[BioC] read.maimages

[Gordon K Smyth]

Dear Assa,

It seems to me that the read.maimages() help page

   help("read.maimages")

answers your question.  The help page, for the version of limma that you 
are using, says

"In the case of Agilent and GenePix, two possible foreground estimators are 
supported: source="genepix" uses the mean foreground estimates while 
source="genepix.median" uses median foreground estimates. Similarly for 
Agilent."

So the help page tells you that read.maimages() reads the mean foreground 
by default, not the median foreground as you say in your email.  So if you 
override the default by reading in the median foreground, it is clear that 
you will get differing results.

If you were to upgrade to the current version of R and the current version 
of limma, there much expanded documentation about reading Agilent files in 
the User's Guide (and the default for agilent has changed).

Please note, I am happy to answer questions about current limma 
documentation.  However, if you follow a third party website that gives 
advice conflicting with the limma documentation, then you should send 
questions to the author of that website.

Best wishes
Gordon

> Date: Mon, 30 Jul 2012 17:16:12 +0200
> From: Assa Yeroslaviz <frymor at gmail.com>
> To: bioconductor <bioconductor at stat.math.ethz.ch>
> Subject: [BioC] read.maimages
>
> Hi BioC User,
>
> I am working for the first time on agilent CGH arrays (singel-channel).
>
> I would like to use the limma package for that>
>
> This is my script:
>  >library(limma)
>
>  >targets <- readTargets("targets.txt")
>  >x <- read.maimages(targets, path="rawData/",
> source="agilent",green.only=TRUE, names = targets$condition)
>  >RG <- read.maimages(targets, path="rawData/", columns = list(G =
> "gMedianSignal", Gb = "gBGMedianSignal", R = "gProcessedSignal",
>   Rb = "gIsPosAndSignif"), annotation = c("Row", "Col","FeatureNum",
> "ControlType","ProbeName"), names = targets$condition)
>
> I tried both examples as I've found an explanation mentioning both of them (
> here<http://matticklab.com/index.php?title=Single_channel_analysis_of_Agilent_microarray_data_with_Limma>).
> My problem is that the results differs slightly from one another:
>
>> RG
> An object of class "RGList"
> $G
>     controll 5_4_chr5 5_3_chr5 5_4_cp 5_3_growth 5_3_cp 5_3_growth
> [1,]    363.0    374.0     1647  678.0      498.5  505.0        642
> [2,]     34.0     24.0       27   34.5       31.0   34.0         31
> [3,]     29.5     23.0       23   30.0       26.0   26.5         30
> [4,]     31.0     23.0       28   28.0       27.0   31.0         29
> [5,]     31.0     25.5       28   27.0       32.0   29.0         31
> 45209 more rows ...
>
>> x
> An object of class "EListRaw"
> $E
>      controll  5_4_chr5   5_3_chr5    5_4_cp 5_3_growth    5_3_cp
> 5_3_growth
> [1,] 361.30160 364.68250 1667.98200 683.31250  506.46670 502.66670
> 649.01610
> [2,]  34.84483  25.94737   29.00000  35.54839   32.28571  33.16949
> 30.70492
> [3,]  31.23438  25.46032   23.61905  31.90164   27.84127  28.95161
> 30.82540
> [4,]  31.65000  24.31818   27.72414  31.83607   28.85484  31.39683
> 30.25000
> [5,]  32.06349  25.93548   28.98413  28.25000   31.44615  28.04615
> 30.78462
> 45209 more rows ...
>
> Even though the differences are very small, I would still like to
> understand them.
> If I understood the manual correctly, limma takes by default the median
> column for both fore- and background.
> The background values are similar (x$Eb and RG$Eb).
>
> What columns does limma uses for the analysis?
>
>
> I would appreciate the help
>
> thanks
> Assa
>
>> sessionInfo()
> R version 2.14.1 (2011-12-22)
> Platform: x86_64-unknown-linux-gnu (64-bit)
>
> locale:
> [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
> [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
> [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
> [7] LC_PAPER=C                 LC_NAME=C
> [9] LC_ADDRESS=C               LC_TELEPHONE=C
> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] limma_3.10.3        BiocInstaller_1.2.1
>
> loaded via a namespace (and not attached):
> [1] tools_2.14.1


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