R/mixOmics.R
In mixOmicsTeam/mixOmics: Omics Data Integration Project
Defines functions
mixOmics
Documented in
mixOmics
# ========================================================================================================
# mixOmics: perform one of the package's function depending on the input data (list or matrix, vector or categerical data, etc)
# ========================================================================================================
#' PLS-derived methods: one function to rule them all!
#'
#' \emph{This is the documentation for mixOmics function from mixOmics package.
#' For package documentation refer to \code{help(package='mixOmics')}}
#'
#' This function performs one of the PLS derived methods included in the
#' mixOmics package that is the most appropriate for your input data, one of
#' (mint).(block).(s)pls(da) depending on your input data (single data, list of
#' data, discrete outcome, \dots{})
#'
#' This function performs one of the PLS derived methods included in the
#' mixOmics package that is the most appropriate for your input data, one of
#' (mint).(block).(s)pls(da).
#'
#' If your input data \code{X} is a matrix, then the algorithm is directed
#' towards one of (mint).(s)pls(da) depending on your input data \code{Y}
#' (factor for the discrete outcome directs the algorithm to DA analysis) and
#' whether you input a \code{study} parameter (MINT analysis) or a \code{keepX}
#' parameter (sparse analysis).
#'
#' If your input data \code{X} is a list of matrices, then the algorithm is
#' directed towards one of (mint).block.(s)pls(da) depending on your input data
#' \code{Y} (factor for the discrete outcome directs the algorithm to DA
#' analysis) and whether you input a \code{study} parameter (MINT analysis) or
#' a \code{keepX} parameter (sparse analysis).
#'
#' More details about the PLS modes in \code{?pls}.
#'
#' @param X Input data. Either a matrix or a list of data sets (called
#' 'blocks') matching on the same samples. Data should be arranged in samples x
#' variables, with samples order matching in all data sets.
#' @param Y Outcome. Either a numeric matrix of responses or a factor or a
#' class vector for the discrete outcome.
#' @param indY To supply if Y is missing, indicates the position of the outcome
#' in the list X
#' @param study grouping factor indicating which samples are from the same
#' study
#' @param ncomp If \code{X} is a data matrix, \code{ncomp} is a single value.
#' If \code{X} is a list of data sets, \code{ncomp} is a numeric vector of
#' length the number of blocks in \code{X}. The number of components to include
#' in the model for each block (does not necessarily need to take the same
#' value for each block).
#' @param keepX Number of variables to keep in the \eqn{X}-loadings
#' @param keepY Number of variables to keep in the \eqn{Y}-loadings
#' @param design numeric matrix of size (number of blocks) x (number of blocks)
#' with only 0 or 1 values. A value of 1 (0) indicates a relationship (no
#' relationship) between the blocks to be modelled. If \code{Y} is provided
#' instead of \code{indY}, the \code{design} matrix is changed to include
#' relationships to \code{Y}.
#' @param tau numeric vector of length the number of blocks in \code{X}. Each
#' regularization parameter will be applied on each block and takes the value
#' between 0 (no regularisation) and 1. If tau = "optimal" the shrinkage
#' paramaters are estimated for each block and each dimension using the Schafer
#' and Strimmer (2005) analytical formula.
#' @param scheme Either "horst", "factorial" or "centroid" (Default:
#' "centroid"), see reference paper.
#' @param mode character string. What type of algorithm to use, (partially)
#' matching one of \code{"regression"}, \code{"canonical"}, \code{"invariant"}
#' or \code{"classic"}. See Details.
#' @param scale Logical. If scale = TRUE, each block is standardized to zero
#' means and unit variances (default: TRUE)
#' @param init Mode of initialization use in the algorithm, either by Singular
#' Value Decompostion of the product of each block of X with Y ("svd") or each
#' block independently ("svd.single") . Default to "svd".
#' @param tol Convergence stopping value.
#' @param max.iter integer, the maximum number of iterations.
