Another presentation of mixOmics dating Sept 2013, which presents the latest developments including time course data, multi-group and multi-block analysis.
Go here.
Another presentation of mixOmics dating Sept 2013, which presents the latest developments including time course data, multi-group and multi-block analysis.
Go here.
Changes in 4.1 ================ New features: ------------- - New S3 method valid for objects of class psl, spls, plsda and splsda - New select.var function to directly extract the selected variables from spls, spca, sipca - New data set vac18 for multilevel data
Our manuscript ‘Insightful graphicalt outputs to explore relationships between two “omics” data sets has been published and explains how to interpret Correlation Circle plots, how relevance networks and CIM are generated from rCCA and sPLS.
Check this very colourful manuscript[intlink id=”202″ type=”page”]here[/intlink]!
Another general presentation of mixOmics dating Dec 2012, which presents some preliminary but exciting results about time course data and the generalisation of PLS to multi block data sets using the approach of our collaborator Arthur Tenenhaus and colleagues.
Go[intlink id=”202″ type=”page”]here[/intlink].
A new general presentation about mixOmics is available (and should be updated for major update of the package) in the [intlink id=”204″ type=”page”]Presentation Section[/intlink].
Lê Cao K.-A. Unravelling `omics’ data with the mixOmics R package, Illustration on several studies. General presentation on mixOmics (last updated 05/04/2012) [Presentation]
Independent Principal Component Analysis (IPCA)
In some case studies, we have identified some limitations when using PCA:
Instead, we propose to apply Independent Principal Component Analysis (IPCA) which combines the advantages of both PCA and Independent Component Analysis (ICA). It uses ICA as a denoising process of the loading vectors produced by PCA to better highlight the important biological entities and reveal insightful patterns in the data.
IPCA offers a better visualization of the data than ICA and with a smaller number of components than PCA.
How to choose the number of components:
The kurtosis measure is used to order the loading vectors to order the Independent Principal Components. We have shown that the kurtosis value is a good post hoc indicator of the number of components to choose, as a sudden drop in the values corresponds to irrelevant dimensions.
Sparse Independent Principal Component Analysis (sIPCA)
Similar to the [intlink id=”129″ type=”page”]sparse PCA[/intlink] version implemented in mixOmics, soft-thresholding is applied in the independent loading vectors in IPCA to perform internal variable selection.
How to choose the number of variables to select:
The number of variables to select is still an open issue. In our paper we proposed to use the Davies Bouldinmeasure which is an index of crisp cluster validity. This index compares the within-cluster scatter with the between-cluster separation.
More details about how to use the ipca.R function in the[intlink id=”233″ type=”page”] case study[/intlink].
A multilevel approach has been added for cross-over design experiments (up to two cross factors), in collaboration with A/Prof B. Liquet (Universite de Bordeaux, France). This approach takes into account the complex structure of repeated measurements from different assays, where different treatments are applied on the same subjects to highlight the treatment effects within subject separately from the biological variation between subject.
Two different frameworks are proposed:
The multilevel function first decomposes the variance in the data sets X (and Y) and applies either sPLS-DA or sPLS on the within-subject deviation. One or two-factor analyses are available for sPLS-DA.
Associated functions include: multilevel.R, tune.multilevel.R, pheatmap.multilevel.R (see examples in methods, graphics and case studies).
This is our first step towards repeated measurements designs.
The package has been updated to version 4.0-1 to implement these methodologies. It now requires the library ‘pheatmap’.
network
and cim
for results from PLS modelvalid
function to PLS-DA and SPLS-DA models validationplot.valid
was modified to display graphical results from valid
function for PLS-DA and SPLS-DA modelscim
and network
functions were modified to obtain the similarity matrix in return valueplotVar
was modified to obtain the coordinates for X and Y variables in return valuepredict
function has been modified to simultaneously run either several or all prediction methods available to predict the classes of the test data from PLS-DA and SPLS-DA modelspca
and spca
are now available to perform Principal Component Analysis (PCA) and sparse PCA for variable selectionplotVar, plot3dVar, plotIndiv, plot3dIndiv
were modified to generate graphical results for pca
and spca