RGCCA

https://github.com/rgcca-factory/rgcca

Science Score: 36.0%

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Repository

Basic Info

Host: GitHub
Owner: rgcca-factory
Language: R
Default Branch: main
Homepage: https://rgcca-factory.github.io/RGCCA/
Size: 30.2 MB

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Stars: 12
Watchers: 5
Forks: 12
Open Issues: 15
Releases: 0

Created almost 7 years ago · Last pushed 11 months ago

Metadata Files

Readme Changelog Contributing Code of conduct Codemeta

R/SGCCA

Version: 3.0.2

Authors:

Fabien GIRKA, Etienne CAMENEN, Caroline PELTIER, Vincent GUILLEMOT, Arnaud GLOAGUEN, Laurent LE BRUSQUET, Arthur TENENHAUS

Key-words:

Regularized Generalized Canonical Correlation Analysis, multi-block data analysis

Contact:

arthur.tenenhaus@centralesupelec.fr

Short description

Performs multiblock component methods (PCA, CCA, PLS, MCOA, GCCA, CPCA, MAXVAR, R/SGCCA, etc.) and produces graphical outputs (e.g. variables and individuals plots) and statistics to assess the robustness/significance of the analysis.

Description
Algorithm
Installation
Installation of a development branch from the git repository
References

Descriptiont

A package for multiblock data analysis (RGCCA - Regularized Generalized Canonical Correlation Analysis) as described in [1-4]. The software produces graphical outputs and statistics to assess the robustness/significance of the analysis.

Algorithm

We consider $J$ data matrices $\mathbf X1 , \dots, \mathbf XJ$. Each $n \times pj$ data matrix $\mathbf Xj = \left[ x{j1}, \dots, x{jpj} \right]$ is called a block and represents a set of $pj$ variables observed on $n$ individuals. The number and the nature of the variables may differ from one block to another, but the individuals must be the same across blocks. We assume that all variables are centered. The objective of RGCCA is to find, for each block, a weighted composite of variables (called block component) $\mathbf yj = \mathbf Xj \mathbf aj, ~ j = 1 ,..., J$ (where $\mathbf aj$ is a column-vector with $p_j$ elements) summarizing the relevant information between and within the blocks. The block components are obtained such that (i) block components explain well their own block and/or (ii) block components that are assumed to be connected are highly correlated. In addition, RGCCA integrates a variable selection procedure, called SGCCA, allowing the identification of the most relevant features.

RGCCA subsumes fifty years of multiblock component methods and is defined as the following optimization problem: $$\underset{\mathbf a1, \dots, \mathbf aJ}{\text{maximize}} \sum{j, k = 1}^J c{jk} g(\text{cov}(\mathbf Xj \mathbf aj, \mathbf Xk \mathbf ak)) \text{ s.t. } (1 - \tauj)\text{var}(\mathbf Xj \mathbf aj) + \tauj \Vert \mathbf a_j \Vert^2 = 1, ~ j = 1, \dots, J.$$

