R: N-integration with Projection to Latent Structures models...

block.pls {mixOmics} | R Documentation

## 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,
    scheme,
    mode,
    scale = TRUE,
    init ,
    tol = 1e-06,
    max.iter = 100,
    near.zero.var = FALSE,
    all.outputs = TRUE)

### Arguments

`X` | A 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.splsda for supervised classification and factor reponse)
`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. 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. Default = `regression`.
`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). Default = `FALSE`.
`all.outputs` | boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

### 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 `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 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 algorthm for each component
`explained_variance` | Percentage of explained variance for each component and each block

### Author(s)

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao

### 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
    # 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)

[Package mixOmics version 6.3.2 Index]