[BioC] Help in the normalization procedures of a cDNA microarray analysis by using limma (problem in dye effect)

Naomi Altman naomi at stat.psu.edu
Tue Jun 19 21:29:40 CEST 2007


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]]
>
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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



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