mixKernel: 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.

Version: 0.1
Depends: R (≥ 2.10), mixOmics, ggplot2
Imports: phyloseq, corrplot, psych, quadprog, LDRTools
Published: 2017-05-18
Author: c(person("Jerome", "Mariette", role = c("aut", "cre"), email="jerome.mariette@inra.fr"), person("Nathalie", "Villa-Vialaneix", role = c("aut"), email="nathalie.villa-vialaneix@inra.fr"))
Maintainer: Jerome Mariette <jerome.mariette at inra.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: mixKernel citation info
Materials: NEWS
CRAN checks: mixKernel results


Reference manual: mixKernel.pdf
Package source: mixKernel_0.1.tar.gz
Windows binaries: r-devel: mixKernel_0.1.zip, r-release: mixKernel_0.1.zip, r-oldrel: mixKernel_0.1.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: mixKernel_0.1.tgz


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