#' @param near.zero.var Logical, see the internal \code{\link{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
#' @return none
#' @author Florian Rohart, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{pls}}, \code{\link{spls}}, \code{\link{plsda}},
#' \code{\link{splsda}}, \code{\link{mint.pls}}, \code{\link{mint.spls}},
#' \code{\link{mint.plsda}}, \code{\link{mint.splsda}},
#' \code{\link{block.pls}}, \code{\link{block.spls}},
#' \code{\link{block.plsda}}, \code{\link{block.splsda}},
#' \code{\link{mint.block.pls}}, \code{\link{mint.block.spls}},
#' \code{\link{mint.block.plsda}}, \code{\link{mint.block.splsda}}
#' @references
#'
#' 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
#'
#' MINT models:
#'
#' 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. (2013). Multi-group
#' PLS Regression: Application to Epidemiology. In New Perspectives in Partial
#' Least Squares and Related Methods, pages 243-255. Springer.
#'
#' Integration of omics data sets:
#'
#' Singh A, Gautier B, Shannon C, Vacher M, Rohart F, Tebbutt S, Lê Cao K-A.
#' DIABLO: an integrative, multi-omics, multivariate method for multi-group
#' classification. \url{http://biorxiv.org/content/early/2016/08/03/067611}
#'
#' Lê Cao, K.-A., Martin, P.G.P., Robert-Granie, C. and Besse, P. (2009).
#' Sparse canonical methods for biological data integration: application to a
#' cross-platform study. \emph{BMC Bioinformatics} \bold{10}:34.
#'
#' Lê Cao, K.-A., Rossouw, D., Robert-Granie, C. and Besse, P. (2008). A sparse
#' PLS for variable selection when integrating Omics data. \emph{Statistical
#' Applications in Genetics and Molecular Biology} \bold{7}, article 35.
#'
#' Tenenhaus A., Phillipe C., Guillemot V., Lê Cao K-A. , Grill J. , Frouin V.
#' (2014), Variable selection for generalized canonical correlation analysis,
#' \emph{Biostatistics}, doi: 10.1093/biostatistics. PMID: 24550197.
#'
#' Sparse SVD:
#'
#' Shen, H. and Huang, J. Z. (2008). Sparse principal component analysis via
#' regularized low rank matrix approximation. \emph{Journal of Multivariate
#' Analysis} \bold{99}, 1015-1034.
#'
#' PLS-DA:
#'
#' Lê Cao K-A, Boitard S and Besse P (2011). Sparse PLS Discriminant Analysis:
#' biologically relevant feature selection and graphical displays for
#' multiclass problems. BMC Bioinformatics 12:253.
#'
#' PLS:
#'
#' Tenenhaus, M. (1998). \emph{La regression PLS: theorie et pratique}. Paris:
#' Editions Technic.
#'
#' Wold H. (1966). Estimation of principal components and related models by
#' iterative least squares. In: Krishnaiah, P. R. (editors), \emph{Multivariate
#' Analysis}. Academic Press, N.Y., 391-420.
#'
#' Abdi H (2010). Partial least squares regression and projection on latent
#' structure regression (PLS Regression). \emph{Wiley Interdisciplinary
#' Reviews: Computational Statistics}, 2(1), 97-106.
#'
#' On multilevel analysis:
#'
#' Liquet, B., Lê Cao, K.-A., Hocini, H. and Thiebaut, R. (2012) A novel
#' approach for biomarker selection and the integration of repeated measures
#' experiments from two platforms. \emph{BMC Bioinformatics} \bold{13}:325.
#'
#' Westerhuis, J. A., van Velzen, E. J., Hoefsloot, H. C., and Smilde, A. K.
#' (2010). Multivariate paired data analysis: multilevel PLSDA versus OPLSDA.
#' \emph{Metabolomics}, \bold{6}(1), 119-128.
#'
#' Visualisations:
#'
#' González I., Lê Cao K.-A., Davis, M.D. and Déjean S. (2013) Insightful
#' graphical outputs to explore relationships between two omics data sets.
#' BioData Mining 5:19.
