% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nemo.R
\name{NEMO}
\alias{NEMO}
\title{NEMO: neighborhood-based multi-omics clustering}
\usage{
NEMO(List, k = NULL, NN = 20, normalize = TRUE)
}
\arguments{
\item{List}{A list of data matrices over the same objects, \strong{objects in
rows}. Sources may cover different subsets of objects (matched by row name)
for the partial-data setting.}

\item{k}{Number of clusters (\code{NULL} = eigengap estimate).}

\item{NN}{Number of neighbours for the affinity graphs and local scaling.}

\item{normalize}{Logical; row-normalise each affinity to a transition matrix
before integration (NEMO default \code{TRUE}).}
}
\value{
A list of class \code{"NEMO"} with \code{FusedM} (integrated
affinity), \code{DistM} (\code{1 - FusedM}), \code{cluster} (labels) and
\code{Clust} (an \code{agnes} object on \code{DistM} for interoperability).
}
\description{
NEMO \insertCite{Wang2014a}{MosaiClusteR} integrates several omics by building
a locally-scaled, k-nearest-neighbour affinity network for each one and
averaging them, for every pair of objects, over only the omics in which both
objects are measured. Partial / missing modalities are therefore handled
without imputation. The integrated affinity is clustered spectrally.
}
\examples{
data(mosaic_toy)
fit <- NEMO(mosaic_toy$List, k = 3, NN = 15)
table(fit$cluster)
}
\references{
\insertCite{Wang2014a}{MosaiClusteR}; Rappoport, N. & Shamir, R.
(2019). NEMO: cancer subtyping by integration of partial multi-omic data.
Bioinformatics 35:3348-3356.
}
\seealso{
\code{\link{SNF}}, \code{\link{spectral_clustering}}
}
