https://perso.math.univ-toulouse.fr/mixomics Omics Data Integration Project Wed, 15 Apr 2015 13:01:05 +0000 en-US hourly 1 http://wordpress.org/?v=4.1 https://perso.math.univ-toulouse.fr/mixomics/2011/08/01/web-interface/ https://perso.math.univ-toulouse.fr/mixomics/2011/08/01/web-interface/#comments Mon, 01 Aug 2011 00:00:27 +0000 http://perso.math.univ-toulouse.fr/mixomics/?p=70 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 Component Analysis (ICA). PCA is a powerful exploratory tool if the biological question is related to the highest variance. ICA was recently proposed in the literature as an alternative to PCA as it optimizes an independence condition that can give more meaningful components. A preprint can be available upon request.

R package and Data: The Liver Toxicity study data has been updated to provide geneBank IDs and gene titles

R package and Data: Two other data sets have been added: Prostate Tumor study (gene expression) and Metabolomic study of Yeast (metabolomics).

Web interface: We are making good progress on our associated web-interface (now deployed on  http://mixomics.qfab.org). Few illustrative examples are also available, and you can download the illustrative examples and run any type of analysis trough the interface. We are currently developing a ‘next level analysis’ to provide pathway enrichment analyses and give the functional annotation of the selected genes using the iHOP database. Do not hesitate to give us some feedback!

‘sletter: we now have a newsletter, to subscribe, send an email to mixomics[at]math.univ-toulouse.fr with no subject in the body.

]]> https://perso.math.univ-toulouse.fr/mixomics/2011/08/01/web-interface/feed/ 0 https://perso.math.univ-toulouse.fr/mixomics/2011/06/01/new-graphics-network-cim/ https://perso.math.univ-toulouse.fr/mixomics/2011/06/01/new-graphics-network-cim/#comments Wed, 01 Jun 2011 00:00:33 +0000 http://perso.math.univ-toulouse.fr/mixomics/?p=68 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 modified to obtain the similarity matrix in return value

The S3 method plotVar was modified to obtain the coordinates for X and Y variables in return value

The predict 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 models

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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

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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 function

valid function can also been performed with missing values

Functions pls, plsda, spls and splsda were modified to identify zero- or near-zero variance predictors

The functions plotVar and plot3dVar were modified to represent only the X variables in the case of PLS-DA and SPLS-DA

The pca function has been improved so that the S3 methods plotIndiv, plot3dIndiv, plotVarand plot3dVar can be used with these new classe

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Currently improving the pca and nipals for further graphical outputs

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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 thepredict function

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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 (updated) to display relevant associations between variables for (r)CCA and (s)PLS, with a new similarity function

A new similarity measure has been included in cim function and the arguments hclusfuncand distfunc to display Clustered image maps (heatmaps)

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3D representation to display samples and variables for (r)CCA

3D representation to display samples and variables for (s)PLS

The argument scaleY has been added to the pls and spls functions

(s)PLS can also be applied when there is only 1 predictor variable

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