Focused Sub-Analysis of Endometriosis Microbial Signatures

Introduction

This vignette demonstrates a focused sub-analysis of microbial signatures associated specifically with endometriosis (and the closely related condition adenomyosis) using BugSigDB. This is the smallest of the four sub-analyses by sample size in the General Women’s Health dataset (42 signatures total across “Endometriosis”, “Adenomyosis”, and “Adenomyosis,Endometriosis”), so thresholds and statistical power should be interpreted with appropriate caution throughout.

Download and Prepare Data

full.dat <- bugsigdbr::importBugSigDB(cache = FALSE)

ind1 <- lengths(full.dat[["MetaPhlAn taxon names"]]) > 0
ind2 <- lengths(full.dat[["NCBI Taxonomy IDs"]]) > 0
dat <- full.dat[ind1 & ind2, ]

Select Endometriosis-Specific Signatures

endometriosis_terms <- c(
  "Endometriosis",
  "Adenomyosis",
  "Adenomyosis,Endometriosis"
)

endo_dat <- dat %>%
  filter(Condition %in% endometriosis_terms) %>%
  distinct()

nrow(endo_dat)

## [1] 56

endo_dat %>%
  count(Condition, sort = TRUE) %>%
  knitr::kable()

Condition	n
Endometriosis	51
Adenomyosis,Endometriosis	3
Adenomyosis	2

Study Characteristics

study.summary <- endo_dat %>%
  count(`Body site`, `Sequencing type`)

knitr::kable(study.summary)

Body site	Sequencing type	n
Cervical cavity	16S	3
Endometrium	16S	3
Feces	16S	20
Feces	WMS	4
Lower part of vagina,Upper part of vagina,Cervical mucus	16S	2
Mouth	16S	5
Ovary	16S	2
Posterior fornix of vagina	16S	4
Upper part of vagina	16S	2
Uterine cervix	16S	1
Uterovesical pouch	16S	2
Vagina	16S	6
Vaginal fluid,Feces,Endocervix	16S	2

endo_dat %>%
  count(Year) %>%
  arrange(Year) %>%
  knitr::kable()

Year	n
2016	5
2018	4
2019	4
2020	9
2021	12
2022	2
2024	18
2025	2

endo_dat %>%
  count(`Body site`, sort = TRUE) %>%
  ggplot(aes(x = reorder(`Body site`, n), y = n)) +
  geom_col(fill = "#FFDE00", width = 0.6) +
  coord_flip() +
  labs(
    title = "Endometriosis Signatures by Body Site",
    x = NULL, y = "Number of Signatures"
  ) +
  theme_minimal(base_size = 11) +
  theme(plot.title = element_text(face = "bold"))

endo_dat %>%
  distinct(PMID, `Location of subjects`, `Sequencing type`) %>%
  filter(!is.na(`Location of subjects`)) %>%
  count(`Location of subjects`, `Sequencing type`) %>%
  ggplot(aes(x = reorder(`Location of subjects`, n), y = n, fill = `Sequencing type`)) +
  geom_col(width = 0.6) +
  coord_flip() +
  # Adds your pastel dark brown palette manually
  scale_fill_manual(values = c("#8B7365", "#A38D80", "#BCABA2")) + 
  labs(
    title = "Endometriosis Studies by Reported Location",
    x = NULL, y = "Number of Independent Studies", fill = "Platform"
  ) +
  theme_minimal(base_size = 11) +
  theme(plot.title = element_text(face = "bold"), legend.position = "bottom")

endo_dat %>%
  distinct(PMID, Year, `Sequencing type`) %>%
  filter(!is.na(Year)) %>%
  count(Year, `Sequencing type`) %>%
  group_by(`Sequencing type`) %>%
  arrange(Year) %>%
  mutate(cumulative_n = cumsum(n)) %>%
  ungroup() %>%
  ggplot(aes(x = Year, y = cumulative_n, color = `Sequencing type`)) +
  geom_line(linewidth = 1.2) +
  geom_point() +
  labs(
    title = "Cumulative Endometriosis Studies Over Time",
    x = "Publication year", y = "Cumulative number of studies", color = "Sequencing type"
  ) +
  theme_minimal(base_size = 11) +
  theme(legend.position = "bottom")

Taxonomic Summary

taxa.table <- createTaxonTable(endo_dat)
knitr::kable(head(taxa.table, 20))

