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 <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.
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