Exploratory Analysis of BugSigDB: A Focus on Meta-Analysis Studies

Introduction

BugSigDB is a manually curated database of published microbial signatures — sets of taxa reported as differentially abundant in human or animal microbiome studies. It covers a wide range of conditions, body sites, and experimental designs.

This report explores the landscape of studies in BugSigDB with an emphasis on meta-analysis entries, which aggregate findings across multiple primary studies. Understanding which conditions and body sites are well-represented by meta-analyses can help guide future curation priorities.

---

Setup

library(bugsigdbr)  # BugSigDB data access
library(dplyr)      # Data manipulation
library(ggplot2)    # Visualisation
library(tidyr)      # Data tidying
library(forcats)    # Factor reordering helpers
library(knitr)      # Table rendering

---

1. Import & Overview

studies <- bugsigdbr::importBugSigDB()

cat("Dimensions:", dim(studies)[1], "rows x", dim(studies)[2], "columns\n")

## Dimensions: 8163 rows x 50 columns

# High-level counts
tibble(
  Metric = c("Total signatures", "Unique studies (PMIDs)", "Unique conditions",
             "Unique body sites", "Unique study designs"),
  Count  = c(
    nrow(studies),
    n_distinct(studies$PMID),
    n_distinct(studies$Condition),
    n_distinct(studies$`Body site`),
    n_distinct(studies$`Study design`)
  )
) |>
  kable(caption = "BugSigDB at a glance")

BugSigDB at a glance

Metric	Count
Total signatures	8163
Unique studies (PMIDs)	1374
Unique conditions	589
Unique body sites	238
Unique study designs	17

---

2. Study Design Distribution

studies |>
  count(`Study design`, name = "n_signatures") |>
  mutate(`Study design` = fct_reorder(`Study design`, n_signatures)) |>
  ggplot(aes(x = `Study design`, y = n_signatures, fill = n_signatures)) +
  geom_col(show.legend = FALSE) +
  scale_fill_gradient(low = "#AED6F1", high = "#1A5276") +
  coord_flip() +
  theme_minimal(base_size = 12) +
  theme(panel.grid.major.y = element_blank()) +
  labs(
    x     = NULL,
    y     = "Number of signatures",
    title = "Frequency of study designs in BugSigDB"
  )

Number of microbial signatures per study design in BugSigDB.

---

3. Isolating Meta-Analysis Studies

BugSigDB contains three study-design values that include meta-analysis.

meta_designs <- c(
  "meta-analysis",
  "case-control,meta-analysis",
  "laboratory experiment,meta-analysis"
)

meta <- studies |>
  filter(`Study design` %in% meta_designs)

cat("Meta-analysis signatures:", nrow(meta), "\n")

## Meta-analysis signatures: 139

cat("Unique studies (PMIDs):  ", n_distinct(meta$PMID), "\n")

## Unique studies (PMIDs):   22

meta |>
  count(`Study design`, name = "Signatures") |>
  arrange(desc(Signatures)) |>
  kable(caption = "Meta-analysis sub-types in BugSigDB")

Meta-analysis sub-types in BugSigDB

Study design	Signatures
meta-analysis	117
case-control,meta-analysis	14
laboratory experiment,meta-analysis	8

---

4. Conditions Covered by Meta-Analyses

meta |>
  count(Condition, name = "n") |>
  slice_max(n, n = 15) |>
  mutate(Condition = fct_reorder(Condition, n)) |>
  ggplot(aes(x = Condition, y = n, fill = n)) +
  geom_col(show.legend = FALSE) +
  scale_fill_gradient(low = "#A9DFBF", high = "#1D8348") +
  coord_flip() +
  theme_minimal(base_size = 12) +
  theme(panel.grid.major.y = element_blank()) +
  labs(
    x     = NULL,
    y     = "Number of signatures",
    title = "Top 15 conditions in meta-analysis studies"
  )

Top 15 conditions studied in BugSigDB meta-analyses by number of signatures.

---

5. Body Sites Covered by Meta-Analyses

meta |>
  count(`Body site`, name = "n") |>
  filter(!is.na(`Body site`)) |>
  slice_max(n, n = 15) |>
  mutate(`Body site` = fct_reorder(`Body site`, n)) |>
  ggplot(aes(x = `Body site`, y = n, fill = n)) +
  geom_col(show.legend = FALSE) +
  scale_fill_gradient(low = "#F9E79F", high = "#B7950B") +
  coord_flip() +
  theme_minimal(base_size = 12) +
  theme(panel.grid.major.y = element_blank()) +
  labs(
    x     = NULL,
    y     = "Number of signatures",
    title = "Top body sites in meta-analysis studies"
  )

Body sites represented in BugSigDB meta-analysis signatures.

---

6. Direction of Dysbiosis in Meta-Analyses

Meta-analyses report taxa as either increased or decreased relative to a reference group. Examining this balance reveals whether the literature tends to highlight enrichment or depletion signals.

meta |>
  filter(!is.na(`Abundance in Group 1`)) |>
  count(`Abundance in Group 1`, name = "n") |>
  ggplot(aes(x = `Abundance in Group 1`, y = n,
             fill = `Abundance in Group 1`)) +
  geom_col(width = 0.5, show.legend = FALSE) +
  scale_fill_manual(values = c("increased" = "#E74C3C",
                               "decreased" = "#2980B9")) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.x = element_blank()) +
  labs(
    x     = "Reported direction",
    y     = "Number of signatures",
    title = "Direction of dysbiosis in meta-analysis signatures"
  )

Direction of microbial change reported in meta-analysis signatures.

