[BioC] Fwd: WGCNA: labeledHeatmap and verboseScatterplot correlations and p-values are not the same

Peter Langfelder peter.langfelder at gmail.com
Mon Aug 11 19:47:32 CEST 2014

Hi Sindre,

please see inline.

On Sat, Aug 9, 2014 at 9:47 AM, Sindre Lee <sindre.lee at medisin.uio.no> wrote:
> Thank you! I understand now, its actually well described on the webpage and
> in the documentation, I should have used more time reading before posting.
> If you have the time, I actually have a few more questions:
> 1. Why is the module size set at min. 30? What will the implications be if
> larger/smaller?

I feel that 30 is a good compromise between robust large modules and
possibly informative but not as robust small modules. You can
certainly adjust the minimum size, although I rarely find modules
below 30 genes interesting. The implication is very simple - smaller
minimum module size will lead to more modules, but whether this
provides more biological information or just more noise is highly

> 2. Is it always better to use the step-by-step network construction and
> module detection?

The step-by-step gives you more options to tweak the procedure or
insert your custom code between the steps. Otherwise they are (nearly)
identical. I say nearly because the blockwise... functions include an
extra step of removing peripheral genes from modules.

> Let me give you some details of my design so you
> understand why I ask. I have two time points, from the same persons, before
> and after an intervention. My immediate idea was to run WGCNA on the time
> point 1, relating it to body weight. Then I run WGCNA on time point 2,
> seeing if the same modules pops up. Finally, I run WGCNA on the log-ratio (
> time point 2- time point 1 ) against log-ratio of body weight. If one or
> more modules shows up in all three runs, they will be prioritised. Now, if I
> only use the one-step approach, no modules show up in all three comparisons,
> while using step-by-step looks more promising. However, if I mix the
> results, using one-step on time point 1, but step-by-step on time point 2,
> its even more interesting.

If you want to see modules that pop up in all 3 data sets, use the
consensus module approach. But it's not clear to me that looking for
modules in data set 1, data set 2, and the log-ratio of the two makes
sense. You could get consensus modules across sets 1 and 2, and then
see if the log-ratio of interesting modules is still associated with
the log-ratio of body weight.


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