mint.block.pls: NP-integration
In mixOmics: Omics Data Integration Project

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

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).

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
)

`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`.

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).

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

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

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.

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

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