Dear all, the following problem: samples are either RNA, or RNA with selective depletion of some forms of RNA. In short, the relative abundance in the second group of samples should always be equal to or smaller than that in the control, but never higher. The difference in abundance might concern a substantial fraction of mRNAs (10-50%). Naturally, when the samples are normalised, since the total transcript abundance in the experimental group is significantly lower, the relative abundance of transcripts with no change will be higher in the experimental group, and artifacts will occur: we will observe genes that are apparently up-regulated, although in reality their levels remain stable. The arrays are two-color Agilent chips. The preferred analysis tool would be limma. What would be the best way to normalise such data? I am considering, at the moment, the following: - completely forget between array normalisation, just doing the background substraction and within array normalisation; - use QPCR to measure several genes in a wide dynamic range, use them to guide normalisation so that the differences in levels fit the observed differences in QPCR Any suggestions are appreciated, Best regards, j. -- -------- Dr. January Weiner 3 -------------------------------------- Max Planck Institute for Infection Biology Charit?platz 1 D-10117 Berlin, Germany Web?? : www.mpiib-berlin.mpg.de Tel? ?? : +49-30-28460514

Hi January On 03/28/2011 05:02 PM, January Weiner wrote: > the following problem: samples are either RNA, or RNA with selective > depletion of some forms of RNA. In short, the relative abundance in > the second group of samples should always be equal to or smaller than > that in the control, but never higher. The difference in abundance > might concern a substantial fraction of mRNAs (10-50%). > > Naturally, when the samples are normalised, since the total transcript > abundance in the experimental group is significantly lower, the > relative abundance of transcripts with no change will be higher in the > experimental group, and artifacts will occur: we will observe genes > that are apparently up-regulated, although in reality their levels > remain stable. We faced the same problem a while ago in a project comparing mRNA from fertilized vs unfertilized Drosophila eggs. In Drosophila eggs, an mRNA degradation machinery is activated when the egg is layed, and many maternally deposited transcripts get degraded within a couple of hours. We had three time points, and in the unfertilized eggs, the transcript levels could only be lower but not higher in the later compared to the earlier time points, similar to your setting. We solved the issue by first using VSN (with an increased trimming quantile), followed by LOESS and then RMA, and this worked very well. Have a look at this image: http://www.embl.de/~anders/misc_pub/FlyEggs_mod_vs_loess.png Each panel is an MA plot, comparing the indicated array with an average over all arrays. The two lines are the LOESS fit lines (with two slightly different settings). Look, for example, at the four arrays for the late unfertilized time point ("unf.3"): The triangle towards the bottom left corner are the decayed genes. They are lower than average (i.e., below y=0) and, as they are gone, also to the left -- hence the triangle. The LOESS line clearly follows the bulk of non-decayed genes and is not deterred by the triangle. Other normalization techniques such as RMA only (without preceding LOESS) or quantile normalization did not do the job. I can send you a code example if you need it. For further details, please see our paper and especially page 4 of the supplement: Thomsen S, Anders S, Chandra Janga S, Huber W, Alonso CR. Genome-wide analysis of mRNA decay patterns during early Drosophila development. Genome Biology, 11 (2010) R93. http://genomebiology.com/2010/11/9/R93 Simon +--- | Dr. Simon Anders, Dipl.-Phys. | European Molecular Biology Laboratory (EMBL), Heidelberg | office phone +49-6221-387-8632 | preferred (permanent) e-mail: sanders at fs.tum.de