#' @keywords multivariate hplot dplot
#' @export
#' @example ./examples/mixOmics-examples.R
mixOmics <- function(X,
Y,
indY, #only use if Y not provided
study, #mint
ncomp,
keepX, #sparse
keepY, #sparse
design, #block
tau = NULL, # rgcca, number between 0,1 or "optimal"
scheme, #block
mode = c("regression", "canonical", "invariant", "classic"), #for DA only regression
scale,
init,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE)
{
mode <- match.arg(mode)
.check_regression_mode <- function(mode) {
if (mode != 'regression') {
warning("using `mode = 'regression'` for discriminant analysis\n")
}
return(NULL)
}
if (is.list(X) & !is.data.frame(X))# either rgcca, sgcca,sgcca-DA, mint.block, mint.block-DA
{
#need to check if Y or indY is a factor, unmap it and then do the checks (no other factors etc)
if ((missing(indY)& missing(Y)) & is.null(tau))
stop("Either 'Y', 'indY' or 'tau' is needed")
if (is.null(tau)) # SGCCA/mint
{
isfactorY = FALSE
if (!missing(Y))
{
if (is.list(Y) & !is.data.frame(X)) stop("Y must be a matrix or a factor")
if (is.factor(Y)) {
#Y = as.factor(Y)
isfactorY = TRUE
}
}else if (!missing(indY)) {
temp = X[[indY]] #not called Y to not be an input of the wrappers
if (is.factor(temp)) {
#temp = as.factor(temp)
isfactorY = TRUE
}
}else if (missing(indY)) {
stop("Either 'Y' or 'indY' is needed")
}
if (isfactorY)# either block.plsda/block.splsda/mint.block.plsda/mint.block.splsda
{
.check_regression_mode(mode)
if (missing(keepX))
{
if (missing(study)) #block.plsda
{
if (missing(scale))
scale = FALSE
message("a block Partial Least Squares - Discriminant Analysis is being performed (block.PLS-DA)")
res = block.plsda(X = X, Y = Y, indY = indY, ncomp = ncomp,design = design,scheme = scheme,
scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.plsda
if (missing(scale))
scale = FALSE
message("a mint block Partial Least Squares - Discriminant Analysis is being performed (mint.block.PLS-DA)")
res = mint.block.plsda(X = X, Y = Y, indY = indY,study = study, ncomp = ncomp,design = design,scheme = scheme,
scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
}
} else {
if (missing(study))# block.splsda
{
if (missing(scale))
scale = FALSE
message("a block sparse Partial Least Squares - Discriminant Analysis is being performed (block.sPLS-DA)")
res = mint.block.splsda(X = X, Y = Y, indY = indY, ncomp = ncomp,keepX = keepX,
design = design,scheme = scheme, scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.splsda
if (missing(scale))
scale = FALSE
message("a mint block sparse Partial Least Squares - Discriminant Analysis is being performed (mint.block.sPLS-DA)")
res = mint.block.splsda(X = X, Y = Y, indY = indY, ncomp = ncomp,study = study,keepX = keepX,
design = design,scheme = scheme,scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
}
}
} else { # either block.pls/block.spls/mint.block.pls/mint.block.spls
if (missing(keepX) )
{
if (missing(study)) #block.pls
{
if (missing(scale))
scale = FALSE
message("a block Partial Least Squares is being performed (block.PLS)")
res = block.pls(X = X, Y = Y, indY = indY, ncomp = ncomp,design = design,scheme = scheme,
mode = mode,scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.pls
if (missing(scale))
scale = FALSE
message("a mint block Partial Least Squares is being performed (mint.block.PLS)")
res = mint.block.pls(X = X, Y = Y, indY = indY,study = study, ncomp = ncomp,design = design,scheme = scheme,
mode = mode,scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
}
} else {
if (missing(study))# block.spls
{
if (missing(scale))
scale = FALSE
message("a block sparse Partial Least Squares is being performed (block.sPLS)")
res = block.spls(X = X, Y = Y, indY = indY, ncomp = ncomp,keepX = keepX,
design = design,scheme = scheme,mode = mode,scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.spls
if (missing(scale))
scale = FALSE
message("a mint block sparse Partial Least Squares is being performed (mint.block.sPLS)")
res = mint.block.spls(X = X, Y = Y, indY = indY, ncomp = ncomp,study = study,keepX = keepX,
design = design,scheme = scheme,mode = mode,scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
}
}
}
} else { # RGCCA
if (!missing(study)) {message("'study' is not used")}
if (missing(keepX) ) #RGCCA
{