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval
Lactobacillus	genus	9	6	3	0.51
Streptococcus	genus	6	5	1	0.22
Bacteroidales	order	6	2	4	0.69
Prevotella	genus	6	4	2	0.69
Coriobacteriaceae	family	5	4	1	0.37
Megasphaera	genus	5	2	3	1.00
Bacteroides	genus	5	3	2	1.00
Enterococcus	genus	4	4	0	0.12
Oscillospira	genus	4	4	0	0.12
Blautia	genus	4	3	1	0.63

Split by Sequencing Type

endo.wms <- endo_dat %>% filter(`Sequencing type` == "WMS")
endo.16s <- endo_dat %>% filter(`Sequencing type` == "16S")

cat("WMS  - signatures:", nrow(endo.wms), "| studies:", n_distinct(endo.wms$PMID), "\n")

## WMS  - signatures: 4 | studies: 1

cat("16S  - signatures:", nrow(endo.16s), "| studies:", n_distinct(endo.16s$PMID), "\n")

## 16S  - signatures: 52 | studies: 16

Long-Format Taxon Table

sigs.endo <- bugsigdbr::getSignatures(endo_dat, tax.id.type = "taxname")
sigs.all <- bugsigdbr::getSignatures(dat, tax.id.type = "taxname")

taxon.long <- purrr::map_dfr(seq_along(sigs.all), function(i) {
  taxa <- sigs.all[[i]]
  if (length(taxa) == 0) return(NULL)
  tibble::tibble(sig_id = i, taxon = taxa)
}) %>%
  left_join(
    dat %>%
      mutate(sig_id = row_number()) %>%
      select(sig_id, PMID, Condition, `Sequencing type`, `Abundance in Group 1`),
    by = "sig_id"
  ) %>%
  rename(direction = `Abundance in Group 1`)

nrow(taxon.long)

## [1] 58971

Taxon Frequency Across Studies

taxon.freq <- taxon.long %>%
  filter(Condition %in% endometriosis_terms) %>%
  distinct(PMID, taxon, direction) %>%
  count(taxon, direction, name = "n_studies") %>%
  group_by(taxon) %>%
  mutate(total_studies = sum(n_studies)) %>%
  ungroup() %>%
  arrange(desc(total_studies))

n_distinct(taxon.freq$taxon)

## [1] 253

taxon.freq %>%
  distinct(taxon, total_studies) %>%
  slice_max(total_studies, n = 15) %>%
  knitr::kable()

taxon	total_studies
Oscillospiraceae	5
Lactobacillus	4
Streptococcus	4
Bacteroides	3
Bifidobacterium	3
Clostridium	3
Collinsella	3
Enterobacteriaceae	3
Faecalibacterium	3
Fusicatenibacter	3
Lachnospiraceae	3
Lactobacillaceae	3
Mycobacteriales	3
Actinomycetes	2
Actinomycetota	2
Alistipes putredinis	2
Bacillota	2
Bacteroides thetaiotaomicron	2
Bifidobacterium adolescentis	2
Bilophila wadsworthia	2
Blautia	2
Burkholderiales	2
Clostridia	2
Clostridiaceae	2
Dorea	2
Eggerthella lenta	2
Enterobacter	2
Enterocloster bolteae	2
Enterocloster clostridioformis	2
Enterococcus	2
Eubacteriales	2
Flavonifractor plautii	2
Gemmiger	2
Klebsiella	2
Lachnospira	2
Lawsonella	2
Moryella	2
Peptoniphilus	2
Peptostreptococcus	2
Pseudomonadota	2
Roseburia	2
Rothia	2
Ruminiclostridium	2
Serratia	2
Staphylococcaceae	2
Subdoligranulum	2
[Clostridium] scindens	2

Core Consensus Signature

concordance <- taxon.long %>%
  filter(Condition %in% endometriosis_terms) %>%
  distinct(PMID, taxon, direction) %>%
  group_by(taxon) %>%
  summarise(
    n_studies       = n_distinct(PMID),
    n_increased     = sum(direction == "increased"),
    n_decreased     = sum(direction == "decreased"),
    concordance_pct = round(max(n_increased, n_decreased) / n_studies * 100, 1),
    majority_dir    = if_else(n_increased >= n_decreased, "increased", "decreased"),
    .groups = "drop"
  ) %>%
  arrange(desc(n_studies), desc(concordance_pct))

