Category Archives: Methods

A new general presentation about mixOmics is available (and should be updated for major update of the package) in the . 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) … Continue reading →

Independent Principal Component Analysis (IPCA) In some case studies, we have identified some limitations when using PCA: PCA assumes that gene expression follows a multivariate normal distribution and recent studies have demonstrated that microarray gene expression measurements follow instead a … Continue reading →

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 … Continue reading →

R package and Methods: IPCA and sparse IPCA functions have been implemented (as well as their associated S3 functions). IPCA stands for Principal Component Analysis with Independent Loadings. It is a combination of the advantages of both PCA and Independent … Continue reading →

New S3 method network and cim for results from PLS model New code for the valid function to PLS-DA and SPLS-DA models validation The S3 method plot.valid was modified to display graphical results from valid function for PLS-DA and SPLS-DA models cim and network functions were … Continue reading →

New function pca and spca are now available to perform Principal Component Analysis (PCA) and sparse PCA for variable selection The S3 methods plotVar, plot3dVar, plotIndiv, plot3dIndiv were modified to generate graphical results for pca and spca

New function plot.valid to display the results of the valid function New code for imgCor function for a nicer representation of the correlation matrices In predict function the argument ‘method’ were replaced by method = c(“max.dist”, “class.dist”, “centroids.dist”, “mahalanobis.dist”) The arguments dendrogram, ColSideColors and RowSideColors were added to the cim … Continue reading →

Currently improving the pca and nipals for further graphical outputs

plsda and splsda have been further improved so that all the S3 functions predict, print, plotIndiv, plot3dIndiv can be used with these new classes Several prediction methods are now available to predict the classes of test data with plsda andsplsda, see argument ‘method’ (max.dist, class.dist, centroids.dist, mahalanobis.dist) in … Continue reading →

plsda and splsda functions are implemented to perform PLS Discriminant Analysis (PLS-DA) and sparse PLS-DA respectively breast.tumors data set is introduced to illustrate the (s)PLS-DA PCA can also been performed with missing values using the NIPALS algorithm and 3D plots are also available for PCA Network … Continue reading →