Tutorials to apply cross decomposition methods in Python (focus on application in neuroimaging)
Let's chat on
First of all, welcome! If you're here, it's because we probably have one thing in common: you're incredibly smart interested in using cross-decomposition algorithms (e.g. Canonical Correlation Analysis - CCA, Partial Least Square - PLS, etc.).
This project has been proposed during the OHBM Hackathon 2020 and should soon gather a set of tutorials to help the application of these methods (especially in neuroscience).
pip install -r requirements.txt
Cross decomposition algorithms look for the relations between two (or more) blocks of variables. These methods are particularly used in neuroimaging to analyze associations between physiological/behavioral variables and brain structure/function. Between unsupervised and supervised modeling, this family of algorithms has many members (e.g. CCA, PLS regression, PLS canonical, PLS-PM, etc.) and many approaches are possible to validate the trained model (e.g. cross validation, bootstrapping, permutation test, etc.). In this project, we propose to write several Python tutorials to help the application and interpretation of these models in practice.
This project brings together a group of collaborators (neuroscientists participating to the 2020 OHBM Hackathon) interested in this issue and you are welcome to join us!
You! As long as you're interested in cross-decomposition algorithms.
We need expertise in Python, tutorial redaction (Jupyter Notebook) and of course cross-decomposition approaches.
In order to help the development of this project, please check out the contributors' guidelines and the roadmap.
During the OHBM Hackathon 2020, please check the kanban board to track the progress of the project.
During the OHBM Hackathon 2020 (June 16 to 18), let's chat on
If you want to report a problem or suggest an enhancement we'd love for you to open an issue at this github repository because then we can get right on it. But you can also contact Léonie Borne by email (leonie.borne AT gmail DOT com) or on twitter.
You might be interested in:
- CCA: Canonical Correlation Analysis
- PLS: Partial Least Square
- PLS-PM: Partial Least Square Path Modeling
- PCA: Principal Component Analysis
- ICA: Independent Component Analysis