message("A RGCCA analysis is being performed")
if (missing(scale))
scale = FALSE
res = wrapper.rgcca(X = X,design = design,tau = tau,ncomp = ncomp,
max.iter = max.iter,scheme = scheme,scale = scale,init = init, tol = tol)
} else { #sparse RGCCA
if (missing(scale))
scale = FALSE
message("A sparse RGCCA analysis is being performed")
res = wrapper.rgcca(X = X,design = design,tau = tau,ncomp = ncomp,keepX = keepX,
max.iter = max.iter,scheme = scheme,scale = scale,init = init, tol = tol)
}
}
#end if (is.list(X))
} else {#either pls,spls, plsda, splsda or mint. pls/spls/plsda/splsda
if (missing(Y))
stop("Y is missing")
if (is.list(Y) & !is.data.frame(X))
stop("Y must be a matrix or a factor")
if (missing(mode)) mode = "regression"
#check for unused inputs (scheme, etc etc)
if (!is.null(tau) | !missing(design) | !missing(init) | !missing(scheme))
{
if (!is.null(tau))
message("'tau' is not used")
if (!missing(design))
message("'design' is not used")
if (!missing(init))
message("'init' is not used")
if (!missing(scheme))
message("'scheme' is not used")
stop("unused input parameters")
}
if (is.factor(Y))#either plsda, splsda
{
.check_regression_mode(mode)
#Check.entry.pls.single(X, ncomp, keepX) # to have the warnings relative to X and Y, instead of blocks
if (length(Y)!=nrow(X))
stop("unequal number of rows in 'X' and 'Y'.")
if (missing(keepX) & missing(keepY)) #plsda, mint.plsda
{
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a Partial Least Squares - Discriminant Analysis is being performed (PLS-DA)")
res = plsda(X = X, Y = Y, ncomp = ncomp,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
} else {# mint
if (missing(scale))
scale = FALSE
message("a mint Partial Least Squares - Discriminant Analysis is being performed (mint.PLS-DA)")
res = mint.plsda(X = X, Y = Y, ncomp = ncomp, study = study,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
}
} else {#splsda, mint.splsda
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a sparse Partial Least Squares - Discriminant Analysis is being performed (sPLS-DA)")
res = splsda(X = X, Y = Y, ncomp = ncomp, keepX = keepX, max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
} else {# mint
if (missing(scale))
scale = FALSE
message("a mint sparse Partial Least Squares - Discriminant Analysis is being performed (mint.sPLS-DA)")
res = mint.splsda(X = X, Y = Y, ncomp = ncomp, study = study,keepX = keepX,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
}
}
} else { #pls or spls
#Check.entry.pls(X, Y, ncomp, keepX, keepY) # to have the warnings relative to X and Y, instead of blocks
if (missing(keepX) & missing(keepY)) #pls, mint.pls
{
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a Partial Least Squares is being performed (PLS)")
res = pls(X = X, Y = Y, ncomp = ncomp, mode = mode,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
} else { # mint
if (missing(scale))
scale = FALSE
message("a mint Partial Least Squares is being performed (mint.PLS)")
res = mint.pls(X = X, Y = Y, ncomp = ncomp, mode = mode, study = study,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
}
} else {
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a sparse Partial Least Squares is being performed (sPLS)")
res = spls(X = X, Y = Y, ncomp = ncomp, mode = mode, keepX = keepX,keepY = keepY,
max.iter = max.iter, tol = tol,
near.zero.var = near.zero.var,scale = scale)
} else {
if (missing(scale))
scale = FALSE
message("a mint sparse Partial Least Squares is being performed (mint.sPLS)")
res = mint.spls(X = X, Y = Y, ncomp = ncomp, mode = mode, study = study,keepX = keepX,keepY = keepY,
max.iter = max.iter, tol = tol,
near.zero.var = near.zero.var,scale = scale)
}
}
}
}
cl = match.call()
res$call = cl
class(res) = c("mixOmics",class(res))
return(invisible(res))
}
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Defines functions mixOmics
Documented in mixOmics
# ========================================================================================================
# mixOmics: perform one of the package's function depending on the input data (list or matrix, vector or categerical data, etc)
# ========================================================================================================
#' PLS-derived methods: one function to rule them all!
