wrapper.rgcca: mixOmics wrapper for Regularised Generalised Canonical...

In mixOmics: Omics Data Integration Project

Description

Wrapper function to perform Regularized Generalised Canonical Correlation Analysis (rGCCA), a generalised approach for the integration of multiple datasets. For more details, see the help(rgcca) from the RGCCA package.

Usage

	
wrapper.rgcca(X,
    design = 1 - diag(length(X)),
    tau = rep(1, length(X)),
    ncomp = 1,
    keepX,
    scheme = "horst",
    scale = TRUE,
    init = "svd.single",
    tol = .Machine$double.eps,
    max.iter=1000,
    near.zero.var = FALSE,
    all.outputs = TRUE)

Arguments

X	a list of data sets (called 'blocks') matching on the same samples. Data in the list should be arranged in samples x variables. NAs are not allowed.
design	numeric matrix of size (number of blocks in X) x (number of blocks in X) with values between 0 and 1. Each value indicates the strenght of the relationship to be modelled between two blocks using sGCCA; a value of 0 indicates no relationship, 1 is the maximum value. If Y is provided instead of indY, the design matrix is changed to include relationships to Y.
tau	numeric vector of length the number of blocks in X. Each regularization parameter will be applied on each block and takes the value between 0 (no regularisation) and 1. If tau = "optimal" the shrinkage paramaters are estimated for each block and each dimension using the Schafer and Strimmer (2005) analytical formula.
ncomp	the number of components to include in the model. Default to 1.
keepX	A vector of same length as X. Each entry keepX[i] is the number of X[[i]]-variables kept in the model.
scheme	Either "horst", "factorial" or "centroid" (Default: "horst").
scale	boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
init	Mode of initialization use in the algorithm, either by Singular Value Decompostion of the product of each block of X with Y ("svd") or each block independently ("svd.single") . Default to "svd.single".
tol	Convergence stopping value.
max.iter	integer, the maximum number of iterations.
near.zero.var	boolean, see the internal nearZeroVar function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations. Default value is FALSE
all.outputs	boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = TRUE.

Details

This wrapper function performs rGCCA (see RGCCA) with 1, … ,ncomp components on each block data set. A supervised or unsupervised model can be run. For a supervised model, the unmap function should be used as an input data set. More details can be found on the package RGCCA.

Value

wrapper.rgcca returns an object of class "rgcca", a list that contains the following components:

data	the input data set (as a list).
design	the input design.
variates	the sgcca components.
loadings	the loadings for each block data set (outer wieght vector).
loadings.star	the laodings, standardised.
tau	the input tau parameter.
scheme	the input schme.
ncomp	the number of components included in the model for each block.
crit	the convergence criterion.
AVE	Indicators of model quality based on the Average Variance Explained (AVE): AVE(for one block), AVE(outer model), AVE(inner model)..
names	list containing the names to be used for individuals and variables.

More details can be found in the references.

Author(s)

Arthur Tenenhaus, Vincent Guillemot and Kim-Anh Lê Cao.

References

Tenenhaus A. and Tenenhaus M., (2011), Regularized Generalized Canonical Correlation Analysis, Psychometrika, Vol. 76, Nr 2, pp 257-284.

Schafer J. and Strimmer K., (2005), A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32.

See Also

wrapper.rgcca, plotIndiv, plotVar, wrapper.sgcca and http://www.mixOmics.org for more details.

Examples

data(nutrimouse)
# need to unmap the Y factor diet
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
# with this design, gene expression and lipids are connected to the diet factor
# design = matrix(c(0,0,1,
#                   0,0,1,
#                   1,1,0), ncol = 3, nrow = 3, byrow = TRUE)

# with this design, gene expression and lipids are connected to the diet factor
# and gene expression and lipids are also connected
design = matrix(c(0,1,1,
                  1,0,1,
                  1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
#note: the tau parameter is the regularization parameter
wrap.result.rgcca = wrapper.rgcca(X = data, design = design, tau = c(1, 1, 0),
                                  ncomp = 2,
                                  scheme = "centroid")
#wrap.result.rgcca

Example output

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2

Loaded mixOmics 6.3.2

Thank you for using mixOmics!

How to apply our methods: http://www.mixOmics.org for some examples.
Questions or comments: email us at mixomics[at]math.univ-toulouse.fr  
Any bugs? https://bitbucket.org/klecao/package-mixomics/issues
Cite us:  citation('mixOmics')
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 

mixOmics documentation built on June 1, 2018, 5:06 p.m.