Dear Bioconductor Mailing List,

We are working with custom-designed Agilent 4x44 arrays in our lab and we use “Agi4x44PreProcess” to perform the pre-processing.  I am looking for some advice about whether or not to summarise probe sets, and if we should, recommendations for methods/approaches to use.

I have been looking over the GMANE archive and I know that probe-set summarisation is a common feature of Affymetrix two-colour data analysis, due to the shorter oligos being used.  In contrast, given the longer length of the oligos with Agilent chips, the consensus opinion seems to be that summarising Agilent probe sets isn’t necessarily a good idea.  Indeed, this may explain why there doesn’t appear to be an obvious R routine to summarise Agilent one-colour probe sets (but please correct me if I am wrong).  We have agreed with this consensus opinion (and agree that probes can behave differently within a probe set) and have so far been running the statistics and clustering analysis where each individual probe (that passes filtering by flags) is kept in the list.

A colleague suggested that we use a non R-based stats and clustering program for differential expression.  In principle this works fine – the problem is that it can’t cope with the large data sets in which the individual probes remain.  We have to break the data set up in to chunks and perform the analysis that way, which sits a little uncomfortably with me.  So the conflict is do we try to summarise the probe sets to try and overcome this problem, or should we keep individual probes separate and look for alternative clustering programs that would enable us to process the intact data set?

So, in summary the questions I have are these:


  1.  Is summarisation ever a good idea for Agilent probe sets (we have 8 probes per transcript), and if so, are their routines in R that would enable us to do this?
  2.  If summarisation is a bad idea for Agilent data sets would taking the median signal intensity be a better strategy?
  3.  Can anybody recommend a good hierarchical clustering routine in R that would be suitable for our Agi one-colour data, whether we take all individual probes or just the median signal intensity?  (I thought maybe oompa or BiClust?)

I would really appreciate any advice or suggestions that people can give me.

Thank you all very much in anticipation of your help.

With Very Best Wishes

Sam England

Samantha England, PhD
Lewis Lab, Syracuse University
Department of Biology
110 Life Sciences Complex
107 College Place
Syracuse
NY 13244, USA

Email: sjenglan@syr.edu
Tel: (1) 315 443 7253 (lab)
Tel: (1) 315 443 1929 (office)


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