MicrobiomeBenchmarkData (original) (raw)
Contents
- 1 Introduction
- 2 Installation
- 3 Sample metadata
- 4 Accessing datasets
- 5 Annotations for each taxa are included in rowData
- 6 Cache
- 7 Session information
Introduction
The MicrobiomeBenchamrkData package provides access to a collection of datasets with biological ground truth for benchmarking differential abundance methods. The datasets are deposited on Zenodo:https://doi.org/10.5281/zenodo.6911026
Installation
## Install BioConductor if not installed
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## Release version (not yet in Bioc, so it doesn't work yet)
BiocManager::install("MicrobiomeBenchmarkData")
## Development version
BiocManager::install("waldronlab/MicrobiomeBenchmarkData") library(MicrobiomeBenchmarkData)
library(purrr)Accessing datasets
Currently, there are 6 datasets available through the MicrobiomeBenchmarkData. These datasets are accessed through the getBenchmarkData function.
Print avaialable datasets
If no arguments are provided, the list of available datasets is printed on screen and a data.frame is returned with the description of the datasets:
dats <- getBenchmarkData()
#> 1 HMP_2012_16S_gingival_V13
#> 2 HMP_2012_16S_gingival_V35
#> 3 HMP_2012_16S_gingival_V35_subset
#> 4 HMP_2012_WMS_gingival
#> 5 Stammler_2016_16S_spikein
#> 6 Ravel_2011_16S_BV
#>
#> Use vignette('datasets', package = 'MicrobiomeBenchmarkData') for a detailed description of the datasets.
#>
#> Use getBenchmarkData(dryrun = FALSE) to import all of the datasets.dats
#> Dataset Dimensions Body.site
#> 1 HMP_2012_16S_gingival_V13 33127 x 311 Gingiva
#> 2 HMP_2012_16S_gingival_V35 17949 x 311 Gingiva
#> 3 HMP_2012_16S_gingival_V35_subset 892 x 76 Gingiva
#> 4 HMP_2012_WMS_gingival 235 x 16 Gingiva
#> 5 Stammler_2016_16S_spikein 247 x 394 Stool
#> 6 Ravel_2011_16S_BV 4036 x 17 Vagina
#> Contrasts
#> 1 Subgingival vs Supragingival plaque.
#> 2 Subgingival vs Supragingival plaque.
#> 3 Subgingival vs Supragingival plaque.
#> 4 Subgingival vs Supragingival plaque.
#> 5 Pre-ASCT (allogeneic stem cell transplantation) vs 14 days after treatment.
#> 6 Healthy vs bacterial vaginosis
#> Biological.ground.truth
#> 1 Enrichment of aerobic taxa in the supragingival plaque and enrichment of anaerobic taxa in the subgingival plaque.
#> 2 Enrichment of aerobic taxa in the supragingival plaque and enrichment of anaerobic taxa in the subgingival plaque.
#> 3 Enrichment of aerobic taxa in the supragingival plaque and enrichment of anaerobic taxa in the subgingival plaque.
#> 4 Enrichment of aerobic taxa in the supragingival plaque and enrichment of anaerobic taxa in the subgingival plaque.
#> 5 Same bacterial loads of the spike-in bacteria across all samples: Salinibacter ruber (extreme halophilic), Rhizobium radiobacter (found in soils and plants), and Alicyclobacillus acidiphilu (thermo-acidophilic).
#> 6 Decrease of Lactobacillus and increase of bacteria isolated during bacterial vaginosis in samples with high Nugent scores (bacterial vaginosis).Access a single dataset
In order to import a dataset, the getBenchmarkData function must be used with the name of the dataset as the first argument (x) and the dryrun argument set to FALSE. The output is a list vector with the dataset imported as a TreeSummarizedExperiment object.
tse <- getBenchmarkData('HMP_2012_16S_gingival_V35_subset', dryrun = FALSE)[[1]]
#> Finished HMP_2012_16S_gingival_V35_subset.
tse
#> class: TreeSummarizedExperiment
#> dim: 892 76
#> metadata(0):
#> assays(1): counts
#> rownames(892): OTU_97.31247 OTU_97.44487 ... OTU_97.45365 OTU_97.45307
#> rowData names(7): kingdom phylum ... genus taxon_annotation
#> colnames(76): 700023057 700023179 ... 700114009 700114338
#> colData names(13): dataset subject_id ... sequencing_method
#> variable_region_16s
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (892 rows)
#> rowTree: 1 phylo tree(s) (892 leaves)
#> colLinks: NULL
#> colTree: NULLAccess a few datasets
Several datasets can be imported simultaneously by giving the names of the different datasets in a character vector:
list_tse <- getBenchmarkData(dats$Dataset[2:4], dryrun = FALSE)
#> Finished HMP_2012_16S_gingival_V35.
