# Cluster the Rows of X into K Clusters Using the AWNCut Method. Builds similarity matrices for the rows of X and the rows of an assisted dataset Z. Clusters them into K groups while conducting feature selection based on the AWNCut method. ```r awncut(X, Z, K, lambda, Tau, B = 500, L = 1000) ``` ## Arguments - `X`: is an n x p1 matrix of n observations and p1 variables. - `Z`: is an n x p2 matrix of n observations and p2 variables. Z is the assistant dataset. - `K`: is the number of clusters. - `lambda`: is a vector of tuning parameter lambda in the objective function. - `Tau`: is a vector of tuning parameters tau to be used in the objective function. - `B`: is the number of iterations in the simulated annealing algorithm. - `L`: is the temperature coefficient in the simulated annealing algorithm. ## Returns A list with the following components: - **lambda**: the value of tuning parameter lambda for the result - **tau**: the value of tuning parameter tau for the result - **Cs**: a matrix of the clustering result - **ws**: a vector of the feature selection result - **OP.value**: the value of the objective function ## Details The algorithm maximizes a sum of the weighed NCut measure for X and assisted dataset Z, with the addition of a correlation measure between the two datasets. Feature selection is implemented by using the average correlation of each feature as a criterion. ## Examples ```r set.seed(123456) #This sets up the initial parameters for the simulation. lambda <- seq(2,6,1) #Tuning parameter lambda Tau <- seq(0.2,0.8,0.2) #Tuning parameter tau n=30; n1=10; n2=10; n3=n-n1-n2 #Sample size p1=10; p2=10; r1=8; r2=8; #Number of variables and noises in each dataset K=3; #Number of clusters mu=1; #Mean of the marginal distribution u1=0.5; #Range of enties in the coefficient matrix library(mvtnorm) epsilon <- matrix(rnorm(n*(p1-r1),0,1), n, (p1-r1)) # Generation of random error Sigma1 <- matrix(rep(0.8,(p1-r1)^2),(p1-r1),(p1-r1)) # Generation of the covariance matrix diag(Sigma1) <- 1 # Generation of the original distribution of the three clusters T1 <- matrix(rmvnorm(n1,mean=rep(-mu,(p1-r1)),sigma=Sigma1),n1,(p1-r1)) T2 <- matrix(rmvnorm(n2,mean=rep(0,(p1-r1)),sigma=Sigma1),n2,(p1-r1)) T3 <- matrix(rmvnorm(n3,mean=rep(mu,(p1-r1)),sigma=Sigma1),n3,(p1-r1)) X1 <- sign(T1)*(exp(abs(T1))) #Generation of signals in X X2 <- sign(T2)*(exp(abs(T2))) X3 <- sign(T3)*(exp(abs(T3))) ep1 <- (matrix(rnorm(n*r1,0,1),n,r1)) #Generation of noises in X X <- rbind(X1,X2,X3) beta1 <- matrix(runif((p1-r1)*(p2-r2),-u1,u1),(p1-r1),(p2-r2)) #Generation of the coefficient matrix Z <- X%*%beta1+epsilon #Generation of signals in Z ep2 <- (matrix(rnorm(n*r2,0.5,1),n,r2)) #Generation of noises in Z X <- cbind(X,ep1) Z <- cbind(Z,ep2) #our method Tune1 <- awncut.selection(X, Z, K, lambda, Tau, B = 20, L = 1000) awncut.result <- awncut(X, Z, 3, Tune1$lam, Tune1$tau, B = 20, L = 1000) ErrorRate(awncut.result[[1]]$Cs, n1, n2) ``` ## References Li, Yang; Bie, Ruofan; Teran Hidalgo, Sebastian; Qin, Yinchen; Wu, Mengyun; Ma, Shuangge. Assisted gene expression-based clustering with AWNCut. (Submitted.) ## Author(s) Ruofan Bie. Maintainer: Sebastian Jose Teran Hidalgo [sebastianteranhidalgo@gmail.com](sebastianteranhidalgo@gmail.com).

awncut function