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 multi-group and generalised PLS-DA for supervised classification.
mint.block.plsda(X,
Y,
indY,
study,
ncomp = 2,
design,
scheme,
mode,
scale = TRUE,
init ,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE,
all.outputs = TRUE)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.
A factor or a class vector indicating the discrete outcome of each sample.
To be supplied if Y is missing, indicates the position of the matrix / vector response in the list X
factor indicating the membership of each sample to each of the studies being combined
Number of components to include in the model (see Details). Default to 2.
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.
Either "horst", "factorial" or "centroid". Default = horst, see reference.
character string. What type of algorithm to use, (partially) matching
one of "regression", "canonical", "invariant" or "classic".
See Details.
boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
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.
Convergence stopping value.
integer, the maximum number of iterations.
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
boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = TRUE.
mint.block.plsda returns an object of class "mint.plsda", "block.plsda", a list
that contains the following components:
the centered and standardized original predictor matrix.
the centered and standardized original response vector or matrix.
the number of components included in the model for each block.
the algorithm used to fit the model.
matrix of coefficients from the regression of X / residual matrices X on the X-variates, to be used internally by predict.
list containing the \(X\) and \(Y\) variates.
list containing the estimated loadings for the variates.
list containing the names to be used for individuals and variables.
list containing the zero- or near-zero predictors information.
the tolerance used in the iterative algorithm, used for subsequent S3 methods
the maximum number of iterations, used for subsequent S3 methods
Number of iterations of the algorthm for each component
The function fits multi-group generalised PLS 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).
On multi-group PLS:
Rohart F, Eslami A, Matigian, N, Bougeard S, L<U+00EA> 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 PLSDA:
Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and L<U+00EA> 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, L<U+00EA> 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. , L<U+00EA> 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, L<U+00EA> Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
spls, summary,
plotIndiv, plotVar, predict, perf, mint.block.spls, mint.block.plsda, mint.block.splsda and http://www.mixOmics.org/mixMINT for more details.
# NOT RUN {
# we will soon provide more examples on our website (data too large to be included in the package
# and still in active development)
# }
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