Hello Maurice,

A quick perusal of the archives would tell you that the point of
normalization is to remove systematic error from the array dataset.
It is sensible thus to transform all spots as is and then average your
technical replicates prior to downstream analysis, otherwise your
metric is subject to error.  If your spot correlation is high then I
don't see how it should matter.
I see from your previous posts that we operate on common grounds - I
work with a 3-species array that includes a pathogen, and is run as a
single color experiment (your post of 2007-June/017582).  We have lots
of spots with poor cross-hybridization, and in addition we work with
inducible genes that are only expressed at certain time points.  The
end result is a large number of empty spots in many but not all slides
for a given probe - this complicates intensity-based culling of spots.
This complication has shaped our approach.  To give you an idea, we
vsn-transform all spots, including ones known to poorly
cross-hybridize and also spots printed only with buffer, average the
dupes, and then analyze only the spots we are interested in.  We
include all spots because they don't really differ from poor-hyb or
no-hyb EST spots, and we include the known poor-hybing spots because
all spots can be informative, and it also makes sense to analyze
everything at once so if you need to go back and look at a different
component, you can.

HTH,

Ben






Hello all,

I'm using vsn to transform a single-colour microarray experiment with 4 lab
lines collected a 5 times, and three biological replicates (60 slides).
Each EST is printed in duplicate, sometimes adjacent to one another and at
other times at the ends of rows and beginning of the following row.

Is it valid to vsn-transform the dataset prior to averaging the duplicate
spots?  The few papers I have that explicitly address minute details of data
preparation do not present consensus - some people average before
normalization, and others use normalization to decrease the variance among
duplicate spots.

It seems to me that providing as much information as possible would be of
benefit to stabilizing variance in a dataset, but on the other hand it seems
that pseudoreplication might bias vsn's accounting for differetially
expressed features.  I'm not much of a statistician, and we're an isolated
facility so I hope that someone can shed light on what is becoming a lively
debate.

With thanks,

Maurice

	[[alternative HTML version deleted]]

