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

Sindre Lee sindre.lee at medisin.uio.no
Mon Aug 11 22:26:38 CEST 2014

Thank you for your answers!

Please see inline for a final comment.

On 2014-08-11 19:47, Peter Langfelder wrote:
> 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
> situation-dependent.
>> 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.

Ok, I will try that, thank you!
My thought was that if the module was related weight (in two data sets) 
and changes (log-ratio) in weight, wouldn't that point to a stronger 
relationship? If not, why?

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