[BioC] Issue with limma and normalization of Agilent data generated with a 20-bit scan
Gordon K Smyth
smyth at wehi.EDU.AU
Wed Mar 17 00:54:42 CET 2010
With the weights, you could easily increase them to 100 without any
likely problems.
Gordon
On Tue, 16 Mar 2010, White, Peter wrote:
> Dear Gordon,
>
> Thanks so much for your detailed response. I did try setting the span to
> and it definitely improved the J-curve. Scroll all the way to the bottom
> of:
>
> http://permalink.gmane.org/gmane.science.biology.informatics.conductor/27771
>
> I also tried setting the span to 0.01 and it looked even better (lower than that and crazy things started to happen):
>
> http://permalink.gmane.org/gmane.science.biology.informatics.conductor/27770
>
> As far as I could see lowering the span had no effect on the elements
> less than an average log2 intensity of 16. I did try adding a weight
> function that elements with a median red or green intensity > 60,000
> were weighted 5, but it made no discernable difference to the default
> normalization.
>
> All the best,
>
> Peter
>
>
>
>> -----Original Message-----
>> From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU]
>> Sent: Monday, March 15, 2010 6:54 PM
>> To: White, Peter
>> Cc: 'Bioconductor mailing list'
>> Subject: RE: [BioC] Issue with limma and normalization of Agilent data
>> generated with a 20-bit scan
>>
>> Dear Peter,
>>
>> The plots on the permalink website do not correspond to the R code in
>> your
>> email. Apparently the plots are from marray package, produced by R
>> code
>> which you do not give. I can't comment on the behaviour of someone
>> else's
>> package.
>>
>> As I've already told you, normalizeWithinArrays() uses all the points,
>> and
>> having an A-value over 16 is not an issue. However, the loess curve is
>> designed to be quite stiff, and also to ignore outliers, and therefore
>> the
>> curve will not follow a highly localized J-curve at the right end of
>> the
>> range, which is what your MA-plot seems to show. The loess curve is
>> designed to follow the main trend, not local trends involving small
>> proportions of the points. If you believe that your platform has a
>> different MvsA relationship for A>16 than for A<16, and this should be
>> removed by the normalization curve, then you can do one of two things.
>> One possibility is simply to make the curve more local by reducing the
>> span paramater:
>>
>> normObj <- normalizeWithinArrays(bgObj, method="loess", span=0.1)
>>
>> Choosing span small enough will certainly remove the J-curve. However
>> I
>> don't recommend this approach, as it is not specific to A>16. I
>> recommend
>> instead that you use the modifyWeights() function to give the spots
>> with
>> A>16 increased weights, so the loess curve will follow it more closely.
>> You might find a combination of slightly reduced span and increased
>> weights will work best.
>>
>> BTW, I would recommend that you use spottypes to display control spots,
>> and weights to downweight them in the loess normalization, rather than
>> hacking NA values into your data. It gives you more information and
>> flexibility.
>>
>> Best wishes
>> Gordon
>>
>> On Mon, 15 Mar 2010, White, Peter wrote:
>>
>>> Dear Gordon,
>>>
>>> The plots are visible in the blog view on gmane.org:
>>>
>>>
>> http://permalink.gmane.org/gmane.science.biology.informatics.conductor/
>> 27731
>>>
>>> I thought you may be on to something with the weights but I tried it
>>> with and without a flag function (also double checked the Agilent
>> file
>>> and the high intensity spots are not flagged). It really does look
>> like
>>> the loess is just not fitted beyond for elements with an A value >
>> 16???
>>> These 20-bit scans from Agilent are quite new and I suspect most
>> folks
>>> with just use the Agilent normalized data rather than starting with
>> the
>>> raw data, so maybe this just hasn't been observed before now?