# Given the small sample size (42 signatures total), the standard >=3/>=75% threshold
# may return very few or zero rows. Loosen to >=2 studies / >=70% concordance if so
# (matching the Cervical Cancer approach), and state that explicitly below.
core.sig <- concordance %>%
  filter(n_studies >= 2, concordance_pct >= 70) %>%
  arrange(majority_dir, desc(n_studies))

nrow(core.sig)

## [1] 22

knitr::kable(core.sig)

taxon	n_studies	n_increased	n_decreased	concordance_pct	majority_dir
Klebsiella	2	0	2	100	decreased
Oscillospiraceae	4	3	2	75	increased
Streptococcus	4	3	1	75	increased
Clostridium	3	3	0	100	increased
Collinsella	3	3	0	100	increased
Enterobacteriaceae	3	3	0	100	increased
Actinomycetes	2	2	0	100	increased
Alistipes putredinis	2	2	0	100	increased
Bacteroides	2	2	1	100	increased
Bifidobacterium adolescentis	2	2	0	100	increased
Clostridia	2	2	0	100	increased
Clostridiaceae	2	2	0	100	increased
Dorea	2	2	0	100	increased
Eubacteriales	2	2	0	100	increased
Fusicatenibacter	2	2	1	100	increased
Lachnospira	2	2	0	100	increased
Lachnospiraceae	2	2	1	100	increased
Moryella	2	2	0	100	increased
Mycobacteriales	2	2	1	100	increased
Peptostreptococcus	2	2	0	100	increased
Pseudomonadota	2	2	0	100	increased
Roseburia	2	2	0	100	increased

Core Signature Visualisation

if (nrow(core.sig) > 0) {
  core.sig %>%
    group_by(majority_dir) %>%
    slice_max(n_studies, n = 15, with_ties = FALSE) %>%
    ungroup() %>%
    mutate(
      direction_label = if_else(majority_dir == "increased", "Increased", "Decreased"),
      taxon_label = paste0(str_wrap(taxon, width = 28), " (", n_studies, ")")
    ) %>%
    ggplot(aes(x = reorder(taxon_label, n_studies), y = n_studies, fill = direction_label)) +
    geom_col(width = 0.7) +
    geom_text(aes(label = paste0(concordance_pct, "%")), hjust = -0.15, size = 3.3) +
    coord_flip(clip = "off") +
    facet_wrap(~direction_label, scales = "free_y", ncol = 1) +
    scale_fill_manual(values = c("Increased" = "#FFF2A3", "Decreased" = "#4A2E1B")) +
    scale_y_continuous(expand = expansion(mult = c(0, 0.35))) +
    labs(
      title = "Core Endometriosis Consensus Signature",
      subtitle = "Taxa in >=2 studies with >=70% direction concordance",
      x = NULL, y = "Number of independent studies", fill = NULL
    ) +
    theme_minimal(base_size = 12) +
    theme(legend.position = "none", plot.title = element_text(face = "bold"))
} else {
  cat("No taxa met the core-signature threshold at this sample size.")
}

Taxon Enrichment Analysis

n.universe <- n_distinct(taxon.long %>% filter(Condition %in% endometriosis_terms) %>% pull(taxon))
n.sigs     <- length(sigs.endo)

enrichment.df <- taxon.freq %>%
  distinct(taxon, total_studies) %>%
  mutate(
    observed         = total_studies,
    expected         = n.sigs / n.universe,
    enrichment_ratio = observed / expected,
    log2_enrichment  = log2(enrichment_ratio),
    p_value = purrr::map_dbl(observed, ~ binom.test(
      x = .x, n = n.sigs, p = 1 / n.universe, alternative = "greater"
    )$p.value),
    p_adj = p.adjust(p_value, method = "BH")
  ) %>%
  arrange(p_adj, desc(log2_enrichment))

sum(enrichment.df$p_adj < 0.05)

