Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/mint.block.plsda.R
Function to integrate data sets measured on the same samples (Nintegration) and to combine multiple independent studies measured on the same variables or predictors (Pintegration) using variants of multigroup and generalised PLSDA for supervised classification.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  mint.block.plsda(
X,
Y,
indY,
study,
ncomp = 2,
design,
scheme,
scale = TRUE,
init,
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 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 
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. 
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 = 
The function fits multigroup 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
).
mint.block.plsda
returns an object of class
"mint.plsda", "block.plsda"
, 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
Xvariates, to be used internally by 
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 nearzero 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 
Florian Rohart, Benoit Gautier, KimAnh Lê Cao, Al J Abadi
On multigroup PLS:
Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao KA (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 multigroup PLS. J. Chemometrics, 28(3), 192201.
On multiple integration with 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 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.
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
spls
, summary
, plotIndiv
,
plotVar
, predict
, perf
,
mint.block.spls
, mint.block.plsda
,
mint.block.splsda
and http://www.mixOmics.org/mixMINT for more
details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  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.plsda(data,Y,study=study, ncomp=2)
res

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