Barnacle is a Python library that implements a sparse tensor decomposition model. It was initially developed for with metatranscriptomic data in mind, but it could feasibly be applied to any multi-way dataset. To learn more about sparse tensor decomposition and its applications, please see the documentation website.
You can install Barnacle and its dependencies by running
pip install barnacle
Using Barnacle usually requires interacting with additional libraries. We recommend using virtual environments to manage this library ecosystem. In particular, we used Poetry environments while developing Barnacle. You can replicate the Barnacle development using the pyproject.toml file published in this repository. If you have Poetry downloaded, running
poetry install
in the same directory as the pyproject.toml file should set up your environment and install dependencies. For more detailed information, refer to the Poetry documentation for installing dependencies.
Details on Barnacle usage can be found on the associated documentation website. The documentation includes:
- An overview of tensors and tensor decomposition.
- A gallery of examples that demonstrate tensor analysis with Barnacle.
- The example jupyer notebooks can be found here
- An API reference of Barnacle modules.
For a more technical discussion of the sparse tensor decomposition model implemented in Barnacle, please see the Methods section of the research article in which we introduce Barnacle (1).
In addition to the example gallery, our research article details using Barnacle to analyze metatranscriptomes of cyanobacterial gene expression in the open ocean. All of the scripts used to conduct those analyses can be found in a separate manuscript repository published alongside the article.
- Blaskowski, Stephen, Marie Roald, Paul M. Berube, Rogier Braakman, and E. Virginia Armbrust. "Simultaneous acclimation to nitrogen and iron scarcity in open ocean cyanobacteria revealed by sparse tensor decomposition of metatranscriptomes." bioRxiv (2024): 2024-07. https://doi.org/10.1101/2024.07.15.603627.