Description Usage Arguments Details Value Author(s) References See Also Examples
Integration of multiple data sets measured on the same samples or observations to classify a discrete outcome, ie. Nintegration with Discriminant Analysis. The method is partly based on Generalised Canonical Correlation Analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  block.plsda(
X,
Y,
indY,
ncomp = 2,
design,
scheme,
scale = TRUE,
init = "svd",
tol = 1e06,
max.iter = 100,
near.zero.var = FALSE,
all.outputs = TRUE
)

X 
A named 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 for the discrete outcome. 
indY 
To supply if 
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 
scheme 
Character, one of 'horst', 'factorial' or 'centroid'. Default =

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 = 
tol 
Numeric, convergence stopping value. 
max.iter 
Integer, the maximum number of iterations. 
near.zero.var 
Logical, see the internal 
all.outputs 
Logical. Computation can be faster when some specific
(and nonessential) outputs are not calculated. Default = 
block.plsda
function fits a horizontal integration PLSDA 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 MBPLS approaches proposed by Kowalski et al., 1989, J Chemom 3(1) and Westerhuis et al., 1998, J Chemom, 12(5).
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 nearzero predictors information. 
iter 
Number of iterations of the algorthm for each component 
explained_variance 
Percentage of explained variance for each component and each block 
Florian Rohart, Benoit Gautier, KimAnh Lê Cao, Al J Abadi
On PLSDA:
Barker M and Rayens W (2003). Partial least squares for discrimination. Journal of Chemometrics 17(3), 166173. PerezEnciso, M. and Tenenhaus, M. (2003). Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLSDA) approach. Human Genetics 112, 581592. Nguyen, D. V. and Rocke, D. M. (2002). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 3950.
On multiple integration with PLSDA: Gunther O., Shin H., Ng R. T. , McMaster W. R., McManus B. M. , Keown P. A. , Tebbutt S.J. , Lê Cao KA. , (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), 68295.
On multiple integration with sPLSDA 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 KA. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
plotIndiv
, plotArrow
,
plotLoadings
, plotVar
, predict
,
perf
, selectVar
, block.pls
,
block.splsda
and http://www.mixOmics.org for more details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  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)

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