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NetCoupler

The goal of NetCoupler is to estimate potential causal links between a set of -omic (e.g. metabolomics, lipidomics) or other high-dimensional metabolic data as a conditional dependency network and either a disease outcome, an exposure, or both. These potential causal links are classified as direct, ambigious, or no effects. This algorithm is largely meant to be used with -omic style data to generate the networks and while theoretically non-omic data could be used, we have not tested it in that context. Given the algorithms nature, it’s primarily designed to be used for exploration of potential mechanisms and used to complement other analyses for a research question. It could also be used to confirm a pre-specified and explicit hypothesis, similar to how structural equation models are used. However, this might be a more niche use.

Why or when might you want to use NetCoupler?

  1. You are interested in asking a research question on how some factor might influence another factor and how it might mediate through a metabolic network.
  2. If you want to explore how a factor might influence a metabolic network or how a metabolic network might influence a factor.
  3. You have an -omic dataset and want another method to explore how it relates to your variable of interest.

Basically, if you’re research question or objective has the general form of:

… So that you can ultimately have an answer that looks like:

There are a few vignettes available in this package:

  • Get Started (vignette("NetCoupler")) describes a simple overview of how and when to use NetCoupler, as well as a basic explanation of some of the components of NetCoupler.
  • Examples with different models (vignette("examples")) lists different models we’ve tested that work with NetCoupler. If you have tried a model out that isn’t listed and seen success, let us know by opening an Issue or submitting a Pull Request (see the contributing guidelines for instructions on doing this).

Installation

To install the official CRAN version, use:

install.packages("NetCoupler")

To install the development version, use:

# install.packages("remotes")
remotes::install_github("NetCoupler/NetCoupler")

Contributing and Code of Conduct

Checkout the guidelines for details on contributing. Please note that the ‘NetCoupler’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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Install

install.packages('NetCoupler')

Monthly Downloads

509

Version

0.1.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Luke Johnston

Last Published

April 8th, 2022

Functions in NetCoupler (0.1.0)

simulated_data

Simulated dataset with an underlying Directed Graph structure for the metabolites.
nc_estimate_links

Compute model estimates between an external (exposure or outcome) variable and a network.
nc_standardize

Standardize the metabolic variables.
%>%

Pipe operator
nc_estimate_network

Create an estimate of the metabolic network as an undirected graph.
classify_options

Classification options for direct, ambigious, and no effect.
as_edge_tbl

Convert network graphs to edge tables as tibbles/data.frames.
pc_estimate_undirected_graph

Estimate the undirected graph of the metabolic data.
reexports

Objects exported from other packages
NetCoupler-package

NetCoupler: Inference of Causal Links Between a Network and an External Variable