_names)]
# sort by length of name (longest first) to match longest names first
cohort_abbr_names <- cohort_abbr_names[order(-nchar(cohort_abbr_names))]
# add cohort names from GWAS catalog not yet added to data-dictionary
gwas_cat_cohorts = gwas_study_info_cohort$COHORT
gwas_cat_cohorts = unlist(strsplit(gwas_cat_cohorts, "\\|"))
gwas_cat_cohorts = gwas_cat_cohorts[!(gwas_cat_cohorts %in% c("", "other", "multiple"))]
cohort_abbr_names = unique(c(cohort_abbr_names,
gwas_cat_cohorts))
# remove small abbreviations that are likely to be false positives
cohort_abbr_names <- cohort_abbr_names[cohort_abbr_names != "DN"]
cohort_abbr_names <- cohort_abbr_names[cohort_abbr_names != "CHB"]
cohort_abbr_names <- cohort_abbr_names[cohort_abbr_names != "FG"]
print("Number of unique cohort abbreviation names:")[1] "Number of unique cohort abbreviation names:"length(cohort_abbr_names)[1] 1029
source spacyr_venv/bin/activate
python3 code/extract_text/spacy_obtain_sentences.py \
--input_dir output/abstracts \
--output_dir output/abstracts
abstract_files <- list.files(here::here("output/abstracts/"),
pattern = "*.json",
full.names = FALSE
) |>
sort()
abstract_pmids = str_remove_all(abstract_files, "_sentences.json$")
abstract_pmids = abstract_pmids[abstract_pmids %in% pmids]
abstracts <- sapply(abstract_pmids,
function(file) {
json_data <- fromJSON(here::here(paste0("output/abstracts/", file, "_sentences.json")))
abstract_lines <- json_data[json_data != ""]
# readLines(here::here(paste0("output/abstracts/",file, ".txt")),
# warn = FALSE) |>
# paste(collapse = " ")
}
)
# check lengths of these vectors are the same
print("Check lengths of vectors are the same:")[1] "Check lengths of vectors are the same:"print(paste("Length of pmids:", length(pmids)))[1] "Length of pmids: 241"print(paste("Length of abstract pmids:", length(abstract_pmids)))[1] "Length of abstract pmids: 239"print(paste("Length of abstracts:", length(abstracts)))[1] "Length of abstracts: 239"print(paste("Length of cohort:", length(cohort)))[1] "Length of cohort: 241"print(paste("Length of date:", length(date)))[1] "Length of date: 241"missing_abstracts = pmids[!(pmids %in% abstract_pmids)]
print("Number of missing abstracts:")[1] "Number of missing abstracts:"length(missing_abstracts)[1] 2#pmids <- pmids[pmids %in% abstract_pmids]extract_cohort_sentences <- function(abstract_list,
cohort_names,
column_name = "COHORT",
tokenize = FALSE,
ignore_case) {
cohort_names_grep <- paste0("\\b", cohort_names, "\\b") # add word boundaries to match whole words only
results <- lapply(seq_along(abstract_list), function(i) {
#abstract <- text_vector[i]
# Split abstract into sentences
if(tokenize) {
sentences <- tokenizers::tokenize_sentences(abstract_list[[i]])[[1]]
} else {
sentences <- abstract_list[[i]]
}
# For each sentence, find all matching cohort names
lapply(seq_along(sentences), function(s) {
sentence <- sentences[s]
# Identify cohort names present in this sentence
matched_cohorts <- cohort_names[str_detect(sentence,
regex(cohort_names_grep,
ignore_case = ignore_case))]
cohort_df <-
data.frame(
article_id = i,
sentence_id = s,
sentence = sentence,
has_cohort = length(matched_cohorts) > 0,
#!!column_name = if (length(matched_cohorts) > 0) str_flatten(unique(matched_cohorts), collapse = "|", na.rm = T) else "",
