[BioC] "normalize.quantiles.robust" produces the SAME intensities from different weights?!

[k. brand]

Dear BioCers,

My interpretation of the function "normalize.quantiles.robust" is the 
ability to selectively increase or reduce the contribution of specific 
chips during normalization.

Although i get different results from equally weighted chips (which is 
the same as "justRMA" as a verification), all variations of unequal 
weightings produce do the SAME intensities?! See 2 examples below.

Can some one find the error in my script, or better yet, give an example 
  of a better way to achieve my objective-

to selectively 'pull' low intensities of duplicate chips form a poor hyb 
(on expired arrays), up to the levels of duplicate chips of a good 
hyb(on unexpired arrays).

TIA,

Karl




 >###                 "weight_is_1&10.R"
 >
 >     setwd("D:/brand 8/CORE-SHELL/Diff methods/ID T0 
all/normalisations/Quantiles-robust")
 >     library(affy)
 >     dat <- ReadAffy()
 >     # list with weights for robust normalization
 >     param <-  list(weights=c(1,1,10,10,1,1,10,10,1,1,10,10))
 >     # performs robust quantile normalization
 >     # summarization is performed using medianpolish
 >     eset <- expresso(dat, bgcorrect.method="rma",
+     normalize.method="quantiles.robust", normalize.param=param,
+     pmcorrect.method="pmonly", summary.method = "medianpolish")
background correction: rma
normalization: quantiles.robust
PM/MM correction : pmonly
expression values: medianpolish
background correcting...done.
normalizing...Chip weights are  1 1 10 10 1 1 10 10 1 1 10 10
Chip weights are  1 1 10 10 1 1 10 10 1 1 10 10
done.
45101 ids to be processed
|                    |
|####################|
 >     exprs2excel(eset, "weight_is_1&10.csv")
 >


for probset 1415670_at
6.268887235	5.946937963	7.191120262	7.087367938	6.145685893 
6.095744472	7.473473796	7.21058045	6.105367399	6.130141537	7.324310607 
7.059578097




 > ###                   "weight_is_10&1.R"
 > dat <- ReadAffy()
 > # list with weights for robust normalization
 > param <-  list(weights=c(10,10,1,1,10,10,1,1,10,10,1,1))
 > # performs robust quantile normalization
 > # summarization is performed using medianpolish
 > eset <- expresso(dat, bgcorrect.method="rma",
+ normalize.method="quantiles.robust", normalize.param=param,
+ pmcorrect.method="pmonly", summary.method = "medianpolish")
background correction: rma
normalization: quantiles.robust
PM/MM correction : pmonly
expression values: medianpolish
background correcting...done.
normalizing...Chip weights are  10 10 1 1 10 10 1 1 10 10 1 1
Chip weights are  10 10 1 1 10 10 1 1 10 10 1 1
done.
45101 ids to be processed
|                    |
|####################|
 > exprs2excel(eset, "weight_is_10&1.csv")

for probset 1415670_at
6.268887235	5.946937963	7.191120262	7.087367938	6.145685893 
6.095744472	7.473473796	7.21058045	6.105367399	6.130141537	7.324310607 
7.059578097



==============================================================

 > sessionInfo()
Version 2.3.0 (2006-04-24)
i386-pc-mingw32

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

other attached packages:
      affyPLM        gcrma  matchprobes     affydata mouse4302cdf 
   affy
      "1.8.0"      "2.4.1"      "1.4.0"      "1.8.0"     "1.12.0" 
"1.10.0"
       affyio      Biobase
      "1.0.0"     "1.10.0"



-- 
Karl Brand <k.brand at erasmusmc.nl>
Department of Cell Biology and Genetics
Erasmus MC
Dr Molewaterplein 50
3015 GE Rotterdam
lab +31 (0)10 408 7409 fax +31 (0)10 408 9468