Last updated: 2025-08-21

Checks: 7 0

Knit directory: genomics_ancest_disease_dispar/

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File Version Author Date Message
Rmd d0edbb8 IJbeasley 2025-08-06 Halfway through converting cohorts
html 88e1648 IJbeasley 2025-08-05 Build site.
Rmd 852dd98 IJbeasley 2025-08-05 Convert icite analysis to workflowr page 
knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE
)
library(httr)
library(jsonlite)
library(dplyr)
library(data.table)
library(purrr)
library(ggplot2)

Overview

  • Retrieves citation metrics from NIH’s iCite API for publications listed in the GWAS Catalog (like in Reales & Wallace, 2023 - sharing gwas data results in more citations)
  • Due to iCite API limitations, PMIDs of papers in the GWAS catalog are queried in chunks.
  • Note: tried iciteR R package (as I believe Reales & Wallace do) but found it didn’t retrieve the complete set of metrics - I think because adding &format=csv to api call seems to cause problems example call: iCiteR::get_metrics(“27599104”)

1. Define Function to Query iCite API

Define a helper function that accepts a chunk of PMIDs and returns a data frame with citation data.

# Function to fetch a chunk of PMIDs from the iCite API
fetch_icite_chunk <- function(pmid_chunk) {
  pmid_vec <- paste0(pmid_chunk, collapse = ",")
  
  # Construct API URL
  url <- paste0("https://icite.od.nih.gov/api/pubs?pmids=", 
                pmid_vec)
  
  # Perform GET request
  response <- GET(url)
  
  # Parse the response content as JSON
  data_list <- fromJSON(content(response, "text"), flatten = TRUE)
  
  # Convert to data frame
  pub_df <- as.data.frame(data_list)
  
  # Remove "data." prefix from column names
  pub_df <- pub_df |> rename_all(~gsub("data.", "", .x))
  
  # Drop large nested citation data (optional)
  pub_df <- pub_df |> select(-c(citedByPmidsByYear))
  
  return(pub_df)
}

2. Load and Clean GWAS Catalog Study Data

Extract unique PMIDs for papers from the GWAS catalog

# Load GWAS Catalog studies
gwas_study_info <- fread("data/gwas_catalog/gwas-catalog-v1.0.3.1-studies-r2025-07-21.tsv",
                         sep = "\t", quote = "")

# Standardize column names (remove spaces)
gwas_study_info <- gwas_study_info |> rename_all(~gsub(" ", "_", .x))

# Extract unique publication information
gwas_study_info <- gwas_study_info |>
  select(FIRST_AUTHOR, DATE, JOURNAL, PUBMED_ID) |>
  distinct()

# Vector of PMIDs
pmid <- gwas_study_info$PUBMED_ID

3. Fetch Citation Metrics from iCite

To comply with iCite rate limits, we split the PMIDs into batches (≤ 400 per request) and apply our fetch function.

# Split PMIDs into chunks of 400
pmid_chunks <- split(pmid, ceiling(seq_along(pmid) / 400))

# Fetch citation metrics for all chunks
all_results <- map_dfr(pmid_chunks, fetch_icite_chunk)

Confirm RCR calculations match provided equation

# Check if RCR ≈ citations_per_year / expected_citations_per_year
check = all_results |>
  select(field_citation_rate,
         expected_citations_per_year,
         citations_per_year,
         relative_citation_ratio) |>
  mutate(calculated_rcr = citations_per_year / expected_citations_per_year)

head(check)
  field_citation_rate expected_citations_per_year citations_per_year
1            7.254935                    2.838550           5.000000
2            7.241446                    2.834556           3.428571
3            8.243854                    3.116852          38.000000
4            8.075742                    3.131311          87.083333
5            8.682365                    3.545011           6.000000
6            8.981703                    3.644344          45.200000
  relative_citation_ratio calculated_rcr
1                1.761462       1.761462
2                1.209562       1.209562
3               12.191789      12.191789
4               27.810498      27.810498
5                1.692519       1.692519
6               12.402780      12.402780
check = check |> 
        filter(!is.na(relative_citation_ratio))

sum(check$calculated_rcr == check$relative_citation_ratio)
[1] 7012
nrow(check)
[1] 7012

