bugphyzz
A harmonized data resource and software for enrichment analysis of microbial physiologies
27 October 2024
Abstract
The bugphyzz package simplifies the import of harmonized microbial annotations into R from diverse sources. These annotations, including extended taxa via ancestral state reconstruction, are organized into tidy data.frame objects, facilitating the creation of microbial signatures. These signatures can be used for enrichment analysis of microbiome omic data, akin to gene set enrichment analysis using tools like EnrichmentBrowser and bugsigdbr, which are also available on Bioconductor. Annotaions are imported from Zenodo.
Package
bugphyzz 0.99.9
Contents
1 Introduction
The bugphyzz package offers a convenient way to import a collection of
harmonized microbial annotations from various sources into R.
These annotations are available on Zenodo.
In addition to being harmonized, some annotations have been extended to
other taxa based on the phylogeny from
‘The All-Species Living Tree Project’
using ancestral state reconstruction (ASR) methods.
The annotations are provided in tabular format and organized into distinct
tidy data.frame objects
(for details, see the “Data schema” section below).
Once imported, these data.frame objects can be used to create microbial
signatures, which are lists of taxa with shared characteristics.
We anticipate these signatures being utilized for enrichment analysis of
microbiome omic data by implementing workflows similar to those used in
gene set enrichment analysis; for example, using the
EnrichmentBrowser
package (a detailed example is provided in a section below).
A similar package in Bioconductor is the bugsigdbr package, which imports literature-published microbial signatures from the BugSigDB database and has been used for bug set enrichment analysis (BSEA). Moreover, the writeGMT function from the bugsigdbr package can export bugphyzz signatures as GMT text files.
2 Data schema
Annotations in bugphyzz represent the link between a taxon (Bacteria/Archaea) and an attribute, as outlined in the data schema provided below.
Taxon-related
Taxonomic data in bugphyzz is standardized according to the NCBI taxonomy:
- NCBI ID: An integer representing the NCBI taxonomy ID (taxid) associated with a taxon.
- Rank: A character string indicating the taxonomy rank, including superkingdom, kingdom, phylum, class, order, family, genus, species, or strain.
- Taxon name: A character string denoting the scientific name of the taxon.
Attribute-related
Attribute data is harmonized using ontology terms. Details of attributes, ontology terms, and ontology libraries can be found in the Attribute and sources article.
- Attribute: A character string describing the name of a trait that can be observed or measured.
- Attribute type: A character string indicating the data type:
- numeric: Attributes with numeric values (e.g., growth temperature: 25°C).
- binary: Attributes with boolean values (e.g., butyrate-producing: TRUE).
- multistate-intersection: A set of related binary attributes (e.g., habitat).
- multistate-union: Attributes with three or more values represented as character strings (e.g., aerophilicity: aerobic, anaerobic, or facultatively anaerobic).
- Attribute value: The possible values that an attribute could take, represented as character strings, booleans, or numbers.
Attribute value-related
Metadata associated with attribute values:
- Attribute source: The source of the information.
- Evidence: The type of evidence supporting an annotation, including:
- EXP: Experiment
- IGC: Inferred from genomic context
- TAS: Traceable author statement
- NAS: Non-traceable author statement
- IBD: Inferred from biological aspect of descendant
- ASR: Ancestral state reconstruction
- Support values:
- Frequency and Score: Confidence that a given taxon exhibits a trait based on the curator’s knowledge or results of ASR or IBD.
- Validation: Score from the 10-fold cross-validation analysis. Matthews correlation coefficient (MCC) for discrete attributes and R-squared for numeric attributes. Default threshold value is 0.5 and above.
- NSTI: Nearest sequence taxon index from PICRUSt2 or the castor package. Relevant for numeric values only.
Attribute source-related
- Confidence in curation: A character string indicating the confidence value of a source based on three criteria: 1) presence of a source, 2) valid references, and 3) peer-reviewed curation. Valid options include high, medium, or low, corresponding to satisfaction of three, two, or one of these criteria.