---

7. Condition × Body-Site Heatmap

The heatmap below shows which condition–body-site pairs have the most meta-analysis signatures, making gaps in coverage immediately visible.

# Identify top conditions and body sites to keep the plot readable
top_conditions <- meta |>
  count(Condition) |>
  slice_max(n, n = 12) |>
  pull(Condition)

top_bodysites <- meta |>
  count(`Body site`) |>
  filter(!is.na(`Body site`)) |>
  slice_max(n, n = 12) |>
  pull(`Body site`)

meta |>
  filter(Condition %in% top_conditions, `Body site` %in% top_bodysites) |>
  count(Condition, `Body site`, name = "n") |>
  complete(Condition, `Body site`, fill = list(n = 0)) |>
  ggplot(aes(x = `Body site`, y = Condition, fill = n)) +
  geom_tile(colour = "white", linewidth = 0.4) +
  geom_text(aes(label = ifelse(n > 0, n, "")),
            size = 3, colour = "white", fontface = "bold") +
  scale_fill_gradient(low = "#EBF5FB", high = "#1A5276", name = "Signatures") +
  theme_minimal(base_size = 11) +
  theme(
    axis.text.x  = element_text(angle = 40, hjust = 1),
    panel.grid   = element_blank()
  ) +
  labs(
    x     = "Body site",
    y     = "Condition",
    title = "Meta-analysis coverage: condition × body site"
  )

Heatmap of meta-analysis signature counts by condition and body site (top 12 of each).

---

8. Publication Trend Over Time

meta |>
  filter(!is.na(Year)) |>
  count(Year, name = "n") |>
  arrange(Year) |>
  mutate(cumulative = cumsum(n)) |>
  ggplot(aes(x = Year, y = cumulative)) +
  geom_area(fill = "#D7BDE2", alpha = 0.6) +
  geom_line(colour = "#6C3483", linewidth = 1) +
  geom_point(colour = "#6C3483", size = 2) +
  theme_minimal(base_size = 12) +
  theme(panel.grid.minor = element_blank()) +
  labs(
    x     = "Publication year",
    y     = "Cumulative signatures",
    title = "Growth of meta-analysis signatures in BugSigDB over time"
  )

Cumulative number of meta-analysis signatures added to BugSigDB by publication year.

---

9. Summary

tibble(
  Aspect    = c("Total meta-analysis signatures",
                "Unique studies (PMIDs)",
                "Most common condition",
                "Most common body site",
                "Most common direction"),
  Finding   = c(
    as.character(nrow(meta)),
    as.character(n_distinct(meta$PMID)),
    meta |> count(Condition)       |> slice_max(n, n=1) |> pull(Condition),
    meta |> count(`Body site`)     |> filter(!is.na(`Body site`)) |>
           slice_max(n, n=1)       |> pull(`Body site`),
    meta |> filter(!is.na(`Abundance in Group 1`)) |>
           count(`Abundance in Group 1`) |>
           slice_max(n, n=1) |> pull(`Abundance in Group 1`)
  )
) |>
  kable(caption = "Key findings from the exploratory analysis")

Key findings from the exploratory analysis

Aspect	Finding
Total meta-analysis signatures	139
Unique studies (PMIDs)	22
Most common condition	Health study participation
Most common body site	Feces
Most common direction	increased

---

Session Info

sessionInfo()

## R version 4.5.2 (2025-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: Africa/Lagos
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] knitr_1.51       forcats_1.0.1    tidyr_1.3.2      ggplot2_4.0.2   
## [5] dplyr_1.2.0      bugsigdbr_1.17.2
## 
## loaded via a namespace (and not attached):
##  [1] bit_4.6.0           gtable_0.3.6        jsonlite_2.0.0     
##  [4] compiler_4.5.2      filelock_1.0.3      tidyselect_1.2.1   
##  [7] blob_1.3.0          jquerylib_0.1.4     scales_1.4.0       
## [10] yaml_2.3.12         fastmap_1.2.0       R6_2.6.1           
## [13] labeling_0.4.3      generics_0.1.4      curl_7.0.0         
## [16] httr2_1.2.2         tibble_3.3.1        DBI_1.3.0          
## [19] bslib_0.10.0        pillar_1.11.1       RColorBrewer_1.1-3 
## [22] rlang_1.1.7         cachem_1.1.0        xfun_0.57          
## [25] sass_0.4.10         S7_0.2.1            bit64_4.6.0-1      
## [28] otel_0.2.0          memoise_2.0.1       RSQLite_2.4.6      
## [31] cli_3.6.5           withr_3.0.2         magrittr_2.0.4     
## [34] digest_0.6.39       grid_4.5.2          rstudioapi_0.18.0  
## [37] dbplyr_2.5.2        rappdirs_0.3.4      BiocFileCache_3.0.0
## [40] lifecycle_1.0.5     vctrs_0.7.2         evaluate_1.0.5     
## [43] glue_1.8.0          farver_2.1.2        rmarkdown_2.30     
## [46] purrr_1.2.1         tools_4.5.2         pkgconfig_2.0.3    
## [49] htmltools_0.5.9