Omics Data Integration Using Kernel Methods

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view. Functions to assess and display important variables are also provided in the package. Jerome Mariette and Nathalie Villa-Vialaneix (2017) .


News


mixKernel 0.3 [2018-11-26] Fixed bugs: fix a bug in kernel.pca.permute with duplicated block variables (reported by Devin Leopold)


mixKernel 0.2 [2017-10-17] New options: Additional kernels : gaussian.radial.basis, poisson


mixKernel 0.1 [2017-05-18] Initial release

Reference manual

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install.packages("mixKernel")

0.3 by Jerome Mariette, 5 months ago


Browse source code at https://github.com/cran/mixKernel


Authors: Jerome Mariette [aut, cre] , Nathalie Villa-Vialaneix [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix, methods

Depends on mixOmics, ggplot2


See at CRAN