#'
#' \emph{This is the documentation for mixOmics function from mixOmics package.
#' For package documentation refer to \code{help(package='mixOmics')}}
#'
#' This function performs one of the PLS derived methods included in the
#' mixOmics package that is the most appropriate for your input data, one of
#' (mint).(block).(s)pls(da) depending on your input data (single data, list of
#' data, discrete outcome, \dots{})
#'
#' This function performs one of the PLS derived methods included in the
#' mixOmics package that is the most appropriate for your input data, one of
#' (mint).(block).(s)pls(da).
#'
#' If your input data \code{X} is a matrix, then the algorithm is directed
#' towards one of (mint).(s)pls(da) depending on your input data \code{Y}
#' (factor for the discrete outcome directs the algorithm to DA analysis) and
#' whether you input a \code{study} parameter (MINT analysis) or a \code{keepX}
#' parameter (sparse analysis).
#'
#' If your input data \code{X} is a list of matrices, then the algorithm is
#' directed towards one of (mint).block.(s)pls(da) depending on your input data
#' \code{Y} (factor for the discrete outcome directs the algorithm to DA
#' analysis) and whether you input a \code{study} parameter (MINT analysis) or
#' a \code{keepX} parameter (sparse analysis).
#'
#' More details about the PLS modes in \code{?pls}.
#'
#' @param X Input data. Either a matrix or a list of data sets (called
#' 'blocks') matching on the same samples. Data should be arranged in samples x
#' variables, with samples order matching in all data sets.
#' @param Y Outcome. Either a numeric matrix of responses or a factor or a
#' class vector for the discrete outcome.
#' @param indY To supply if Y is missing, indicates the position of the outcome
#' in the list X
#' @param study grouping factor indicating which samples are from the same
#' study
#' @param ncomp If \code{X} is a data matrix, \code{ncomp} is a single value.
#' If \code{X} is a list of data sets, \code{ncomp} is a numeric vector of
#' length the number of blocks in \code{X}. The number of components to include
#' in the model for each block (does not necessarily need to take the same
#' value for each block).
#' @param keepX Number of variables to keep in the \eqn{X}-loadings
#' @param keepY Number of variables to keep in the \eqn{Y}-loadings
#' @param design numeric matrix of size (number of blocks) x (number of blocks)
#' with only 0 or 1 values. A value of 1 (0) indicates a relationship (no
#' relationship) between the blocks to be modelled. If \code{Y} is provided
#' instead of \code{indY}, the \code{design} matrix is changed to include
#' relationships to \code{Y}.
#' @param tau numeric vector of length the number of blocks in \code{X}. Each
#' regularization parameter will be applied on each block and takes the value
#' between 0 (no regularisation) and 1. If tau = "optimal" the shrinkage
#' paramaters are estimated for each block and each dimension using the Schafer
#' and Strimmer (2005) analytical formula.
#' @param scheme Either "horst", "factorial" or "centroid" (Default:
#' "centroid"), see reference paper.
#' @param mode character string. What type of algorithm to use, (partially)
#' matching one of \code{"regression"}, \code{"canonical"}, \code{"invariant"}
#' or \code{"classic"}. See Details.
#' @param scale Logical. If scale = TRUE, each block is standardized to zero
#' means and unit variances (default: TRUE)
#' @param init Mode of initialization use in the algorithm, either by Singular
#' Value Decompostion of the product of each block of X with Y ("svd") or each
#' block independently ("svd.single") . Default to "svd".
#' @param tol Convergence stopping value.
#' @param max.iter integer, the maximum number of iterations.
#' @param near.zero.var Logical, see the internal \code{\link{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
#' @return none
#' @author Florian Rohart, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{pls}}, \code{\link{spls}}, \code{\link{plsda}},
#' \code{\link{splsda}}, \code{\link{mint.pls}}, \code{\link{mint.spls}},
#' \code{\link{mint.plsda}}, \code{\link{mint.splsda}},
#' \code{\link{block.pls}}, \code{\link{block.spls}},
#' \code{\link{block.plsda}}, \code{\link{block.splsda}},
#' \code{\link{mint.block.pls}}, \code{\link{mint.block.spls}},
#' \code{\link{mint.block.plsda}}, \code{\link{mint.block.splsda}}
#' @references
#'
#' 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
#'
#' MINT models:
#'
#' 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. (2013). Multi-group
#' PLS Regression: Application to Epidemiology. In New Perspectives in Partial
#' Least Squares and Related Methods, pages 243-255. Springer.