Additional information
Description of sources and attributes: https://waldronlab.io/bugphyzz/articles/attributes.html
Description of ontology evidence codes: https://geneontology.org/docs/guide-go-evidence-codes/
Description of frequency keywords and scores were based on: https://grammarist.com/grammar/adverbs-of-frequency/
IBD and ASR were performed with taxPPro: https://github.com/waldronlab/taxPPro
3 Analysis and Stats
This vignette serves as an introduction to the basic functionalities of bugphyzz. For a more in-depth analysis and detailed statistics utilizing bugphyzz annotations, please visit: https://github.com/waldronlab/bugphyzzAnalyses
4 Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("bugphyzz")5 Import bugphyzz data
Load the bugphyzz package and additional packages for data manipulation:
library(bugphyzz)
library(dplyr)
library(purrr)bugphyzz data is imported using the importBugphyzz function,
resulting in a list of tidy data frames. Each data frame corresponds to an
attribute or a group of related attributes. This is particularly evident in
the case of the multistate-union type described in the data schema above,
where related attributes are grouped together in a single data frame. Available
attribute names can be inspected with the names function:
bp <- importBugphyzz()
names(bp)
#> [1] "animal pathogen"
#> [2] "antimicrobial sensitivity"
#> [3] "biofilm formation"
#> [4] "butyrate-producing bacteria"
#> [5] "extreme environment"
#> [6] "health associated"
#> [7] "host-associated"
#> [8] "hydrogen gas producing"
#> [9] "lactate producing"
#> [10] "motility"
#> [11] "plant pathogenicity"
#> [12] "sphingolipid producing"
#> [13] "spore formation"
#> [14] "aerophilicity"
#> [15] "antimicrobial resistance"
#> [16] "arrangement"
#> [17] "biosafety level"
#> [18] "cogem pathogenicity rating"
#> [19] "disease association"
#> [20] "gram stain"
#> [21] "habitat"
#> [22] "hemolysis"
#> [23] "shape"
#> [24] "spore shape"
#> [25] "coding genes"
#> [26] "genome size"
#> [27] "growth temperature"
#> [28] "length"
#> [29] "mutation rate per site per generation"
#> [30] "mutation rate per site per year"
#> [31] "optimal ph"
#> [32] "width"Let’s take a glimpse at one of the data frames:
glimpse(bp$aerophilicity, width = 50)
#> Rows: 17,966
#> Columns: 12
#> $ NCBI_ID <int> 117743, 118001, 1…
#> $ Taxon_name <chr> "Flavobacteriia",…
#> $ Rank <chr> "class", "class",…
#> $ Attribute <chr> "aerophilicity", …
#> $ Attribute_value <chr> "anaerobic", "ana…
#> $ Evidence <chr> "asr", "asr", "as…
#> $ Frequency <chr> "sometimes", "usu…
#> $ Score <dbl> 0.6911516, 0.9992…
#> $ Attribute_source <chr> NA, NA, NA, NA, N…
#> $ Confidence_in_curation <chr> NA, NA, NA, NA, N…
#> $ Attribute_type <chr> "multistate-inter…
#> $ Validation <dbl> 0.84, 0.84, 0.84,…Compare the column names with the data schema described above.
6 Create microbial signatures
bugphyzz’s primary function is to facilitate the creation of microbial
signatures, which are essentially lists of microbes sharing specific taxonomy
ranks and attribute values. Once the data frames containing attribute
information are imported, the makeSignatures function can be employed to
generate these signatures. makeSignatures offers various filtering options,
including evidence, frequency, and minimum and maximum values for numeric
attributes. For more precise filtering requirements, users can leverage
standard data manipulation functions on the relevant data frame,
such as dplyr::filter.
