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@noel0925sbcglobalnet-1574

Last seen 7.1 years ago

In the paper: Exploration, Normalization and Summaries
of High Density Oligonucleotide Array Probe Level Data
the following statement regarding the
bimodality of log2(PM) values and RMA background
corrected PM values can be found- "The same bimodal
effect is seen when we stratisfy by log2(PM), thus it
is not an artifact of conditioning on sums." (p4).
I am a little confused by this as I thought that
indeed an artifact of the convolution!
Clearly, the background corrected intensity
values are given by E(S | O) or the conditional
expectation of the signal given what we observe; where
the observed signal is the convolution of a normally
distributed background (N) mean mu variance sigma^2
(B~ N(u, ??^2)) and an exponentially distributed
signal (S) with mean alpha (S~ exp(??)).
There have been several postings regarding this matter
in the Bioconductor archives and all seem to point to
this. Have I misunderstood?
In particular was the following post:
https://stat.ethz.ch/pipermail/bioconductor/2004-August/005908.html
(See below the response from zwu at jhsph.edu
The original question I got was about the bimodal
distribution of gcrma
result from probe intensities with unimodel
distribution. My answer was
that the "change" was not necessarily surprising.
For example , when you have "true log signal" from a
bimodal distribution
logS=c(rnorm(1000,3,1),rnorm(1000,8,2))
# You will see this has two peaks
par(mfrow=c(2,2))
plot(density(logS))
#if the background, log(non-specific binding) come
from
logB=rnorm(2000,6,1)
#then when you plot the histogram of convolution in
log scale,
plot(density(log(exp(logS)+exp(logB))))
#you see only one peak, and this would be "before
gcrma".
This explanation made sense to me, but seems to
contradict what is stated in the paper.
Also, can someone explain the difference between RMA
background version1 vs version2?
Best regards,
Noel