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TaxSEA is designed for microbiome researchers seeking deeper functional insights into their datasets.A common question in human microbiome research is whether a detected pattern of microbial changes has been previously reported in other diseases or contexts. However, current methods to answer this question are highly limited, often requiring researchers to manually search literature, cross-reference multiple databases, or repurpose tools not originally designed for microbiome data. TaxSEA directly addresses this gap by integrating multiple public microbiome databases, allowing researchers to systematically test for enrichment of known disease signatures, metabolite producers, or other biologically relevant taxon sets. TaxSEA is available as R package.
TaxSEA takes as input a vector of genus or species names and a rank. For example log2 fold changes or Spearman's rho. TaxSEA then uses a Kolmogorov-Smirnov test to identify if a particular group of species or genera (i.e. a set of taxa such as butyrate producers) are skewed to one end of the distribution .
Simply put, TaxSEA allows users to rapidly convert species/genus level changes to alterations in
- Metabolite producers
- Disease signatures
- Previously published associations
Note: Although TaxSEA in principle can be applied to microbiome data from any source, the databases utilized largely cover human associated microbiomes and the human gut microbiome in particular. As such TaxSEA will likely perform best on human gut microbiome data.
By default TaxSEA utilizes taxon sets generated from five reference databases (gutMGene, GMrepo v2, MiMeDB, mBodyMap, BugSigDB). See below for examples of using custom databases or taxonomically defined taxon sets.
Please cite the appropriate database if using:
- Cheng et al. gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites Nucleic Acids Res. 2022.
- Dai et al. GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison Nucleic Acids Res. 2022.
- Wishart et. al. MiMeDB: the Human Microbial Metabolome Database Nucleic Acids Res. 2023.
- Jin et al. mBodyMap: a curated database for microbes across human body and their associations with health and diseases. Nucleic Acids Res. 2022.
- Geistlinger et al. BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures. Nature Biotech. 2023.
library(devtools)
install_github("feargalr/TaxSEA")
library(TaxSEA)
library(TaxSEA)
# Retrieve taxon sets containing Bifidobacterium longum.
blong.sets <- get_taxon_sets(taxon="Bifidobacterium_longum")
# Run TaxSEA with test data provided
data(TaxSEA_test_data)
taxsea_results <- TaxSEA(taxon_ranks=TaxSEA_test_data)
#Enrichments among metabolite producers from gutMgene and MiMeDB
metabolites.df <- taxsea_results$Metabolite_producers
#Enrichments among health and disease signatures from GMRepoV2 and mBodyMap
disease.df <- taxsea_results$Health_associations
#Enrichments amongh published associations from BugSigDB
bsdb.df <- taxsea_results$BugSigdB
All that is required for TaxSEA is a vector in R containing ranks (e.g. log2 fold changes) and names (E.g. species/genus). TaxSEA will not work for ranks higher than species or genus. The input should be for all taxa tested, and not limited to only a pre-defined set (e.g. do not use a threshold for significance or remove any taxa). See example below for format. TaxSEA will lookup and convert taxon names to NCBI taxonomic identifiers. TaxSEA stores a commonly observed identifiers internally and so will only look up whatever is not covered to save time.
Input IDs should be in the format of like one of the following
- Species name. E.g. "Bifidobacterium longum", "Bifidobacterium_longum"
- Genus name. E.g. "Bifidobacterium"
- NCBI ID E.g. 216816
#Input IDs with the full taxonomic lineage should be split up. E.g.
