[BioC] Looking for an opinion about Affymetrix signals

[James MacDonald]

Hi Remo,

If you have triplicates, I would recommend using a statistical test
(t-test, F-test, etc.) to determine which genes are differentially
expressed. You probably won't have much power to detect differences, but
you can try to increase your power by using an empirical Bayes
adjustment to your variance estimate (using EBayes or limma packages).

In general you would use log transformed data for most statisitical
tests, so if you want to filter your data further using fold change, you
should use geometric means.

HTH,

Jim



James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
>>> <sanges at biogem.it> 03/10/04 9:05 PM >>>
: 

I apologize if this topic is a little bit OT,
but I think this is the better list in which discuss about
mathematical and statistical issues relating Affymetrix chips.

I work in a service but I have very poor feedback from users.
Furthermore I am a biologist so I ask your opinion based on your
experience and knowledge.

Generally I use in the analysis gcrma background subtraction,
quantile normalization, pm-only and medianpolish.
Usually this analysis are conducted on an experiment in which
each point has a biological triplicate.

Now I am thinking at the more robust way to infer fold changes.
For robust I means 'nearest to biology' and 'statistically acceptable'.
My problem is how to summarize replicate signals for each probe before
compute ratios.

What do you think is the best way to have a mean signal from replicates?
Arithmetic mean or geometric men?
Is one of this approach wrong?
Is the choose of the approach dependent from the homogeneity of
replicates?

Thank you

Best Regards

Remo Sanges
BioGeM

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