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
A novel FLI1 exonic circular RNA promotes metastasis in breast cancer by coordinately regulating TET1 and DNMT1	2018	473	Genome biology
Genome editing in potato via CRISPR‐Cas9 ribonucleoprotein delivery	2018	424	Physiologia Plantarum
Deletion of a Csf1r enhancer selectively impacts CSF1R expression and development of tissue macrophage populations	2019	378	Nature Communications
RAP2 mediates mechanoresponses of the Hippo pathway	2018	377	Nature
Efficient CRISPR-Cas9–mediated genome editing in Plasmodium falciparum	2014	241	Nature Methods
A highly efficient single-step, markerless strategy for multi-copy chromosomal integration of large biochemical pathways in Saccharomyces cerevisiae	2015	215	Metabolic Engineering
Cysteine oxidation of copper transporter CTR1 drives VEGFR2 signalling and angiogenesis	2022	197	Nature Cell Biology
Genome-scale engineering of Saccharomyces cerevisiae with single-nucleotide precision	2018	196	Nature Biotechnology
A CRISPR way for accelerating improvement of food crops	2020	192	Nature Food
The helicase domain of Polθ counteracts RPA to promote alt-NHEJ	2017	192	Nature Structural & Molecular Biology

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: 43 nodes, 583 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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