Description Usage Arguments Details Value Author(s) References See Also Examples
Function to combine multiple independent studies measured on the same variables or predictors (Pintegration) using variants of multigroup sparse PLSDA for supervised classification with variable selection.
1 2 3 4 5 6 7 8 9 10 11 12 
X 
numeric matrix of predictors combining multiple independent studies
on the same set of predictors. 
Y 
A factor or a class vector indicating the discrete outcome of each sample. 
ncomp 
Integer, the number of components to include in the model. Default to 2. 
study 
Factor, indicating the membership of each sample to each of the studies being combined 
keepX 
numeric vector indicating the number of variables to select in

scale 
Logical. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE) 
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 = 
mint.splsda
function fits a vertical sparse PLSDA models with
ncomp
components in which several independent studies measured on the
same variables are integrated. The aim is to classify the discrete outcome
Y
and select variables that explain the outcome. The study
factor indicates the membership of each sample in each study. We advise to
only combine studies with more than 3 samples as the function performs
internal scaling per study, and where all outcome categories are
represented.
X
can contain missing values. Missing values are handled by being
disregarded during the cross product computations in the algorithm
mint.splsda
without having to delete rows with missing data.
Alternatively, missing data can be imputed prior using the nipals
function.
The type of deflation used is 'regression'
for discriminant algorithms.
i.e. no deflation is performed on Y.
Variable selection is performed on each component for X
via input
parameter keepX
.
Useful graphical outputs are available, e.g. plotIndiv
,
plotLoadings
, plotVar
.
mint.splsda
returns an object of class "mint.splsda",
"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. 
ind.mat 
the centered and standardized original response vector or matrix. 
ncomp 
the number of components included in the model. 
study 
The study grouping factor 
mode 
the algorithm used to fit the model. 
keepX 
Number of variables used to build each component of X 
variates 
list containing the variates of X  global variates. 
loadings 
list containing the estimated loadings for the variates  global loadings. 
variates.partial 
list containing the variates of X relative to each study  partial variates. 
loadings.partial 
list containing the estimated loadings for the partial variates  partial loadings. 
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 study (note that contrary to PCA, this amount may not decrease as the aim of the method is not to maximise the variance, but the covariance between X and the dummy matrix Y). 
Florian Rohart, KimAnh Lê Cao, Al J Abadi
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.
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.pls
, mint.plsda
, mint.plsda
and http://www.mixOmics.org/mixMINT for more details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  data(stemcells)
#  feature selection
res = mint.splsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 3, keepX = c(10, 5, 15),
study = stemcells$study)
plotIndiv(res)
#plot studyspecific outputs for all studies
plotIndiv(res, study = "all.partial")
## Not run:
#plot studyspecific outputs for study "2"
plotIndiv(res, study = "2")
#plot studyspecific outputs for study "2", "3" and "4"
plotIndiv(res, study = c(2, 3, 4))
## End(Not run)

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