ConsensusClusterPlus: How to extract most contributing features for each cluster
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2.1 years ago
komal.rathi ★ 4.1k

Hi,

I am using the R package ConsensusClusterPlus. Here is an example with the ALL data:

library(ConsensusClusterPlus)
library(ALL)
data(ALL)
d = exprs(ALL)

res <- ConsensusClusterPlus(d,
clusterAlg = "pam",
distance = "spearman",
plot = NULL,
reps = 1000,
maxK = 10,
pItem = 0.8,
pFeature = 1,
seed = 100)


So if I want to get information on the cluster membership for each sample when k = 5, I would get it by using:

cluster5 <- res[[5]]
> head(cluster5\$consensusClass, n = 10)
01005 01010 03002 04006 04007 04008 04010 04016 06002 08001
1     2     1     2     1     1     2     1     1     3


My question is: how do I extract the most contributing features (or genes in this case) in each cluster?

R consensusclusterplus • 852 views
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Since you are clustering patients/samples using expression values, my best guess would be to separate patients based on cluster membership, e.g. For cluster 1, get a matrix of patients that are only associated with cluster 1 and compare the gene expression between other clusters. You can use something like a Wilcox test. Sort results based on fold-change or P-values.

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Hi, It is an answer, but would like to know whether you have found a way to extracting the most contributing features for each cluster? I am also stuck at this point.