#COHORT = if (length(matched_cohorts) > 0) str_flatten(unique(matched_cohorts), collapse = "|", na.rm = T) else "",
stringsAsFactors = FALSE
)
# Add dynamic column
cohort_df[[column_name]] <- if (length(matched_cohorts) > 0) str_flatten(unique(matched_cohorts), collapse = "|", na.rm = T) else ""
return(cohort_df)
}) |> bind_rows()
})
results <- bind_rows(results)
results$pubmed_id <- names(abstract_list)[results$article_id]
return(results) #returns a df of sentences, with labelled columns for whether they contain cohort names, and which cohort names they contain (if any)
# id of sentence, and abstract / article
}# extract sentences with cohort full names (case-insensitive matching, as full names are less likely to be ambiguous)
cohort_sentences_df_p1 <- extract_cohort_sentences(abstracts,
cohort_full_names,
ignore_case = TRUE
)
# extract sentences with cohort abbreviation names (case-sensitive matching, as abbreviations are more likely to be ambiguous)
cohort_sentences_df_p2 = extract_cohort_sentences(abstracts,
cohort_abbr_names,
ignore_case = FALSE
)
cohort_sentences_df =
bind_rows(cohort_sentences_df_p1,
cohort_sentences_df_p2
)
cohort_sentences_df =
cohort_sentences_df |>
distinct()
separate_cohorts <- function(COHORT) {
if (any(grepl("\\|", COHORT))) {
return(unlist(strsplit(COHORT, "\\|")))
} else {
return(COHORT)
}
}
cohort_sentences_df =
cohort_sentences_df |>
group_by(pubmed_id,
article_id,
sentence_id,
sentence) |>
summarise(
COHORT = str_flatten(unique(separate_cohorts(COHORT)),
collapse = "|",
na.rm = T),
has_cohort = ifelse(any(COHORT != ""), TRUE, FALSE)
) |>
ungroup() `summarise()` has grouped output by 'pubmed_id', 'article_id', 'sentence_id'.
You can override using the `.groups` argument.cohort_sentences_df =
cohort_sentences_df |>
mutate(COHORT = str_remove_all(COHORT,
pattern = "\\|$|^\\|"
)
) json_list <-
cohort_sentences_df |>
filter(has_cohort) |>
pull(sentence) |>
as.list()
# Write to JSON file
writeLines(toJSON(json_list,
auto_unbox = TRUE,
pretty = TRUE),
here::here("output/doccano/abstract_cohort_sentences.json"))
json_list <-
cohort_sentences_df |>
filter(!has_cohort) |>
pull(sentence) |>
as.list()
# Write to JSON file
writeLines(toJSON(json_list,
auto_unbox = TRUE,
pretty = TRUE),
here::here("output/doccano/abstract_non_cohort_sentences.json"))# remove stop words and common words that are not informative for cohort context
removal_words <- c("the", "and", "of", "in", "to", "with", "a",
"for", "on", "by", "is", "are", "was", "were", "each", "all", "had", "have", "it",
"as", "from", "that", "this", "which", "be", "at", "or", "an", "then", "than", "into",
"if", "not", "only", "both", "same", "after", "across", "between", "out", "up", "any",
"we", "our", "us", "these", "within", "per",
"used", "using", "use",
"0.01", "0.05", "0.5", "1", "one", "2", "two", "3", "three", "iii",
"4", "5", "6", "p", "8", "10", "value", "significant", "30",
"i", "r", "wide"
)
cohort_sentences_words <-
cohort_sentences_df |>
filter(has_cohort) |>
pull(sentence) |>
tokenizers::tokenize_words() |>
unlist()
cohort_sentence_words_df =
data.frame(word = cohort_sentences_words) |>
filter(!(tolower(word) %in% removal_words)) |>
group_by(word) |>
summarise(n_in_cohort = n()) |>
filter(n_in_cohort > 5)
