- recipe bioconductor-ropls
PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data
- Homepage:
- License:
CeCILL
- Recipe:
- 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:
1.42.0-0,1.38.0-0,1.34.0-0,1.32.0-0,1.30.0-0,1.26.0-0,1.24.0-0,1.22.0-1,1.22.0-0,1.42.0-0,1.38.0-0,1.34.0-0,1.32.0-0,1.30.0-0,1.26.0-0,1.24.0-0,1.22.0-1,1.22.0-0,1.20.0-0,1.18.0-0,1.16.0-1,1.14.1-0,1.14.0-0,1.12.0-0,1.10.0-0,1.8.0-0,1.6.0-0,1.4.4-1,1.4.4-0,1.4.2-0,1.2.14-1,1.2.14-0- Depends:
on bioconductor-biobase
>=2.70.0,<2.71.0on bioconductor-multiassayexperiment
>=1.36.0,<1.37.0on bioconductor-multidataset
>=1.38.0,<1.39.0on bioconductor-summarizedexperiment
>=1.40.0,<1.41.0on r-base
>=4.5,<4.6.0a0on r-ggplot2
on r-plotly
- Additional platforms:
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install bioconductor-ropls
to add into an existing workspace instead, run:
pixi add bioconductor-ropls
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install bioconductor-ropls
Alternatively, to install into a new environment, run:
conda create -n envname bioconductor-ropls
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/bioconductor-ropls:<tag>
(see bioconductor-ropls/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-ropls/README.html)
