% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/getMOIC.R

\name{getMOIC}

\alias{getMOIC}

\title{Get subtypes from multi-omics integrative clustering}

\usage{

getMOIC(

  data = NULL,

  methodslist = list("SNF", "CIMLR", "PINSPlus", "NEMO", "COCA", "MoCluster",

    "LRAcluster", "ConsensusClustering", "IntNMF", "iClusterBayes"),

  N.clust = NULL,

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

  ...

)

}

\arguments{

\item{data}{List of matrices (Maximum number of matrices is 6).}

\item{methodslist}{A string list specifying one or multiple methods to run (See Details).}

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

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

\item{...}{Additionnal parameters for each method (only works when only one method chosen)}

}

\value{

A list of results returned by each specified algorithms.

}

\description{

Using `getMOIC()`, users can choose one out of the ten algorithms embedded in `MOVICS`. Users can implement multi-omics clustering in a simplest way of which the only requirement is to specify and at least specify a list of matrices (argument of `data`), a number of cluster (argument of `N.clust`), and clustering method (argument of `methodslist`) in `getMOIC()`. It is possible to pass various arguments that are specific to each method. Of course, users can also directly call different algorithms by using functions start with `get` and end with the name of the algorithm (e.g., `getSNF`; please refer to `?get%algorithm_name%` for more details about the editable arguments)

}

\details{

Method for integrative clustering will be chosed according to the value of argument 'methodslist':

If \code{methodslist == "IntNMF"}, Integrative clustering methods using Non-Negative Matrix Factorization

If \code{methodslist == "SNF"}, Similarity network fusion.

If \code{methodslist == "LRAcluster"}, Integrated cancer omics data analysis by low rank approximation.

If \code{methodslist == "PINSPlus"}, Perturbation Clustering for data integration and disease subtyping

If \code{methodslist == "ConsensusClustering"}, Consensus clustering

If \code{methodslist == "NEMO"}, Neighborhood based multi-omics clustering

If \code{methodslist == "COCA"}, Cluster Of Clusters Analysis

If \code{methodslist == "CIMLR"}, Cancer Integration via Multikernel Learning (Support Feature Selection)

If \code{methodslist == "MoCluster"}, Identifying joint patterns across multiple omics data sets (Support Feature Selection)

If \code{methodslist == "iClusterBayes"}, Integrative clustering of multiple genomic data by fitting a Bayesian latent variable model (Support Feature Selection)

}

\examples{

# There is no example and please refer to vignette.

}

\references{

Pierre-Jean M, Deleuze J F, Le Floch E, et al. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration[J]. Briefings in Bioinformatics, 2019.

intNMF:

Chalise P, Fridley BL. Integrative clustering of multi-level omic data based on non-negative matrix factorization algorithm. PLoS One. 2017;12(5):e0176278.

iClusterBayes:

Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics. 2018;19(1):71-86.

SNF:

Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333-337.

Mocluster:

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

LRAcluster:

Wu D, Wang D, Zhang MQ, Gu J. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics. 2015;16:1022.

CIMLR:

Ramazzotti D, Lal A, Wang B, Batzoglou S, Sidow A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun. 2018;9(1):4453.

PINSPlus:

Nguyen H, Shrestha S, Draghici S, Nguyen T. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics. 2019;35(16):2843-2846.

ConsensusClustering:

Monti S, Tamayo P, Mesirov J, et al. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning. 2003;52:91-118.

NEMO:

Rappoport N, Shamir R. NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics. 2019;35(18):3348-3356.

COCA:

Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944.

}