mint.block.spls: NP-integration for integration with variable selection
In ajabadi/mixOmics2: 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 (P-integration) using variants of sparse multi-group and generalised PLS with variable selection (unsupervised analysis).

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mint.block.spls(X, Y, indY, study, 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 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 block.splsda for supervised classification and factor reponse)
`indY`	To supply 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.
`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. By default all variables are kept in the model.
`design`	numeric matrix of size (number of blocks in X) x (number of blocks in X) with 0 or 1 values. A value of 1 (0) indicates a relationship (no relationship) between the blocks to be modelled. 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.
`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). Setting this argument to FALSE (when appropriate) will speed up the computations. Default value is FALSE
`all.outputs`	boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

The function fits sparse 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.spls returns an object of class "mint.spls", "block.spls", 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 algorthm for each component

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao

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.pls, mint.block.plsda, mint.block.splsda and http://www.mixOmics.org/mixMINT for more details.

# 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.splsda(data, Y, study=study, ncomp=2,
keepX = list(mrna=c(10,10), mirna=c(20,20)))

res