Dear professor Dvinge, Thank you very much for your response. The MA plot, the direction of plot is completely in reverse order to microarray studies, which is expected since in qPCR the higher results (Cts) corresponds to less expressed genes, in contrary to intensities in array studies. On 12/19/11, Heidi Dvinge <heidi at="" ebi.ac.uk=""> wrote: > Hi Ali, > >> Hi everyone, >> >> Newbie here. I was wondering whether an MA plot for highthrouput qPCR >> array (~760 genes) shows the bias? > > The best way to find out is probably to try it :). Remember that (for log2 > values) M and A are simply defined as: > M = R - G > A = (R + G)/2 > You can therefore calculate these values for whatever samples you have, > either assigning 'R' and 'G' to two different samples, or for example let > one of them be the mean across all samples. > > Alternatively, there is an increasing number of qPCR-specific packages in > Bioconductor. HTqPCR contains a number of functions for visualising data > quality + potential biases and for normalising or removing 'bad' samples. > In addition, if you do a search on > http://bioconductor.org/packages/release/bioc/ there's more packages you > might be interested in. > > HTH > \Heidi > >> Especially because in contrary to >> array results, lower expression values have higher "signals". Would >> transformation of results help? How may I do that? >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > >