MGnifyR

An R package for searching and retrieving data from the EBI Metagenomics resource. In most cases, MGnifyR interacts directly with the JSONAPI, rather than relying on downloading analyses outputs as TSV files. Thus it is more general - allowing for example the intuitive combining of multiple studies and analyses into a single workflow, but is in some cases slower than the afformentioned direct access. Local caching of results on disk is implemented to help counter some of the overheads, but data downloads can be slow - particularly for functional annotation retrieval.

MGnifyR package is part of miaverse microbiome analysis ecosystem enabling usage of mia and other miaverse packages.

This research has received funding from the Horizon 2020 Programme of the European Union within the FindingPheno project under grant agreement No 952914. FindingPheno, an EU-funded project, is dedicated to developing computational tools and methodologies for the integration and analysis of multi-omics data. Its primary objective is to deepen our understanding of the interactions between hosts and their microbiomes. You can find more information on FindingPheno website.

Requirements

devtools # for installation
mia
plyr
dplyr
reshape2

httr
urltools

Installation

Bioc-release

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MGnifyR")

Bioc-devel

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("MGnifyR")

GitHub

remotes::install_github("EBI-Metagenomics/MGnifyR")

Basic usage

For more detailed instructions read the associated function help and vignette (vignette("MGNifyR"))

library(MGnifyR)

# Set up the MGnify client instance
mgclnt <- MgnifyClient(usecache = TRUE, cache_dir = '/tmp/MGnify_cache')

# Retrieve the list of analyses associated with a study
accession_list <- searchAnalysis(mgclnt, "studies", "MGYS00005058", usecache = TRUE)

# Download all associated study/sample and analysis metadata
meta_dataframe <- getMetadata(mgclnt, accession_list, usecache = TRUE)

# Convert analyses outputs to a single `MultiAssayExperiment` object
mae <- getResult(mgclnt, meta_dataframe$analysis_accession, usecache = TRUE)
mae