mint.spls: P-integration with variable selection
In mixOmics: Omics Data Integration Project

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 (P-integration) using variants of multi-group sparse PLS for variable selection (unsupervised analysis).

mint.spls(
  X,
  Y,
  ncomp = 2,
  mode = c("regression", "canonical", "invariant", "classic"),
  study,
  keepX = rep(ncol(X), ncomp),
  keepY = rep(ncol(Y), 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`	Matrix or vector response for a multivariate regression framework. Data should be continuous variables (see `mint.splsda` for supervised classification and factor response)
`ncomp`	Positive Integer. The number of components to include in the model. Default to 2.
`mode`	Character string indicating the type of PLS algorithm to use. One of `"regression"`, `"canonical"`, `"invariant"` or `"classic"`. 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.
`keepY`	numeric vector indicating the number of variables to select in `Y` on each component. By default all variables are kept in the model.
`scale`	Logical. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
`tol`	Positive numeric used as convergence criteria/tolerance during the iterative process. Default to `1e-06`.
`max.iter`	Integer, the maximum number of iterations. Default to 100.
`near.zero.var`	Logical, see the internal `nearZeroVar` function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations. Default value is FALSE.
`all.outputs`	Logical. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

mint.spls 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 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. `NA`s are allowed.
`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 near-zero predictors information.
`iter`	Number of iterations of the algorithm for each component

prop_expl_var

The amount of the variance explained by each variate / component divided by the total variance in the data for each study (after removing the possible missing values) using the definition of 'redundancy'. Note that contrary to PCA, this amount may not decrease in the following components as the aim of the method is not to maximise the variance, but the covariance between data sets (including the dummy matrix representation of the outcome variable in case of the supervised approaches).

Florian Rohart, Kim-Anh Lê Cao, Al J Abadi

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.

spls, summary, plotIndiv, plotVar, predict, perf, mint.pls, mint.plsda, mint.splsda and http://www.mixOmics.org/mixMINT for more details.

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 study-specific outputs for all studies
plotIndiv(res, study = "all.partial")

## Not run: 
#plot study-specific outputs for study "2"
plotIndiv(res, study = "2", col = 1:3, legend = TRUE)

## End(Not run)