[BioC] No replicates and differential analysis !!

[Magnus Rattray]

Hi Nicolas,

I tried to send this message to the mailing list yesterday but it seems 
not to have got through.

Like others I'd caution against using no replicates at all, but I think 
we have some methods that are useful with no or very few replicates. 
Like Ben Bolstad said in a previous message, I would suggest that you 
use the probe-level analysis to estimate the technical variance. We have 
a method, multi-mgMOS, which estimates the log-signal and associates 
this estimate with credibility intervals (either variances or 
percentiles of the posterior log-signal). A paper describing this method 
is available from,

http://bioinformatics.oxfordjournals.org/cgi/content/short/bti583v1

and the most recent code and other publications are on the project web-site,

http://umber.sbs.man.ac.uk/resources/puma/

An older version of mmgMOS is available in bioconductor.

More recently we have developed a Bayesian t-test, PPLR, which allows 
this technical variance to be included in determining differential 
expression from replicated conditions. The PPLR code also works with no 
replicates, in which case it just uses the technical variance. R code is 
available from the above website (the paper is submitted and we will get 
PPLR into bioconductor shortly).

I suggest you try mmgMOS, followed by mean or median centering of the 
log-signal estimates, followed by PPLR. Let me know how you get on - 
these are new methods and feedback is very welcome.

Regards,
Magnus.

-- 
Dr Magnus Rattray
School of Computer Science,
University of Manchester,
Oxford Road,
Manchester M13 9PL, UK.

Tel. +44-161-275-6187
Fax. +44-161-275-6204

 > Hello,
 >
 > Does anybody know a R package or function to compare expression level
 > (affy data) of two groups with no replicates in each group ? In fact,
 > just compare one array to an other.
 > The purpose is to find differentially expressed genes.
 > We cannot used statistical test (not enougth replicates), but we can
 > used graphical approach based on scatter plot, and outliers detection
 > approach.
 >
 > Thanks for your help,
 > Regards
 >
 > Nicolas.
 >