The scheme function $g$ is any continuous convex function and allows to consider different optimization criteria. Typical choices of $g$ are the identity (horst scheme, leading to maximizing the sum of covariances between block components), the absolute value (centroid scheme, yielding maximization of the sum of the absolute values of the covariances), the square function (factorial scheme, thereby maximizing the sum of squared covariances), or, more generally, for any even integer $m$, $g(x) = x^m$ ($m$-scheme, maximizing the power of $m$ of the sum of covariances). The horst scheme penalizes structural negative correlation between block components while both the centroid scheme and the $m$-scheme enable two components to be negatively correlated. According to [5], a fair model is a model where all blocks contribute equally to the solution in opposition to a model dominated by only a few of the $J$ sets. If fairness is a major objective, the user must choose $m = 1$. $m > 1$ is preferable if the user wants to discriminate between blocks. In practice, $m$ is equal to 1, 2 or 4. The higher the value of $m$ the more the method acts as block selector [5].
The design matrix $\mathbf C$ is a symmetric $J \times J$ matrix of nonnegative elements describing the network of connections between blocks the user wants to take into account. Usually, $c_{jk} = 1$ for two connected blocks and 0 otherwise.
The $\tauj$ are called shrinkage parameters or regularization parameters ranging from 0 to 1. $\tauj$ enables interpolate smoothly between maximizing the covariance and maximizing the correlation. Setting the $\tauj$ to 0 will force the block components to unit variance ($\text{var}(\mathbf Xj \mathbf aj) = 1$). In this case, the covariance criterion boils down to the correlation. The correlation criterion is better in explaining the correlated structure across datasets, thus discarding the variance within each individual dataset. Setting $\tauj$ to 1 will normalize the block weight vectors ($\Vert \mathbf aj \Vert = 1$), which applies the covariance criterion. A value between 0 and 1 will lead to a compromise between the two first options and correspond to the following constraint $(1 − \tauj) \text{var}(\mathbf Xj \mathbf aj) + \tauj \Vert \mathbf aj \Vert^2 = 1$. In the RGCCA package, for each block, the determination of the shrinkage parameter can be made fully automatic by using the analytical formula proposed by (Schäfer and Strimmer 2005 [6]), by permutation or K fold cross-validation. Moreover, we can define the choice of the shrinkage parameters by providing interpretations on the properties of the resulting block components:
- $\tau_j = 1$ yields the maximization of a covariance-based criterion. It is recommended when the user wants a stable component (large variance) while simultaneously taking into account the correlations between blocks. The user must, however, be aware that variance dominates over correlation.
- $\tauj = 0$ yields the maximization of a correlation-based criterion. It is recommended when the user wants to maximize correlations between connected components. This option can yield unstable solutions in case of multi-collinearity and cannot be used when a data block is rank deficient (e.g. $n < pj$).
- $0 < \tau_j < 1$ is a good compromise between variance and correlation: the block components are simultaneously stable and as well correlated as possible with their connected block components. This setting can be used when the data block is rank deficient.

The quality and interpretability of the RGCCA block components $\mathbf yj = \mathbf Xj \mathbf aj, ~ j = 1 , \dots, J$ are likely affected by the usefulness and relevance of the variables of each block. Accordingly, it is an important issue to identify within each block a subset of significant variables which are active in the relationships between blocks. SGCCA extends RGCCA to address this issue of variable selection. Specifically, RGCCA with all $\tauj$ equal to 1 is combined with an L1-penalty that gives rise to SGCCA [3]. The SGCCA optimization problem is defined with $sj$, a user defined positive constant that determines the amount of sparsity through the additional constraint $\Vert \mathbf aj \Vert1 \leq sj, ~ j = 1, \dots, J$. The smaller the $sj$, the larger the degree of sparsity for $\mathbf aj$. The sparsity parameter $sj$ is usually set by cross-validation or permutation. Alternatively, values of $sj$ can simply be chosen to result in desired amounts of sparsity.

Installation

Required:

Software: R (≥ 3.2.0)
R libraries: see the DESCRIPTION file.

install.packages("RGCCA")

See the vignette for an introduction to the package.

Installation of a development branch from the git repository

Required:

Software: R (≥ 3.2.0)
R libraries: see the DESCRIPTION file.
The R library devtools.

remove.packages("RGCCA") devtools::install_github(repo="https://github.com/rgcca-factory/RGCCA.git", ref = "main")

References

Tenenhaus, M., Tenenhaus, A., & Groenen, P. J. (2017). Regularized generalized canonical correlation analysis: a framework for sequential multiblock component methods. Psychometrika, 82(3), 737-777.
Tenenhaus, A., Philippe, C., & Frouin, V. (2015). Kernel generalized canonical correlation analysis. Computational Statistics & Data Analysis, 90, 114-131.
Tenenhaus, A., Philippe, C., Guillemot, V., Le Cao, K. A., Grill, J., & Frouin, V. (2014). Variable selection for generalized canonical correlation analysis. Biostatistics, 15(3), 569-583.
Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257.
Van de Geer, J. P. (1984). Linear relations among K sets of variables. Psychometrika, 49(1), 79-94.
Schäfer, J., & Strimmer, K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical applications in genetics and molecular biology, 4(1).
Tenenhaus, A., & Tenenhaus, M. (2014). Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis. European Journal of operational research, 238(2), 391-403.

