[BioC] Analysis of Differentially Expressed Genes using Microarray Technology

[Thomas H. Hampton]

This is a great question. Obviously, with an N of 1, you are sorely limited in what 
you can say. In your consideration, you need to consider the ratio between your 
two samples, but you should also consider whether the ratio is based on a gene 
that is well-expressed in your system. Most genes are not very well expressed, 
and so many of your "largest" ratios will involve genes that are expressed at very 
low levels -- low enough so you might wonder whether the ratio is just pure 
noise. If I were you, I would look first at genes that are well expressed and show a large 
ratio, as well. You may also want to place emphasis on genes that are well understood
and well annotated. At the end of the day, you can only make very preliminary statements
of course, but you may see something that is worth following up on...

Best

Tom
On Sep 10, 2012, at 2:38 PM, Eleonora Lusito wrote:

> Dear BioC users, I have a question regarding microarray data analysis (Human
> Affymetrix one color). My point is that I have just 1 sample TREATMENT and 1
> sample REFERENCE. Neither technical replicates nor biological replicates are
> available. A statistical test to find differentially expressed genes between
> the two conditions seems to me impossible (even the simple t-test) due to the
> absence of replicates. People who asked me to do the analysis were interested
> only in finding the genes changing between the two conditions. In this
> conditions, in my opinion only the fold change is possible just to give a
> general view of the behavior of the genes. Any other suggestion about this
> issue?
> 
> Thanks a lot
> 
> E.
> 
> -- 
> Eleonora Lusito
> Computational Biology PhD student
> Molecular Medicine Program
> via Ripamonti 435, 20141 Milano, Italy
> 
> Phone number: +390294375160
> e-mail: eleonora.lusito at ieo.eu
> 
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