An R interface to omixer-rpm, the tool for metabolic module profiling of microbiome samples
R and Java8 (Docker users, please make sure Java8 is part of the R image)
Download the latest binary (omixeRpm_x.y.z.tar.gz) from the release page, then install it as follows after replacing x.y.z by the correct version
R CMD INSTALL omixeRpm_x.y.z.tar.gz
Download the latest source from the release page, then install it as follows after replacing x.y.z by the correct version
R CMD INSTALL omixer-rpmR-x.y.z.tar.gz
Start the docker container
docker run --rm -ti -p 3838:3838 -v $PWD:/workspace omixer/shinyrpm:0.1then open the browser at http://localhost:3838/sample-apps/rpm/
Download the example matrix.tsv (raw link) form the test directory.
library(omixerRpm)
# read a functional profile matrix into R or create it inside R. Please note that row.names should not be used while reading the matrix.
dat <- read.table("matrix.tsv", header=T, sep="\t")
# Run the module mapping on the loaded table.
mods <- rpm(dat, minimum.coverage=0.3, annotation = 1)
# alternatively run the mapping without loading the table into R.
mods <- rpm("matrix.tsv", minimum.coverage=0.3, annotation = 1)
# Load the default mapping database
db <- loadDefaultDB()
# get the name of the first predicted module
getNames(db, mods@annotation[1,])
# get the abundance|coverage as a data.frame with module id and description
coverage <- asDataFrame(mods, "coverage")
- load one of the bundled databases. Type listDB() to check the list of available databases
db <- loadDB("GBMs.v1.0")
- load an external database. Please refer to this module.list and module.names for examples
db <- ModuleDB(directory = "/path/to/moduledb/", modules = "module.list", module.names.file="module.names")
- Gut Brain Modules, Valles-Colomer et al. 2019, The neuroactive potential of the human gut microbiota in quality of life and depression, Nature Microbiology 2019.
- Gut Metabolic Modules, Vieira-Silva et al. 2016, Species-function relationships shape ecological properties of the human gut microbiome, Nature Microbiology 2016.
omixer-rpmR was developed as part of GOmixer. If you use omixer-rpmR in your work please cite:
Youssef Darzi, Gwen Falony, Sara Silva, Jeroen Raes. Towards biome-specific analysis of meta-omics data, The ISME journal, 2015.
GNU General Public License v3.0. The bundled omixer-rpm.jar is licensed under an Academic Non-commercial Software License Agreement