% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/getMoCluster.R

\name{getMoCluster}

\alias{getMoCluster}

\title{Get subtypes from MoCluster}

\usage{

getMoCluster(

  data = NULL,

  N.clust = NULL,

  type = rep("gaussian", length(data)),

  ncomp = NULL,

  method = "CPCA",

  option = "lambda1",

  k = 10,

  center = TRUE,

  scale = TRUE,

  clusterAlg = "ward.D"

)

}

\arguments{

\item{data}{List of matrices.}

\item{N.clust}{Number of clusters.}

\item{type}{Data type corresponding to the list of matrics, which can be gaussian, binomial or possion.}

\item{ncomp}{An integer value to indicate the number of components to calculate. To calculate more components requires longer computational time.}

\item{method}{A string value can be one of CPCA, GCCA and MCIA; CPCA by default.}

\item{option}{A string value could be one of c('lambda1', 'inertia', 'uniform') to indicate how the different matrices should be normalized.}

\item{k}{A numeric value to indicate the absolute number (if k >= 1) or the proportion (if 0 < k < 1) of non-zero coefficients for the variable loading vectors. It could be a single value or a vector has the same length as x so the sparsity of individual matrix could be different.}

\item{center}{A logical value to indicate if the variables should be centered. TRUE by default.}

\item{scale}{A logical value to indicate if the variables should be scaled. TRUE by default.}

\item{clusterAlg}{A string value to indicate the cluster algorithm for distance.}

}

\value{

A list with the following components:

        \code{fit}        an object returned by \link[mogsa]{mbpca}.

        \code{clust.res}  a data.frame storing sample ID and corresponding clusters.

        \code{feat.res}   the results of features selection process.

        \code{clust.dend} a dendrogram of sample clustering.

        \code{mo.method}  a string value indicating the method used for multi-omics integrative clustering.

}

\description{

This function wraps the MoCluster (Multiple omics data integrative clustering) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

}

\examples{

# There is no example and please refer to vignette.

}

\references{

Meng C, Helm D, Frejno M, Kuster B (2016). moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets. J Proteome Res, 15(3):755-765.

}