[BioC] read.maimages

Wed Aug 1 08:40:29 CEST 2012

Dear Assa,

No, the User's Guide is correct.  As I said in my last email, the default 
for agilent has changed between the older version that you were using and 
the current version.  The documentation with each version of limma 
correctly described the behaviour of that version of the software.

Best wishes
Gordon

On Wed, 1 Aug 2012, Assa Yeroslaviz wrote:

> Hi Gordon,
>
> 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."
>>
>
>
> I have updated to the latest version of R (2.15.1) and limma (3.12.1).
> I should have read the help page. But I read the User's manual (I think the
> latest version - from June, 10th 2012).
> The manual says differently (page 18):
> the default values for agilent are not the same as for genepix.
> by just stating 'source="agilent" ', limma takes both fore- and background
> median signals.
>
> So maybe the manual needs an update.
>
>
>> 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.
>>
>
> On this website, I just reacted to a comment supposedly made by you, or at
> least was given by you. I did contact the guy who posted it, but he said he
> can't help me. This is why I posted it here.
>
> Again, thanks for clarifying the problem
>
> Assa
>
>>
>> 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<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<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
>>>

______________________________________________________________________
The information in this email is confidential and intend...{{dropped:4}}