block.pls: N-integration with Projection to Latent Structures models...

In mixOmicsTeam/mixOmics: Omics Data Integration Project

N-integration with Projection to Latent Structures models (PLS)

Description

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

Usage

block.pls(
  X,
  Y,
  indY,
  ncomp = 2,
  design,
  mode,
  scale = TRUE,
  tol = 1e-06,
  max.iter = 100,
  near.zero.var = FALSE,
  all.outputs = TRUE,
  verbose.call = FALSE
)

Arguments

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 ?block.plsda for supervised classification and factor response).
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.
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. Alternatively, one of c('null', 'full') indicating a disconnected or fully connected design, respecively, or a numeric between 0 and 1 which will designate all off-diagonal elements of a fully connected design (see examples in block.splsda). If Y is provided instead of indY, the design matrix is changed to include relationships to Y.
mode	Character string indicating the type of PLS algorithm to use. One of "regression", "canonical", "invariant" or "classic". See Details.
scale	Logical. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
tol	Positive numeric used as convergence criteria/tolerance during the iterative process. Default to 1e-06.
max.iter	Integer, the maximum number of iterations. Default to 100.
near.zero.var	Logical, 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	Logical. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = TRUE.
verbose.call	Logical (Default=FALSE), if set to TRUE then the $call component of the returned object will contain the variable values for all parameters. Note that this may cause large memory usage.

Details

block.spls function fits a horizontal integration PLS 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 impute.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 the argument 'scheme' has now been hardcoded to 'horst' and 'init' to 'svd.single'.

Note that our method is partly based on Generalised Canonical Correlation Analysis and differs from the MB-PLS approaches proposed by Kowalski et al., 1989, J Chemom 3(1) and Westerhuis et al., 1998, J Chemom, 12(5).

Value

block.pls returns an object of class 'block.pls', 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.
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 algorithm for each component
prop_expl_var	Percentage of explained variance for each component and each block
call	if verbose.call = FALSE, then just the function call is returned. If verbose.call = TRUE then all the inputted values are accessable via this component

Author(s)

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao, Al J Abadi

References

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.

See Also

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

Examples

# Example with TCGA multi omics 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)

TCGA.block.pls = block.pls(X = data, Y = breast.TCGA$data.train$protein, 
                           ncomp = ncomp, design = design)
TCGA.block.pls
## use design = 'full'
TCGA.block.pls = block.pls(X = data, Y = breast.TCGA$data.train$protein, 
                           ncomp = ncomp, design = 'full')
# in plotindiv we color the samples per breast subtype group but the method is unsupervised!
# here Y is the protein data set
plotIndiv(TCGA.block.pls, group =  breast.TCGA$data.train$subtype, ind.names = FALSE)

mixOmicsTeam/mixOmics documentation built on Dec. 3, 2024, 11:15 p.m.