Package: MicrobiomeBenchmarkData 1.15.1

Samuel Gamboa

MicrobiomeBenchmarkData: Datasets for benchmarking in microbiome research

The MicrobiomeBenchmarkData package provides functionality to access microbiome datasets suitable for benchmarking. These datasets have some biological truth, which allows to have expected results for comparison. The datasets come from various published sources and are provided as TreeSummarizedExperiment objects. Currently, only datasets suitable for benchmarking differential abundance methods are available.

Authors:Samuel Gamboa [aut, cre], Levi Waldron [aut], Marcel Ramos [ctb], NCI [fnd]

MicrobiomeBenchmarkData_1.15.1.tar.gz
MicrobiomeBenchmarkData_1.15.1.zip(r-4.7)MicrobiomeBenchmarkData_1.15.1.zip(r-4.6)MicrobiomeBenchmarkData_1.15.1.zip(r-4.5)
MicrobiomeBenchmarkData_1.15.1.tgz(r-4.6-any)MicrobiomeBenchmarkData_1.15.1.tgz(r-4.5-any)
MicrobiomeBenchmarkData_1.15.1.tar.gz(r-4.7-any)MicrobiomeBenchmarkData_1.15.1.tar.gz(r-4.6-any)
MicrobiomeBenchmarkData_1.15.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MicrobiomeBenchmarkData/json (API)

# Install 'MicrobiomeBenchmarkData' in R:
install.packages('MicrobiomeBenchmarkData', repos = c('https://waldronlab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/waldronlab/microbiomebenchmarkdata/issues

Pkgdown/docs site:https://waldronlab.io

Datasets:

On CRAN:

Conda:

experimentdatamicrobiomedatareproducibleresearchsequencingdatabioconductor-packager01ca230551

4.23 score 19 scripts 3 exports 75 dependencies

Last updated from:ed900aa65c. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE346
source / vignettesOK349
linux-release-x86_64NOTE329
macos-release-arm64NOTE207
macos-oldrel-arm64NOTE201
windows-develNOTE285
windows-releaseNOTE256
windows-oldrelNOTE280
wasm-releaseOK164

Exports:getBenchmarkDataremoveCachescml

Dependencies:abindapeaskpassBHBiobaseBiocFileCacheBiocGenericsBiocParallelBiostringsbitbit64blobcachemclicodetoolscpp11crayoncurlDBIdbplyrDelayedArraydigestdplyrfastmapfilelockformatRfsfutile.loggerfutile.optionsgenericsGenomicRangesgluehttr2IRangesjsonlitelambda.rlatticelazyevallifecyclemagrittrMatrixMatrixGenericsmatrixStatsmemoisenlmeopensslpillarpkgconfigpurrrR6rappdirsRcpprlangRSQLiteS4ArraysS4VectorsSeqinfoSingleCellExperimentsnowSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselecttidytreetreeioTreeSummarizedExperimentutf8vctrswithrXVectoryulab.utils

Recalibration of the Stammler_2016_16S_spikein dataset
Import data | Ids of the spike-in bacteria | Recalibrate based on Salinibacter ruber abundance. | A more convenient way using the scml function included in the package: | Session information
Rendered fromrecalibrare_spikein_data.Rmdusingknitr::rmarkdown

Last update: 2024-09-02
Started: 2022-07-06

Datasets in MicrobiomeBenchmarkData
Rendered fromdatasets.Rmdusingknitr::rmarkdown

Last update: 2022-10-26
Started: 2021-09-24

MicrobiomeBenchmarkData
Introduction | Installation | Sample metadata | Accessing datasets | Print avaialable datasets | Access a single dataset | Access a few datasets | Access all of the datasets | Annotations for each taxa are included in rowData | Cache | Session information
Rendered fromMicrobiomeBenchmarkData.Rmdusingknitr::rmarkdown

Last update: 2022-10-26
Started: 2021-09-07

Readme and manuals

Help Manual

Help pageTopics
Get datasetgetBenchmarkData
MicrobiomeBenchmarkDataMicrobiomeBenchmarkData-package MicrobiomeBenchmarkData
Remove cacheremoveCache
sampleMetadatasampleMetadata
SCML: spike-in-based calibration to total microbial loadscml