Contents

1 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

2 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)

3 Sample metadata

All sample metadata is merged into a single data frame and provided as a data object:

data('sampleMetadata', package = 'MicrobiomeBenchmarkData')
## Get columns present in all samples
sample_metadata <- sampleMetadata |> 
    discard(~any(is.na(.x))) |> 
    head()
knitr::kable(sample_metadata)
dataset sample_id body_site library_size pmid study_condition sequencing_method
HMP_2012_16S_gingival_V13 700103497 oral_cavity 5356 22699609 control 16S
HMP_2012_16S_gingival_V13 700106940 oral_cavity 4489 22699609 control 16S
HMP_2012_16S_gingival_V13 700097304 oral_cavity 3043 22699609 control 16S
HMP_2012_16S_gingival_V13 700099015 oral_cavity 2832 22699609 control 16S
HMP_2012_16S_gingival_V13 700097644 oral_cavity 2815 22699609 control 16S
HMP_2012_16S_gingival_V13 700097247 oral_cavity 6333 22699609 control 16S

4 Accessing datasets

Currently, there are 6 datasets available through the MicrobiomeBenchmarkData. These datasets are accessed through the getBenchmarkData function.

4.2 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: NULL

4.3 Access 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 slots

4.4 Access 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 slots

5 Annotations for each taxa are included in rowData

The biological annotations of each taxa are provided as a column in the rowData 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_anaerobic

6 Cache

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()

7 Session information

sessionInfo()
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#> 
#> 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            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> 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_1.0.2                     MicrobiomeBenchmarkData_1.6.0  
#>  [3] TreeSummarizedExperiment_2.12.0 Biostrings_2.72.0              
#>  [5] XVector_0.44.0                  SingleCellExperiment_1.26.0    
#>  [7] SummarizedExperiment_1.34.0     Biobase_2.64.0                 
#>  [9] GenomicRanges_1.56.0            GenomeInfoDb_1.40.0            
#> [11] IRanges_2.38.0                  S4Vectors_0.42.0               
#> [13] BiocGenerics_0.50.0             MatrixGenerics_1.16.0          
#> [15] matrixStats_1.3.0               BiocStyle_2.32.0               
#> 
#> loaded via a namespace (and not attached):
#>  [1] xfun_0.43               bslib_0.7.0             lattice_0.22-6         
#>  [4] yulab.utils_0.1.4       vctrs_0.6.5             tools_4.4.0            
#>  [7] generics_0.1.3          curl_5.2.1              parallel_4.4.0         
#> [10] RSQLite_2.3.6           tibble_3.2.1            fansi_1.0.6            
#> [13] blob_1.2.4              pkgconfig_2.0.3         Matrix_1.7-0           
#> [16] dbplyr_2.5.0            lifecycle_1.0.4         GenomeInfoDbData_1.2.12
#> [19] compiler_4.4.0          treeio_1.28.0           codetools_0.2-20       
#> [22] htmltools_0.5.8.1       sass_0.4.9              lazyeval_0.2.2         
#> [25] yaml_2.3.8              tidyr_1.3.1             pillar_1.9.0           
#> [28] crayon_1.5.2            jquerylib_0.1.4         BiocParallel_1.38.0    
#> [31] DelayedArray_0.30.0     cachem_1.0.8            abind_1.4-5            
#> [34] nlme_3.1-164            tidyselect_1.2.1        digest_0.6.35          
#> [37] dplyr_1.1.4             bookdown_0.39           fastmap_1.1.1          
#> [40] grid_4.4.0              cli_3.6.2               SparseArray_1.4.0      
#> [43] magrittr_2.0.3          S4Arrays_1.4.0          utf8_1.2.4             
#> [46] ape_5.8                 withr_3.0.0             filelock_1.0.3         
#> [49] UCSC.utils_1.0.0        bit64_4.0.5             rmarkdown_2.26         
#> [52] httr_1.4.7              bit_4.0.5               memoise_2.0.1          
#> [55] evaluate_0.23           knitr_1.46              BiocFileCache_2.12.0   
#> [58] rlang_1.1.3             Rcpp_1.0.12             DBI_1.2.2              
#> [61] glue_1.7.0              tidytree_0.4.6          BiocManager_1.30.22    
#> [64] jsonlite_1.8.8          R6_2.5.1                fs_1.6.4               
#> [67] zlibbioc_1.50.0