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

In mixOmics: Omics Data Integration Project

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,
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 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	Number of components to include in the model (see Details). 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.
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.

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 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 algorthm for each component

Author(s)

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao

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

See Also

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

Examples

# we will soon provide more examples on our website (data too large to be included in the package
# and still in active development)

Example output

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2

Loaded mixOmics 6.3.2

Thank you for using mixOmics!

How to apply our methods: http://www.mixOmics.org for some examples.
Questions or comments: email us at mixomics[at]math.univ-toulouse.fr  
Any bugs? https://bitbucket.org/klecao/package-mixomics/issues
Cite us:  citation('mixOmics')
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 

mixOmics documentation built on June 1, 2018, 5:06 p.m.