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Rajarshi Guha
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@rajarshi-guha-3531
Last seen 8.5 years ago

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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