Visual exploration of data

4. Distribution of citation metrics

# Example: Distribution of Relative Citation Ratios (RCR)
ggplot(all_results, aes(x = relative_citation_ratio)) +
  geom_histogram(bins = 50) +
  theme_minimal() +
  labs(title = "Distribution of RCR among GWAS publications",
       x = "Relative Citation Ratio (RCR)",
       y = "Count")

Version Author Date
88e1648 IJbeasley 2025-08-05 
# Summary of citation counts
summary(all_results$relative_citation_ratio)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
  0.0000   0.7022   1.5072   3.6552   3.4473 214.2744      313 
# Distribution of raw citation counts
ggplot(all_results, aes(x = citation_count)) +
  geom_histogram() +
  theme_bw() + 
  labs(title = "Distribution of Raw Citation Counts among GWAS catalog publications")

Version Author Date
88e1648 IJbeasley 2025-08-05 
# Summary of citation counts
summary(all_results$citation_count)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   11.00   30.00   98.85   88.00 7167.00

5. Investigate Missing and Zero RCRs

# Publication Year of Papers with NA RCR
all_results |>
  filter(is.na(relative_citation_ratio)) |>
  ggplot(aes(x = year)) +
  geom_histogram() +
  scale_x_continuous(breaks = seq(min(all_results$year, na.rm = TRUE),
                                  max(all_results$year, na.rm = TRUE), 1)) +
  theme_bw() +
  labs(title = "Publication Year of Papers with NA RCR")

Version Author Date
88e1648 IJbeasley 2025-08-05 
# Count of NA RCR publications
all_results |>
  filter(is.na(relative_citation_ratio)) |>
  nrow()
[1] 313
# Values for other citation metrics for papers with
# NA RCR 
all_results |>
  filter(is.na(relative_citation_ratio)) |>
  select(field_citation_rate,
         expected_citations_per_year,
         citation_count,
         citations_per_year,
         relative_citation_ratio) |>
  head()
  field_citation_rate expected_citations_per_year citation_count
1           13.822219                          NA              3
2           10.571710                          NA              1
3                  NA                          NA              0
4           14.848473                          NA              2
5            8.535105                          NA              1
6            5.326717                          NA              4
  citations_per_year relative_citation_ratio
1                  3                      NA
2                  1                      NA
3                  0                      NA
4                  2                      NA
5                  1                      NA
6                  4                      NA
# Publication Year of Paper with zero RCR
all_results |>
  filter(relative_citation_ratio == 0) |>
  ggplot(aes(x = year)) +
  geom_histogram() +
  scale_x_continuous(breaks = seq(min(all_results$year, na.rm = TRUE),
                                  max(all_results$year, na.rm = TRUE), 1)) +
  theme_bw() + 
  labs(title = "Publication Year of Papers with Zero RCR")

Version Author Date
88e1648 IJbeasley 2025-08-05 
# Count of zero RCR publications
all_results |>
  filter(relative_citation_ratio == 0) |>
  nrow()
[1] 36
# Values for other citation metrics for papers with
# zero RCR 
all_results |>
  filter(relative_citation_ratio == 0) |>
  select(field_citation_rate,
         expected_citations_per_year,
         citation_count,
         citations_per_year,
         relative_citation_ratio) |>
  head()
  field_citation_rate expected_citations_per_year citation_count
1           0.9545455                   0.7909298              0
2           3.0000000                   1.6893112              0
3           2.1426202                   0.8013203              0
4           2.2334348                   0.8352841              0
5           2.5710387                   0.9615448              0
6           1.2419355                   0.4644725              0
  citations_per_year relative_citation_ratio
1                  0                       0
2                  0                       0
3                  0                       0
4                  0                       0
5                  0                       0
6                  0                       0

6. Correlations Between Citation Metrics

# Spearman correlation: citation count vs RCR
cor(all_results$citation_count,
    all_results$relative_citation_ratio,
    method = "spearman",
    use = "pairwise.complete.obs")
[1] 0.8763393
# Spearman correlation: citations per year vs RCR
cor(all_results$citations_per_year,
    all_results$relative_citation_ratio,
    method = "spearman",
    use = "pairwise.complete.obs")
[1] 0.9867908

