mint.spls: P-integration with variable selection

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

View source: R/mint.spls.R

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

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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)

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 reponse)

ncomp

Number of components to include in the model. Default to 2

mode

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See Details.

study

grouping factor indicating which samples are from the same study

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

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.

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 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).

Author(s)

Florian Rohart, Kim-Anh Lê Cao

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

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# we will soon provide more examples on our website (data too large to be included in the package)

mixOmics documentation built on June 1, 2018, 5:06 p.m.

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