[BioC] RMA - answer found- AND ANOTHER QUESTION

[Richard Friedman]

Dear Benilton, Naomi, Ben, and Monnie,

	Thank you for your answers and references. I know understand RMA well
enough to explain it simply  to my class and to the experimentalists 
for whom I provide
bioinformatics support  I had misunderstood a critical aspect of the 
algorithm previously.
The point you cleared up also has consequences for my understanding of 
GCRMA,
so that I will reread those papers and probably have questions on that 
as well.

	One quick question: To what do the boundaries of the boxes in the affy 
boxplots refer?
Quartiles?
Standard deviations?
Other?

Thanks and best wishes,
Rich

On Jan 30, 2006, at 6:48 PM, Benilton Carvalho wrote:

> Dear Dr. Friedman,
>
> I'd say that what Rafa wants to say in his paper is:
>
> The PMs are a combination of background noise and signal:
> (PM.inj = bg.inj + s.ijn)
>
> We're not interested in this noisy information. We really want the 
> signal s.ijn. But since we can only observe the noisy information 
> (PM), we have to estimate the signal.
>
> Supposed you observed PM = 5,827.82. Then the formulae below answer 
> the following question: what's the best value for the true signal if 
> the noisy observation is 5,827.82?
>
> The usual answer for the is: the average... E(sijn | PMinj)
>
> The hypothetical thing here would be: consider a situation where you 
> always know the true signal s.ijn. Now, write down all the signals 
> s.ijn_k where PM = 5,827.82 and take the mean of such s.ijn_k.
>
> Of course we can not have such situation in real life. For this 
> reason, if one assumes distributions (exponential for the signal and 
> normal for the background - remember PM = signal + background), we can 
> simulate the real signal and the real background in order to get the 
> noisy observation (PM) and then we're good to use the hypothetical 
> situation above.
>
> Such simulation would consume a good portion of time, but the 
> distribuitions above (normal and exponential) allow us to derive the 
> exact answer without the need of simulation.
>
> I'm sure this is not the best way to explain, but I hope it helps.
>
> kind regards,
>
> benilton
>
>
>
>
>> Naomi,
>>
>> 	I am trying to understand RMA better myself at the moment. I cannot
>> understand how
>> the background is obtained from page 6 of the pdf file that you cite
>> below. In
>> the RMA paper (Biostat 4, 249-264). Dr. Irizarry and coworkers state
>> "An alternative
>> background correction is to consider B(PM(ijn))=E(sijn)|PMijn). ... To
>> obtain a computationally
>> feasible B(.) we consider a closed form transformation obtained when
>> sijn
>> is exponential and bgijn is normal."
>> 	This text seems to refer to the graphs on page 6 of what you cite
>> below, but I don't
>> understand it. Can you (or anybody out there) please explain it in
>> simpler language.
>>
>> Thanks and best wishes,
>> Rich
>>
>> On Jan 30, 2006, at 5:24 PM, Naomi Altman wrote:
>>
>>> I found the answer to my questions on Dr. Irizarry's website in his
>>> talk:
>>>
>>> http://www.iobion.com/slides/RMA/rma.pdf
>>>
>>> Naomi S. Altman                                814-865-3791 (voice)
>>> Associate Professor
>>> Dept. of Statistics                              814-863-7114 (fax)
>>> Penn State University                         814-865-1348 
>>> (Statistics)
>>> University Park, PA 16802-2111
>>>
>>> _______________________________________________
>>> Bioconductor mailing list
>>> Bioconductor at stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>
>> ------------------------------------------------------------
>> Richard A. Friedman, PhD
>> Associate Research Scientist
>> Herbert Irving Comprehensive Cancer Center
>> Oncoinformatics Core
>> Lecturer
>> Department of Biomedical Informatics
>> Box 95, Room 130BB or P&S 1-420C
>> Columbia University Medical Center
>> 630 W. 168th St.
>> New York, NY 10032
>> (212)305-6901 (5-6901) (voice)
>> friedman at cancercenter.columbia.edu
>> http://cancercenter.columbia.edu/~friedman/
>>
>> "42 is the answer. Dylan got it wrong. 'Blowin'
>> in the wind' is not the answer. It isn't even
>> a number' " - Rose Friedman, age 9
>>
>> _______________________________________________
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>> Bioconductor at stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>
>
>
> --
> Benilton Carvalho
> PhD Candidate
> Department of Biostatistics
> Bloomberg School of Public Health
> Johns Hopkins University
> Baltimore, MD USA
>
------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist
Herbert Irving Comprehensive Cancer Center
Oncoinformatics Core
Lecturer
Department of Biomedical Informatics
Box 95, Room 130BB or P&S 1-420C
Columbia University Medical Center
630 W. 168th St.
New York, NY 10032
(212)305-6901 (5-6901) (voice)
friedman at cancercenter.columbia.edu
http://cancercenter.columbia.edu/~friedman/

"42 is the answer. Dylan got it wrong. 'Blowin'
in the wind' is not the answer. It isn't even
a number' " - Rose Friedman, age 9