non_cohort_sentences_words <-
cohort_sentences_df |>
filter(!has_cohort) |>
pull(sentence) |>
tokenizers::tokenize_words() |>
unlist()
# sample down non-cohort sentences words
set.seed(500)
non_cohort_sentences_words_sample <- sample(non_cohort_sentences_words,
size = length(cohort_sentences_words),
replace = FALSE)
non_cohort_sentence_words_df =
data.frame(word = non_cohort_sentences_words_sample) |>
filter(!(tolower(word) %in% removal_words)) |>
group_by(word) |>
summarise(n_not_cohort = n())
n_sentences <- cohort_sentences_df |>
filter(COHORT != "") |>
select(article_id, sentence_id) |>
distinct() |>
nrow()
print("Words more common in sentences with cohort names than sentences without cohort names:")[1] "Words more common in sentences with cohort names than sentences without cohort names:"left_join(cohort_sentence_words_df,
non_cohort_sentence_words_df,
by = "word"
) |>
mutate(n_not_cohort = ifelse(is.na(n_not_cohort), 0, n_not_cohort)) |>
mutate(delta_n = n_in_cohort - n_not_cohort) |>
mutate(recall = n_in_cohort / n_sentences) |>
arrange(desc(delta_n)) |>
head(20)# A tibble: 20 × 5
word n_in_cohort n_not_cohort delta_n recall
<chr> <int> <dbl> <dbl> <dbl>
1 biobank 53 0 53 0.310
2 study 62 19 43 0.363
3 uk 41 1 40 0.240
4 data 38 9 29 0.222
5 n 29 4 25 0.170
6 cases 35 16 19 0.205
7 controls 32 15 17 0.187
8 consortium 15 0 15 0.0877
9 cohorts 15 2 13 0.0877
10 project 13 0 13 0.0760
11 african 12 0 12 0.0702
12 based 18 7 11 0.105
13 genomes 11 0 11 0.0643
14 individuals 25 14 11 0.146
15 performed 25 14 11 0.146
16 1000 10 0 10 0.0585
17 19 10 0 10 0.0585
18 ancestry 14 4 10 0.0819
19 analysis 40 31 9 0.234
20 cohort 15 6 9 0.0877print("Number of abstracts containing probable cohort reference")[1] "Number of abstracts containing probable cohort reference"abstracts_cohorts <- cohort_sentences_df |>
filter(COHORT != "") |>
pull(article_id) |>
unique()
n_abstracts_cohorts = abstracts_cohorts |>
length()
print(n_abstracts_cohorts)[1] 99print("Percentage of sampled abstracts containing probable cohort reference:")[1] "Percentage of sampled abstracts containing probable cohort reference:"100 * n_abstracts_cohorts / length(unique(pmids))[1] 41.07884print("Number of sentences containing probable cohort reference")[1] "Number of sentences containing probable cohort reference" cohort_sentences_df |>
filter(COHORT != "") |>
select(article_id, sentence_id) |>
distinct() |>
nrow()[1] 171print("Number of entities")[1] "Number of entities"cohort_sentences_df |>
filter(COHORT != "") |>
pull(COHORT) |>
str_split("\\|") |>
unlist() |>
length()[1] 279print("Number of unique cohorts referenced (not unique cohort names, but unique strings in the COHORT column):")[1] "Number of unique cohorts referenced (not unique cohort names, but unique strings in the COHORT column):"detected_cohorts <- cohort_sentences_df |>
filter(COHORT != "") |>
pull(COHORT) |>
str_split("\\|") |>
unlist()
n_cohorts <- detected_cohorts |>
unique() |>
length()
print(n_cohorts)[1] 115print("Most common cohort names detected (by number of sentences they are mentioned in):")[1] "Most common cohort names detected (by number of sentences they are mentioned in):"data.frame(cohort = detected_cohorts) |>
group_by(cohort) |>
summarise(n = n()) |>
arrange(desc(n)) |>
head(10)# A tibble: 10 × 2
