MatrixLM

Description

This package can estimates matrix linear models. The core functions to obtain closed-form least squares estimates for matrix linear models. Variance shrinkage is adapted from Ledoit & Wolf (2003)^[1].

An extension of MatrixLM for applications in high-throughput genetic screens is the GeneticScreens package. See the associated paper, "Matrix linear models for high-throughput chemical genetic screens", and its reproducible code for more details.

MatrixLMnet is a related package that implements algorithms for L1-penalized estimates for matrix linear models. See the associated paper, "Sparse matrix linear models for structured high-throughput data", and its reproducible code for more details.

Installation

The MatrixLM package can be installed by running:

using Pkg
Pkg.add("MatrixLM")

or from the julia REPL, press ] to enter pkg mode, and execute the following command:

add MatrixLM

For the most recent (development) version, use:

using Pkg
Pkg.add(url = "https://github.com/senresearch/MatrixLM.jl", rev="main")

Contributing

We appreciate contributions from users including reporting bugs, fixing issues, improving performance and adding new features.

Questions

If you have questions about contributing or using MatrixLM package, please communicate with the authors via GitHub.

Citing MatrixLM

If you use MatrixLM in a scientific publication, please consider citing the following paper:

Jane W Liang, Robert J Nichols, Śaunak Sen, Matrix Linear Models for High-Throughput Chemical Genetic Screens, Genetics, Volume 212, Issue 4, 1 August 2019, Pages 1063–1073, https://doi.org/10.1534/genetics.119.302299

@article{10.1534/genetics.119.302299,
    author = {Liang, Jane W and Nichols, Robert J and Sen, Śaunak},
    title = "{Matrix Linear Models for High-Throughput Chemical Genetic Screens}",
    journal = {Genetics},
    volume = {212},
    number = {4},
    pages = {1063-1073},
    year = {2019},
    month = {06},
    issn = {1943-2631},
    doi = {10.1534/genetics.119.302299},
    url = {https://doi.org/10.1534/genetics.119.302299},
    eprint = {https://academic.oup.com/genetics/article-pdf/212/4/1063/42105135/genetics1063.pdf},
}

References