Overview

OmicFlow is a generalised data structure for fast and efficient loading of various sparse omics data. It can handle metataxonomics/metagenomics data in text or BIOM and extends to proteomics and other omics types. It also supports non-sparse data, but it's performance peaks in sparsity.

Installation

The latest stable version can be installed from CRAN.

install.packages('OmicFlow', dependencies = TRUE)

The development version is available on GitHub.

install.packages('pak') # if not yet installed
pak::pkg_install('agusinac/OmicFlow@dev')

Usage

Initialize the metagenomics or any omics object from a file-path or pre-loaded object.

library("OmicFlow")
metadata_file <- system.file("extdata", "metadata.tsv", package = "OmicFlow")
counts_file <- system.file("extdata", "counts.tsv", package = "OmicFlow")
features_file <- system.file("extdata", "features.tsv", package = "OmicFlow")
tree_file <- system.file("extdata", "tree.newick", package = "OmicFlow")
taxa <- metagenomics$new(
    metaData = metadata_file,
    countData = counts_file,
    featureData = features_file,
    treeData = tree_file
)
taxa$feature_subset(Kingdom == "Bacteria")
taxa$scale(method = "tss")
# Access variables directly
taxa$metaData
taxa$countData
taxa$featureData
taxa$treeData
# Change variables & enjoy the automatic sync
taxa$featureData <- taxa$featureData[1:100, ]
# Inspect what functions variables are available to the class
taxa$

If you are new to OmicFlow, the best place to start is the Introduction to OmicFlow.

Docker

Example: Outputs a report.html file in current work directory

docker pull agusinac/autoflow:latest
docker run -it --rm -v \
    "$(pwd)":/data \             # Mount the data in a temporary directory
    -w /data \                   # set working directory
    -u $(id -u):$(id -g) \       # non-root user
    agusinac/autoflow:latest \
    autoflow \                   # autoflow R script
    -b /data/biom_with_taxonomy_hdf5.biom \
    -m /data/metadata.tsv

Support

If you are having issues, please create a ticket

Acknowledgements

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