cohort n
<chr> <int>
1 UK Biobank 38
2 1000 Genomes 10
3 Biobank Japan 7
4 HTN 7
5 WTCCC 7
6 Taiwan 6
7 AA-DHS 5
8 COPDGene 5
9 DCCT 5
10 DHS 5print("Most common cohort names detected (by number of abstracts they are mentioned in):")[1] "Most common cohort names detected (by number of abstracts they are mentioned in):"cohort_sentences_df |>
filter(COHORT != "") |>
select(COHORT, article_id) |>
tidyr::separate_rows(COHORT, sep = "\\|") |>
distinct() |>
group_by(COHORT) |>
summarise(n_abstracts = n()) |>
arrange(desc(n_abstracts)) |>
head(5)# A tibble: 5 × 2
COHORT n_abstracts
<chr> <int>
1 UK Biobank 29
2 1000 Genomes 9
3 Biobank Japan 6
4 FinnGen 4
5 Taiwan 4Using this to correct for the over-representation of sentences without cohort names, by identifying well-matched sentences
# words that indicate sample or cohort is being discussed:
sample_terms <- c(
# Your original list
"ancestry", "biobank", "cases", "cohort", "controls",
"consortium", "consortia", "descent", "founder",
"enrolled", "enrollment", "ethnicity", "heritage",
"isolate", "isolated", "individuals", "participants",
"patients", "population", "recruitment", "registry",
"sample", "study", "subjects", "volunteer",
# Additional core terms
"recruited", "enroll", "ascertained", "volunteers",
"probands", "affected", "unaffected", "families", "twins",
"cohorts", "populations", "samples", "subgroup", "subset",
"subcohort", "demographic", "ethnic", "racial",
"admixed", "admixture", "ancestral", "origin",
"case-control", "prospective", "genotyped", "sequenced",
"meta-analysis", "pooled", "residing", "nationwide",
# Useful variants
"enrollees", "sampling", "lineage", "self-reported",
"self-identified", "replication", "discovery", "validation"
)
control_cohort_sentences_df <-
cohort_sentences_df |>
filter(COHORT == "" & CONTEXT == "") |>
filter(grepl(paste(sample_terms, collapse = "|"),
sentence,
ignore.case = TRUE)
) #|>
print("Number of sentences without (detected) cohort names or cohort-context but with sample-related context:")[1] "Number of sentences without (detected) cohort names or cohort-context but with sample-related context:"nrow(control_cohort_sentences_df)[1] 706filtered_cohort_sentences_df =
bind_rows(filtered_cohort_sentences_df,
control_cohort_sentences_df
) |>
distinct()
print("Percentage of sentences with cohort names vs other cohort-related context")[1] "Percentage of sentences with cohort names vs other cohort-related context"total_n_sentences <- nrow(filtered_cohort_sentences_df)
filtered_cohort_sentences_df |>
group_by(has_cohort) |>
summarise(n = n(),
percentage = round(100 * n/total_n_sentences, digits = 2)
)# A tibble: 2 × 3
has_cohort n percentage
<lgl> <int> <dbl>
1 FALSE 920 84.3
2 TRUE 171 15.7# file path for intermediate json file output
json_file = here::here("output/doccano/abstracts_with_cohort_info.json")
# file path for final jsonl file output
jsonl_file = here::here("output/doccano/abstracts_with_cohort_info.jsonl")
convert_to_doccano_json_sentence_level <- function(cohort_sentences_df) {
# set up json list
doccano_list <- list()
example_id <- 1
for(current_sentence in cohort_sentences_df$sentence) {
# Filter cohort sentences that match this sentence (safe matching)
df <- cohort_sentences_df |>
dplyr::filter(sentence == current_sentence)
