[BioC] results of justPlier vs. RMA

[Michal Okoniewski]

Generally plier shows good correspondence with RMA. 
There are penalty parameters that tune the optimization of the 
plier model. Some of them may make plier a bit more "RMA-like".
For instance, in my experiments, setting concpenalty=0.1 removes
a lot of the artifacts of different fold change between plier and RMA. 
Still, the vast majority of the fold changes are similar - 
running a density plot instead of a scatterplot shows it well.  

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of He, Yiwen
(NIH/CIT) [C]
Sent: 09 November 2006 21:38
To: bioconductor at stat.math.ethz.ch
Cc: microarray
Subject: [BioC] results of justPlier vs. RMA

Hi,
When comparing the results of RMA from the affy package and justPlier
from the plier package, I found that the summarized data are clustered
very differently. The cluster of the RMA results is very close to MAS5
values, showing 5 groups. However, PLIER results show a very different
and strange clustering pattern, almost like using single linkage. (See
attached figure)

I used Pearson correlation coefficient distance and average linkage for
all clustering.

I remember attending a web talk about PLIER and the speaker mentioned
post-processing scaling in PLIER. Is that done in justPlier? Or is this
an additional step that needs to follow justPlier? Or is it something
else?

Thank you for your help!

Here is my code:
> sessionInfo()
R version 2.4.0 (2006-10-03)
sparc-sun-solaris2.8

locale:
C

attached base packages:
[1] "tools"     "methods"   "stats"     "graphics"  "grDevices" "utils"
[7] "datasets"  "base"

other attached packages:
   plier     affy   affyio  Biobase
 "1.4.0" "1.12.0"  "1.2.0" "1.12.2"


> eset1 <- rma(myData)
> exprs(eset1) <- 2^exprs(eset1)

> eset2 <- justPlier(myData, normalize=T)
> exprs(eset2) <- 2^exprs(eset2)

Yiwen He
 
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