R/mint.plsda.R

In mixOmics: Omics Data Integration Project

# ========================================================================================================
# mint.plsda: perform a vertical PLS-DA on a combination of experiments, input as a matrix in X
# this function is a particular setting of internal_mint.block,
# the formatting of the input is checked in internal_wrapper.mint, which then call 'internal_mint.block'
# ========================================================================================================

#' P-integration with Projection to Latent Structures models (PLS) with
#' Discriminant Analysis
#' 
#' Function to combine multiple independent studies measured on the same
#' variables or predictors (P-integration) using variants of multi-group PLS-DA
#' for supervised classification.
#' 
#' \code{mint.plsda} function fits a vertical PLS-DA models with \code{ncomp}
#' components in which several independent studies measured on the same
#' variables are integrated. The aim is to classify the discrete outcome
#' \code{Y}. The \code{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.
#' 
#' \code{X} can contain missing values. Missing values are handled by being
#' disregarded during the cross product computations in the algorithm
#' \code{mint.plsda} without having to delete rows with missing data.
#' Alternatively, missing data can be imputed prior using the
#' \code{impute.nipals} function.
#' 
#' The type of deflation used is \code{'regression'} for discriminant algorithms.
#' i.e. no deflation is performed on Y.
#' 
#' Useful graphical outputs are available, e.g. \code{\link{plotIndiv}},
#' \code{\link{plotLoadings}}, \code{\link{plotVar}}.
#' 
#' @inheritParams mint.pls
#' @param Y A factor or a class vector indicating the discrete outcome of each
#' sample.
#' @return \code{mint.plsda} returns an object of class \code{"mint.plsda",
#' "plsda"}, a list that contains the following components:
#' 
#' \item{X}{the centered and standardized original predictor matrix.}
#' \item{Y}{original factor} \item{ind.mat}{the centered and standardized
#' original response vector or matrix.} \item{ncomp}{the number of components
#' included in the model.} \item{study}{The study grouping factor}
#' \item{mode}{the algorithm used to fit the model.} \item{variates}{list
#' containing the variates of X - global variates.} \item{loadings}{list
#' containing the estimated loadings for the variates - global loadings.}
#' \item{variates.partial}{list containing the variates of X relative to each
#' study - partial variates.} \item{loadings.partial}{list containing the
#' estimated loadings for the partial variates - partial loadings.}
#' \item{names}{list containing the names to be used for individuals and
#' variables.} \item{nzv}{list containing the zero- or near-zero predictors
#' information.} \item{iter}{Number of iterations of the algorithm for each
#' component} \item{prop_expl_var}{Percentage of explained variance for
#' each component and each study after setting possible missing values to zero
#' (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 Florian Rohart, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{spls}}, \code{\link{summary}}, \code{\link{plotIndiv}},
#' \code{\link{plotVar}}, \code{\link{predict}}, \code{\link{perf}},
#' \code{\link{mint.pls}}, \code{\link{mint.spls}}, \code{\link{mint.splsda}}
#' and http://www.mixOmics.org/mixMINT for more details.
#' @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
#' @keywords regression multivariate
#' @export
#' @examples
#' 
#' data(stemcells)
#' 
#' res = mint.plsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 3,
#' study = stemcells$study)
#' 
#' plotIndiv(res)
#' 
#' #plot study-specific outputs for all studies
#' plotIndiv(res, study = "all.partial")
#' 
#' \dontrun{
#' #plot study-specific outputs for study "2"
#' plotIndiv(res, study = "2", col = 1:3, legend = TRUE)
#' }
#' 
mint.plsda <- function(X,
                       Y,
                       ncomp = 2,
                       study,
                       scale = TRUE,
                       tol = 1e-06,
                       max.iter = 100,
                       near.zero.var = FALSE,
                       all.outputs = TRUE)
{
    #-- validation des arguments --#
    # most of the checks are done in 'internal_wrapper.mint'
    
    if (is.null(Y))
        stop("'Y' has to be something else than NULL.")
    
    if (is.null(dim(Y)))
    {
        Y = factor(Y)
    }  else {
        stop("'Y' should be a factor or a class vector.")
    }
    Y.mat = unmap(Y)
    colnames(Y.mat) = levels(Y
    )
    
    X = as.matrix(X)
    
    if (length(study) != nrow(X))
        stop(paste0("'study' must be a factor of length ", nrow(X), "."))
    
    if (sum(apply(table(Y, study) != 0, 2, sum) == 1) > 0)
        stop(
            "At least one study only contains a single level of the multi-levels outcome Y. The MINT algorithm cannot be computed."
        )
    
    if (sum(apply(table(Y, study) == 0, 2, sum) > 0) > 0)
        warning(
            "At least one study does not contain all the levels of the outcome Y. The MINT algorithm might not perform as expected."
        )
    
    # call to 'internal_wrapper.mint'
    result <- internal_wrapper.mint(
        X = X,
        Y = Y.mat,
        study = study,
        ncomp = ncomp,
        scale = scale,
        near.zero.var = near.zero.var,
        mode = 'regression',
        max.iter = max.iter,
        tol = tol,
        all.outputs = all.outputs
    )
    
    # choose the desired output from 'result'
    out <- list(
        call = match.call(),
        X = result$A[-result$indY][[1]],
        Y = Y,
        ind.mat = result$A[result$indY][[1]],
        ncomp = result$ncomp,
        study = result$study,
        mode = result$mode,
        variates = result$variates,
        loadings = result$loadings,
        variates.partial = result$variates.partial,
        loadings.partial = result$loadings.partial,
        names = result$names,
        tol = result$tol,
        iter = result$iter,
        max.iter = result$max.iter,
        nzv = result$nzv,
        scale = result$scale,
        prop_expl_var = result$prop_expl_var
    )
    
    class(out) <- c("mint.plsda", "mixo_plsda", "mixo_pls", "DA")
    return(invisible(out))
    
    
}