[BioC] Limma: questions about data pre-processing

[Vladimir Krasikov]

Dear Axel,

Thanks a lot for the info.


> Dear Vladimir,
>
> I'll only answer or comment on some of your questions and leave
> the others for the true experts...
>
> Q2: yes, for example using package arrayQualityMetrics, if you know
> the array layout. FES output usually contains columns Col and Row for
> spot coordinates but apparently your "service provider" has removed
> them. I could send you a coordinates <--> oligo mapping by email if you
> can tell me your array type -- is it 1x44K, 4x44K or 4x44Kv2?
> Alternatively,
> you can try to find that information on Agilent's eArray web site:
> earray.chem.agilent.com

I will try to figure it out

> Q5: for a common reference design, dye swaps are not required and
> I would not apply a loess normalization -- depending on what you have
> hybridized as the common reference, the assumptions may not hold.
> As for the between-array normalization, Rquantile may also be an
> option for your design and boxplots and density plots may be used
> for judging the results.

Common reference is commercial something.
Thanks for another method of between array normalization.
As far as I have no assumptions about any single gene regulation in my  
conditions,
all methods are equal for me.
However maybe you have some tips on how to judge which of the  
normalizations
suited best for the particular experiment.
All kind of density and box-plots and MA plots look more or less the same
in any applied normalizations.

Regards
Vladimir


>
> Cheers,
>
>  - axel
>
>
> Axel Klenk
> Research Informatician
> Actelion Pharmaceuticals Ltd / Gewerbestrasse 16 / CH-4123 Allschwil /
> Switzerland
>
>
>
>
> From:
> "Vladimir Krasikov" <v.v.krasikov at gmail.com>
> To:
> bioconductor at r-project.org
> Date:
> 07.02.2012 14:27
> Subject:
> [BioC] Limma: questions about data pre-processing
> Sent by:
> bioconductor-bounces at r-project.org
>
>
>
> Dear limma experts
>
> During creating the pipe-line for dissecting differential gene expression
> in frame of limma,
> several questions have arose.
>
> Experiment:
> I have 62 two-color Agilent human arrays.
> The samples are from several human more or less related to each other
> disorders and vary in age, sex, disease duration and diagnosis.
> Company that made hybridizations performed all hybs in one direction (no
> dye-swaps),
> where all samples were in G channel and common Ref in R channel,
> and unfortunately provided us only excepts of Feature Extraction
> which contained info on G, Gb, R, Rb, and FNO (non-uniformity outliers)
> and separate gene annotation table.
>
> I performed generic import of the data and assigned zero-weight to the  
> FNO
> spots:
> I analyzed density and MA-plots, box-plots of M-values, G and R channels
> and box-plots of background intensities,
> and removed from experiment 1 array with aberrant raw G-channel density.
> (I will discuss experiment description later, when come to the linear
> model)
>
> Q1: Is there a rationale of down-weighting FNO (around 100-200 spots per
> array) for background correction and further normalization?
> Q2: Is there way to make image representation of Agilent microarray (for
> each channel and backgrounds)?
>      In another words is there known 'layout' for human 44K Agilent?
>
> Next I corrected the background with:
>> RG.b <- backgroundCorrect(RG.raw, method="minimum", offset=50)
> (recommended method=normexp produced shifted curves for five arrays after
> taking a look on density plots,
> and box-plots for separate G and R channels also look less uniform as
> compared with 'minimum' method)
>
> Q3: I guess it is also possible to remove those 5 arrays from the
> experiment. Is it fair?
> Q4: What kind of reasoning should be used for the choice between
> background subtraction methods?
>
> Then performed standard loess within array normalization:
>> MA.loess <- normalizeWithinArrays(RG.b, method="loess",bc.method="none")
>
> Q5: Do I need to perform between array normalization?
>      How to judge which of the methods (non, scale, quantile, Aquantile)
> is
> best for my experiment?
>
> For now I decide to stuck with background=minimum, within=loess, and
> between=is under the question
>
> Next I would like to ask questions about
> linear model of my experiment, but I will make it in a next help request
>
> Thanks a lot in advance
>
> and finally
>> sessionInfo()
> R version 2.14.1 (2011-12-22)
> Platform: i386-pc-mingw32/i386 (32-bit)
>
> locale:
> [1] LC_COLLATE=Dutch_Netherlands.1252  LC_CTYPE=Dutch_Netherlands.1252
> [3] LC_MONETARY=Dutch_Netherlands.1252 LC_NUMERIC=C
> [5] LC_TIME=Dutch_Netherlands.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] limma_3.10.2
>>
>
> With kind regards
> Vladimir
> --
>
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