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.

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Description file content

Package
mixKernel
Type
Package
Title
Omics Data Integration Using Kernel Methods
Version
0.2
Date
2018-09-11
Depends
R (>= 2.10), mixOmics, ggplot2
Imports
vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix, methods
Maintainer
Jerome Mariette
Description
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.
License
GPL (>= 2)
Repository
CRAN
Date/Publication
2018-09-11 14:50:03 UTC
Packaged
2018-09-11 13:49:43 UTC; jmariette
NeedsCompilation
no
LazyData
true
Author
Jerome Mariette [aut, cre], Nathalie Villa-Vialaneix [aut]

install.packages('mixKernel')

0.2

2 months ago

Jerome Mariette

GPL (>= 2)

Depends on

R (>= 2.10), mixOmics, ggplot2

Imports

vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix, methods

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