mint.splsda: P-integration with Discriminant Analysis and variable...

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

View source: R/mint.splsda.R

Description

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.

Usage

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mint.splsda(
  X,
  Y,
  ncomp = 2,
  study,
  keepX = rep(ncol(X), 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

A factor or a class vector indicating the discrete outcome of each sample.

ncomp

Integer, the number of components to include in the model. Default to 2.

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

Logical. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)

tol

Numeric, convergence stopping value.

max.iter

Integer, the maximum number of iterations.

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.

Details

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 deflation used is 'regression' for discriminant algorithms. i.e. no deflation is performed on Y.

Variable selection is performed on each component for X via input parameter keepX.

Useful graphical outputs are available, e.g. plotIndiv, plotLoadings, plotVar.

Value

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).

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.

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

See Also

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

Examples

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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")

## Not run: 
#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))

## End(Not run)

mixOmics documentation built on Nov. 8, 2020, 11:12 p.m.