>>>
>>> Thanks,
>>>
>>> Peter
>>>
>>> Below is the code I used:
>>>
>>> library(limma)
>>> agilentFiles <- list.files(pattern="U")
>>> rawObj <- read.maimages(agilentFiles,
>>> columns = list(G = "gMedianSignal", Gb = "gBGMedianSignal",
>>> R = "rMedianSignal", Rb = "rBGMedianSignal"),
>>> annotation= c("ProbeName", "SystematicName","ControlType"))
>>> #Remove spike controls and remove background signals
>>> bgObj <- rawObj
>>> posControls <- grep(T,rawObj$genes$ControlType == 1)
>>> bgObj$G[posControls,] <- NA
>>> bgObj$R[posControls,] <- NA
>>> bgObj$Gb <- bgObj$Rb <- NULL
>>> #Loess normalize
>>> normObj <- normalizeWithinArrays(bgObj, method="loess",
>> weights=NULL)
>>> #Plot MvA
>>> for (i in 1:ncol(normObj)) {
>>> figureName <- paste(i, " MvA Plots")
>>> mat <- matrix(c(3,1,2),nrow=3,ncol=1)
>>> layout(mat,heights=c(1,10,10))
>>> plotMA(rawObj, array=i, main = "Pre-Normalization MvA",
>>> ylim=c(-3.5,3.5), zero.weights=TRUE)
>>> abline(0,0)
>>> plotMA(normObj, array=i, main = "Normalized MvA",
>>> ylim=c(-3.5,3.5), zero.weights=TRUE)
>>> abline(0,0)
>>> layout(1)
>>> mtext(figureName, cex=1.25, line=3)
>>> savePlot(filename=figureName, type=c("png"), device=dev.cur())
>>> }
>>>
>>>> sessionInfo()
>>> R version 2.10.1 (2009-12-14)
>>> i386-pc-mingw32
>>>
>>> locale:
>>> [1] LC_COLLATE=English_United States.1252
>>> [2] LC_CTYPE=English_United States.1252
>>> [3] LC_MONETARY=English_United States.1252
>>> [4] LC_NUMERIC=C
>>> [5] LC_TIME=English_United States.1252
>>>
>>> attached base packages:
>>> [1] grDevices datasets splines graphics stats tcltk utils
>>> [8] methods base
>>>
>>> other attached packages:
>>> [1] limma_3.2.2 svSocket_0.9-48 TinnR_1.0.3 R2HTML_1.59-1
>>> [5] Hmisc_3.7-0 survival_2.35-9
>>>
>>> loaded via a namespace (and not attached):
>>> [1] cluster_1.12.1 grid_2.10.1 lattice_0.18-3 svMisc_0.9-56
>> tools_2.10.1
>>>
>>>> -----Original Message-----
>>>> From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU]
>>>> Sent: Saturday, March 13, 2010 6:39 PM
>>>> To: White, Peter
>>>> Cc: Bioconductor mailing list
>>>> Subject: [BioC] Issue with limma and normalization of Agilent data
>>>> generated with a 20-bit scan
>>>>
>>>> Dear Peter,
>>>>
>>>> You can't send attachments to the Bioconductor mailing list, so I
>> have
>>>> not
>>>> seen your plots. However I am not aware of any issue such as you
>>>> describe. The limma function normalizeWithinArrays includes all
>> spots
>>>> in
>>>> the normalization, regardless of how large the A-value is. You
>> haven't
>>>> shown us any code, or any problem we can reproduce, so we can't tell
>>>> whether or not you're doing something wrong. We don't know whether
>>>> you're
>>>> using probe weights, whether you've filtered control spots, etc etc.
>>>>
>>>> Best wishes
>>>> Gordon
>>>>
>>>>> Date: Fri, 12 Mar 2010 10:21:41 -0500
>>>>> From: "White, Peter" <Peter.White at nationwidechildrens.org>
>>>>> To: "'bioconductor at stat.math.ethz.ch'"
>>>>> <bioconductor at stat.math.ethz.ch>
>>>>> Subject: [BioC] Issue with limma and normalization of Agilent data
>>>>> generated with a 20-bit scan
>>>>> Content-Type: text/plain
>>>>>
>>>>> I have noticed an issue with the limma normalizeWithinArrays
>> function
>>>>> (and also with marray and maNorm). When normalizing two color data
>>>>> generated with an Agilent 20-bt scanner it fails to normalize the
>>>> high
>>>>> intensity data (i.e. any points with an A value > 16). In our
>> dataset
>>>> we
>>>>> have in excess of 400 elements with red and green intensities
>> ranging
>>>>> from 65500 to 475100. When we loess normalize the data any points
>>>> beyond
>>>>> A=16 appear to be untouched by the normalization. If the attached
>>>>> figures come through this should be clear - when using maNorm and
>>>> maPlot
>>>>> it will plot the loess line and you can see it stop at 16.
>>>>>
>>>>> Is it possible for loess normalization to be extended to this
>> higher
>>>>> intensity data? Or am I just doing something wrong?
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Peter
>>>>>
>>>>>
>>>>> Peter White, Ph.D.
>>>>> Director, Biomedical Genomics
>> Core<http://genomics.nchresearch.org/>
>>>>> Research Assistant Professor of Pediatrics
>>>>> The Research Institute at
>>>>> Nationwide Children's Hospital and
>>>>> The Ohio State University
>>>>>
>>>>> Mailing Address:
>>>>>
>>>>> The Research Institute at
>>>>> Nationwide Children's Hospital
>>>>> 700 Children's Drive, W510
>>>>> Columbus, OH 43205
>>>>>
>>>>> Assistant (Jennifer Neelans): (614) 722-2915
>>>>> Office: (614) 355-2671
>>>>> Lab: (614) 355-5252
>>>>> Fax: (614) 722-2818
>>>>> Web: http://genomics.nchresearch.org/
>>
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