Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more.
'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering),
while incorporating the dependencies across multiple types of Omics data.
(SJ Teran Hidalgo et al (2017),

The NCutYX package includes functions for clustering genomic data using graph theory. Each function in this package is a variation on the NCut measure used to cluster vertices in a graph. The running theme is to use data sets from different sources and types to improve the clustering results.

- The ncut function clusters the columns of a data set using the classical normalized cut measure from graph theory.
- The ancut function clusters one type of data, say gene expressions, with the help of a second type of data, like copy number aberrations.
- The muncut function clusters a three-layered graph into K different clusters of 3 different data types, say gene expression, copy number aberrations and proteins.
- The pwncut function clusters the columns of X into K clusters by giving a weight for each cluster while penalizing them to be similar to each other.
- The mlbncut function works similarly to muncut but it also clusters samples into R clusters.
- The awncut builds similarity matrices for the row of X and an assisted dataset Z. Clusters them into K groups while conducting feature selection based on the AWNCut method.

To install:

- latest development version:
- install and load package devtools
`install_github("Seborinos/NCutYX")`

The Normalized Cut (NCut) clusters the columns of Y into K groups using the NCut graph measure. Builds a similarity matrix for the columns of Y and clusters them into K groups based on the NCut graph measure. Correlation, Euclidean and Gaussian distances can be used to construct the similarity matrix. The NCut measure is minimized using the cross entropy method, a Monte Carlo optimization technique.

The Assisted NCut (ANcut) clusters the columns of a data set Y into K groups with the help of an external data set X, which is associated linearly with Y.

This example shows how to use the muncut function. MuNCut clusters the columns of data from 3 different sources. It clusters the columns of Z, Y and X into K clusters by representing each data type as one network layer. It represents the Z layer depending on Y, and the Y layer depending on X. Elastic net can be used before the clustering procedure by using the predictions of Z and Y instead of the actual values to improve the cluster results. The function muncut will output K clusters of columns of Z, Y and X.

- Sebastian J. Teran Hidalgo and Shuangge Ma. “Clustering Multilayer
Omics Data using MuNCut.”
*Revise and resubmit.*

The Penalized Weighted NCut (PWNCut) clusters the columns of X into K clusters by giving a weighted cluster membership while shrinking weights towards each other.

- Sebastian J. Teran Hidalgo, Mengyun Wu and Shuangge Ma. “Penalized
and weighted clustering of gene expression data using PWNCut.”
*Submitted.*

The Multilayer Biclustering NCut (MLBNCut) clusters the columns and the rows simultaneously of data from 3 different sources. It clusters the columns of Z,Y and X into K clusters and the samples into R clusters by representing each data type as one network layer. It represents the Z layer depending on Y, and the Y layer depending on X. This function will output K clusters of columns of Z, Y and X and R clusters of the samples.

- Sebastian J. Teran Hidalgo and Shuangge Ma. “Multilayer Biclustering
of Omics Data using MLBNCut.”
*Work in progress.*

The Assisted Weighted NCut builds the similarity matrices for the rows of X and an assisted dataset Z. Clusters them into K groups while conducting feature selection based on the AWNCut method.

- Li, Yang; Bie, Ruofan; Teran Hidalgo, Sebastian; Qin, Yinchen; Wu,
Mengyun; Ma, Shuangge. “Assisted gene expression-based clustering
with AWNCut.”
*Submitted.*