Dear List,
   
  Following Maurice's question (below), could somebody shed a light on what is the right way of filtering low quality data, before or after normalization? As I understand, the general consensus for Affy data is that one should normalize using all raw data. What would you suggest for other single-channel platforms, for example AB1700? This platform has its own QC measurements such as FLAGs and S/N ratios (high FLAGs indicate poor detection, S/N<3 indicate that such signals are too low to be properly measured). It is recommended by ABI to filter out signals with "bad" FLAGs or Signal/Noise ratios before differential expression analysis. I would be extremely grateful to you if you could explain whether these "bad" signals should be filtered out before or after normalization step.
   
  Thank you for your help.
  With kind regards,
  Lev.

Maurice Melancon <dmso12@gmail.com> wrote:
  Hello,

I worked with a single-colour multi-species array. The dataset had limited
options for transformation but disparately needed it, and vsn fit the bill
nicely. I was advised to include all non-empty spots in the normalization,
even those that I am certain show hybridization due to non-target binding,
because all spots contain useful information. To qualify for a spot being
'present' or 'absent', the creators of the array used the criteria of
intensity above 2 SD of empty spots as their criteria. This has proven to
be far too liberal for my purposes, considering the nature of the array, and
considering that while the dataset was in desparate need of scaling, the
empty spots have little to no hybridization and thus the criteria for
dropping spots prior to normalization or anything else is lax (imho).

With vsn dataset in hand, the consensus seems to be among my collaborators
to count only those spots that meet a certain criteria based upon the vsn
data as present, and to test only those for differential expression. One
then calculates FDR based upon this subset of the data. Is this fair? Can
you ignore spots used in normalization because you know them to be
unreliable probes? Or must this subset be eliminated prior to
transformation?

This is along a similar line to something posted earlier, where someone
asked if it was allowable to normalize only a subset of the spots on an
array, and to me the bigger question is how to fairly exclude spots that you
are not interested in.

With thanks,

Maurice

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