degPatterns results exists unsimilar pattern in a cluster about degreport HOT 1 OPEN

Hi @etbuface,

thank you for the details.

This function works in the following way:

1-make pair-wise correlations between the input genes (that they should be significant genes defined by some other method, like DESeq2)
2-hierarchical clustering
3-cut the tree at a given point

The third point is the one will define the cluster you see. With Consensus Cluster option one, it may give better clusters, but it is not always the case. This option will use the ConsesusCluster package to define groups.

It is normal to find clusters that go almost identical, but you can see there is always a little different. I use the plot to then merge the groups to make more sense with your biology. If that little difference is not important, it makes sense to put all together.

It is common as well to find some genes that show a bigger difference when you plot the non-scale value, but the scale value should show the same pattern, even if the difference is not equal.

There is a couple of plots in the output of the function if you save it into a variable that may help you define the cutoff (http://lpantano.github.io/DEGreport/reference/degPatterns.html#value benchmarking). Look at http://lpantano.github.io/DEGreport/reference/degPlotCluster.html to see how to plot using different cutoffs.

At the end of the days, the last step is arbitrary, and some genes will go to a cluster even if they are not similar because when you cut the tree they will be part of a group. That is the reason I added reduce to remove those cases.

You are right about scale, it shouldn't be different, it is more a historical parameter and I probably should remove it.

I hope this helps.

from degreport.