Examples:
- Create signatures of taxon names at the genus level for the aerophilicity attribute (discrete):
aer_sigs_g <- makeSignatures(
dat = bp[["aerophilicity"]], taxIdType = "Taxon_name", taxLevel = "genus"
)
map(aer_sigs_g, head)
#> $`bugphyz:aerophilicity|aerobic`
#> [1] "Cellvibrio" "Acidipila" "Hydrotalea" "Saprospira"
#> [5] "Nitrosarchaeum" "Halopelagius"
#>
#> $`bugphyz:aerophilicity|anaerobic`
#> [1] "Microaerobacter" "Desulfitispora" "Desulfurispira"
#> [4] "Pseudoflavonifractor" "Chromatium" "Ectothiorhodospira"
#>
#> $`bugphyz:aerophilicity|facultatively anaerobic`
#> [1] "Capnocytophaga" "Kistimonas" "Trueperella" "Telmatobacter"
#> [5] "Alishewanella" "Muricauda"- Create signatures of taxon names at the species level for the growth temperature attribute (numeric):
gt_sigs_sp <- makeSignatures(
dat = bp[["growth temperature"]], taxIdType = "Taxon_name",
taxLevel = 'species'
)
map(gt_sigs_sp, head)
#> $`bugphyzz:growth temperature|hyperthermophile| > 60 & <= 121`
#> [1] "Metallosphaera cuprina" "Pyrococcus yayanosii"
#> [3] "Methanothermobacter crinale" "Acidilobus aceticus"
#> [5] "Thermanaerovibrio velox" "Thermoanaerobacter italicus"
#>
#> $`bugphyzz:growth temperature|mesophile| > 25 & <= 45`
#> [1] "Ancylobacter aquaticus" "Leptospira alexanderi"
#> [3] "Halosaccharopolyspora lacisalsi" "Garritya polymorpha"
#> [5] "Sporobacterium olearium" "Borreliella lusitaniae"
#>
#> $`bugphyzz:growth temperature|psychrophile| > -10.01 & <= 25`
#> [1] "Cryobacterium roopkundense" "Hugenholtzia roseola"
#> [3] "Halopseudomonas formosensis" "Occallatibacter riparius"
#> [5] "Occallatibacter savannae" "Pectinatus sottacetonis"
#>
#> $`bugphyzz:growth temperature|thermophile| > 45 & <= 60`
#> [1] "Alicyclobacillus cellulosilyticus" "Defluviitoga tunisiensis"
#> [3] "Fervidobacterium thailandense" "Polycladomyces subterraneus"
#> [5] "Desulfofalx alkaliphila" "Marinitoga camini"- Create signatures with a custom threshold for the growth temperature attribute (numeric):
gt_sigs_mix <- makeSignatures(
dat = bp[["growth temperature"]], taxIdType = "Taxon_name",
taxLevel = "mixed", min = 0, max = 25
)
map(gt_sigs_mix, head)
#> $`bugphyzz:growth temperature| >=0 & <=25`
#> [1] "Cryobacterium roopkundense" "Hugenholtzia roseola"
#> [3] "Halopseudomonas formosensis" "Occallatibacter riparius"
#> [5] "Occallatibacter savannae" "Pectinatus sottacetonis"- Create signatures for the animal pathogen attribute (boolean):
ap_sigs_mix <- makeSignatures(
dat = bp[["animal pathogen"]], taxIdType = "NCBI_ID",
taxLevel = "mixed", evidence = c("exp", "igc", "nas", "tas")
)
map(ap_sigs_mix, head)
#> $`bugphyz:animal pathogen|FALSE`
#> [1] 100225 1003110 1006 1008 1008460 101192
#>
#> $`bugphyz:animal pathogen|TRUE`
#> [1] 100053 100901 1015 1017 1018 1019- Create signatures for all of the bugphyzz data frames:
sigs <- map(bp, makeSignatures) |>
list_flatten(name_spec = "{inner}")
length(sigs)
#> [1] 123head(map(sigs, head))
#> $`bugphyz:animal pathogen|FALSE`
#> [1] 100225 1003110 1006 1008 1008460 101192
#>
#> $`bugphyz:animal pathogen|TRUE`
#> [1] 100053 1004150 1004159 1004165 1004166 100469
#>
#> $`bugphyz:antimicrobial sensitivity|FALSE`
#> [1] 100225 1008 1008460 101192 101385 101534
#>
#> $`bugphyz:antimicrobial sensitivity|TRUE`
#> [1] 1003110 100379 101564 101571 1031 103232
#>
#> $`bugphyz:biofilm formation|FALSE`
#> [1] 1006 1053 105841 105972 1079800 109790
#>
#> $`bugphyz:biofilm formation|TRUE`
#> [1] 100053 1018 102684 102862 1033739 10338467 Run a bug set enrichment analysis
Bugphyzz signatures are suitable for conducting bug set enrichment analysis using existing tools available in R. In this example, we will perform a set enrichment analysis using a dataset with a known biological ground truth.