## [1] 13

knitr::kable(head(enrichment.df, 15))

taxon	total_studies	observed	expected	enrichment_ratio	log2_enrichment	p_value	p_adj
Oscillospiraceae	5	5	0.2213439	22.589286	4.497567	0.0000031	0.0007882
Lactobacillus	4	4	0.2213439	18.071429	4.175639	0.0000761	0.0064158
Streptococcus	4	4	0.2213439	18.071429	4.175639	0.0000761	0.0064158
Bacteroides	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Bifidobacterium	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Clostridium	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Collinsella	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Enterobacteriaceae	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Faecalibacterium	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Fusicatenibacter	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Lachnospiraceae	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Lactobacillaceae	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Mycobacteriales	3	3	0.2213439	13.553571	3.760601	0.0014637	0.0284859
Actinomycetes	2	2	0.2213439	9.035714	3.175639	0.0208905	0.1124530
Actinomycetota	2	2	0.2213439	9.035714	3.175639	0.0208905	0.1124530

Monte Carlo Critical N

siglengths <- lengths(endo_dat$`MetaPhlAn taxon names`)

criticalN <- getCriticalN(
  relevant.sigs = endo_dat$`MetaPhlAn taxon names`,
  siglengths = siglengths
)

criticalN

## $critical_n
## 95% 
##  12 
## 
## $ci_lower
## 2.5% 
##   12 
## 
## $ci_upper
## 97.5% 
##    13 
## 
## $simulated_values
##    [1] 11  9 10 10  7 10  9 10  7  8 10  9  8  9  9  7  9 14 11 10  9 10  7 13
##   [25]  8  9 10 10  9 12 11 11  6  8 10 10  8  9  9 10 10  8 11  7  9 11  8  9
##   [49] 12 12 11  8 10  9 11 10  9  8  8 11  7 14  9  9  9  9  8  9  8 10  8 15
##   [73]  9 11 10 11 10 11  6 10  8  9  8  9  6 10  9  9 10 10 10  9 12 10 13  7
##   [97] 10  8  9 14  8  9 10  8 10  9 11 10 12  7  8  8  8  9 12 10  9  8 10  9
##  [121] 11  7  7  8  6  8 12  7  9  8  9  9  8 12  9  8  9  8  7  9  9  9 10 10
##  [145] 10  8  7  7  8  7  9 11 11  8 14  8  8  9 10  8  7  7  8  8  8  7  8  8
##  [169]  8 12  8 12 11  9  8  9 10  9  9  8  8  8 10  8  9 13 11 11  8  8 11 10
##  [193] 10  7  8 11 11  7  9 12 11  7  7  8 11 10 10  9  9  8  9 10  8 10  8  8
##  [217]  9 11  8  8  8  8 11  8  8 11 10 11  9  8 10  9  8 10  9  9  9  9 10  7
##  [241] 12  9  8 11  7  8  7 10 13 10  9 11  7  8  7 11  8  8 11 13 10 10  9 10
##  [265]  8  7  8 10 10  9 10 10 11  8 10  9 11 10  7 15 11  7 15  7  7 10 11 10
##  [289]  8  7  8  9 10  9  8 10  9 12  9  9 10  9 11 11  8 10  7  9 11  8 11 10
##  [313]  9 10  7  8  9 14 10 14  9 10  8 10  8  9  9  8 11 12  8  9  6  7  8  7
##  [337]  8 10 12 10 11 11  9  8  9  7  9  8  9  9  8 10  9  9 10  7 11  8 10 11
##  [361]  8  8  8 10  9 10 11 10 10 12 10 12  9 12 11 11  7  9  8  9  9  9  9  8
##  [385]  7 12  8 10 10  9 10 10 11 11  8 11  7  9 10  8 12 10  9 10 11  7  9  9
##  [409] 10  8 10 12  8  7 11  9  9  9 10  9  8  8 11  7 10  7  9  9 10  8 10 14
##  [433]  9 10 10 10 13 10 11  9 10 10  9  8 10 11 13 10 13 12  9 10  7  7 10 11
##  [457] 10  9  8  8  7 10 11  9 10  9 11 10  9 10  8  9  9 11  9  7 10  9  9 10
##  [481] 11  9  7 10  8  8 11 12 10  8  9  9 11  9 10 10  8  7  9 10 11  8  8 10
##  [505]  7 10 12  8 12  9 12  9 11 10  9  9 10  8  8  7 12  8  8 12 10 10 12  8
##  [529]  8 12  8 10 10  7  9 11  9  7  7  9  9  7  5 10 12  7 10 11  8  9 12  8
##  [553] 10 10 12  8  7  7  8 10  8 10 11 12 10  7 10  7 12 10 10  9  9  9 12 11
##  [577]  7 10 10  9 11 10  9 10 13 11 10  6 11  8 11 11  9 13 14  7  8  8 11 11
##  [601]  9  7 11  9 10  8 14 12 11  8  7  7  7  8 10  8 11  7 10 10 11 11 11 10
##  [625]  8 12  9 11  9  8 10 13 10  8  9  8 11 12  9 10  8 11 12  7  7  9  8  7
##  [649] 10  7  9  7  7 11  8 11  8  8  8  8  6 11  9 10  9  9  8  8  9  7 10  9
##  [673]  8 10  8  9 13 10  9 10  8  6  7  8 14  7  9 13  7  9 10  8  8 10 11  7
##  [697] 10  9 10 11  8  7 11  8  9  8 12  8  7  8 10  9 10  9 13  9  9  9  8  8
##  [721] 13  9 11 12  8  9  8 10 10  8 11 12  8  7 12  8  9  9 13  9  8 10  9  9
##  [745] 11 11  8 12 10  8 10  9  8 11 10 10  8 10 10  6  7  9 12 12  9  8  7 13
##  [769]  9 13 10  8  9  9 12  9  7  6  9  8  9 10  8 13 11  8  9 10 13  9  8  8
##  [793]  9  9 10 11  9  9 10  8 13  8  9  8  8  9  9  8  7  9  9  9  7  8 13  9
##  [817] 10 14 10 10  8  9  9  7 10  8 10  8 10  8 10  9  8  7 11  8  7 11  7  8
##  [841] 11  7 11  9  9 11  9  8  9  9  6  9  9  8  7  8 12 12  8  9  7 10  8  9
##  [865]  9 10  7  8  8 10 12 11  7  8  9  7  9  9 12  9  8 10  8  9  9 12  8 11
##  [889]  8  9 11  8 10  8  7 11 10  7 14  8 14 10  7 10  8 10 10  9  7  9 11  9
##  [913]  9  9  9  9 11 10 12  8  9 10  9  7  8 10 11  8  9  7 10  7  8 10 10 12
##  [937]  7  9 14  9  9 10  8  8  9 15 10 11  6 10 11  9 12  7 11  8  9  9  9 10
##  [961]  9  9  9 10 10 10 13 12  8  8  8  9  7 16 10  9 11  9 11  9 11  7 11  9
##  [985]  8  7  7 10 10  6 10 11  8  6  9  9  8  7  8 10
## 
## $alpha
## [1] 0.05
## 
## $quantile_label
## [1] "95th percentile"

