Package: mixKernel
Type: Package
Title: Omics Data Integration Using Kernel Methods
Version: 0.7
Date: 2021-06-15
Depends: R (>= 3.5.0), mixOmics, ggplot2, reticulate (>= 1.14)
Imports: vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix,
methods
Authors@R: c(person("Jerome", "Mariette", role = c("aut", "cre"), email="jerome.mariette@inrae.fr"),
person("Celine", "Brouard", role = c("aut"), email="celine.brouard@inrae.fr"),
person("Remi", "Flamary", role = c("aut"), email="remi.flamary@polytechnique.edu"),
person("Nathalie", "Vialaneix", role = c("aut"), email="nathalie.vialaneix@inrae.fr"))
Maintainer: Jerome Mariette <jerome.mariette@inrae.fr>
Author: Jerome Mariette [aut, cre],
Celine Brouard [aut],
Remi Flamary [aut],
Nathalie Vialaneix [aut]
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. Jerome Mariette and
Nathalie Villa-Vialaneix (2018) <doi:10.1093/bioinformatics/btx682>.
License: GPL (>= 2)
Repository: CRAN
Date/Publication: 2021-06-15 11:50:02 UTC
Packaged: 2021-06-15 11:33:58 UTC; jmariette
NeedsCompilation: no
LazyData: true
Config/reticulate: list( packages = list( list(package = "autograd",
pip = TRUE), list(package = "numpy", pip = TRUE), list(package
= "scipy", pip = TRUE), list(package = "sklearn", pip = TRUE) )
)

important variables are also provided in the package. Ref: Jerome Mariette
and Nathalie Villa-Vialaneix (2018) <doi:10.1093/bioinformatics/btx682>.

Package: mixOmics
Type: Package
Title: Integrate Omics data project
Version: 2.6
Date: 2010-02-15
Depends: igraph, rgl
Author: Sebastien Dejean, Ignacio Gonzalez and Kim-Anh Le Cao
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
Description: The package supplies two efficients methodologies:
regularized CCA and sparse PLS to unravel relationships between
two heterogeneous data sets of size (nxp) and (nxq) where the p
and q variables are measured on the same samples or individuals
n. These data may come from high throughput technologies, such
as omics data (e.g. transcriptomics, metabolomics or proteomics
data) that require an integrative or joint analysis. However,
mixOmics can also be applied to any other large data sets where
p+q>>n. rCCA is a regularized version of CCA to deal with the
large number of variables. sPLS allows variable selection in a
one step procedure and two frameworks are proposed: regression
and canonical analysis. Numerous graphical outputs are provided
to help interpreting the results.
License: GPL (>= 2)
Repository: CRAN
Date/Publication: 2010-02-25 11:10:16
Packaged: 2010-02-24 01:35:28 UTC; klecao