Package: mixKernel 0.9-2

Nathalie Vialaneix

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 <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>.

Authors:Nathalie Vialaneix [aut, cre], Celine Brouard [aut], Remi Flamary [aut], Julien Henry [aut], Jerome Mariette [aut]

mixKernel_0.9-2.tar.gz
mixKernel_0.9-2.tar.gz(r-4.7-any)mixKernel_0.9-2.tar.gz(r-4.6-any)
mixKernel_0.9-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mixKernel/json (API)

# Install 'mixKernel' in R:
install.packages('mixKernel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Pkgdown/docs site:https://mixkernel.clementine.wf

Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.12 score 22 scripts 389 downloads 3 mentions 9 exports 115 dependencies

Last updated from:5dc064b138. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK268
source / vignettesOK255
linux-release-x86_64OK277
wasm-releaseOK229

Exports:center.scalecim.kernelcombine.kernelscompute.kernelkernel.pcakernel.pca.permutemixKernel.users.guideplotVar.kernel.pcaselect.features

Dependencies:ade4apebase64encBHBiobaseBiocGenericsBiocParallelbiomformatBiostringsbslibcachemcliclustercodetoolscommonmarkcorpcorcorrplotcpp11crayondata.tabledigestdplyrellipseevaluatefarverfastmapfontawesomeforeachformatRfsfutile.loggerfutile.optionsgenericsggplot2ggrepelglueGPArotationgridExtragtableherehighrhtmltoolshtmlwidgetsigraphIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglambda.rlatticeLDRToolslifecyclelitedownmagrittrmarkdownMASSMatrixmatrixStatsmemoisemgcvmimemixOmicsmnormtmulttestnlmepermutephyloseqpillarpixmappkgconfigplyrpngpsychpurrrquadprogR6rappdirsrARPACKRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLreshape2reticulaterglrlangrmarkdownrprojrootRSpectraS4VectorsS7sassscalesSeqinfosnowspstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsveganviridisLitewithrxfunXVectoryaml

Data Integration using Unsupervised Multiple Kernel Learning
Introduction | Loading TARA Ocean datasets | Multiple kernel computation | Individual kernel computation | Combined kernel computation | Exploratory analysis: Kernel Principal Component Analysis (KPCA) | Perform KPCA | Assessing important variables | Selecting relevant variables | References | Session information
Rendered frommixKernelUsersGuide.Rmdusingknitr::rmarkdown

Last update: 2025-04-19
Started: 2022-01-13

Installation instruction for mixKernel
Installation of python dependencies | Installation of Bioconductor dependencies | mixKernel installation
Rendered froma-mixKernelInstallation.Rmdusingknitr::rmarkdown

Last update: 2024-01-28
Started: 2022-01-13