mint.splsda: P-integration with Discriminant Analysis and variable...
In mixOmics: Omics Data Integration Project

Description Usage Arguments Details Value Author(s) References See Also Examples

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)

`X`	numeric matrix of predictors combining multiple independent studies on the same set of predictors. `NA`s are allowed.
`Y`	A factor or a class vector indicating the discrete outcome of each sample.
`ncomp`	Number of components to include in the model (see Details). Default to 2
`mode`	character string. What type of algorithm to use, (partially) matching one of `"regression"` or `"canonical"`. See Details.
`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 `X` on each component. By default all variables are kept in the model.
`scale`	boleean. If scale = TRUE, each block is standardized to zero means and unit variances. Default = `TRUE`.
`tol`	Convergence stopping value.
`max.iter`	integer, the maximum number of iterations.
`near.zero.var`	boolean, see the internal `nearZeroVar` function (should be set to TRUE in particular for data with many zero values). Default = `FALSE`.
`all.outputs`	boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

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).

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 near-zero 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, Kim-Anh Lê Cao

Rohart F, Eslami A, Matigian, N, Bougeard S, Lê 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 article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. 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.

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))