gregfa/matrixlm.jl

Core functions to obtain closed-form least squares estimates for matrix linear models.

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).

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")

For the most recent version, use:

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

Alternatively, you can also install MatrixLM from the julia REPL. Press ] to enter pkg mode again, and enter the following:

add MatrixLM

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 author form github.

Citing MatrixLM

If you use MatrixLM in a scientific publication, please consider citing 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},
}