mint.spls: P-integration with variable selection

In mixOmicsTeam/mixOmics: Omics Data Integration Project

P-integration with variable selection

Description

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).

Usage

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,
  verbose.call = FALSE
)

Arguments

X	numeric matrix of predictors combining multiple independent studies on the same set of predictors. NAs 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.
verbose.call	Logical (Default=FALSE), if set to TRUE then the $call component of the returned object will contain the variable values for all parameters. Note that this may cause large memory usage.

Details

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.

Value

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. NAs 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).
call	if verbose.call = FALSE, then just the function call is returned. If verbose.call = TRUE then all the inputted values are accessable via this component

Author(s)

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

References

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.

See Also

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

Examples

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)

mixOmicsTeam/mixOmics documentation built on Nov. 19, 2024, 11:53 a.m.