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. Jerome Mariette and Nathalie Villa-Vialaneix (2017) <doi:10.1093/bioinformatics/btx682>.

Version: 0.3
Depends: R (≥ 2.10), mixOmics, ggplot2
Imports: vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix, methods
Published: 2018-11-26
Author: Jerome Mariette [aut, cre], Nathalie Villa-Vialaneix [aut]
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_0.3.tar.gz
Windows binaries: r-devel: mixKernel_0.3.zip, r-release: mixKernel_0.3.zip, r-oldrel: mixKernel_0.3.zip
OS X binaries: r-release: mixKernel_0.3.tgz, r-oldrel: mixKernel_0.3.tgz
Old sources: mixKernel archive


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