block.spls: N-integration and feature selection with sparse Projection to...
In mixOmics: Omics Data Integration Project

Description Usage Arguments Details Value Author(s) References See Also Examples

Integration of multiple data sets measured on the same samples or observations, with variable selection in each data set, ie. N-integration. The method is partly based on Generalised Canonical Correlation Analysis.

block.spls(X,
Y,
indY,
ncomp = 2,
keepX,
keepY,
design,
scheme,
mode,
scale = TRUE,
init ,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE,
all.outputs = TRUE)

`X`	A list of data sets (called 'blocks') measured on the same samples. Data in the list should be arranged in matrices, samples x variables, with samples order matching in all data sets.
`Y`	Matrix response for a multivariate regression framework. Data should be continuous variables (see block.splsda for supervised classification and factor reponse)
`indY`	To supply if Y is missing, indicates the position of the matrix response in the list `X`
`ncomp`	the number of components to include in the model. Default to 2. Applies to all blocks.
`keepX`	A list of same length as X. Each entry is the number of variables to select in each of the blocks of X for each component. By default all variables are kept in the model.
`keepY`	Only if Y is provided. Each entry is the number of variables to select in each of the blocks of Y for each component.
`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; 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`.
`scheme`	Either "horst", "factorial" or "centroid". Default = `horst`, see reference.
`mode`	character string. What type of algorithm to use, (partially) matching one of `"regression"`, `"canonical"`, `"invariant"` or `"classic"`. See Details. Default = `regression`.
`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 = `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). Default = `FALSE`.
`all.outputs`	boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

block.spls function fits a horizontal sPLS model with a specified number of components per block). An outcome needs to be provided, either by Y or by its position indY in the list of blocks X. Multi (continuous)response are supported. X and Y can contain missing values. Missing values are handled by being disregarded during the cross product computations in the algorithm block.pls without having to delete rows with missing data. Alternatively, missing data can be imputed prior using the nipals function.

The type of algorithm to use is specified with the mode argument. Four PLS algorithms are available: PLS regression ("regression"), PLS canonical analysis ("canonical"), redundancy analysis ("invariant") and the classical PLS algorithm ("classic") (see References and ?pls for more details).

Note that our method is partly based on sparse Generalised Canonical Correlation Analysis and differs from the MB-PLS approaches proposed by Kowalski et al., 1989, J Chemom 3(1), Westerhuis et al., 1998, J Chemom, 12(5) and sparse variants Li et al., 2012, Bioinformatics 28(19); Karaman et al (2014), Metabolomics, 11(2); Kawaguchi et al., 2017, Biostatistics.

Variable selection is performed on each component for each block of X, and for Y if specified, via input parameter keepX and keepY.

Note that if Y is missing and indY is provided, then variable selection on Y is performed by specifying the input parameter directly in keepX (no keepY is needed).

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

`X`	the centered and standardized original predictor matrix.
`indY`	the position of the outcome Y in the output list X.
`ncomp`	the number of components included in the model for each block.
`mode`	the algorithm used to fit the model.
`keepX`	Number of variables used to build each component of each block
`keepY`	Number of variables used to build each component of Y
`variates`	list containing the variates of each block of X.
`loadings`	list containing the estimated loadings for the variates.
`names`	list containing the names to be used for individuals and variables.
`nzv`	list containing the zero- or near-zero predictors information.
`iter`	Number of iterations of the algorthm for each component
`explained_variance`	Percentage of explained variance for each component and each block

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao

Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.

Wold H. (1966). Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P. R. (editors), Multivariate Analysis. Academic Press, N.Y., 391-420.

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

Tenenhaus A., Philippe C., Guillemot V, Lê Cao K.A., Grill J, Frouin V. Variable selection for generalized canonical correlation analysis. Biostatistics. kxu001

plotIndiv, plotArrow, plotLoadings, plotVar, predict, perf, selectVar, block.pls, block.splsda and http://www.mixOmics.org for more details.

# Example with multi omics TCGA study
# -----------------------------
data("breast.TCGA")
# this is the X data as a list of mRNA and miRNA; the Y data set is a single data set of proteins
data = list(mrna = breast.TCGA$data.train$mrna, mirna = breast.TCGA$data.train$mirna)
# set up a full design where every block is connected
design = matrix(1, ncol = length(data), nrow = length(data),
dimnames = list(names(data), names(data)))
diag(design) =  0
design
# set number of component per data set
ncomp = c(2)
# set number of variables to select, per component and per data set (this is set arbitrarily)
list.keepX = list(mrna = rep(20, 2), mirna = rep(10,2))
list.keepY = c(rep(10, 2))

TCGA.block.spls = block.spls(X = data, Y = breast.TCGA$data.train$protein,
ncomp = ncomp, keepX = list.keepX, keepY = list.keepY, design = design)
TCGA.block.spls
# in plotindiv we color the samples per breast subtype group but the method is unsupervised!
plotIndiv(TCGA.block.spls, group =  breast.TCGA$data.train$subtype, ind.names = FALSE)
# illustrates coefficient weights in each block
plotLoadings(TCGA.block.spls, ncomp = 1)
plotVar(TCGA.block.spls, style = 'graphics', legend = TRUE)
network(TCGA.block.spls)