mint.block.pls: NP-integration

In mixOmicsTeam/mixOmics: Omics Data Integration Project

NP-integration

Description

Function to integrate data sets measured on the same samples (N-integration) and to combine multiple independent studies measured on the same variables or predictors (P-integration) using variants of multi-group and generalised PLS (unsupervised analysis).

Usage

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

Arguments

X	A named list of data sets (called 'blocks') measured on the same samples. Data in the list should be arranged in samples x variables, with samples order matching in all data sets.
Y	Matrix or vector response for a multivariate regression framework. Data should be continuous variables (see ?mint.block.splsda for supervised classification and factor response).
indY	To be supplied if Y is missing, indicates the position of the matrix / vector response in the list X
study	Factor, indicating the membership of each sample to each of the studies being combined
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.
scheme	Character, one of 'horst', 'factorial' or 'centroid'. Default = 'horst', see reference.
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)
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 = svd.single
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.

Details

The function fits multi-group generalised PLS models with a specified number of ncomp components. 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 more details in ?pls).

Value

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

X	the centered and standardized original predictor matrix.
Y	the centered and standardized original response vector or matrix.
ncomp	the number of components included in the model for each block.
mode	the algorithm used to fit the model.
mat.c	matrix of coefficients from the regression of X / residual matrices X on the X-variates, to be used internally by predict.
variates	list containing the X and Y variates.
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.
tol	the tolerance used in the iterative algorithm, used for subsequent S3 methods
max.iter	the maximum number of iterations, used for subsequent S3 methods
iter	Number of iterations of the algorithm for each component

Author(s)

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

References

Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.

Eslami, A., Qannari, E. M., Kohler, A., and Bougeard, S. (2014). Algorithms for multi-group PLS. J. Chemometrics, 28(3), 192-201.

See Also

spls, summary, plotIndiv, plotVar, predict, perf, mint.block.spls, mint.block.plsda, mint.block.splsda and http://www.mixOmics.org/mixMINT for more details.

Examples

data(breast.TCGA)

# for the purpose of this example, we create data that fit in the context of
# this function.
# We consider the training set as study1 and the test set as another
# independent study2.

study = c(rep("study1",150), rep("study2",70))

# to put the data in the MINT format, we rbind the two studies
mrna = rbind(breast.TCGA$data.train$mrna, breast.TCGA$data.test$mrna)
mirna = rbind(breast.TCGA$data.train$mirna, breast.TCGA$data.test$mirna)

# For the purpose of this example, we create a continuous response by
# taking the first mrna variable, and removing it from the data
Y = mrna[,1]
mrna = mrna[,-1]

data = list(mrna = mrna, mirna = mirna)

# we can now apply the function
res = mint.block.plsda(data, Y, study=study, ncomp=2)

res

mixOmicsTeam/mixOmics documentation built on Nov. 4, 2024, 8:56 a.m.