https://github.com/shu-hai/D-CCA

A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets (JASA-20 paper)
https://github.com/shu-hai/D-CCA

data-fusion data-integration high-dimensional-data integrative-analysis multiblock-structures multiview

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A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets (JASA-20 paper)

• Host: GitHub
• URL: https://github.com/shu-hai/D-CCA
• Owner: shu-hai
• Created: 2019-01-09T05:41:58.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-08-10T04:45:26.000Z (about 2 years ago)
• Last Synced: 2024-08-02T20:43:40.873Z (3 months ago)
• Topics: data-fusion, data-integration, high-dimensional-data, integrative-analysis, multiblock-structures, multiview
• Language: Python
• Homepage:
• Size: 3.71 MB
• Stars: 10
• Watchers: 1
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-multi-omics - D-CCA - Shu - Decomposition-based Canonical Correlation Analysis - [paper](https://doi.org/10.1080/01621459.2018.1543599) (Software packages and methods / Multi-omics correlation or factor analysis)

README

        
# D-CCA: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets

This python package implements the D-CCA method proposed in [1] for K=2 datasets. See [example.py](https://github.com/shu-hai/D-CCA/blob/master/example.py) for details, with Python 3.5 or above. For K>2 datasets, please use the [D-GCCA](https://github.com/shu-hai/D-GCCA) method.

D-CCA conducts the following decomposition:

 for 

where  and  share the same latent factors, but  and  have uncorrelated latent factors.

Note that  should be row-mean centered.

Please cite the article [1] for this package, which is available [here](https://www.researchgate.net/publication/329691934_D-CCA_A_Decomposition-based_Canonical_Correlation_Analysis_for_High-Dimensional_Datasets).

[1] Hai Shu, Xiao Wang & Hongtu Zhu (2020) D-CCA: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets. Journal of the American Statistical Association, 115(529): 292-306. [DOI: 10.1080/01621459.2018.1543599](https://doi.org/10.1080/01621459.2018.1543599)