Description Usage Arguments Examples
cobra
computes a convex biclustering path via Dykstra-like Proximal Algorithm
1 |
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 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ## 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])
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Loading required package: Matrix
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
[1] 1
[1] 1
[1] "***** Completed gamma = 1 *****"
[1] "***** Completed gamma = 2 *****"
[1] "***** Completed gamma = 3 *****"
[1] "***** Completed gamma = 4 *****"
[1] "***** Completed gamma = 5 *****"
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