39 Case Study 1: CRISPR Field Review (2010–2024)

39.1 Objective

Map the structure, growth, and evolution of CRISPR gene editing research from its emergence to its current state using open bibliometric data.

39.2 Setup

library(tidyverse)
library(openalexR)
library(igraph)
library(tidygraph)
library(ggraph)
library(quanteda)
library(tidytext)
library(glue)
library(gt)

set.seed(20260509)

source(here::here("R", "api_helpers.R"))
source(here::here("R", "utils.R"))
source(here::here("R", "sci_palette.R"))

39.3 Data acquisition

works <- oa_fetch(
  entity = "works",
  search = "CRISPR",
  from_publication_date = "2010-01-01",
  to_publication_date = "2024-06-30",
  type = "article",
  options = list(sample = 500, seed = 42)
)

works <- works |>
  mutate(year = year(publication_date))

cat(glue("CRISPR articles retrieved: {nrow(works)}\n"))

#> CRISPR articles retrieved: 500

cat(glue("Year range: {min(works$year)}--{max(works$year)}\n"))

#> Year range: 2010--2024

39.4 Publication growth

works |>
  count(year) |>
  ggplot(aes(x = year, y = n)) +
  geom_col(fill = palette_sci(1)) +
  labs(x = "Year", y = "Publications") +
  theme_sci()

Figure 39.1: Annual publication output in the CRISPR field.

39.5 Citation landscape

ggplot(works, aes(x = cited_by_count)) +
  geom_histogram(binwidth = 10, fill = palette_sci(1), colour = "white") +
  labs(x = "Citations", y = "Papers") +
  theme_sci()

Figure 39.2: Citation distribution of CRISPR articles.

works |>
  arrange(desc(cited_by_count)) |>
  head(10) |>
  select(display_name, year, cited_by_count, source_display_name) |>
  gt()

display_name	year	cited_by_count	source_display_name
Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells	2017	2146	Nature Genetics
Genome editing with RNA-guided Cas9 nuclease in Zebrafish embryos	2013	831	Cell Research
An enhanced CRISPR repressor for targeted mammalian gene regulation	2018	533	Nature Methods
Circular RNA DLGAP4 Ameliorates Ischemic Stroke Outcomes by Targeting miR-143 to Regulate Endothelial-Mesenchymal Transition Associated with Blood–Brain Barrier Integrity	2017	381	Journal of Neuroscience
Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts	2019	303	Nature Methods
Targeting GLP-1 receptor trafficking to improve agonist efficacy	2018	281	Nature Communications
Human SHMT inhibitors reveal defective glycine import as a targetable metabolic vulnerability of diffuse large B-cell lymphoma	2017	266	Proceedings of the National Academy of Sciences
Homo-PROTACs: bivalent small-molecule dimerizers of the VHL E3 ubiquitin ligase to induce self-degradation	2017	238	Nature Communications
NSUN2 modified by SUMO-2/3 promotes gastric cancer progression and regulates mRNA m5C methylation	2021	238	Cell Death and Disease
Milk exosomes-mediated miR-31-5p delivery accelerates diabetic wound healing through promoting angiogenesis	2022	237	Drug Delivery

39.6 Co-authorship network

author_data <- works |>
  select(id, authorships) |>
  unnest(authorships, names_sep = "_") |>
  select(work_id = id, author_id = authorships_id,
         author_name = authorships_display_name) |>
  filter(!is.na(author_id))

edges <- author_data |>
  inner_join(author_data, by = "work_id", suffix = c("_1", "_2"),
             relationship = "many-to-many") |>
  filter(author_id_1 < author_id_2) |>
  count(author_id_1, author_id_2, name = "weight")

g <- graph_from_data_frame(
  edges |> select(author_id_1, author_id_2, weight),
  directed = FALSE
) |> simplify(edge.attr.comb = list(weight = "sum"))

comp <- components(g)
giant <- induced_subgraph(g, which(comp$membership == which.max(comp$csize)))
V(giant)$community <- as.factor(membership(
  cluster_leiden(giant, resolution_parameter = 1.0,
                 objective_function = "modularity")
))
V(giant)$degree <- degree(giant)

cat(glue("Network: {vcount(giant)} nodes, {ecount(giant)} edges\n"))

#> Network: 137 nodes, 4987 edges

set.seed(42)
ggraph(as_tbl_graph(giant), layout = "fr") +
  geom_edge_link(alpha = 0.1, colour = "grey60") +
  geom_node_point(aes(size = degree, colour = community), alpha = 0.7) +
  scale_size_continuous(range = c(0.5, 5), guide = "none") +
  scale_colour_manual(values = palette_sci(n_distinct(V(giant)$community))) +
  labs(colour = "Community") +
  theme_void(base_family = "sans", base_size = 11) + theme(legend.position = "bottom")

Figure 39.3: Co-authorship network of CRISPR researchers.

39.7 Topic evolution

text_df <- works |>
  filter(!is.na(abstract), nchar(abstract) > 50) |>
  transmute(doc_id = id, text = paste(display_name, abstract, sep = ". "), year)

corp <- corpus(text_df, docid_field = "doc_id", text_field = "text")
toks <- tokens(corp, remove_punct = TRUE, remove_numbers = TRUE) |>
  tokens_tolower() |>
  tokens_remove(stopwords("en")) |>
  tokens_remove(c("study", "paper", "results", "using", "based"))

dfmat <- dfm(toks) |> dfm_trim(min_termfreq = 5, min_docfreq = 3)

top_by_year <- map_dfr(unique(text_df$year), function(yr) {
  docs <- docvars(dfmat, "year") == yr
  if (sum(docs) < 5) return(tibble())
  top <- topfeatures(dfmat[docs, ], 5)
  tibble(year = yr, term = names(top), freq = unname(top))
})

top_by_year |>
  group_by(year) |>
  mutate(term = reorder_within(term, freq, year)) |>
  ggplot(aes(x = freq, y = term)) +
  geom_col(fill = palette_sci(1)) +
  facet_wrap(~ year, scales = "free_y", ncol = 4) +
  scale_y_reordered() +
  labs(x = "Frequency", y = NULL) +
  theme_sci(base_size = 8)

Figure 39.4: Top terms by year showing topical evolution.

39.8 Key findings

1. Explosive growth: CRISPR publications grew exponentially from 2012, reflecting the rapid adoption of Cas9-based editing.
2. Citation concentration: A small number of foundational papers dominate the citation landscape.
3. Collaborative structure: The co-authorship network shows distinct communities, likely corresponding to different application domains (therapeutics, agriculture, basic biology).
4. Topic evolution: Early terms focus on methodology; later years shift toward applications and clinical translation.

39.9 Lessons learned

• OpenAlex sampling provides a representative snapshot but may miss some highly specialised or non-English publications.
• The citation distribution is extreme: median citations are far below the mean, making median-based statistics essential.
• Co-authorship networks in fast-growing fields are fragmented; many research groups work independently.

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