Tutorial 2. Data reduction

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Description

This tutorial focus on dimensionality-reduction techniques (PCA, ICA, etc.) that can provide useful data preprocessing when the number of variables exceeds the number of samples.

Useful references

• Section 5.2 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)

Hi all,

I will add the contributors to this project during the OHBM Hackathon 2020 here.
Let's do a first test.

If people are thinking of continuing the project I am happy to advise and comment in discussions. I am wondering if we can have more people as contributors and monitor the project? @LeonieBorne will remain as the owner of the project. @nadinespy seems really keen and I do think it's great to have more people with fresh eyes.

On the practical side, I would highly recommend people adding dependencies so people can work on the same versions of libraries.

Tutorial 3. Model selection

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Or do you have a question about this tutorial?
Let us know here!

Description

This tutorial introduce to the different techniques used to evaluate/validate/select the model.

• How to choose the optimal number of latent sources of variation to be extracted?
• How to evaluate the contribution of each individual input variable to the overall modeling solution?
• How to compare the models?

Useful references

• Section 5.3 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)
• Section 4.6 PLS-PM: "PLS Path Modeling with R" Gaston Sanchez
• Comparison CCA/PLS: Rahim, Mehdi, Bertrand Thirion, and Gaël Varoquaux. "Multi-output predictions from neuroimaging: assessing reduced-rank linear models." 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2017.
• Permutation inference for CCA: Winkler, Anderson M., et al. "Permutation inference for Canonical Correlation Analysis." arXiv preprint arXiv:2002.10046 (2020).

In order to write the different tutorials, we need open access databases to play with. Feel free to suggest here if you have any ideas, or to start looking for one on OpenNeuro!

Tutorial 1. Data preprocessing

Would you like to participate in the writing of this tutorial?
Or do you have a question about this tutorial?
Let us know here!

Description

This tutorial focus on minimal data preprocessing, usually required as for most machine-learning methods, with among other things:

• z-scoring of each variable,
• outlier detection,
• missing values processing,
• deconfounding procedures.

Useful references

• Section 5.1 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)

Roadmap

This issue contains the roadmap of this project. It's a place to start to investigate the issues that you can contribute to.

Please note that the list of tutorials proposed are by no means exhaustive. If you wish to add/modify some of them, do not hesitate to suggest it by creating a new issue!

General

Here is a (non-exhaustive) list of points to be dealt with before/during/after the tutorials have been written.

• #4 Find databases to use in the tutorials on OpenNeuro
• Export the tutorials in NeuroLibre

Tutorial 0. Introduction #5

The objective of this introductory tutorial is to explain the general principles of cross-decomposition algorithms, their possible applications and practical considerations. It should introduce and refer to the other tutorials.

This tutorial should also give an overview of the different cross-decomposition algorithms that exist, including CCA, PLS regression, PLS canonical, PLS-PM (for more than 2-blocks of variables), etc.

Useful references

• Cross-decomposition in scikit-learn: scikit-learn documentation for the cross-decomposition module (CCA, PLS regression, PLS canonical). Note that the documentation should be updated soon (see current pull request, corresponding branch).
• PLS-PM: "PLS Path Modeling with R" Gaston Sanchez
• PLS-PM in Python
• PLS methods for neuroimaging: Krishnan, Anjali, et al. "Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review." Neuroimage 56.2 (2011): 455-475.
• CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)

Tutorial 1. Data preprocessing #6

This tutorial focus on minimal data preprocessing, usually required as for most machine-learning methods, with among other things:

• z-scoring of each variable,
• outlier detection,
• missing values processing,
• deconfounding procedures.

Useful references

• Section 5.1 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)

Tutorial 2. Data reduction #7

This tutorial focus on dimensionality-reduction techniques (PCA, ICA, etc.) that can provide useful data preprocessing when the number of variables exceeds the number of samples.

Useful references

• Section 5.2 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)

Tutorial 3. Model selection #8

This tutorial introduce to the different techniques used to evaluate/validate/select the model.

• How to choose the optimal number of latent sources of variation to be extracted?
• How to evaluate the contribution of each individual input variable to the overall modeling solution?
• How to compare the models?

Useful references

• Section 5.3 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)
• Section 4.6 PLS-PM: "PLS Path Modeling with R" Gaston Sanchez
• Comparison CCA/PLS: Rahim, Mehdi, Bertrand Thirion, and Gaël Varoquaux. "Multi-output predictions from neuroimaging: assessing reduced-rank linear models." 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2017.
• Permutation inference for CCA: Winkler, Anderson M., et al. "Permutation inference for Canonical Correlation Analysis." arXiv preprint arXiv:2002.10046 (2020).

Tutorial 0. Introduction

Would you like to participate in the writing of this tutorial?
Or do you have a question about this tutorial?
Let us know here!

Description

The objective of this introductory tutorial is to explain the general principles of cross-decomposition algorithms, their possible applications and practical considerations. It should introduce and refer to the other tutorials.

This tutorial should also give an overview of the different cross-decomposition algorithms that exist, including CCA, PLS regression, PLS canonical, PLS-PM (for more than 2-blocks of variables), etc.

Useful references

• Cross-decomposition in scikit-learn: scikit-learn documentation for the cross-decomposition module (CCA, PLS regression, PLS canonical). Note that the documentation should be updated soon (see current pull request, corresponding branch).
• PLS-PM: "PLS Path Modeling with R" Gaston Sanchez
• PLS-PM in Python
• PLS methods for neuroimaging: Krishnan, Anjali, et al. "Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review." Neuroimage 56.2 (2011): 455-475.
• CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)