Hi James,
   
  That is great! Thank you.
  I will have a look and try to select AFFXs which are suitable biologically.
  Whould you say that highly expressed (to increase the accuracy of signal detection) and least variable AFFXs would do as the basis for the selection of genes for connectivity analysis?
  I am VERY grateful for your response. The only thing that I did not get was whether you liked the idea of analysing variations across a population of individuals (which usually are considered to be the same!)?
   
  Thank you a lot for help!
  Lev.
   
  
James MacDonald <jmacdon@med.umich.edu> wrote:
  Hi Lev,

Not all the AFFX probes are the same. Here is a link that outlines what each one is used for. I wouldn't necessarily use all of the AFFX probes, but which one you use is dependent on how the chips were processed.

http://www.abdn.ac.uk/ims/facilities/microarray/documents/chipcontrols.doc 


>>> Lev Soinov wrote:
> Hi James,
> 
> Thank you very much for your reply!
> Just a quick question:
> - how would you recommend to estimate the technichal component of 
> variability? E.g. by measuring the average (or maybe max, min) variability of 
> AFFX (control) probes?
> - if AFFX's variabilty were a good measure of technical variability, would 
> you agree that taking for connectivity analysis only "interesting" (e.g. 
> differentially expressed) genes with overall variability say 2-4 times higher 
> than AFFX variability would make sense?
> 
> Thank you a lot for the link!
> Lev.
> 
> "James W. MacDonald" wrote:
> Hi Lev,
> 
> Lev Soinov wrote:
>> Dear List, It was a lot of e-mails from me recently for which I
>> apologise and hope that my questions were of interest to you. I would
>> be very much interested in your views on the following. It is
>> accepted that microarray measurements contain technical and
>> biological variations and that for single channel (e.g. Affy)
>> normalised log2 data biological variation is ~2-4 times higher than
>> technical. 
> 
> I'm not sure I would agree with that. The technical variability is 
> highly correlated with the abilities of the lab techs who do the RNA 
> extraction and the Affy molecular biology steps, and there is a huge 
> range of abilities in the world of lab techs.
> 
> In addition, biological variability is dependent on what system you are 
> considering. If for instance you take some cell lines and pour bleach on 
> one set and leave the other alone, then you are correct. The biological 
> variability will likely be larger than technical varibility. On the 
> other hand, I have seen plenty of experiments where the biological 
> variability was much smaller than the technical variability, and as a 
> result the number of 'significant' genes was less than you would expect 
> by chance, given no difference between the sample types.
> 
> Now, let's say we have N arrays representing N separate
>> individuals (or animals) of a certain type (wild type or under the
>> same treatment condition). Thus, for a given gene, variations in its
>> expression level represent variations across individuals. Suppose
>> that for each gene we calculate its mean expression level across all
>> arrays (across population) and then in each array classify genes as
>> expressed above/below their average across population levels. Now for
>> each gene we have a binary profile (above/below) across population of
>> N individuals. We can further perform say simple correlation analysis
>> and find those genes which concurrently expressed above/below their
>> population means within the given set of N arrays. This may provide
>> some information on their connectivity, etc. What would you say about
>> this? Is it a valid suggestion? Or the technical component of
>> variation will not allow doing this? If it were relevant, what would
>> be the sample size sufficient for finding accurate correlations? Do
>> you know of any Bioconductor tools that may be applied here for
>> sample size calculations? Looking forward to hearing your opinions, 
> 
> I don't like the idea of reducing data to a binary distribution without 
> a clear reason to do so. For instance, some classifiers work better if 
> you can reduce the phenotypes to a binary outcome (e.g., good/bad 
> survival outcome). However, by reducing to a binary distribution you are 
> throwing out (possibly valuable) information, so I think it is 
> reasonable to show why using the continuous distribution of data you 
> started with isn't workable.
> 
> If you want to detect connectivity of genes, you might want to look at 
> the work Steve Horvath has done:
> 
> http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ 
> 
> Best,
> 
> Jim
> 
> 
>> Lev.
> 
> 
>> 
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