mint.block.splsda: NP-integration with Discriminant Analysis and variable...
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mint.block.splsda

R Documentation

NP-integration with Discriminant Analysis and variable selection

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 sparse multi-group and generalised PLS-DA for supervised classification and variable selection.

Usage

mint.block.splsda(
  X,
  Y,
  indY,
  study,
  ncomp = 2,
  keepX,
  design,
  scheme,
  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`	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 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.
`keepX`	A named 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.
`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.
`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 sparse multi-group generalised PLS Discriminant Analysis models with a specified number of ncomp components. 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 impute.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.splsda returns an object of class "mint.splsda", "block.splsda", 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

On multi-group PLS: 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.

On multiple integration with sparse PLSDA: 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

Tenenhaus A., Philippe C., Guillemot V, Lê Cao K.A., Grill J, Frouin V. Variable selection for generalized canonical correlation analysis. Biostatistics. kxu001

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.

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

Examples


data(breast.TCGA)

# for the purpose of this example, we consider the training set as study1 and
# the test set as another independent study2.
study = c(rep("study1",150), rep("study2",70))

mrna = rbind(breast.TCGA$data.train$mrna, breast.TCGA$data.test$mrna)
mirna = rbind(breast.TCGA$data.train$mirna, breast.TCGA$data.test$mirna)
data = list(mrna = mrna, mirna = mirna)

Y = c(breast.TCGA$data.train$subtype, breast.TCGA$data.test$subtype)

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

res

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