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: 0.4
Depends: R (≥ 3.5.0), mixOmics, ggplot2, reticulate (≥ 1.14)
Imports: vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix, methods
Published: 2020-02-26
Author: Jerome Mariette [aut, cre], Celine Brouard [aut], Remi Flamary [aut], Nathalie Vialaneix [aut]
Maintainer: Jerome Mariette <jerome.mariette at inrae.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_0.4.tar.gz
Windows binaries: r-devel: mixKernel_0.4.zip, r-release: mixKernel_0.4.zip, r-oldrel: mixKernel_0.4.zip
macOS binaries: r-release: mixKernel_0.4.tgz, r-oldrel: mixKernel_0.4.tgz
Old sources: mixKernel archive


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