Unsupervised classifications about nmf HOT 3 OPEN

Yes, NMF actually gives you a biclustering model, grouping patients (columns) and genes (rows), which have expression pattern that are characteristic to each patient group.
The cluster memberships are returned by

predict(res)

, based on the most contributing basis component in each patient. The contributions of each basis component is given the matrix H ( X = W * H) by

coef(res)

You can see the contribution patterns with

# default is to scale contributions so sum up to one
coefmap(res)
# consensus matrix
consensusmap(res)

from nmf.

Thanks for the response. I tried to estimate the rank to get the best possibility. you can see the results for ranks 3:6 in the figure. Clustering data into 3 clusters has the highest cophenetic but rank 3 also has the highest dispersion. Do you think I can support my idea of clustering the samples into 3 in this situation?

from nmf.

Higher the dispersion the better, as it measures how much distinct the consensus clusters are. So these two measures are actually consistent.

from nmf.