for (i in seq_len(nrow(df))) {
matched_cohort <- df$COHORT[i]
pubmed_id_val <- df$pubmed_id[i]
date_val <- df$date[i]
cohort_val <- df$cohort[i]
country_val <- df$country[i]
if(matched_cohort == ""){
doccano_list[[example_id]] <- list(
text = current_sentence,
pubmed_id = pubmed_id_val,
date = date_val,
country = country_val,
gwas_cat_cohort_label = cohort_val,
label = list()
)
} else {
# Find the location of all matches of the cohort name in the sentence
if(grepl("\\|", matched_cohort)) {
# If multiple cohort names, separate
matched_cohort <- unlist(strsplit(matched_cohort, split = "\\|"))
match_locations <- list()
for(current_matched_cohort in matched_cohort){
matches <- str_locate_all(current_sentence,
fixed(current_matched_cohort,
ignore_case = TRUE)
)[[1]]
match_locations <- append(match_locations, list(matches))
}
# Combine all match locations into a single matrix
matches <- do.call(rbind, match_locations)
} else {
matches <- str_locate_all(current_sentence,
fixed(matched_cohort,
ignore_case = TRUE)
)[[1]]
}
# Convert matches to 0-based indexing (for doccano)
matches[, "start"] <- matches[, "start"] - 1
# Turn match locations into entity list
entities <- list()
for(k in seq_len(nrow(matches))) {
entities <- append(entities, list(list(
start_offset = matches[k, "start"],
end_offset = matches[k, "end"],
label = "COHORT"
)))
}
# Create Doccano JSON entry
doccano_list[[example_id]] <- list(
text = current_sentence,
pubmed_id = pubmed_id_val,
date = date_val,
country = country_val,
gwas_cat_cohort_label = cohort_val,
label = entities
)
}
}
example_id <- example_id + 1
}
# Convert named list label format to vector format [start, end, label]
doccano_list <- lapply(doccano_list, function(x) {
x$label <- lapply(x$label, function(l) {
c(l$start_offset, l$end_offset, l$label)
})
x
})
return(doccano_list)
}
# Join metadata onto sentences before passing to function
meta_data_df <- data.frame(
pubmed_id = as.character(pmids),
cohort = cohort,
date = date,
country = country
)
filtered_cohort_sentences_df <- left_join(
filtered_cohort_sentences_df,
meta_data_df,
by = "pubmed_id"
)
# Create JSON as a list
json_list <- convert_to_doccano_json_sentence_level(
cohort_sentences_df = filtered_cohort_sentences_df
)
# Write to JSON file
writeLines(toJSON(json_list,
auto_unbox = TRUE,
pretty = TRUE),
json_file)
json_data <- fromJSON(json_file,
simplifyVector = FALSE)
# Open connection to JSONL file
con <- file(jsonl_file, "w")
# Loop over each element (object) and write as one line
for (i in seq_along(json_data)) {
writeLines(toJSON(json_data[[i]], auto_unbox = TRUE), con)
}
# Close connection
close(con)
cat("JSONL saved to:", jsonl_file, "\n")JSONL saved to: /Users/ibeasley/code/genomics_ancest_disease_dispar/output/doccano/abstracts_with_cohort_info.jsonl
source spacyr_venv/bin/activate
python3 code/extract_text/spacy_obtain_sentences.py \
--input_dir output/fulltexts/methods_sections \
--output_dir output/fulltexts/methods_sentences
methods_sections <- list.files(here::here("output/fulltexts/methods_sentences/"),
pattern = "*.json",
full.names = TRUE
)
pmcids_with_methods <- methods_sections |>
gsub(pattern = ".*/", replacement = "") |>
gsub(pattern = "_methods_sentences\\.json$", replacement = "")
pubmeds_with_methods <- converted_ids |>
