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.


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Version 0.1.0 [2017-05-18] Initial release

Reference manual

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install.packages("mixKernel")

0.1 by Jerome Mariette, 9 months ago


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


Authors: c(person("Jerome", "Mariette", role = c("aut", "cre"), email="[email protected]"), person("Nathalie", "Villa-Vialaneix", role = c("aut"), email="[email protected]"))


Documentation:   PDF Manual  


GPL (>= 2) license


Imports phyloseq, corrplot, psych, quadprog, LDRTools

Depends on mixOmics, ggplot2


See at CRAN