7. Relationships between citation metrics (scatterplots)

# RCR vs citation count (log x-axis)
ggplot(all_results, aes(x = citation_count + 1, y = relative_citation_ratio)) +
  scale_x_log10() +
  geom_point() +
  theme_bw()

Version Author Date
88e1648 IJbeasley 2025-08-05 
# RCR vs expected citations per year
ggplot(all_results, aes(x = expected_citations_per_year, y = relative_citation_ratio)) +
  scale_x_log10() +
  geom_point() +
  theme_bw()

Version Author Date
88e1648 IJbeasley 2025-08-05 
# RCR vs field citation rate
ggplot(all_results, aes(x = field_citation_rate, y = relative_citation_ratio)) +
  scale_x_log10() +
  geom_point() +
  theme_bw()

Version Author Date
88e1648 IJbeasley 2025-08-05

8. RCR Over Time (Like in Reales & Wallace)

Reales & Wallace observe bump in the RCR of papers published in the last two years (their conclusion is that citation metrics stabilise after two year) - do we observe the same trend?

# RCR vs publication year (raw scale)
all_results |>
  filter(!is.na(relative_citation_ratio), relative_citation_ratio != 0) |>
  ggplot(aes(x = year, y = relative_citation_ratio)) +
  geom_point() +
  theme_bw() + 
  labs(x = 'Year')

Version Author Date
88e1648 IJbeasley 2025-08-05 
# RCR vs year (log y-axis)
all_results |>
  filter(!is.na(relative_citation_ratio), relative_citation_ratio != 0) |>
  ggplot(aes(x = year, y = relative_citation_ratio)) +
  scale_y_log10() +
  geom_point() +
  theme_bw() + 
  labs(title = "RCR over time",
       x = 'Year')

Version Author Date
88e1648 IJbeasley 2025-08-05 
# Boxplot of RCRs per year
all_results |>
  filter(!is.na(relative_citation_ratio), relative_citation_ratio != 0) |>
  ggplot(aes(x = factor(year), y = relative_citation_ratio)) +
  geom_boxplot(outlier.size = 0.5) +
  scale_y_log10() +
  theme_bw() + 
  labs(x = 'Year')

Version Author Date
88e1648 IJbeasley 2025-08-05 
# Citations per year over time
all_results |>
  filter(!is.na(relative_citation_ratio), relative_citation_ratio != 0) |>
  ggplot(aes(x = year, y = citations_per_year)) +
  geom_point() +
  theme_bw()

Version Author Date
88e1648 IJbeasley 2025-08-05 

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6

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] ggplot2_3.5.2     purrr_1.1.0       data.table_1.17.8 dplyr_1.1.4      
[5] jsonlite_2.0.0    httr_1.4.7        workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       compiler_4.3.1     renv_1.0.3         promises_1.3.3    
 [5] tidyselect_1.2.1   Rcpp_1.1.0         stringr_1.5.1      git2r_0.36.2      
 [9] callr_3.7.6        later_1.4.2        jquerylib_0.1.4    scales_1.4.0      
[13] yaml_2.3.10        fastmap_1.2.0      R6_2.6.1           labeling_0.4.3    
[17] generics_0.1.4     curl_6.4.0         knitr_1.50         tibble_3.3.0      
[21] rprojroot_2.1.0    RColorBrewer_1.1-3 bslib_0.9.0        pillar_1.11.0     
[25] rlang_1.1.6        cachem_1.1.0       stringi_1.8.7      httpuv_1.6.16     
[29] xfun_0.52          getPass_0.2-4      fs_1.6.6           sass_0.4.10       
[33] cli_3.6.5          withr_3.0.2        magrittr_2.0.3     ps_1.9.1          
[37] grid_4.3.1         digest_0.6.37      processx_3.8.6     rstudioapi_0.17.1 
[41] lifecycle_1.0.4    vctrs_0.6.5        evaluate_1.0.4     glue_1.8.0        
[45] farver_2.1.2       whisker_0.4.1      rmarkdown_2.29     tools_4.3.1       
[49] pkgconfig_2.0.3    htmltools_0.5.8.1