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) .


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

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.4 by Jerome Mariette, 3 months ago

Browse source code at

Authors: Jerome Mariette [aut, cre] , Celine Brouard [aut] , Remi Flamary [aut] , Nathalie Vialaneix [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

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

Depends on mixOmics, ggplot2, reticulate

See at CRAN