hist(
  criticalN$simulated_values,
  breaks = 30,
  main = "Monte Carlo Simulation of Critical N (Endometriosis)",
  xlab = "Maximum frequency under the null model"
)

abline(v = criticalN$critical_n, col = "violet", lwd = 2, lty = 2)

Cross-Condition Taxa Overlap Analysis

endo_taxa <- taxon.long %>%
  filter(Condition %in% endometriosis_terms) %>%
  distinct(taxon) %>%
  pull(taxon)

shared_taxa_counts <- taxon.long %>%
  filter(taxon %in% endo_taxa) %>%
  group_by(taxon) %>%
  summarise(
    total_womens_health_studies = n_distinct(PMID),
    distinct_conditions_linked = n_distinct(Condition),
    top_other_conditions = paste(
      head(setdiff(unique(Condition), endometriosis_terms), 3),
      collapse = ", "
    ),
    .groups = "drop"
  ) %>%
  arrange(desc(distinct_conditions_linked))

shared_taxa_counts %>%
  slice_max(distinct_conditions_linked, n = 20, with_ties = FALSE) %>%
  knitr::kable(
    caption = "Top Endometriosis-Associated Taxa and Their Links to Other Conditions",
    col.names = c("Taxon Name", "Total WH Studies", "Total Distinct Conditions", "Other Conditions it Often Appears In")
  )