The dataset originates from the Human Microbiome Project (2012) and compares subgingival and supragingival plaque. This data will be imported using the MicrobiomeBenchmarkData package. For the implementation of the enrichment analysis, we will utilize the Gene Set Enrichment Analysis (GSEA) method available in the EnrichmentBrowser package. The expected outcome is an enrichment of aerobic taxa in the supragingival plaque (positive enrichment score) and anaerobic taxa in the subgingival plaque (negative enrichment score).
Load necessary packages:
library(EnrichmentBrowser)
library(MicrobiomeBenchmarkData)
library(mia)Import benchmark data:
dat_name <- 'HMP_2012_16S_gingival_V35'
tse <- MicrobiomeBenchmarkData::getBenchmarkData(dat_name, dryrun = FALSE)[[1]]
#> Finished HMP_2012_16S_gingival_V35.
tse_genus <- mia::splitByRanks(tse)$genus
min_n_samples <- round(ncol(tse_genus) * 0.2)
tse_subset <- tse_genus[rowSums(assay(tse_genus) >= 1) >= min_n_samples,]
tse_subset
#> class: TreeSummarizedExperiment
#> dim: 37 311
#> metadata(1): agglomerated_by_rank
#> assays(1): counts
#> rownames(37): Abiotrophia Actinobacillus ... Treponema Veillonella
#> rowData names(7): superkingdom phylum ... genus taxon_annotation
#> colnames(311): 700103497 700106940 ... 700111586 700109119
#> colData names(15): dataset subject_id ... sequencing_method
#> variable_region_16s
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (37 rows)
#> rowTree: 1 phylo tree(s) (45364 leaves)
#> colLinks: NULL
#> colTree: NULLLet’s use the edgeR method for differential abundance analysis and obtain sets of microbes. Subgingival plaque will be used as reference or “control”, so negative values will mean enrichment in the subgingival plaque and positive values will mean enrichment in the supragingival plaque.
Perform differential abundance (DA) analysis:
tse_subset$GROUP <- ifelse(
tse_subset$body_subsite == 'subgingival_plaque', 0, 1
)
se <- EnrichmentBrowser::deAna(
expr = tse_subset, de.method = 'edgeR', padj.method = 'fdr',
filter.by.expr = FALSE,
)It’s recommended to perform a normalization step of the counts before running GSEA. From the original GSEA user guide: “GSEA does not normalize RNA-seq data. RNA-seq data must be normalized for between-sample comparisons using an external normalization procedure (e.g. those in DESeq2 or Voom).”
In this example, we are treating the microbiome
data as RNA-seq (see: https://link.springer.com/article/10.1186/s13059-020-02104-1).
Let’s use the limma::voom function.
A glimpse to the assay stored in the SE:
assay(se)[1:5, 1:5] # counts
#> 700103497 700106940 700097304 700099015 700097644
#> Abiotrophia 9 22 19 0 0
#> Actinobacillus 0 2 7 0 1
#> Actinomyces 1875 1012 12 499 248
#> Aggregatibacter 1084 157 215 0 144
#> Atopobium 1 0 1 0 18From the ?limma::voom documentation, input should be “a numeric matrix
containing raw counts…”. Note that the assay in the SummarizedExperiment
will be replaced with normalized counts.
