I have microarray data, which is 2 colour agilent human of 3 technical replicates. Green dye case and Red dye control. I have analysed in Limma, normalising within arrays and between arrays using aQuantile normalisation. I also have some Next gen RNAseq data that has been mapped to the Refseq transcriptome and I have these raw counts. However there are no replicates; only one case and one control. I want to plot how the Log2 Fold change is correlated between the two data sets as they are looking at similar samples. The microarray data is easy as Limma reports log2 fold change but NGS on the other hand does not. What would be the best package/approach to generating a log2 fold change of the next gen counts? I am thinking they should be quantile normalised as the microarray data is????

Hi John, You have not really told us enough to help you but I will try to guess about what you need anyways. To just get started, the thing you most need is to count how many of your aligned reads in the two samples are landing on each gene of interest. You probably want to look at the Shortread and GenomicFeatures packages to get your alignments read in and some annotations to compare them to, and then you will want to use countOverlaps() from the GenomicRanges package to count how many reads land in each annotation element for each sample. Then you will at least have counts that you can play with. (and compare to the quantifications from your microarray experiment). Once you have that you can at least start to think about fold changes. Hope this helps, Marc On 07/12/2011 12:18 PM, john herbert wrote: > I have microarray data, which is 2 colour agilent human of 3 technical > replicates. > Green dye case and Red dye control. I have analysed in Limma, > normalising within arrays and between arrays using aQuantile > normalisation. > I also have some Next gen RNAseq data that has been mapped to the > Refseq transcriptome and I have these raw counts. > However there are no replicates; only one case and one control. > I want to plot how the Log2 Fold change is correlated between the two > data sets as they are looking at similar samples. > The microarray data is easy as Limma reports log2 fold change but NGS > on the other hand does not. > What would be the best package/approach to generating a log2 fold > change of the next gen counts? > I am thinking they should be quantile normalised as the microarray data is???? > > _______________________________________________ > 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