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

block.plsda {mixOmics} | R Documentation

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

### Description

Integration of multiple data sets measured on the same samples or observations to classify a discrete outcome, ie. N-integration with Discriminant Analysis. The method is partly based on Generalised Canonical Correlation Analysis.

### Usage

    block.plsda(X,
    Y,
    indY,
    ncomp = 2,
    design,
    scheme,
    mode,
    scale = TRUE,
    init = "svd",
    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` | A factor or a class vector indicating the discrete outcome of each sample.
`indY` | To be supplied if Y is missing, indicates the position of the factor / class vector outcome 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`.
`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.plsda` function fits a horizontal integration PLS-DA model with a specified number of components per block). A factor indicating the discrete outcome needs to be provided, either by `Y` or by its position `indY` in the list of blocks `X`.

`X` 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.plsda` returns an object of class `"block.plsda","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

On PLSDA:

Barker M and Rayens W (2003). Partial least squares for discrimination. Journal of Chemometrics 17(3), 166-173. Perez-Enciso, M. and Tenenhaus, M. (2003). Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics 112, 581-592. Nguyen, D. V. and Rocke, D. M. (2002). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 39-50.

On multiple integration with PLS-DA: Gunther O., Shin H., Ng R. T. , McMaster W. R., McManus B. M. , Keown P. A. , Tebbutt S.J. , Lê Cao K-A. , (2014) Novel multivariate methods for integration of genomics and proteomics data: Applications in a kidney transplant rejection study, OMICS: A journal of integrative biology, 18(11), 682-95.

On multiple integration with sPLS-DA and 4 data blocks:

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê Cao K.A. (2016). DIABLO: multi omics integration for biomarker discovery. BioRxiv available here: http://biorxiv.org/content/early/2016/08/03/067611

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

### See Also

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

### Examples

    data(nutrimouse)
    data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = nutrimouse$diet)
    # with this design, all blocks are connected
    design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3,
    byrow = TRUE, dimnames = list(names(data), names(data)))

    res = block.plsda(X = data, indY = 3) # indY indicates where the outcome Y is in the list X
    plotIndiv(res, ind.names = FALSE, legend = TRUE)
    plotVar(res)

    ## Not run:
    # when Y is provided
    res2 = block.plsda(list(gene = nutrimouse$gene, lipid = nutrimouse$lipid),
        Y = nutrimouse$diet, ncomp = 2)
    plotIndiv(res2)
    plotVar(res2)

    ## End(Not run)

[Package mixOmics version 6.3.2 Index]