Sparse reduced-rank regression for exploratory visualization of paired multivariate data

This repository holds all the code and all the data for the following manuscript:

• Kobak D, Bernaerts Y, Weis MA, Scala F, Tolias AS, Berens P (2021), Sparse reduced-rank regression for exploratory visualisation of paired multivariate data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70 (4), 980--1000.

BioRxiv link (first version from 2018): https://www.biorxiv.org/content/10.1101/302208v3

Jan 2025: switching from glmnet_py to scikit_learn

All analysis in the 2021 paper was done using the sRRR implementation based on the glmnet_py package. This package can cause conflicts with many versions of scipy and gets increasingly difficult to work with. We therefore now provide an equivalent sRRR implementation based on the MultiTaskElasticNet class from scikit-learn, and recommend this version for all users from now on. Please find it in sparseRRR_scikit.py and demo_scikit.ipynb.

In this implementation, we adopted the alpha and l1_ratio nomenclature from scikit-learn, meaning that alpha controls the strength of the overall group lasso + ridge regularization and l1_ratio controls how much lasso vs. ridge penalty is used (l1_ratio=1 corresponds to pure group lasso).