#'
#' Integration of omics data sets:
#'
#' Singh A, Gautier B, Shannon C, Vacher M, Rohart F, Tebbutt S, Lê Cao K-A.
#' DIABLO: an integrative, multi-omics, multivariate method for multi-group
#' classification. \url{http://biorxiv.org/content/early/2016/08/03/067611}
#'
#' Lê Cao, K.-A., Martin, P.G.P., Robert-Granie, C. and Besse, P. (2009).
#' Sparse canonical methods for biological data integration: application to a
#' cross-platform study. \emph{BMC Bioinformatics} \bold{10}:34.
#'
#' Lê Cao, K.-A., Rossouw, D., Robert-Granie, C. and Besse, P. (2008). A sparse
#' PLS for variable selection when integrating Omics data. \emph{Statistical
#' Applications in Genetics and Molecular Biology} \bold{7}, article 35.
#'
#' Tenenhaus A., Phillipe C., Guillemot V., Lê Cao K-A. , Grill J. , Frouin V.
#' (2014), Variable selection for generalized canonical correlation analysis,
#' \emph{Biostatistics}, doi: 10.1093/biostatistics. PMID: 24550197.
#'
#' Sparse SVD:
#'
#' Shen, H. and Huang, J. Z. (2008). Sparse principal component analysis via
#' regularized low rank matrix approximation. \emph{Journal of Multivariate
#' Analysis} \bold{99}, 1015-1034.
#'
#' PLS-DA:
#'
#' Lê Cao K-A, Boitard S and Besse P (2011). Sparse PLS Discriminant Analysis:
#' biologically relevant feature selection and graphical displays for
#' multiclass problems. BMC Bioinformatics 12:253.
#'
#' PLS:
#'
#' Tenenhaus, M. (1998). \emph{La regression PLS: theorie et pratique}. Paris:
#' Editions Technic.
#'
#' Wold H. (1966). Estimation of principal components and related models by
#' iterative least squares. In: Krishnaiah, P. R. (editors), \emph{Multivariate
#' Analysis}. Academic Press, N.Y., 391-420.
#'
#' Abdi H (2010). Partial least squares regression and projection on latent
#' structure regression (PLS Regression). \emph{Wiley Interdisciplinary
#' Reviews: Computational Statistics}, 2(1), 97-106.
#'
#' On multilevel analysis:
#'
#' Liquet, B., Lê Cao, K.-A., Hocini, H. and Thiebaut, R. (2012) A novel
#' approach for biomarker selection and the integration of repeated measures
#' experiments from two platforms. \emph{BMC Bioinformatics} \bold{13}:325.
#'
#' Westerhuis, J. A., van Velzen, E. J., Hoefsloot, H. C., and Smilde, A. K.
#' (2010). Multivariate paired data analysis: multilevel PLSDA versus OPLSDA.
#' \emph{Metabolomics}, \bold{6}(1), 119-128.
#'
#' Visualisations:
#'
#' González I., Lê Cao K.-A., Davis, M.D. and Déjean S. (2013) Insightful
#' graphical outputs to explore relationships between two omics data sets.
#' BioData Mining 5:19.
