ConsensusClusterPlus: How to extract most contributing features for each cluster

Question

[komal.rathi]

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",
                     finalLinkage = "average",
                     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?

[halo22]

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.

[aUser]

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.