I have only *two* datasets from normal and cancer samples. Each samples contain mRNA microarray expressions. The final aim is to identify mRNAs that are significantly down/upregulated. What is the best R package we can use for this? - G.V. [[alternative HTML version deleted]]

There's no straightforward way to quantify fixed vs. random effects when you only have one measurement of any given quantity per group. You can subtract one set of measurements from the other and look at which changes appear to be the largest, but without additional samples, you will not know what proportion of those changes are random (independent of group) and what proportion are fixed (dependent upon what group they come from). If you want to separate those two types of effects, you have to run replicates. Best, --t On Sun, Jan 27, 2013 at 9:07 PM, Gundala Viswanath <gundalav@gmail.com>wrote: > I have only *two* datasets from normal and cancer samples. > > Each samples contain mRNA microarray expressions. > The final aim is to identify mRNAs that are significantly > down/upregulated. > > What is the best R package we can use for this? > > - G.V. > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > -- *A model is a lie that helps you see the truth.* * * Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> [[alternative HTML version deleted]]