#' @keywords multivariate hplot dplot
#' @export
#' @example ./examples/mixOmics-examples.R
mixOmics <- function(X,
Y,
indY, #only use if Y not provided
study, #mint
ncomp,
keepX, #sparse
keepY, #sparse
design, #block
tau = NULL, # rgcca, number between 0,1 or "optimal"
scheme, #block
mode = c("regression", "canonical", "invariant", "classic"), #for DA only regression
scale,
init,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE)
{
mode <- match.arg(mode)
.check_regression_mode <- function(mode) {
if (mode != 'regression') {
warning("using `mode = 'regression'` for discriminant analysis\n")
}
return(NULL)
}
if (is.list(X) & !is.data.frame(X))# either rgcca, sgcca,sgcca-DA, mint.block, mint.block-DA
{
#need to check if Y or indY is a factor, unmap it and then do the checks (no other factors etc)
if ((missing(indY)& missing(Y)) & is.null(tau))
stop("Either 'Y', 'indY' or 'tau' is needed")
if (is.null(tau)) # SGCCA/mint
{
isfactorY = FALSE
if (!missing(Y))
{
if (is.list(Y) & !is.data.frame(X)) stop("Y must be a matrix or a factor")
if (is.factor(Y)) {
#Y = as.factor(Y)
isfactorY = TRUE
}
}else if (!missing(indY)) {
temp = X[[indY]] #not called Y to not be an input of the wrappers
if (is.factor(temp)) {
#temp = as.factor(temp)
isfactorY = TRUE
}
}else if (missing(indY)) {
stop("Either 'Y' or 'indY' is needed")
}
if (isfactorY)# either block.plsda/block.splsda/mint.block.plsda/mint.block.splsda
{
.check_regression_mode(mode)
if (missing(keepX))
{
if (missing(study)) #block.plsda
{
if (missing(scale))
scale = FALSE
message("a block Partial Least Squares - Discriminant Analysis is being performed (block.PLS-DA)")
res = block.plsda(X = X, Y = Y, indY = indY, ncomp = ncomp,design = design,scheme = scheme,
scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.plsda
if (missing(scale))
scale = FALSE
message("a mint block Partial Least Squares - Discriminant Analysis is being performed (mint.block.PLS-DA)")
res = mint.block.plsda(X = X, Y = Y, indY = indY,study = study, ncomp = ncomp,design = design,scheme = scheme,
scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
}
} else {
if (missing(study))# block.splsda
{
if (missing(scale))
scale = FALSE
message("a block sparse Partial Least Squares - Discriminant Analysis is being performed (block.sPLS-DA)")
res = mint.block.splsda(X = X, Y = Y, indY = indY, ncomp = ncomp,keepX = keepX,
design = design,scheme = scheme, scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.splsda
if (missing(scale))
scale = FALSE
message("a mint block sparse Partial Least Squares - Discriminant Analysis is being performed (mint.block.sPLS-DA)")
res = mint.block.splsda(X = X, Y = Y, indY = indY, ncomp = ncomp,study = study,keepX = keepX,
design = design,scheme = scheme,scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
}
}
} else { # either block.pls/block.spls/mint.block.pls/mint.block.spls
if (missing(keepX) )
{
if (missing(study)) #block.pls
{
if (missing(scale))
scale = FALSE
message("a block Partial Least Squares is being performed (block.PLS)")
res = block.pls(X = X, Y = Y, indY = indY, ncomp = ncomp,design = design,scheme = scheme,
mode = mode,scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.pls
if (missing(scale))
scale = FALSE
message("a mint block Partial Least Squares is being performed (mint.block.PLS)")
res = mint.block.pls(X = X, Y = Y, indY = indY,study = study, ncomp = ncomp,design = design,scheme = scheme,
mode = mode,scale = scale, init = init,tol = tol, max.iter = max.iter,near.zero.var = near.zero.var)
}
} else {
if (missing(study))# block.spls
{
if (missing(scale))
scale = FALSE
message("a block sparse Partial Least Squares is being performed (block.sPLS)")
res = block.spls(X = X, Y = Y, indY = indY, ncomp = ncomp,keepX = keepX,
design = design,scheme = scheme,mode = mode,scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
} else {# mint.block.spls
if (missing(scale))
scale = FALSE
message("a mint block sparse Partial Least Squares is being performed (mint.block.sPLS)")
res = mint.block.spls(X = X, Y = Y, indY = indY, ncomp = ncomp,study = study,keepX = keepX,
