Exploratory Analysis of MASLD Microbial Signatures

Introduction

This vignette demonstrates an exploratory analysis of microbial signatures associated with metabolic dysfunction-associated steatotic liver disease (MASLD) using data obtained from BugSigDB and functions provided by the bugSigSimple package.

Analysis objectives

Downloading the data from BugSigDB
Filtering of the data to select relevant signatures and remove similar/duplicate signatures
Description of the studies
Description of the taxa
Basic statistical tests
Monte Carlo simulation to calculate critical N
Heatmaps and dendrograms

Making sure packages are installed

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("devtools", "tidyverse", "kableExtra"))
BiocManager::install(c("waldronlab/bugSigSimple", "waldronlab/BugSigDBStats", "waldronlab/bugsigdbr"))

Download BugSigDB data

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

dim(full.dat)

## [1] 7425   51

The BugSigDB database was downloaded using the importBugSigDB() function from the bugsigdbr package. The result contains all curated microbial signatures together with study metadata such as disease, body site, sequencing platform, and abundance direction.

Remove empty signatures

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

dat <- full.dat[ind1 & ind2, ]

dim(dat)

## [1] 7425   51

Signatures without annotated taxa were removed to ensure that only complete microbial signatures were included in downstream analyses.

pattern <- paste(
  "Metabolic dysfunction-associated steatotic liver disease",
  "Metabolic dysfunction-associated liver disease",
  "Metabolic dysfunction-associated steatohepatitis",
  "Non-alcoholic steatohepatitis",
  "Nonalcoholic steatohepatitis",
  "Non alcoholic steatohepatitis",
  "Non-alcoholic fatty liver disease",
  "Nonalcoholic fatty liver disease",
  "Non alcoholic fatty liver disease",
  "MASLD",
  "MAFLD",
  "MASH",
  "NASH",
  "NAFLD",
  sep = "|"
)

masld.dat <- dat %>%
  filter(
    grepl(pattern, Condition, ignore.case = TRUE) |
      grepl(pattern, `Group 0 name`, ignore.case = TRUE) |
      grepl(pattern, `Group 1 name`, ignore.case = TRUE)
  ) %>%
  filter(
    grepl(pattern, Condition, ignore.case = TRUE)
  )

nrow(masld.dat)

## [1] 43

masld.dat %>%
  count(Condition, sort = TRUE)

##                           Condition  n
## 1 Non-alcoholic fatty liver disease 37
## 2     Non-alcoholic steatohepatitis  6

To capture the complete body of evidence related to metabolic dysfunction-associated steatotic liver disease (MASLD), signatures were identified using both the current disease terminology (MASLD) and historical terms (MAFLD, NAFLD, and NASH). Disease-related terms were searched across the Condition, Group 0 name, and Group 1 name fields to recover studies curated under different naming conventions. Finally, only records whose primary condition corresponded to a MASLD-related disease were retained, excluding studies focused primarily on clinical biomarkers or laboratory measurements.

Remove similar signatures

masld.filtered <- masld.dat %>%
  distinct()

nrow(masld.filtered)

## [1] 43

Exact duplicate records were removed using distinct() to reduce redundancy before downstream analyses. This step retains unique MASLD-associated microbial signatures while preserving the overall study representation.

Study characteristics

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

knitr::kable(study.summary)

Body site	Sequencing type	n
Feces	16S	35
Feces	PCR	2
Feces	WMS	3
Saliva	WMS	1
Supragingival dental plaque	WMS	2

The selected MASLD signatures span multiple studies and sequencing approaches. Summarizing study characteristics provides context for interpreting downstream analyses and highlights the diversity of experimental designs represented in BugSigDB.

Taxonomic summary

taxa.table <- createTaxonTable(masld.filtered)

head(taxa.table)

## # A tibble: 6 × 6
##   `Taxon Name`       `Taxonomic Level` total_signatures increased_signatures
##   <chr>              <chr>                        <int>                <int>
## 1 Porphyromonadaceae family                           9                    3
## 2 Bacteroides        genus                            7                    2
## 3 Dorea              genus                            6                    4
## 4 Roseburia          genus                            6                    3
## 5 Prevotella         genus                            6                    2
## 6 Alistipes          genus                            6                    2
## # ℹ 2 more variables: decreased_signatures <int>, `Binomial Test pval` <dbl>

knitr::kable(head(taxa.table,10))

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval
Porphyromonadaceae	family	9	3	6	0.510
Bacteroides	genus	7	2	5	0.450
Dorea	genus	6	4	2	0.690
Roseburia	genus	6	3	3	1.000
Prevotella	genus	6	2	4	0.690
Alistipes	genus	6	2	4	0.690
Lactobacillus	genus	5	4	1	0.370
Streptococcaceae	family	5	2	3	1.000
Lachnoclostridium	genus	5	5	0	0.062
Prevotellaceae	family	5	1	4	0.370

The taxonomic summary identifies microorganisms that recur across MASLD-associated signatures. Frequently reported taxa may represent microbial features that are consistently observed across independent MASLD studies.