filter(pmcids %in% pmcids_with_methods) |>
pull(PMID)
pubmeds_with_methods <- c(pubmeds_with_methods,
grep("PMC", pmcids_with_methods, value = TRUE, invert = TRUE)
)
print("Number of papers with methods sections extracted:")[1] "Number of papers with methods sections extracted:"sum(pubmeds_with_methods %in% pmids)[1] 212pmids_methods_to_get <- pubmeds_with_methods[pubmeds_with_methods %in% pmids]
pmcids_methods_to_get <- converted_ids |>
filter(PMID %in% pmids_methods_to_get) |>
pull(pmcids)
ids_methods_to_get <- c(pmids_methods_to_get, pmcids_methods_to_get)
ids_methods_to_get <- ids_methods_to_get[ids_methods_to_get != ""]
files_to_get <-
paste0(ids_methods_to_get,
"_methods_sentences.json") |>
sort()
methods_sections_to_get <- methods_sections[basename(methods_sections) %in% files_to_get]
methods_sections_to_get <- basename(methods_sections_to_get) |>
gsub(pattern = "_methods_sentences\\.json$", replacement = "")
# read in methods sections for available papers
methods_texts <- sapply(methods_sections_to_get,
function(id) {
file <- here::here(paste0("output/fulltexts/methods_sentences/",
id,
"_methods_sentences.json"))
json_data <- fromJSON(file)
abstract_lines <- json_data[json_data != ""]
# readLines(file,
# warn = FALSE,
# encoding = "UTF-8") |>
# paste(collapse = " ")
}
)
# convert pmcids to pubmeds
names(methods_texts) <- sapply(names(methods_texts), function(id) {
if(grepl("^PMC", id)) {
pmcid <- id
pubmed_id <- converted_ids$PMID[converted_ids$pmcids == pmcid]
if(length(pubmed_id) > 0) {
return(as.character(pubmed_id))
} else {
return(NA)
}
} else {
return(id)
}
})
# order methods texts by pmids
methods_texts <- methods_texts[order(names(methods_texts))]cohort_sentences_words <-
methods_cohort_sentences_df |>
filter(has_cohort) |>
pull(sentence) |>
tokenizers::tokenize_words() |>
unlist()
cohort_sentence_words_df =
data.frame(word = cohort_sentences_words) |>
filter(!(tolower(word) %in% removal_words)) |>
group_by(word) |>
summarise(n_in_cohort = n()) |>
filter(n_in_cohort > 5)
non_cohort_sentences_words <-
methods_cohort_sentences_df |>
filter(!has_cohort) |>
pull(sentence) |>
tokenizers::tokenize_words() |>
unlist()
# sample down non-cohort sentences words
set.seed(500)
non_cohort_sentences_words_sample <- sample(non_cohort_sentences_words,
size = length(cohort_sentences_words),
replace = FALSE)
non_cohort_sentence_words_df =
data.frame(word = non_cohort_sentences_words_sample) |>
filter(!(tolower(word) %in% removal_words)) |>
group_by(word) |>
summarise(n_not_cohort = n())
print("Words more common in sentences with cohort names than sentences without cohort names:")[1] "Words more common in sentences with cohort names than sentences without cohort names:"left_join(cohort_sentence_words_df,
non_cohort_sentence_words_df,
by = "word"
) |>
mutate(n_not_cohort = ifelse(is.na(n_not_cohort), 0, n_not_cohort)) |>
mutate(delta_n = n_in_cohort - n_not_cohort) |>
arrange(desc(delta_n)) |>
head(20)# A tibble: 20 × 4
word n_in_cohort n_not_cohort delta_n
<chr> <int> <dbl> <dbl>
1 study 523 189 334
2 biobank 300 6 294
3 uk 289 12 277
4 data 503 279 224
5 1000 184 7 177
6 genomes 177 3 174
7 project 165 12 153
8 cohort 212 67 145
9 reference 190 45 145
10 ukb 145 0 145
11 consortium 134 7 127
12 participants 194 82 112
