Description Usage Arguments Value Author(s) References See Also Examples
Given multiple genomic data types (e.g., copy number, gene expression, DNA methylation) measured in the same set of samples, iCluster fits a regularized latent variable model based clustering that generates an integrated cluster assigment based on joint inference across data types
1 |
datasets |
A list object containing m data matrices representing m different genomic data types measured in a set of n samples. For each matrix, the rows represent samples, and the columns represent genomic features. |
k |
Number of subtypes. |
lambda |
Vector of length-m lasso penalty terms. |
scalar |
If TRUE, assumes scalar covariance matrix Psi. Default is FALSE. |
max.iter |
Maximum iteration for the EM algorithm. |
epsilon |
EM algorithm convegence criterion. |
A list with the following elements.
meanZ |
Relaxed cluster indicator matrix. |
beta |
Coefficient matrix. |
clusters |
Cluster assigment. |
conv.rate |
Convergence history. |
Ronglai Shen shenr@mskcc.org
Ronglai Shen, Adam Olshen, Marc Ladanyi. (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906-2912.
breast.chr17
,plotiCluster
, compute.pod
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(breast.chr17)
fit=iCluster(breast.chr17, k=4, lambda=c(0.2,0.2))
plotiCluster(fit=fit, label=rownames(breast.chr17[[2]]))
compute.pod(fit)
#library(gplots)
#library(lattice)
#col.scheme = alist()
#col.scheme[[1]] = bluered(256)
#col.scheme[[2]] = greenred(256)
#cn.image=breast.chr17[[2]]
#cn.image[cn.image>1.5]=1.5
#cn.image[cn.image< -1.5]= -1.5
#exp.image=breast.chr17[[1]]
#exp.image[exp.image>3]=3
#exp.image[exp.image< -3]=3
#plotHeatmap(fit, datasets=list(cn.image,exp.image), type=c("gaussian","gaussian"),
# row.order=c(FALSE,FALSE), width=5, col.scheme=col.scheme)
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