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.
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)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.
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 factor / class vector outcome in the list X
the number of components to include in the model. Default to 2. Applies to all blocks.
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.
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. Default = regression.
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.
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). Default = FALSE.
boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = TRUE.
block.plsda returns an object of class "block.plsda","block.pls", a list
that contains the following components:
the centered and standardized original predictor matrix.
the position of the outcome Y in the output list X.
the number of components included in the model for each block.
the algorithm used to fit the model.
list containing the variates of each block of X.
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.
Number of iterations of the algorthm for each component
Percentage of explained variance for each component and each block
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).
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. , Le 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 Le Cao K.A. (2016). DIABLO: multi omics integration for biomarker discovery. BioRxiv available here: http://biorxiv.org/content/early/2016/08/03/067611
mixOmics manuscript:
Rohart F, Gautier B, Singh A, Le Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. BioRxiv available here: http://biorxiv.org/content/early/2017/02/14/108597
plotIndiv, plotArrow, plotLoadings, plotVar, predict, perf, selectVar, block.pls, block.splsda and http://www.mixOmics.org for more details.
# NOT RUN {
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)
# }
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