[BioC] Breaking the "most genes not differentially expressed" assumption

[Wolfgang Huber]

Hi Paolo

Some suggestions here:

1.) The correlation plot 
http://www.iee.uu.se/zooekol/pdf/hemiarray_qc_correlationplot.pdf looks 
bizarre. Can you explain what it shows, and why you think it is 
consistent with a successful experiment?

2.) How does the array index relate to whether the sample is 
male/female? Could it be that further experimental factors (time, lab, 
reagent batch) are confounded with sex?

3.) I am puzzled by your sessionInfo(). How could you run "rma" without 
having a cdf package loaded?

4.) You could try using different normalisation methods. The quantile 
normalisation used within rma is rather aggressive. You could try 
methods based on affine linear or local polynomial regression.

Best wishes
      Wolfgang

------------------------------------------------
Wolfgang Huber, EMBL, http://www.ebi.ac.uk/huber



Paolo Innocenti ha scritto:
> Hi all,
> 
> I have dataset of 120 Affy arrays, 60 males and 60 females.
> The expression profiles of the 2 groups differs dramatically, i.e. if I 
> run a standard RMA + limma, I have ~90% of the genes differentially 
> expressed. Also, downregulated genes are twice as many than upregulated 
> genes, although if I impose a cutoff of two-fold difference in 
> expression, they are almost equal (15% up and 15% down).
> This is clearly breaking the assumption that most of the genes on the 
> array should not be differentially expressed, but the result is in line 
> with the current knowledge of sex-biased gene expression in my model 
> organism.
> 
> I have done some quality control plots, available here:
> - Boxplot:
> http://www.iee.uu.se/zooekol/pdf/hemiarray_qc_boxplot.pdf
> 
> - Frequency histogram:
> http://www.iee.uu.se/zooekol/pdf/hemiarray_qc_histogram.pdf
> 
> - RLE and NUSE plots:
> http://www.iee.uu.se/zooekol/pdf/hemiarray_qc_RLEandNUSE1.pdf
> 
> - CorrelationPlot:
> http://www.iee.uu.se/zooekol/pdf/hemiarray_qc_correlationplot.pdf
> 
> - PCA, after RMA normalization:
> http://www.iee.uu.se/zooekol/pdf/hemiarray_qc_pca.pdf
> 
> Now, my questions are:
> 1) Is my issue really a issue? If so, how can I perform a robust 
> normalization of my arrays?
> 
> 2) Is there a tool to assess how "robust" your pre-processing method is 
> in respect to this issue?
> 
> 3) Sex-biased gene expression is not the only biological question in my 
> experiment. Is the massive size of this effect going to affect the 
> "detectability" of other smaller effects? (through normalization or 
> correction for multiple testing or other?)
> 
> Thanks,
> paolo
> 
>