13 cases 240 132 108
14 european 153 46 107
15 phase 118 13 105
16 panel 115 26 89
17 samples 216 129 87
18 individuals 193 109 84
19 ukbb 84 0 84
20 studies 149 68 81control_methods_cohort_sentences_df <-
methods_cohort_sentences_df |>
filter(COHORT == "" & CONTEXT == "") |>
filter(grepl(paste(sample_terms, collapse = "|"),
sentence,
ignore.case = TRUE)
) #|>
print("Number of sentences without (detected) cohort names or cohort-context but with sample-related context:")[1] "Number of sentences without (detected) cohort names or cohort-context but with sample-related context:"nrow(control_methods_cohort_sentences_df)[1] 4358filtered_methods_cohort_sentences_df =
bind_rows(filtered_methods_cohort_sentences_df,
control_methods_cohort_sentences_df
) |>
distinct()
print("Percentage of sentences with cohort names vs other cohort-related context")[1] "Percentage of sentences with cohort names vs other cohort-related context"total_n_sentences <- nrow(filtered_methods_cohort_sentences_df)
filtered_methods_cohort_sentences_df |>
group_by(has_cohort) |>
summarise(n = n(),
percentage = round(100 * n/total_n_sentences, digits = 2)
)# A tibble: 2 × 3
has_cohort n percentage
<lgl> <int> <dbl>
1 FALSE 5510 74.6
2 TRUE 1878 25.4# file path for intermediate json file output
json_file = here::here("output/doccano/methods_with_cohort_info.json")
# file path for final jsonl file output
jsonl_file = here::here("output/doccano/methods_with_cohort_info.jsonl")
filtered_methods_cohort_sentences_df <- left_join(
filtered_methods_cohort_sentences_df,
meta_data_df,
by = "pubmed_id"
)
# Create JSON as a list
json_list <- convert_to_doccano_json_sentence_level(
cohort_sentences_df = filtered_methods_cohort_sentences_df
)
# Write to JSON file
writeLines(toJSON(json_list,
auto_unbox = TRUE,
pretty = TRUE),
json_file)
json_data <- fromJSON(json_file,
simplifyVector = FALSE)
# Open connection to JSONL file
con <- file(jsonl_file, "w")
# Loop over each element (object) and write as one line
for (i in seq_along(json_data)) {
writeLines(toJSON(json_data[[i]], auto_unbox = TRUE), con)
}
# Close connection
close(con)
cat("JSONL saved to:", jsonl_file, "\n")JSONL saved to: /Users/ibeasley/code/genomics_ancest_disease_dispar/output/doccano/methods_with_cohort_info.jsonl
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.7.3
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 jsonlite_2.0.0 xml2_1.4.0 rentrez_1.2.4
[5] httr_1.4.7 stringi_1.8.7 dplyr_1.1.4 readxl_1.4.5
[9] stringr_1.6.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.10 utf8_1.2.6 generics_0.1.4
[4] tidyr_1.3.1 renv_1.0.3 digest_0.6.37
[7] magrittr_2.0.4 evaluate_1.0.5 fastmap_1.2.0
[10] cellranger_1.1.0 rprojroot_2.1.0 processx_3.8.6
[13] whisker_0.4.1 ps_1.9.1 promises_1.3.3
[16] BiocManager_1.30.26 purrr_1.1.0 XML_3.99-0.19
[19] jquerylib_0.1.4 cli_3.6.5 rlang_1.1.6
[22] withr_3.0.2 cachem_1.1.0 yaml_2.3.10
[25] tools_4.3.1 httpuv_1.6.16 here_1.0.1
[28] vctrs_0.6.5 R6_2.6.1 lifecycle_1.0.4
[31] git2r_0.36.2 fs_1.6.6 pkgconfig_2.0.3
[34] callr_3.7.6 pillar_1.11.1 bslib_0.9.0
[37] later_1.4.4 glue_1.8.0 data.table_1.17.8
[40] Rcpp_1.1.0 xfun_0.55 tibble_3.3.0
[43] tidyselect_1.2.1 rstudioapi_0.17.1 knitr_1.50
[46] htmltools_0.5.8.1 SnowballC_0.7.1 rmarkdown_2.30
[49] compiler_4.3.1 getPass_0.2-4