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k. brand
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@k-brand-1874
Last seen 10.2 years ago
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
