Migration: multivariate

tools/multivariate/.gitignore

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+runit/output/
+tool_test_output.*
+.#*
+\#*#
+*~
+.swp
+.*.swp
+NA
+planemo-venv
+.Rproj.user

tools/multivariate/.shed.yml

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+name: multivariate
+owner: ethevenot
+description: '[W4M][Metabolomics][LC-MS][GC-MS][NMR] Multivariate analysis by PCA, PLS(-DA), and OPLS(-DA).'
+homepage_url: http://workflow4metabolomics.org
+long_description: 'Part of the W4M project (http://workflow4metabolomics.org). "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 (Wold et al, 2001; Thenenhaus, 1998; Wehrens, 2011; Eriksson et al, 2006; Trygg et al, 2007). Orthogonal Partial Least Squares ("OPLS") enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation (Trygg and Wold, 2002). 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 module provides metrics and graphics to determine the optimal number of components (e.g. with the "R2" and "Q2" coefficients; Wold et al, 2001; Thenenhaus, 1998; Eriksson et al, 2006), check the "validity of the model" by permutation testing (Szymanska et al, 2012), detect "outliers" (Wold et al, 2001; Thenenhaus, 1998; Hubert et al, 2005), and provide several metrics to assess the importance of the variables in the model (e.g. "Variable Importance in Projection" or regression coefficients; Wold et al, 2001; Mehmood et al, 2012; Galindo-Prieto et al, 2014). The module is an implementation of the "ropls" R package available from Bioconductor (Thevenot et al, 2015).'
+remote_repository_url: https://github.com/workflow4metabolomics/tools-metabolomics/
+homepage_url: https://github.com/odisce/
+categories:
+- Metabolomics
+- Transcriptomics
+- Proteomics
+- Statistics

tools/multivariate/.travis.yml

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+notifications:
+ email:
+  recipients:
+    - etienne.thevenot@cea.fr
+
+before_install:
+ - sudo sed -i -e '$adeb http://cran.univ-paris1.fr/bin/linux/ubuntu trusty/' /etc/apt/sources.list
+ - sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9
+ - sudo apt-get update
+ - sudo apt-get install -y --no-install-recommends r-base
+ - sudo R -e "install.packages('batch', dependencies = TRUE, repos = 'https://cloud.r-project.org/')"
+    - sudo R -e "source('http://bioconductor.org/biocLite.R') ; biocLite('ropls')"
+ - sudo apt-get install -y python-virtualenv
+
+script:
+ - make test
+ - make planemolint
+ - make planemotest