design = design,scheme = scheme,mode = mode,scale = scale, init = init,tol = tol,
max.iter = max.iter,near.zero.var = near.zero.var)
}
}
}
} else { # RGCCA
if (!missing(study)) {message("'study' is not used")}
if (missing(keepX) ) #RGCCA
{
message("A RGCCA analysis is being performed")
if (missing(scale))
scale = FALSE
res = wrapper.rgcca(X = X,design = design,tau = tau,ncomp = ncomp,
max.iter = max.iter,scheme = scheme,scale = scale,init = init, tol = tol)
} else { #sparse RGCCA
if (missing(scale))
scale = FALSE
message("A sparse RGCCA analysis is being performed")
res = wrapper.rgcca(X = X,design = design,tau = tau,ncomp = ncomp,keepX = keepX,
max.iter = max.iter,scheme = scheme,scale = scale,init = init, tol = tol)
}
}
#end if (is.list(X))
} else {#either pls,spls, plsda, splsda or mint. pls/spls/plsda/splsda
if (missing(Y))
stop("Y is missing")
if (is.list(Y) & !is.data.frame(X))
stop("Y must be a matrix or a factor")
if (missing(mode)) mode = "regression"
#check for unused inputs (scheme, etc etc)
if (!is.null(tau) | !missing(design) | !missing(init) | !missing(scheme))
{
if (!is.null(tau))
message("'tau' is not used")
if (!missing(design))
message("'design' is not used")
if (!missing(init))
message("'init' is not used")
if (!missing(scheme))
message("'scheme' is not used")
stop("unused input parameters")
}
if (is.factor(Y))#either plsda, splsda
{
.check_regression_mode(mode)
#Check.entry.pls.single(X, ncomp, keepX) # to have the warnings relative to X and Y, instead of blocks
if (length(Y)!=nrow(X))
stop("unequal number of rows in 'X' and 'Y'.")
if (missing(keepX) & missing(keepY)) #plsda, mint.plsda
{
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a Partial Least Squares - Discriminant Analysis is being performed (PLS-DA)")
res = plsda(X = X, Y = Y, ncomp = ncomp,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
} else {# mint
if (missing(scale))
scale = FALSE
message("a mint Partial Least Squares - Discriminant Analysis is being performed (mint.PLS-DA)")
res = mint.plsda(X = X, Y = Y, ncomp = ncomp, study = study,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
}
} else {#splsda, mint.splsda
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a sparse Partial Least Squares - Discriminant Analysis is being performed (sPLS-DA)")
res = splsda(X = X, Y = Y, ncomp = ncomp, keepX = keepX, max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
} else {# mint
if (missing(scale))
scale = FALSE
message("a mint sparse Partial Least Squares - Discriminant Analysis is being performed (mint.sPLS-DA)")
res = mint.splsda(X = X, Y = Y, ncomp = ncomp, study = study,keepX = keepX,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
}
}
} else { #pls or spls
#Check.entry.pls(X, Y, ncomp, keepX, keepY) # to have the warnings relative to X and Y, instead of blocks
if (missing(keepX) & missing(keepY)) #pls, mint.pls
{
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a Partial Least Squares is being performed (PLS)")
res = pls(X = X, Y = Y, ncomp = ncomp, mode = mode,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
} else { # mint
if (missing(scale))
scale = FALSE
message("a mint Partial Least Squares is being performed (mint.PLS)")
res = mint.pls(X = X, Y = Y, ncomp = ncomp, mode = mode, study = study,
max.iter = max.iter, tol = tol, near.zero.var = near.zero.var,scale = scale)
}
} else {
if (missing(study))
{
if (missing(scale))
scale = TRUE
message("a sparse Partial Least Squares is being performed (sPLS)")
res = spls(X = X, Y = Y, ncomp = ncomp, mode = mode, keepX = keepX,keepY = keepY,
max.iter = max.iter, tol = tol,
near.zero.var = near.zero.var,scale = scale)
} else {
if (missing(scale))
scale = FALSE
message("a mint sparse Partial Least Squares is being performed (mint.sPLS)")
res = mint.spls(X = X, Y = Y, ncomp = ncomp, mode = mode, study = study,keepX = keepX,keepY = keepY,
max.iter = max.iter, tol = tol,
near.zero.var = near.zero.var,scale = scale)
}
}
}
}
cl = match.call()
res$call = cl
class(res) = c("mixOmics",class(res))
return(invisible(res))
}
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