Perform normalization step:
dat <- data.frame(colData(se))
design <- stats::model.matrix(~ GROUP, data = dat)
assay(se) <- limma::voom(
counts = assay(se), design = design, plot = FALSE
)$EThe output is a “numeric matrix of normalized expression values on the
log2 scale” as described in the ?lima::voom documentation. This output
is ready for GSEA.
assay(se)[1:5, 1:5] # normalized counts
#> 700103497 700106940 700097304 700099015 700097644
#> Abiotrophia 10.038187 12.162796 12.294386 6.301074 4.935801
#> Actinobacillus 5.790260 8.992871 10.915875 6.301074 6.520764
#> Actinomyces 17.663319 17.654649 11.652840 16.265415 13.892903
#> Aggregatibacter 16.873074 14.970151 15.760528 6.301074 13.110727
#> Atopobium 7.375222 6.670943 8.593947 6.301074 10.145255Perform GSEA and display the results:
gsea <- EnrichmentBrowser::sbea(
method = 'gsea', se = se, gs = aer_sigs_g, perm = 1000,
# Alpha is the FDR threshold (calculated above) to consider a feature as
# significant.
alpha = 0.1
)
gsea_tbl <- as.data.frame(gsea$res.tbl) |>
mutate(
GENE.SET = ifelse(PVAL < 0.05, paste0(GENE.SET, ' *'), GENE.SET),
PVAL = round(PVAL, 3),
) |>
dplyr::rename(BUG.SET = GENE.SET)
knitr::kable(gsea_tbl)| BUG.SET | ES | NES | PVAL |
|---|---|---|---|
| bugphyz:aerophilicity|aerobic * | 0.974 | 1.920 | 0.000 |
| bugphyz:aerophilicity|anaerobic * | -0.861 | -1.650 | 0.015 |
| bugphyz:aerophilicity|facultatively anaerobic | 0.317 | 0.709 | 0.810 |
8 Get signatures associated with a specific microbe
To retrieve all signature names associated with a specific taxon,
users can utilize the getTaxonSignatures function.
Let’s see an example using Escherichia coli (taxid: 562).
Get all signature names associated to E. coli:
getTaxonSignatures(tax = "Escherichia coli", bp = bp)
#> character(0)Get all signature names associated to the E. coli taxid:
getTaxonSignatures(tax = "562", bp = bp)
#> [1] "bugphyz:animal pathogen|FALSE"
#> [2] "bugphyz:extreme environment|TRUE"
#> [3] "bugphyz:health associated|FALSE"
#> [4] "bugphyz:host-associated|TRUE"
#> [5] "bugphyz:motility|FALSE"
#> [6] "bugphyz:plant pathogenicity|FALSE"
#> [7] "bugphyz:spore formation|FALSE"
#> [8] "bugphyz:aerophilicity|aerobic"
#> [9] "bugphyz:aerophilicity|facultatively anaerobic"
#> [10] "bugphyz:arrangement|paired cells"
#> [11] "bugphyz:biosafety level|biosafety level 1"
#> [12] "bugphyz:cogem pathogenicity rating|cogem pathogenicity rating 2"
#> [13] "bugphyz:gram stain|gram stain negative"
#> [14] "bugphyz:habitat|digestive system"
#> [15] "bugphyz:habitat|feces"
#> [16] "bugphyz:habitat|human microbiome"
#> [17] "bugphyz:shape|bacillus"
#> [18] "bugphyzz:growth temperature|mesophile| > 25 & <= 45"
#> [19] "bugphyzz:mutation rate per site per generation|slow| > 0.78 & <= 2.92"
#> [20] "bugphyzz:mutation rate per site per year|slow| > 0.08 & <= 7.5"
#> [21] "bugphyzz:optimal ph|neutral| > 6 & <= 8"9 Session information
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> date 2024-10-27
#> pandoc 3.1.3 @ /usr/bin/ (via rmarkdown)
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