[BioC] Un-balanced dye-swaps and LIMMA
naomi at stat.psu.edu
Tue May 25 14:42:25 CEST 2004
Here is what I have been telling people about dye-swapping. I would be
very interested if anyone thinks this is wrong.
The dye by gene interaction is due to the labeling chemistry. Therefore,
unless there are big allelic differences between the biological samples,
dye-swapping should be done between biological reps. Technical dye-swaps
are a waste of resources unless the cost of taking biological samples is
Regarding the qq plots - I am not sure what you are plotting.
If you are plotting the quantiles of WT vs quantiles of Treated, then the
QQ plot should be straight in the center, and curved at the ends. The
curved ends are differentially expressing genes. The curvature could be in
either direction depending on the thickness of the tails of the
distribution relative to the center.
At 11:25 AM 5/24/2004 +0200, Matthew Hannah wrote:
>I have some questions regarding some cDNA array data I've been asked to
>The design is slightly different to the standard designs, in that independent
>biological replicates (different plants within the same experiment) have been
>hybridised to different arrays. Therefore there are biological dye-swaps
>Array 1 - WT plant 1/Treated plant 1
>Array 2 - Treated plant 2/WT plant 2
>Array 3 - WT plant 3/Treated plant 3
>Analysing these data using LIMMA, lmfit and ebayes (as in html manual)
>some odd looking qq plots that I have some questions about. All analyses used
>print-tip loess followed by quantile normalisation, with different BG
>BG- NONE - produces a normal looking single S-curved line, but both ends
>same side of the 1/1 line.
>BG-minimum - looks alright, although the extreme values at the upper end cross
>back over the 1/1 line.
>BG- subtract - the qq plot separates at both ends into 3 lines (presumably
>each array), which clearly isn't normal.
>My question is whether these are likely to result from the unbalanced
>the independent plant (rather than pooled) RNA used. More generally is it
>to treat the individual channels of cDNA data in any way similar to single
>data (like affy?) after quantile normalising between arrays, or will the
>array differences always be too great? Also is it generally considered
>best to use
>local BG correction, or non-corrected values and then eliminate bad spots
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
Naomi S. Altman 814-865-3791 (voice)
Bioinformatics Consulting Center
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348 (Statistics)
University Park, PA 16802-2111
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