x <- "d__Bacteria.p__Actinobacteriota.c__Actinomycetes.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium"
x <- strsplit(x,split="\\.")[[1]][6]
x <- gsub("g__","",x)
## Running this through a vector of IDs may look something like the following
#new_ids <- sapply(as.character(old_ids),function(y) {strsplit(x = y,split="\\.")[[1]][6]})
#new_ids <- gsub("g__","",new_ids)
## Example test data
library(TaxSEA)
data("TaxSEA_test_data")
head(sample(TaxSEA_test_data),4)
data("TaxSEA_test_data")
taxsea_results <- TaxSEA(taxon_ranks=TaxSEA_test_data)
#Enrichments among metabolite producers from gutMgene and MiMeDB
metabolites.df <- taxsea_results$Metabolite_producers
#Enrichments among health and disease signatures from GMRepoV2 and mBodyMap
disease.df <- taxsea_results$Health_associations
#Enrichments among published associations from BugSigDB
bsdb.df <- taxsea_results$BugSigDB
The test data provided with TaxSEA consists of log2 fold changes comparing between healthy and IBD. The count data for this was downloaded from curatedMetagenomeData and fold changes generated with LinDA.
- Hall et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients** Genome Med. 2017 Nov 28;9(1):103.
- Pasolli et al. Accessible, curated metagenomic data through ExperimentHub. Nat Methods. 2017 Oct 31;14(11):1023-1024. doi: 10.1038/nmeth.4468.
- Zhou et al. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol. 2022 Apr 14;23(1):95.
> head(sample(TaxSEA_test_data),4)
Bacteroides_thetaiotaomicron Blautia_sp_CAG_257 Ruminococcus bromii Clostridium_disporicum
1.908 3.650 -5.038 0.300
The output is a list of three data frames providing enrichment results for metabolite produers, health/disease associations, and published signatures from BugSigDB. Each dataframe has 5 columns
- taxonSetName - The name of the taxon set tested
- median_rank - This is simply the median rank across all detected members in the set. This allows you to see the direction of change
- P value - Kolmogorov-Smirnov test P value.
- FDR - P value adjusted for multiple testing.
- TaxonSet - Returns list of taxa in the set to show what is driving the signal
Many users may want to utilise TaxSEA with a custom database. For example for testing if there is a flag in the TaxSEA function "custom_db" which expects as input a named list of vectors. This is the same format as the default TaxSEA database. Note: using the custom_db flag disables the automatic ID conversion and NCBI API lookup. However we have functionality available via other functions
# Perform enrichment analysis using TaxSEA
custom_taxsea_results <- TaxSEA(taxon_ranks = log2_fold_changes, custom_db = custom_taxon_sets)
custom_taxsea_results <- custom_taxsea_results$custom_sets
In addition to taxon sets defined by function or phenotype, users can define sets based on taxonomy. Current methods to test at higher taxonomic levels (e.g., genus or family), involve aggregating counts but with this approach opposing shifts in individual species may cancel each other out, obscuring meaningful biological patterns. For instance, antibiotic treatment may suppress certain species while allowing resistant species within the same genus to expand and occupy the vacant niche, creating an ecological shift that appears as no net change at broader taxonomic levels. Here we utilise data from Chng et al. demonstrating this in a comparsion between Atopic dermatitis and controls.
