cobra {cvxbiclustr} | R Documentation |
cobra
computes a convex biclustering path via Dykstra-like Proximal Algorithm
cobra(X, E_row, E_col, w_row, w_col, gamma, max_iter = 100, tol = 0.001)
X |
The data matrix to be clustered. The rows are the features, and the columns are the samples. |
E_row |
Edge-incidence matrix for row graph |
E_col |
Edge-incidence matrix for column graph |
w_row |
Vector of weights for row graph |
w_col |
Vector of weights for column graph |
gamma |
A sequence of regularization parameters for row and column shrinkage |
max_iter |
Maximum number of iterations |
tol |
Stopping criterion |
## Create bicluster path ## Example: Lung X <- lung X <- X - mean(X) X <- X/norm(X,'f') ## Create annotation for heatmap types <- colnames(lung) ty <- as.numeric(factor(types)) cols <- rainbow(4) YlGnBu5 <- c('#ffffd9','#c7e9b4','#41b6c4','#225ea8','#081d58') hmcols <- colorRampPalette(YlGnBu5)(256) ## Construct weights and edge-incidence matrices phi <- 0.5; k <- 5 wts <- gkn_weights(X,phi=phi,k_row=k,k_col=k) w_row <- wts$w_row w_col <- wts$w_col E_row <- wts$E_row E_col <- wts$E_col ## Connected Components of Row and Column Graphs wts$nRowComp wts$nColComp #### Initialize path parameters and structures nGamma <- 5 gammaSeq <- 10**seq(0,3,length.out=nGamma) ## Generate solution path sol <- cobra_validate(X,E_row,E_col,w_row,w_col,gammaSeq) ix <- 4 heatmap(sol$U[[ix]],col=hmcols,labRow=NA,labCol=NA,ColSideCol=cols[ty])