Owner

Name: rgcca-factory
Login: rgcca-factory
Kind: organization

Repositories: 4
Profile: https://github.com/rgcca-factory

CodeMeta (codemeta.json)

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GitHub Events

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Watch event: 2
Issue comment event: 15
Push event: 15
Pull request event: 2
Fork event: 1
Create event: 4

Last Year

Watch event: 2
Issue comment event: 15
Push event: 15
Pull request event: 2
Fork event: 1
Create event: 4

Committers

Last synced: over 2 years ago

All Time

Total Commits: 1,019
Total Committers: 14
Avg Commits per committer: 72.786
Development Distribution Score (DDS): 0.69

Past Year

Commits: 250
Committers: 4
Avg Commits per committer: 62.5
Development Distribution Score (DDS): 0.144

Top Committers

Name	Email	Commits
GFabien	f**a@s**r	316
Etienne Camenen	e**n@g**m	223
cpeltier	c**r@i**g	217
Tenenhaus	a**s@c**r	99
Arnaud	a**n@s**r	80
Fabien	f**a@g**m	44
Caroline Peltier	c**r@g**m	17
llrs	l**a@g**m	12
ChemoSens	c**r@i**r	4
Arnaud	a**n@g**m	2
arthur	a**r@t**r	2
abourrelier	a**r@i**g	1
Tenenhaus	a**s@s**r	1
GFabien	3****n	1

Committer Domains (Top 20 + Academic)

supelec.fr: 3 icm-institute.org: 2 inrae.fr: 1 centralesupelec.fr: 1

Issues and Pull Requests

Last synced: 10 months ago

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Total issues: 13
Total pull requests: 76
Average time to close issues: 5 months
Average time to close pull requests: 20 days
Total issue authors: 9
Total pull request authors: 7
Average comments per issue: 6.46
Average comments per pull request: 0.33
Merged pull requests: 59
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 3
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 1
Average comments per issue: 4.0
Average comments per pull request: 0.33
Merged pull requests: 0
Bot issues: 0
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JChRoy (1)
EGoujon (1)

Pull Request Authors

GFabien (55)
AGloaguen (13)
Tenenhaus (5)
llrs (2)
bernt-matthias (1)
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Packages

Total packages: 2
Total downloads:
- cran 482 last-month
Total docker downloads: 18

Total dependent packages: 4
(may contain duplicates)
Total dependent repositories: 9
(may contain duplicates)
Total versions: 10
Total maintainers: 1

cran.r-project.org: RGCCA

Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data

Homepage: https://github.com/rgcca-factory/RGCCA
Documentation: http://cran.r-project.org/web/packages/RGCCA/RGCCA.pdf
License: GPL-3
Latest release: 3.0.3
published over 2 years ago

Versions: 9
Dependent Packages: 4
Dependent Repositories: 9
Downloads: 482 Last month
Docker Downloads: 18

Rankings

Forks count: 6.8%

Dependent packages count: 9.3%

Dependent repos count: 9.8%

Average: 12.6%

Stargazers count: 18.3%

Downloads: 18.7%

Maintainers (1)

arthur.tenenhaus@centralesupelec.fr

Last synced: 10 months ago

conda-forge.org: r-rgcca

Homepage: https://rgcca-factory.github.io/RGCCA/
License: GPL-2.0-or-later
Latest release: 2.1.2
published over 7 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 34.0%

Average: 42.6%

Dependent packages count: 51.2%

Last synced: 10 months ago

Dependencies

DESCRIPTION cran

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R >= 3.2 depends
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pbapply * imports
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scales * imports
stats * imports
utils * imports
DT * suggests
bsplus * suggests
devtools * suggests
ggrepel * suggests
igraph * suggests
knitr * suggests
magrittr * suggests
markdown * suggests
nnet * suggests
openxlsx * suggests
optparse * suggests
pander * suggests
rmarkdown * suggests
shiny * suggests
shinyjs * suggests
testthat * suggests
visNetwork * suggests

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actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite
r-lib/actions/setup-tinytex v2 composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action v4.4.1 composite
actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite
r-lib/actions/setup-tinytex v2 composite

.github/workflows/pr-commands.yaml actions

actions/checkout v3 composite
r-lib/actions/pr-fetch v2 composite
r-lib/actions/pr-push v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/test-coverage.yaml actions

actions/checkout v3 composite
actions/upload-artifact v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

RGCCA

Science Score: 36.0%

Repository

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README.md

R/SGCCA

Version: 3.0.2

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Descriptiont

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Installation of a development branch from the git repository

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CodeMeta (codemeta.json)

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cran.r-project.org: RGCCA

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conda-forge.org: r-rgcca

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