#### Applying TaxSEA functionality to taxonomic ranks
# This script applies TaxSEA to identify taxonomic enrichment at different taxonomic levels.
# Specifically, we analyze enrichment at the family level using metagenomic data
# Load required libraries
library(TaxSEA)
library(curatedMetagenomicData)
library(tidyverse)
library(phyloseq)
library(MicrobiomeStat)
library(dplyr)
# Load sample metadata
metadata_all <- sampleMetadata
# Filter metadata for the specific study (ChngKR_2016)
metadata <- metadata_all %>%
filter(study_name == "ChngKR_2016") %>%
column_to_rownames('sample_id')
# Extract count data using curatedMetagenomicData
cmd_data <- curatedMetagenomicData(
pattern = "ChngKR_2016.relative_abundance",
counts = TRUE,
dryrun = FALSE
)
# Convert the extracted data to a count matrix
counts_data <- assay(cmd_data[[1]])
counts_data <- counts_data[, rownames(metadata)] # Subset to relevant samples
# Filter taxa with at least one sample having counts > 100
counts_data <- counts_data[apply(counts_data > 100, 1, sum) > 0, ]
# Extract species names from taxonomic strings
species_names <- gsub("s__", "", sapply(rownames(counts_data), function(y) strsplit(y, "\\|")[[1]][7]))
rownames(counts_data) <- species_names
# Create a taxonomic lineage dataframe
# Remove taxonomic prefixes (k__, p__, c__, etc.) and separate into taxonomic ranks
# make data frame of taxon lineages
taxon_lineages <- data.frame(Name = species_names,
Lineage = names(species_names)) %>%
mutate(Lineage = str_remove_all(Lineage, '[kpcofgs]__')) %>%
separate(col = Lineage, into = c('kingdom', 'phylum', 'class',
'order', 'family', 'genus', 'species'),
sep = '\\|') %>%
mutate(name = Name) %>%
remove_rownames() %>%
column_to_rownames('name')
# Perform differential abundance testing using LinDA
metadata$study_condition <- factor(metadata$study_condition, levels = c("control", "AD"))
linda_results <- linda(
feature.dat = counts_data,
meta.dat = metadata,
formula = '~study_condition',
feature.dat.type = 'count',
prev.filter = 0.05
)
# Extract log2 fold change values for differential taxa
linda_results <- linda_results$output$study_conditionAD
log2_fold_changes <- linda_results$log2FoldChange
names(log2_fold_changes) <- rownames(linda_results)
# Define the taxonomic rank for enrichment analysis
selected_taxon_level <- 'genus' # Modify as needed (e.g., genus, phylum)
# Create a named list of species grouped by taxonomic rank
custom_taxon_sets <- taxon_lineages %>%
group_by(.data[[selected_taxon_level]]) %>%
summarise(species = list(species), .groups = "drop") %>%
deframe()
# Perform enrichment analysis using TaxSEA
custom_taxsea_results <- TaxSEA(taxon_ranks = log2_fold_changes, custom_db = custom_taxon_sets)
custom_taxsea_results <- custom_taxsea_results$custom_sets
The results above were generated by using TaxSEA on the output of a differential abundance analysis comparing between disease and control. The input was per spcies log2 fold changes between taxa in Inflammatory Bowel disease and control. TaxSEA identified a significant depletion in the producers of certain short chain fatty acids. Using barplots we can show the overall signatures identified as significantly different. We can then highlight the individual species contributing to this signature on a volcano plot.
The format of BugSigDB is that each publication is entered as a "Study", and within this there is different experiments and signatures. For example signature 1 may be taxa increased in an experiment, and signature 2 taxa that are decreased. Users can find out more by querying the BugSigDB. See below for an example.
library(bugsigdbr) #This package is installable via Bioconductor
bsdb <- importBugSigDB() #Import database
#E.g. if the BugSigDB identifier you found enriched was #bsdb:74/1/2_obesity:obese_vs_non-obese_DOWN
#This is Study 74, Experiment 1, Signature 2
bsdb[bsdb$Study=="Study 74" &
bsdb$Experiment=="Experiment 1" &
bsdb$Signature=="Signature 2",]
The TaxSEA function by default uses the Kolmogorov Smirnov test and the original idea was inspired by gene set enrichment analyses from RNASeq. Should users wish to use an alternative gene set enrichment analysis tool the database is formatted in such a way that should be possible. See below for an example with fast gene set enrichment analysis (fgsea).
library(fgsea) #This package is installable via Bioconductor
data(TaxSEA_DB)
#Convert input names to NCBI taxon ids
names(TaxSEA_test_data) = get_ncbi_taxon_ids(names(TaxSEA_test_data))
TaxSEA_test_data = TaxSEA_test_data[!is.na(names(TaxSEA_test_data))]
#Run fgsea
fgsea_results <- fgsea(TaxSEA_db, TaxSEA_test_data, minSize=5, maxSize=500)