[BioC] LIMMA: Why does eBayes expression differ from observed?
Axel.Klenk at Actelion.Com
Axel.Klenk at Actelion.Com
Thu Jul 30 15:02:57 CEST 2009
(sorry, forgot to CC to the list.)
first of all, the code you have posted cannot work because it never assigns
of your computations to any variables. In order to help you, we need to
know at least
1) what code you have really run,
2) your refdesign matrix,
3) the values from topTable() and RG.MA() for at least one example probe,
4) HOW you backcalculated R, G, and FC from topTable(), and
5) the obligatory output of sessionInfo() although this doesn't really
sound like a version issue. :-)
AFAIK, eBayes() will affect the t, p, and B statistics computed from your M
values but not
the AveExpr and logFC.
RG.MA() backcalculates background-adjusted and normalized R and G values,
so I am
not sure what you mean by "observed" RG -- you don't get back the original
R, Rb, G, Gb
from an MAList via RG.MA().
Actelion Pharmaceuticals Ltd / Gewerbestrasse 16 / CH-4123 Allschwil /
<edwin.groot at biol
.de> bioconductor at stat.math.ethz.ch
Sent by: cc
ces at stat.math.eth Subject
z.ch [BioC] LIMMA: Why does eBayes
expression differ from observed?
I am getting odd results from a common reference design analysis of
two-colour data in LIMMA. Previously I had analyzed only
simple-comparison designs. Hopefully you can help restore credibility
of Bioconductor to my supervisor.
Why does the AveExpr and logFC reported in topTable() differ from the
replicates in my MA object?
The analysis is a textbook example of comparing a series of mutants to
the wild type. Wild type is always green. The summary is as follows:
#Regenerate the RG from MA
Backcalculating the topTable AveExpr and logFC to green, red and FC
gives the following expressions as an example:
WT: 31 mut1: 298 FC: 9.5
Compare that to the average of 9 WT and 3 mut1 in the RG.MA(MA) list:
WT: 58 mut1: 611 FC: 10.5
A survey of other probes gives an over and underestimate of the
observed RG from 1.5 to 5 times.
What is the explanation for that?
What should I troubleshoot, besides looking at the usual BG and FG
distributions and MA plots?
Dr. Edwin Groot, postdoctoral associate
Institut fuer Biologie III
79104 Freiburg, Deutschland
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