Function to combine multiple independent studies measured on the same variables or predictors (P-integration) using variants of multi-group sparse PLS-DA for supervised classification with variable selection.
mint.splsda(X,
Y,
ncomp = 2,
mode = c("regression", "canonical", "invariant", "classic"),
study,
keepX = rep(ncol(X), ncomp),
scale = TRUE,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE,
all.outputs = TRUE)numeric matrix of predictors combining multiple independent studies on the same set of predictors. NAs are allowed.
A factor or a class vector indicating the discrete outcome of each sample.
Number of components to include in the model (see Details). Default to 2
character string. What type of algorithm to use, (partially) matching
one of "regression" or "canonical". See Details.
factor indicating the membership of each sample to each of the studies being combined
numeric vector indicating the number of variables to select in X on each component, or remaining components should keepX.constraint be provided. By default all variables are kept in the model.
boleean. If scale = TRUE, each block is standardized
to zero means and unit variances. Default = TRUE.
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.
mint.splsda returns an object of class "mint.splsda", "splsda", a list
that contains the following components:
the centered and standardized original predictor matrix.
the centered and standardized original response vector or matrix.
the centered and standardized original response vector or matrix.
the number of components included in the model.
The study grouping factor
the algorithm used to fit the model.
Number of variables used to build each component of X
list containing the variates of X - global variates.
list containing the estimated loadings for the variates - global loadings.
list containing the variates of X relative to each study - partial variates.
list containing the estimated loadings for the partial variates - partial loadings.
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 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).
mint.splsda function fits a vertical sparse PLS-DA 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 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).
Constraint model. To improve performance, a new argument was recently added to fit a constraint model (constraint=TRUE). The model is run on the optimal list of selected features keepX.constraint from the previous components, as opposed to considering only a specified number of features keepX. Such strategy was implemented in the sister package bootPLS and successfully applied in our recent integrative study (Rohart et al., 2016). Our experience has shown that the constraint model can substantially improve the performance of the method (see tune.mint.splsda).
Variable selection is performed on each component for X via input parameter keepX and keepX.constraint afor a constraint model. keepX.constraint specifies variable names to be selected in the previous components of the model, see example.
Useful graphical outputs are available, e.g. plotIndiv, plotLoadings, plotVar.
Rohart F, Eslami A, Matigian, N, Bougeard S, Le Cao K-A (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 multi-group PLS. J. Chemometrics, 28(3), 192-201.
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
spls, summary,
plotIndiv, plotVar, predict, perf, mint.pls, mint.plsda, mint.plsda and http://www.mixOmics.org/mixMINT for more details.
# NOT RUN {
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 study-specific outputs for all studies
plotIndiv(res, study = "all.partial")
#plot study-specific outputs for study "2"
plotIndiv(res, study = "2")
#plot study-specific outputs for study "2", "3" and "4"
plotIndiv(res, study = c(2, 3, 4))
# }
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