PubMatrixR with Ligand Receptors

Introduction

WNT ligands and their receptors do not pair off neatly. A given ligand can bind several receptors, and the literature reflects that: some ligand-receptor combinations turn up in paper after paper, others almost never.

This vignette counts those co-occurrences. It compares 19 WNT ligands against 15 receptors (FZD1-10, LRP5/6, ROR1/2, RYK), which gives a 15x19 grid of PubMed counts. Bear in mind what the numbers actually measure: how often two gene symbols appear in the same record, not whether the paper found them to interact.

library(PubMatrixR)
library(knitr)
library(kableExtra)
library(dplyr)
library(pheatmap)
library(ggplot2)

A <- c(
  "WNT1", "WNT2", "WNT2B", "WNT3", "WNT3A", "WNT4", "WNT5A", "WNT5B",
  "WNT6", "WNT7A", "WNT7B", "WNT8A", "WNT8B", "WNT9A", "WNT9B",
  "WNT10A", "WNT10B", "WNT11", "WNT16"
)

B <- c(
  "FZD1", "FZD2", "FZD3", "FZD4", "FZD5", "FZD6", "FZD7",
  "FZD8", "FZD9", "FZD10", "LRP5", "LRP6", "ROR1", "ROR2", "RYK"
)

Running the search

A grid this size is 285 separate PubMed searches. With an API key that takes about half a minute; without one, closer to two. The vignette skips the live call by default and fills in a synthetic matrix, so the page builds whether or not NCBI is reachable. Every number below is fake. Swap in your own gene lists and run the live version to get real ones.

NCBI API Key (Recommended)

For better performance and higher rate limits, we recommend obtaining an NCBI API key:

Without API key: 3 requests per second
With API key: 10 requests per second

To obtain your free NCBI API key, visit: https://support.nlm.nih.gov/kbArticle/?pn=KA-05317

Once you have your API key, pass it to PubMatrix() like this:

result <- PubMatrix(
  A = A,
  B = B,
  API.key = "your_api_key_here",
  Database = "pubmed"
)

For live rendering, this vignette picks up the key from the NCBI_API_KEY environment variable instead of hardcoding it, so no key is stored in the file:

NCBI_API_KEY=your_api_key_here PUBMATRIX_LIVE_VIGNETTE=true \
  Rscript -e 'pkgdown::build_site()'

current_year <- as.integer(format(Sys.Date(), "%Y"))
result <- PubMatrix(
  A = A,
  B = B,
  API.key = ncbi_api_key,
  Database = "pubmed",
  daterange = c(1990, current_year),
  outfile = "pubmatrix_result"
)

# Offline deterministic example used for vignette rendering/package checks.
result <- outer(seq_along(B), seq_along(A), function(i, j) {
  10 + (i * 5) + (j * 4) + ((i + j) %% 5) * 2 + ((i * j) %% 6)
})
result <- as.data.frame(result, check.names = FALSE)
colnames(result) <- A
rownames(result) <- B

Which genes get the most attention

Before looking at pairs, check the totals. These bar charts sum each gene’s row or column and colour it by its strongest partner on the other list, so you can see which receptor dominates a given ligand’s literature and the other way round.

# Create data frame for List A genes (rows) colored by List B genes (columns)
a_genes_data <- data.frame(
  gene = rownames(result),
  total_pubs = rowSums(result),
  stringsAsFactors = FALSE
)

# Add color coding based on max overlap with B genes
a_genes_data$max_b_gene <- apply(result, 1, function(x) colnames(result)[which.max(x)])
a_genes_data$max_overlap <- apply(result, 1, max)

# Create data frame for List B genes (columns) colored by List A genes (rows)
b_genes_data <- data.frame(
  gene = colnames(result),
  total_pubs = colSums(result),
  stringsAsFactors = FALSE
)

# Add color coding based on max overlap with A genes
b_genes_data$max_a_gene <- apply(result, 2, function(x) rownames(result)[which.max(x)])
b_genes_data$max_overlap <- apply(result, 2, max)

# Plot A genes colored by their strongest B gene partner
p1 <- ggplot(a_genes_data, aes(x = reorder(gene, total_pubs), y = total_pubs, fill = max_b_gene)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "List A Genes by Publication Count",
    subtitle = "Colored by strongest List B gene partner",
    x = "Genes (List A)",
    y = "Total Publications",
    fill = "Strongest B Partner"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_fill_viridis_d()


# Plot B genes colored by their strongest A gene partner
p2 <- ggplot(b_genes_data, aes(x = reorder(gene, total_pubs), y = total_pubs, fill = max_a_gene)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "List B Genes by Publication Count",
    subtitle = "Colored by strongest List A gene partner",
    x = "Genes (List B)",
    y = "Total Publications",
    fill = "Strongest A Partner"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_fill_viridis_d()


print(p1)

print(p2)

