[BioC] effect of normalization on analysis of differential knockdown

[Rajarshi Guha]

Hi, I am analysing the results from a drug sensitization siRNA screen  
and am trying to determine which genes are being differentially  
knocked down (between a vehicle only run and a dosed run).

Each gene is targeted by 4 siRNA's and my initial strategy has been to  
consider the signals from the 4 siRNA's to be individual samples for  
that gene. Then I perform a paired t-test on the 4 signals for a given  
gene across the two conditions. I then calculate Storey's q-values  
based on the resultant p-values.

The question: does/should the normalization of the plates have an  
effect on the results of the above analysis? For example, I considered  
two normalization schemes - 1) normalizing each plate to the median of  
a separate negative control plate and 2) B-score normalization.

If I rank the genes based on their q-values I get 2 very different  
rankings for the two normalization schemes. Furthermore, the q- & p- 
values differ greatly. In the case of median normalization I get a  
number of q-values < 0.05 but when using B-score I get a single gene  
with a q-value < 0.05 (and the next closest value is 0.58).

Thinking that this study is analogous to differential expression  
studies in microarrays, I tried running my dataset through the SAM  
method (via siggenes). Using this method, the B-score normalized data  
leads to no hits (and a pi0 = 1) whereas the median normalization  
method leads to lots of hits.

I can see that B-score normalized data would differ in character from  
median normalized data (seeing that the actual signals are replaced  
with scaled residuals) - but is it to be expected that normalization  
schemes would lead to such different results in this type of analysis?

Any pointers would be appreciated.

Thanks,

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Rajarshi Guha  <rajarshi.guha at gmail.com>
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