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Old 04-30-2013, 06:15 AM   #3
Location: Germany

Join Date: Sep 2012
Posts: 15

I didn't do qPCR validation of the second data set, but if I do parallel analyses for each set of replicates (at least in the CLC software) I do see up-regulation of a number of known genes within each replicate set of timepoints. There is a bit of variation in timing, etc. but the genes I expect to go up do go up.

The problem comes when I try to do statistics, then the large variance in levels between the replicates makes the p-values really big for most of my "known" up-regulated genes.
I'm considering whether I need to do some sort of paired comparison, but then I'm not sure if I'll have to do separate analyses for each timepoint, comparing each timepoint to 0hrs, and then if I do that, do I have to make an even more severe significance correction if I'm effectively doing 4 separate tests...I wish I'd taken statistics more recently than 10 years ago.

On a partly unrelated note, the more I look through my data, the more I feel like cufflinks/cuffdiff is just not ideal for bacterial genomes. I feel like it doesn't deal well with the whole "many genes are in operons" issue. Has anyone else had experience with this and did you find something better?

And are there any programs that don't lump sense and antisense transcripts when counting reads mapping to a particular genomic region (also a somewhat bacteria-specific problem, I think)?
amcloon is offline   Reply With Quote