The full matrix

Raw PubMed publication counts for every ligand-receptor pair. Rows are FZD/LRP/ROR/RYK receptors, columns are WNT ligands.

kable(result,
  caption = "Co-occurrence Matrix: WNT Genes (Publication Counts)",
  align = "c",
  format = if (knitr::pandoc_to() == "html") "html" else "markdown"
) %>%
  kableExtra::kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE,
    position = "center"
  ) %>%
  kableExtra::add_header_above(c(" " = 1, "Wnt Genes" = length(A)))

Co-occurrence Matrix: WNT Genes (Publication Counts)
	Wnt Genes
	WNT1	WNT2	WNT2B	WNT3	WNT3A	WNT4	WNT5A	WNT5B	WNT6	WNT7A	WNT7B	WNT8A	WNT8B	WNT9A	WNT9B	WNT10A	WNT10B	WNT11	WNT16
FZD1	24	31	38	35	42	43	50	57	54	61	68	69	76	73	80	87	94	95	92
FZD2	32	40	32	40	48	50	58	56	58	66	74	76	74	82	84	92	100	92	100
FZD3	40	33	42	45	54	57	56	59	68	71	80	73	82	85	94	97	96	99	108
FZD4	38	42	46	56	60	54	64	68	72	82	76	80	90	94	98	98	102	106	116
FZD5	46	51	56	61	56	61	72	77	82	77	82	87	98	103	98	103	108	113	124
FZD6	48	54	60	56	62	68	74	80	76	82	88	94	100	96	102	108	114	120	116
FZD7	56	63	60	67	74	75	82	79	86	93	100	101	98	105	112	119	126	117	124
FZD8	64	62	64	72	80	82	80	88	90	98	106	98	106	114	116	124	122	124	132
FZD9	62	65	74	77	86	79	88	91	100	103	102	105	114	117	126	119	128	131	140
FZD10	70	74	78	88	82	86	96	100	104	104	108	112	122	126	120	130	134	138	148
LRP5	78	83	88	83	88	93	104	109	104	109	114	119	130	125	130	135	140	145	146
LRP6	80	86	82	88	94	100	106	102	108	114	120	126	122	128	134	140	146	142	148
ROR1	88	85	92	99	106	107	104	111	118	125	132	123	130	137	144	151	148	149	156
ROR2	86	94	96	104	112	104	112	120	122	130	128	130	138	146	148	146	154	156	164
RYK	94	97	106	109	108	111	120	123	132	125	134	137	146	149	148	151	160	163	172

Heatmaps

Nobody reads a 15x19 table of numbers. The heatmap shows the same data as colour, and show_numbers = TRUE keeps the counts in the cells if you still want them.

plot_pubmatrix_heatmap(
  matrix = result,
  title = "WNT - Ligands v/s Receptors",
  show_numbers = TRUE
)

Dropping the numbers makes the pattern easier to see when you care about the shape rather than the exact counts.

pubmatrix_heatmap(matrix = result)

System Information

sessionInfo()

## R version 4.6.1 (2026-06-24)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.6.2
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/London
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_4.0.3    pheatmap_1.0.13  dplyr_1.2.1      kableExtra_1.4.1
## [5] knitr_1.51       PubMatrixR_1.0.1
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6       jsonlite_2.0.0     compiler_4.6.1     tidyselect_1.2.1  
##  [5] xml2_1.6.0         stringr_1.6.0      parallel_4.6.1     jquerylib_0.1.4   
##  [9] systemfonts_1.3.2  scales_1.4.0       textshaping_1.0.5  yaml_2.3.12       
## [13] fastmap_1.2.0      R6_2.6.1           labeling_0.4.3     generics_0.1.4    
## [17] tibble_3.3.1       svglite_2.2.2      bslib_0.12.0       pillar_1.11.1     
## [21] RColorBrewer_1.1-3 readODS_2.3.5      rlang_1.3.0        cachem_1.1.0      
## [25] stringi_1.8.9      xfun_0.60          S7_0.2.2           sass_0.4.10       
## [29] otel_0.2.0         viridisLite_0.4.3  cli_3.6.6          withr_3.0.3       
## [33] magrittr_2.0.5     grid_4.6.1         digest_0.6.39      rstudioapi_0.19.0 
## [37] pbapply_1.7-4      lifecycle_1.0.5    vctrs_0.7.3        evaluate_1.0.5    
## [41] glue_1.8.1         farver_2.1.2       rmarkdown_2.31     tools_4.6.1       
## [45] pkgconfig_2.0.3    htmltools_0.5.9