Description Usage Arguments Details Value Author(s) References See Also Examples
Function to integrate and combine multiple independent studies measured on the same variables or predictors (Pintegration) using variants of multigroup sparse PLS for variable selection (unsupervised analysis).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 
X 
numeric matrix of predictors combining multiple independent studies
on the same set of predictors. 
Y 
Matrix or vector response for a multivariate regression framework.
Data should be continuous variables (see 
ncomp 
Integer, the number of components to include in the model. Default to 2. 
mode 
Character string. What type of algorithm to use, (partially)
matching one of 
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

keepY 
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.spls
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 explain the continuous outcome
Y
and selecting correlated features between both data sets X
and Y
. 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.
Multi (continuous)response are supported. X
and Y
can contain
missing values. Missing values are handled by being disregarded during the
cross product computations in the algorithm mint.spls
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
).
Variable selection is performed on each component for each block of
X
, and for Y
if specified, via input parameter keepX
and keepY
.
Useful graphical outputs are available, e.g. plotIndiv
,
plotLoadings
, plotVar
.
mint.spls
returns an object of class
"mint.spls","spls"
, a list that contains the following components:
X 
numeric matrix of predictors combining multiple independent studies
on the same set of predictors. 
Y 
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 
keepY 
Number of variables used to build each component of Y 
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 data sets). 
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.
spls
, summary
, plotIndiv
,
plotVar
, predict
, perf
,
mint.pls
, mint.plsda
, mint.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 16 17 18  data(stemcells)
# for the purpose of this example, we artificially
# create a continuous response Y by taking gene 1.
res = mint.spls(X = stemcells$gene[,1], Y = stemcells$gene[,1], 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", col = 1:3, legend = TRUE)
## End(Not run)

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