An R package containing all of the curated datasets from MicrobiomeDB.org and a helper function to list which datasets are available.
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Use the R package remotes to install microbiomeData. From the R command prompt:
remotes::install_github('microbiomeDB/microbiomeData')If you encounter difficulties installing Maaslin2 try installing from GitHub directly:
remotes::install_github("biobakery/Maaslin2")If you encounter errors related to gfortran when installing SpiecEasi, particularly errors like:
ld: library 'emutls_w' not found
ld: warning: search path '/opt/gfortran/lib' not found
This typically occurs when gfortran is installed via Homebrew but R expects it in a different location. The recommended solution is to install the official gfortran from the R project:
- Download and install gfortran from: https://github.com/R-macos/gcc-14-branch/releases
- Restart R and try installing again
Alternatively, if you have gfortran installed via Homebrew (/opt/homebrew/bin/gfortran), you may need to create symlinks or set appropriate environment variables.
Starting with version 1.0.7, this package depends on mbioUtils instead of veupathUtils. If you're updating from an older version and encounter issues, you may need to:
# Remove old installation
remove.packages(c("microbiomeData", "MicrobiomeDB", "microbiomeComputations", "veupathUtils"))
# Reinstall fresh
remotes::install_github('microbiomeDB/microbiomeData')This package contains all of the curated datasets from MicrobiomeDB.org. It is an extension to the MicrobiomeDB R package which can be used to analyze and visualize these data. That package is installed and attached with this one.
You can get a list of all curated datasets available within this package by doing the following:
microbiomeData::getCuratedDatsetNames()
myData <- microbiomeData::DiabImmuneOnce you have your favorite dataset as myData, you can ask for specific collections from that dataset. A collection is any group of variables that represent a biologically coherent concept and are measured over a comparable range. Relative abundances at a specific taxonomic rank are an example. That might look something like:
getCollectionNames(myData) # will print the names of collections
myCollection <- getCollection(myData, '16S (V4) Species')