mixKernel

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.