Top Endometriosis-Associated Taxa and Their Links to Other Conditions
Taxon Name	Total WH Studies	Total Distinct Conditions	Other Conditions it Often Appears In
Streptococcus	347	214	Granulomatosis with Polyangiitis, Diet, Autism
Prevotella	354	211	Illness severity status, Antimicrobial agent, Obesity
Lactobacillus	335	201	Antimicrobial agent, Oxygen, Illness severity status
Bacteroides	345	199	Autism, Autoimmune disease, Obesity
Lachnospiraceae	331	199	Autoimmune disease, Smoking behavior, Irritable bowel syndrome
Bacillota	313	190	Type II diabetes mellitus, Autism, Autoimmune disease
Faecalibacterium	278	183	Obesity, Autoimmune disease, Irritable bowel syndrome
Oscillospiraceae	273	183	Autoimmune disease, Smoking behavior, Colorectal carcinoma
Bifidobacterium	275	177	Food allergy, Irritable bowel syndrome, Autism
Clostridium	258	175	Irritable bowel syndrome, Autism, Autoimmune disease
Actinomycetota	265	171	Autism, Ethnic group,Place of residence measurement, Ulcerative colitis
Pseudomonadota	241	166	Autism, Autoimmune disease, Ethnic group,Place of residence measurement
Roseburia	228	165	Autoimmune disease, Smoking behavior, Irritable bowel syndrome
Blautia	246	164	Autoimmune disease, Irritable bowel syndrome, Colorectal cancer
Veillonella	241	161	Obesity, Antimicrobial agent, Oxygen
Bacteroidota	247	158	Autism, Autoimmune disease, Irritable bowel syndrome
Eubacteriales	219	156	Autoimmune disease, Colorectal carcinoma, Psoriasis
Enterobacteriaceae	213	155	Autoimmune disease, Irritable bowel syndrome, Sampling site
Haemophilus	208	153	Illness severity status, Diet, Autoimmune disease
Clostridia	188	140	Type II diabetes mellitus, Autoimmune disease, Colorectal carcinoma

Signature Similarity and Heatmap

allsigs <- bugsigdbr::getSignatures(endo_dat, tax.id.type = "taxname")
allsigs <- allsigs[sapply(allsigs, length) > 1]

if (length(allsigs) > 1) {
  jmat <- BugSigDBStats::calcJaccardSimilarity(allsigs)
  siglengths <- sapply(allsigs, length)

  ha <- HeatmapAnnotation(
    `Signature\nLength` = anno_barplot(siglengths, gp = gpar(fill = "#4682B4"), height = unit(1.2, "cm"))
  )
  hr <- rowAnnotation(
    `Signature\nLength` = anno_barplot(siglengths, gp = gpar(fill = "#4682B4"), width = unit(1.2, "cm"))
  )

  hm <- Heatmap(
    jmat,
    top_annotation = ha,
    left_annotation = hr,
    row_names_gp = gpar(fontsize = 7),
    column_names_gp = gpar(fontsize = 7),
    column_names_rot = 45,
    column_title = "Heatmap of Endometriosis Microbial Signatures",
    column_title_gp = gpar(fontsize = 14, fontface = "bold"),
    heatmap_legend_param = list(title = "Jaccard Similarity", direction = "horizontal", legend_width = unit(6, "cm"))
  )

  draw(hm, heatmap_legend_side = "bottom", padding = unit(c(5, 5, 5, 10), "mm"))

  png(filename = "Endometriosis_heatmap.png", width = 10, height = 9, units = "in", res = 300)
  draw(hm, heatmap_legend_side = "bottom", padding = unit(c(5, 5, 5, 10), "mm"))
  dev.off()
} else {
  cat("Fewer than 2 multi-taxon signatures available - heatmap not generated.")
}

## agg_png 
##       2

Note: with only 42 total signatures for this sub-analysis, the heatmap is small enough that row/column labels are kept visible (unlike General/Breast Cancer, which had to hide labels given hundreds of signatures) - adjust row_names_gp/column_names_gp font size further if labels still overlap once you see the actual output.

Dendrogram

if (exists("jmat") && nrow(jmat) > 1) {
  hc <- hclust(as.dist(1 - jmat))
  dend <- as.dendrogram(hc)

  par(mar = c(5, 2, 4, 10))

  plot(
    dend,
    horiz = TRUE,
    main = "Hierarchical Cluster Map of Endometriosis Microbial Signatures",
    xlab = "Distance (1 - Jaccard similarity)",
    cex.main = 1.2
  )
}

Condition-Specific Analysis with Cross-Condition Mapping (BugSigDB)

Sally Karimi Kinyua