#> Finished HMP_2012_16S_gingival_V35_subset.
#> Finished HMP_2012_WMS_gingival.
str(list_tse, max.level = 1)
#> List of 3
#> $ HMP_2012_16S_gingival_V35 :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ HMP_2012_16S_gingival_V35_subset:Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ HMP_2012_WMS_gingival :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slotsAccess all of the datasets
If all of the datasets must to be imported, this can be done by providing the dryrun = FALSE argument alone.
mbd <- getBenchmarkData(dryrun = FALSE)
#> Finished HMP_2012_16S_gingival_V13.
#> Finished HMP_2012_16S_gingival_V35.
#> Finished HMP_2012_16S_gingival_V35_subset.
#> Finished HMP_2012_WMS_gingival.
#> Warning: No taxonomy_tree available for Ravel_2011_16S_BV.
#> Finished Ravel_2011_16S_BV.
#> Warning: No taxonomy_tree available for Stammler_2016_16S_spikein.
#> Finished Stammler_2016_16S_spikein.
str(mbd, max.level = 1)
#> List of 6
#> $ HMP_2012_16S_gingival_V13 :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ HMP_2012_16S_gingival_V35 :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ HMP_2012_16S_gingival_V35_subset:Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ HMP_2012_WMS_gingival :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ Ravel_2011_16S_BV :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slots
#> $ Stammler_2016_16S_spikein :Formal class 'TreeSummarizedExperiment' [package "TreeSummarizedExperiment"] with 14 slotsAnnotations for each taxa are included in rowData
The biological annotations of each taxa are provided as a column in therowData slot of the TreeSummarizedExperiment.
## In the case, the column is named as taxon_annotation
tse <- mbd$HMP_2012_16S_gingival_V35_subset
rowData(tse)
#> DataFrame with 892 rows and 7 columns
#> kingdom phylum class order
#> <character> <character> <character> <character>
#> OTU_97.31247 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.44487 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.34979 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.34572 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.42259 Bacteria Firmicutes Bacilli Lactobacillales
#> ... ... ... ... ...
#> OTU_97.44294 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.45429 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.44375 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.45365 Bacteria Firmicutes Bacilli Lactobacillales
#> OTU_97.45307 Bacteria Firmicutes Bacilli Lactobacillales
#> family genus taxon_annotation
#> <character> <character> <character>
#> OTU_97.31247 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.44487 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.34979 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.34572 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.42259 Streptococcaceae Streptococcus facultative_anaerobic
#> ... ... ... ...
#> OTU_97.44294 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.45429 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.44375 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.45365 Streptococcaceae Streptococcus facultative_anaerobic
#> OTU_97.45307 Streptococcaceae Streptococcus facultative_anaerobicCache
The datasets are cached so they’re only downloaded once. The cache and all of the files contained in it can be removed with the removeCache function.
removeCache()Session information
sessionInfo()
#> R version 4.5.0 RC (2025-04-04 r88126)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
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#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] purrr_1.0.4 MicrobiomeBenchmarkData_1.10.0
#> [3] TreeSummarizedExperiment_2.16.0 Biostrings_2.76.0
#> [5] XVector_0.48.0 SingleCellExperiment_1.30.0
#> [7] SummarizedExperiment_1.38.0 Biobase_2.68.0
#> [9] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
#> [11] IRanges_2.42.0 S4Vectors_0.46.0
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#> [15] MatrixGenerics_1.20.0 matrixStats_1.5.0
#> [17] BiocStyle_2.36.0
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#> loaded via a namespace (and not attached):
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#> [4] vctrs_0.6.5 tools_4.5.0 yulab.utils_0.2.0
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