[BioC] Analysis of Differentially Expressed Genes using Microarray Technology

[Eleonora Lusito]

Hi Tom,
thanks a lot for your suggestions! I'll ask them for a marker!
Thanks again


Best

E.


 Thomas H. Hampton (Thomas.H.Hampton at dartmouth.edu) wrote:
 >
 > 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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 > > Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
 > >
 >
 >
 >

-- 
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