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I think you are pretty much stuck unless you have spiking controls on the array. The labeling efficiency is taken care of by the dye-swap design, but the overall reduction in transcription cannot be estimated without a stable standard. --Naomi At 12:20 PM 6/19/2007, Alex Tsoi wrote: >Dear all, > >I have four cDNA microarray data, two of them are control group, and the >other two are the experimental group (the two replicates within each group >has dye swamp) > >The design is: > >Control: > >First chip: >Green dye (time point 1) >Red dye (time point 2) > >Second chip: >Green dye (time point 2) >Red dye (time point 1) > > > > >Experimental group: > >First chip: >Green dye (time point 1) >Red dye (time point 2) > >Second chip: >Green dye (time point 2) >Red dye (time point 1) > > >Since we stop the transcription process after time point 1, so the mRNA >levels in time point 2 should be lower than the one in time point 1 ; >and we find that for some reason the red dye is much less effecicient than >the green dye, so in each chip the red dye is distributed in a lower density >than that of the green dye (no matter what time point does it represent) > >My goal is to identify those mRNA in the experimental group that has fold >change (in the two time points) different from that of the control group. > >I tried to use > > normalizeWithinArrays(method="loess", bc.method="normexp") > > normalizeBetweenArrays(method="quantile") > >but I eventually end up with all the dyes in each chip having very similar >distribution (but in fact I would like to have the time point 2 has similar >distirbution across each chip, and time point 1 has the similar distribution >across each chip, and the time point 2 should have lower intensity than that >of time point 1) > >I apologize that this is pretty much a procedure for microarray analysis >rather than a bioconductor - specific question, >and I greatly appreciate any help from the group. > > >-- >Lam C. Tsoi (Alex) >Medical University of South Carolina > > [[alternative HTML version deleted]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111