Package: mixKernel 0.9-1
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:
mixKernel_0.9-1.tar.gz
mixKernel_0.9-1.tar.gz(r-4.5-noble)mixKernel_0.9-1.tar.gz(r-4.4-noble)
mixKernel_0.9-1.tgz(r-4.4-emscripten)mixKernel_0.9-1.tgz(r-4.3-emscripten)
mixKernel.pdf |mixKernel.html✨
mixKernel/json (API)
NEWS
| # Install 'mixKernel' in R: |
| install.packages('mixKernel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- TARAoceans - TARA ocean microbiome data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:245b17ef1a. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 23 2024 |
| R-4.5-linux | OK | Nov 23 2024 |
Exports:center.scalecim.kernelcombine.kernelscompute.kernelkernel.pcakernel.pca.permutemixKernel.users.guideplotVar.kernel.pcaselect.features
Dependencies:ade4apeaskpassbase64encBHBiobaseBiocGenericsBiocParallelbiomformatBiostringsbslibcachemcliclustercodetoolscolorspacecommonmarkcorpcorcorrplotcpp11crayoncurldata.tabledigestdplyrellipseevaluatefansifarverfastmapfontawesomeforeachformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataggplot2ggrepelglueGPArotationgridExtragsignalgtableherehighrhtmltoolshtmlwidgetshttrigraphIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglambda.rlatticeLDRToolslifecyclemagrittrmarkdownMASSMatrixmatrixStatsmemoisemgcvmimemixOmicsmnormtmulttestmunsellnlmeopensslpermutephyloseqpillarpixmappkgconfigplyrpngpracmapsychpurrrquadprogR6rappdirsrARPACKRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLreshape2reticulaterglrhdf5rhdf5filtersRhdf5librlangrmarkdownrprojrootRSpectraS4VectorssassscalessnowspstringistringrsurvivalsystibbletidyrtidyselecttinytexUCSC.utilsutf8vctrsveganviridisLitewithrxfunXVectoryamlzlibbioc
Installation instruction for mixKernel
Rendered froma-mixKernelInstallation.Rmdusingknitr::rmarkdownon Nov 23 2024.Last update: 2024-01-28
Started: 2022-01-13
Data Integration using Unsupervised Multiple Kernel Learning
Rendered frommixKernelUsersGuide.Rmdusingknitr::rmarkdownon Nov 23 2024.Last update: 2024-01-28
Started: 2022-01-13
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Center and scale | center.scale |
| Compute and display similarities between multiple kernels | cim.kernel |
| Combine multiple kernels into a meta-kernel | combine.kernels |
| Compute a kernel | compute.kernel |
| Kernel Principal Components Analysis | kernel.pca |
| Assess variable importance | kernel.pca.permute |
| View mixKernel User's Guide | mixKernel.users.guide |
| Plot importance of variables in kernel PCA | plotVar.kernel.pca |
| Select important features | select.features |
| TARA ocean microbiome data | TARAoceans |