Hi,
This experiment involves the expression analysis between 2 batches of 
AGE variable samples which were normalized separately because the
batches did not cluster together.  The age groups of the second set
were in between the ages from the first set and all the data are desired
to be analyzed together.  Now what happened is that the separately normalized 
expression values became graphed together by the lab researchers and plotted expression
values vs age.  Now with the diseased samples there were genes which showed
a "trend" with age where the R-squared were between .16 and .4.  From my
training I get that this trend only is explains 16-40% of the data and would
not be significant.  However, using Prism these R-squares are called significantly
different from zero.  These researchers explain to me that this is the way data is presented
in their field and that an R-squared of .16-.4 is considered excellent results.  Indeed,
with non-diseased individuals the R-squared are zero for these genes.
I understand that in their field "any" trend is better than no trend, especially since
the samples are hetergeneous.  However, this is not what is taught in statistics.
These graphs will be submitted to Journals under my authorship and I am 
a bit shaken-up.
Would you please comment to me about your thoughs about the combination
of the 2 sets of expression values and the significance of the R-squared values.
Thanks,
Lana


Lana Schaffer
Biostatistics/Informatics
The Scripps Research Institute
DNA Array Core Facility
La Jolla, CA 92037
(858) 784-2263
(858) 784-2994
schaffer@scripps.edu



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