[BioC] Breaking the "most genes not differentially expressed" assumption
paolo.innocenti at ebc.uu.se
Mon Apr 27 17:25:22 CEST 2009
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
I have done some quality control plots, available here:
- Frequency histogram:
- RLE and NUSE plots:
- PCA, after RMA normalization:
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?)
Department of Animal Ecology, EBC
75236 Uppsala, Sweden
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