https://github.com/berenslab/patch-seq-rrr

Sparse reduced-rank regression

https://github.com/berenslab/patch-seq-rrr

Science Score: 23.0%

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    Found 2 DOI reference(s) in README
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    Links to: biorxiv.org, wiley.com
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    Low similarity (8.1%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Sparse reduced-rank regression

Basic Info
  • Host: GitHub
  • Owner: berenslab
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 123 MB
Statistics
  • Stars: 20
  • Watchers: 5
  • Forks: 5
  • Open Issues: 1
  • Releases: 0
Created about 8 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

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

Sparse reduced-rank regression for Patch-seq data

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

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).

Owner

  • Name: Berens Lab @ University of Tübingen
  • Login: berenslab
  • Kind: organization
  • Email: philipp.berens@uni-tuebingen.de
  • Location: Tübingen, Germany

Department of Data Science at the Hertie Institute for AI in Brain Health, University of Tübingen

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Last Year
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Last synced: 10 months ago

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Past Year
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  • Average time to close issues: N/A
  • Average time to close pull requests: 2 days
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  • Yves33 (2)
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  • ybernaerts (1)
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