recipe bioconductor-ropls

PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/ropls.html

License:

CeCILL

Recipe:

/bioconductor-ropls/meta.yaml

Links:

biotools: ropls

Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment).

package bioconductor-ropls

versions:

1.34.0-0,  1.32.0-0,  1.30.0-0,  1.26.0-0,  1.24.0-0,  1.22.0-1,  1.22.0-0,  1.20.0-0,  1.18.0-0, 

1.34.0-0,  1.32.0-0,  1.30.0-0,  1.26.0-0,  1.24.0-0,  1.22.0-1,  1.22.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.14.1-0,  1.14.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-0,  1.6.0-0,  1.4.4-1,  1.4.4-0,  1.4.2-0,  1.2.14-1,  1.2.14-0

depends bioconductor-biobase:

>=2.62.0,<2.63.0

depends bioconductor-multiassayexperiment:

>=1.28.0,<1.29.0

depends bioconductor-multidataset:

>=1.30.0,<1.31.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-base:

>=4.3,<4.4.0a0

depends r-ggplot2:

depends r-plotly:

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-ropls

and update with::

   mamba update bioconductor-ropls

To create a new environment, run:

mamba create --name myenvname bioconductor-ropls

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/bioconductor-ropls:<tag>

(see `bioconductor-ropls/tags`_ for valid values for ``<tag>``)

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