Group Factor Analysis (GFA)

Python implementation of GFA that can be used to uncover relationships among multiple groups (e.g., data modalities) in complete and incomplete data sets.

Description of the files:

• analysis_syntdata.py: script to run experiments on synthetic data generated from two or more groups. This module was used to run the experiments on synthetic data described in [1](insert link).
• visualization_syntdata.py: plot and save the results of the experiments on synthetic data.
• analysis_HCP.py: this script was used to run the experiments on the HCP data described in [1].
• visualization_HCP.py: plot and save the results the experiments on the HCP data described in [1].
• GFA_original.py: implementation of the original GFA model proposed in [2][3].
• GFA_missingdata.py: implementation of our GFA extensions proposed in [1] to handle missing data.
• utils.py: GFA tools for multi-output prediction and missig data prediction.
• demo_2groups.ipynb: jupyter notebook to run our GFA implementation using synthetic data generated from 2 groups with missing data.
• demo_3groups.ipynb: jupyter notebook to run our GFA implementation using synthetic data generated from 3 groups with missing data.

Installation

• Clone the repository.
• Create and activate a virtual environment.
• Install the necessary packages by running:

    pip install -r requeriments.txt

Citation

If you want to use this repository for running experiments on your data with GFA, please consider citing:

@article{Ferreira2021,
      title={A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets}, 
      author={Fabio S. Ferreira, Agoston Mihalik, Rick A. Adams, John Ashburner, Janaina Mourao-Miranda},
      year={2022},
      journal = {NeuroImage}
}

References

[1] Ferreira FS, Mihalik A, Adams RA, Ashburner J, Mourao-Miranda J. A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets. NeuroImage (2022).

[2] Virtanen S, Klami A, Khan S, Kaski S. Bayesian Group Factor Analysis. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1269-1277 (2012).

[3] Klami A, Virtanen S, Kaski S. Bayesian Canonical Correlation Analysis. J Mach Learn Res 14:965–1003 (2013).