Monte Carlo critical N

siglengths <- lengths(masld.filtered$`MetaPhlAn taxon names`)

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

criticalN

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

The Monte Carlo simulation estimates the critical number of independent studies required before repeated observation of a taxon is unlikely to occur by chance.

Plot critical N

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

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

The simulated distribution illustrates the study count threshold required for statistical significance under random sampling.

Signature similarity

allsigs <- bugsigdbr::getSignatures(
  masld.filtered,
  tax.id.type = "taxname"
)

# Keep signatures containing at least two taxa
allsigs <- allsigs[sapply(allsigs, length) > 1]

length(allsigs)

## [1] 39

# Pairwise overlaps
mydists <- BugSigDBStats::calcPairwiseOverlaps(allsigs)

dim(mydists)

## [1] 144   8

# Jaccard similarity matrix
jmat <- BugSigDBStats::calcJaccardSimilarity(allsigs)

# Keep BugSigDB study/experiment identifiers
row_labels <- sub("^bsdb:", "PMID", sub("_.*$", "", rownames(jmat)))
col_labels <- sub("^bsdb:", "PMID", sub("_.*$", "", colnames(jmat)))

Microbial signatures were converted into a pairwise Jaccard similarity matrix using functions provided by BugSigDBStats. This matrix was subsequently used for both the heatmap and hierarchical clustering analyses.

Heatmap

library(grid)
library(ComplexHeatmap)

# Calculate signature lengths
siglengths <- sapply(allsigs, length)

# Short labels for display
short.labels <- paste0("S", seq_len(nrow(jmat)))

# Top annotation
ha <- HeatmapAnnotation(
  `Signature\nLength` = anno_barplot(
    siglengths,
    gp = gpar(fill = 4:(length(siglengths) + 3)),
    annotation_name_gp = gpar(
      fontsize = 10,
      lineheight = 0.8
    )
  )
)

# Left annotation
hr <- rowAnnotation(
  `Signature\nLength` = anno_barplot(
    siglengths,
    gp = gpar(fill = 4:(length(siglengths) + 3)),
    annotation_name_gp = gpar(
      fontsize = 10,
      lineheight = 0.8
    )
  )
)

# Create heatmap
hm <- Heatmap(
  jmat,

  top_annotation = ha,
  left_annotation = hr,

  row_names_max_width = unit(10, "cm"),
  column_names_max_height = unit(3, "cm"),

  row_labels = row_labels,
  column_labels = col_labels,

  row_names_gp = gpar(fontsize = 8),
  column_names_gp = gpar(fontsize = 8),

  column_title = "Heatmap of Similarity of MASLD Microbial Signatures",
  column_title_gp = gpar(fontsize = 15),

  heatmap_legend_param = list(
    title = "Jaccard Similarity",
    direction = "horizontal"
  )
)

# Draw heatmap
draw(
  hm,
  heatmap_legend_side = "bottom",
  annotation_legend_side = "right"
)

# Save the heatmap as a high-resolution PNG
png(
  filename = file.path(tempdir(), "MASLD_heatmap.png"),
  width = 14,
  height = 10,
  units = "in",
  res = 300
)

draw(
  hm,
  heatmap_legend_side = "bottom",
  annotation_legend_side = "right"
)

dev.off()

## agg_png 
##       2

The heatmap visualizes pairwise Jaccard similarities among MASLD-associated microbial signatures, highlighting groups of studies with similar taxonomic compositions.

Dendrogram

# Convert similarity to distance
hc <- hclust(as.dist(1 - jmat))

plot(
  hc,
  labels = row_labels,
  main = "Hierarchical Cluster Map of MASLD Microbial Signatures",
  xlab = "",
  sub = "",
  cex = 0.6
)

Hierarchical clustering groups microbial signatures according to taxonomic similarity, revealing patterns of shared microbial composition across MASLD studies.

Discussion

This vignette demonstrates a complete exploratory analysis of MASLD-associated microbial signatures curated in BugSigDB using the existing functionality provided by bugSigSimple. After selecting disease-relevant signatures and reducing redundancy, study characteristics and taxonomic patterns were summarized, Monte Carlo simulation estimated the critical number of supporting studies, and clustering methods visualized relationships among microbial signatures. Together, these analyses illustrate a reproducible workflow for exploring disease-associated microbial signatures while reusing established package functions rather than implementing new analytical methods.

Peace Daniel

09 November, 2026

Introduction

Analysis objectives

Making sure packages are installed

Download BugSigDB data

Remove empty signatures

Remove similar signatures

Study characteristics

Taxonomic summary

Monte Carlo critical N

Plot critical N

Signature similarity

Heatmap

Dendrogram

Discussion

Exploratory Analysis of MASLD Microbial Signatures

Peace Daniel

09 November, 2026

Introduction

Analysis objectives

Making sure packages are installed

Download BugSigDB data

Remove empty signatures

Select MASLD-related signatures

Remove similar signatures

Study characteristics

Taxonomic summary

Monte Carlo critical N

Plot critical N

Signature similarity

Heatmap

Dendrogram

Discussion