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. Nintegration. The method is partly based on Generalised Canonical Correlation Analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 
X 
A named 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 
indY 
To supply if 
ncomp 
the number of components to include in the model. Default to 2. Applies to all blocks. 
keepX 
A named 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 (and not 
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 
scheme 
Character, one of 'horst', 'factorial' or 'centroid'. Default =

mode 
Character string. What type of algorithm to use, (partially)
matching one of 
scale 
Logical. 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 Decomposition of the product of each block of X with Y ('svd') or each
block independently ('svd.single'). Default = 
tol 
Numeric, convergence stopping value. 
max.iter 
Integer, the maximum number of iterations. 
near.zero.var 
Logical, see the internal 
all.outputs 
Logical. Computation can be faster when some specific
(and nonessential) outputs are not calculated. Default = 
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 MBPLS 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 nearzero 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, KimAnh Lê Cao, Al J Abadi
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., 391420.
Tenenhaus A. and Tenenhaus M., (2011), Regularized Generalized Canonical Correlation Analysis, Psychometrika, Vol. 76, Nr 2, pp 257284.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  # 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)
## Not run:
network(TCGA.block.spls)
## End(Not run)

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.univtoulouse.fr
Any bugs? https://bitbucket.org/klecao/packagemixomics/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
mrna mirna
mrna 0 1
mirna 1 0
Design matrix has changed to include Y; each block will be linked to Y.
Call:
block.spls(X = data, Y = breast.TCGA$data.train$protein, ncomp = ncomp, keepX = list.keepX, keepY = list.keepY, design = design)
sGCCA with 2 components on block 1 named mrna
sGCCA with 2 components on block 2 named mirna
sGCCA with 2 components on block 3 named Y
Dimension of block 1 is 150 200
Dimension of block 2 is 150 184
Dimension of block 3 is 150 142
Selection of 20 20 variables on each of the sGCCA components on the block 1
Selection of 10 10 variables on each of the sGCCA components on the block 2
Selection of 10 10 variables on each of the sGCCA components on the block 3
Main numerical outputs:

loading vectors: see object$loadings
variates: see object$variates
variable names: see object$names
Functions to visualise samples:

plotIndiv, plotArrow
Functions to visualise variables:

plotVar, plotLoadings, network
Other functions:

selectVar
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