Hi Henrik,
Many thanks for your detailed response even when I didn't include some 
important information.

See comments below.

On 3/11/10 23:00, "Henrik Bengtsson" <henrik.bengts...@gmail.com> wrote:


On Wed, Nov 3, 2010 at 6:03 AM, Oscar Rueda <oscar.ru...@cancer.org.uk> wrote:

> Hi all,
> I'm trying to normalize several thousands of tumor samples using aroma. The
> BAF profiles I get using
>> AvgCnPlm(csN, mergeStrands=TRUE, combineAlleles=FALSE)
> are quite noisy, so I have started using ACNE for getting 'cleaner' ones.

Yes, BAF profiles are quite noisy if not taking into account
SNP-specific crosstalk effects, which ACNE and some other methods do.

It is not clear from your message what chip type you are working on,
but here I will assume GenomeWideSNP_6.

Yes, This is GenomeWideSNP_6.

> The problem is that I don't want to use the tumors as a reference, because
> some of them have a lot of alterations.

I understand this concern.  Though, note that with a robust estimator
together with the assumption that at any given SNP the majority of
samples/arrays are normal/diploid, - (CA,CB) = (2,0), (1,1) or (0,2) -
then the estimates of the crosstalk parameters should be ok.  However,
I agree that if you know a sample is not-diploid it is best to exclude
it.  The safe version of the latter is to only use "normal" samples.

Before answering your specific ACNE-related questions/problems, are
you aware of the TumorBoost method which is designed for the case
where you have a matched normal for each tumor?

Bengtsson, H.; Neuvial, P. & Speed, T. P. TumorBoost: Normalization of
allele-specic tumor copy numbers from a single pair of tumor-normal
genotyping microarrays BMC Bioinformatics, 2010.  (Downloadable via

Using that, you can normalize the tumor BAFs utilizing the match
normal so that the normalized BAFs have much(!) greater
signal-to-noise ratios.  It won't/cannot normalize your normal samples
- only the tumors - but if that is all you need it may be easier to
use.  It also needs a matched normal for *each* tumor; you cannot
normalize tumors without a matched normal.  Another advantage is that
if you preprocess with AS-CRMAv2 and BAF normalize with TumorBoost you
have a truly single-pair processing method, i.e. you can process your
tumor-normal pairs one by one independently of all other pairs you

You will find vignettes from TumorBoost at
http://aroma-project.org/vignettes/.  FYI, we are aware that the
high-level version of TumorBoost for the aroma framework is still a
bit tedious to apply.  We simply haven't decided on the final design
where to put the result files etc.  Regardless, the results are still
correct.  Depending on what you are doing downstream, you might find
the low-level/all-in-memory version much easier to use.

Thanks for this! I remember reading that paper some time ago, so I'll 
definitely give it
a try for the matched pairs.

> With aroma I used the 'target=0'
> option to get log intensities

That option I do not follow?!?  Where do you use 'target=0'?  Even if
you get signals that are on average near zero, I suspect that you are
misinterpreting the results/output, which most likely are *not*
log-intensities.  Please show some code - actually show all you code
to avoid ambiguities.

This is the code I use:

> library(aroma.affymetrix)
> cdf <- AffymetrixCdfFile$byChipType("GenomeWideSNP_6", tags="Full")
> gi <- getGenomeInformation(cdf)
> si <- getSnpInformation(cdf)
> geneinform <- getGenomeInformation(cdf)
> snpinform <- getSnpInformation(cdf)
> cs.comb<- AffymetrixCelSet$byName("Project1", chipType = 
> "GenomeWideSNP_6",cdf = cdf)
> getFullName(cs.comb)
> getPath(cs.comb)
> getNames(cs.comb)
> samplenames = getNames(cs.comb)
> setCdf(cs.comb, cdf)
> acc <- AllelicCrosstalkCalibration(cs.comb,model="CRMAv2")
> csC <- process(acc, ram=28,verbose=verbose)
> bpn <- BasePositionNormalization(csC, target="zero")
> csN <- process(bpn, ram=28,verbose=verbose)
> plm <- AvgCnPlm(csN, mergeStrands=TRUE, combineAlleles=FALSE)
> if (length(findUnitsTodo(plm)) > 0) {
>   units <- fitCnProbes(plm, verbose=verbose)
>   str(units)
>   units <- fit(plm, ram=28, verbose=verbose)
>   str(units)
> }
> ces  <- getChipEffectSet(plm)
> fln <- FragmentLengthNormalization(ces, target="zero")
> cesN <- process(fln, verbose=verbose)
> cdf.mono <- getCdf(cesN);
> for (kk in 1:24) {
>        units <- getUnitsOnChromosome(geneinform, chromosome=kk);
>        pos <- getPositions(geneinform, units=units);
>        unitNames <- getUnitNames(cdf.mono, units=units);
>        theta <- extractMatrix(cesN, units=1, field = "theta");
>        theta <- colnames(theta)
>        Vals <- extractTheta(cesN, units=units)
>        Vals <- apply(Vals, c(1,2), cbind)
>        rownames(Vals) <- unitNames;
>        colnames(Vals) <- rep(theta, rep(2, length(theta)))
>        pos.log2I.val<-cbind(pos,Vals)
>        write.table(pos.log2I.val, 
> paste("Project1_log2I.pos_Chrm",kk,".txt",sep=""))
>        }

So my understanding is that these are intensities, not ratios. I can obtain 
ratios later using a reference.

> and then I normalized them against my pool of
> normals or against them matched normal if available  just subtracting the
> values.

I need to see your code to know what you are doing.

> For ACNE it seems I have to specify the reference, so I created folders for
> each of my matched pairs and used
>> NmfSnpPlm(csN, mergeStrands=TRUE, refs=c(FALSE, TRUE))
> To indicate that the second array is the normal and the first the tumor.
> Is this the best way to proceed in my situation?

Nope.  When you find yourself having to do tedious "tricks" such as
splitting up your data set to data sets containing a single
tumor-normal pair take it as sign for "there must be a better way to
do this" (which I guess is one reason why you posted this message).

That's what I was thinking! :-)

> The former command produces the following values for chromosome 2:
>> summary(data)
> total             freqB
> Min.   :    0.0   Min.   :    NA
>  1st Qu.:    0.0   1st Qu.:    NA
>  Median :  905.6   Median :    NA
>  Mean   : 2345.2   Mean   :   NaN
>  3rd Qu.: 4192.8   3rd Qu.:    NA
>  Max.   :53200.6   Max.   :    NA
>                   NA's   :153732
> So it's clear that I'm doing something wrong. Does anyone know what is it?

The problem is that you are asking the ACNE method to estimate the
(SNP-specific) crosstalk parameters using only two arrays, actually
only one since you specify that it is only the normal sample that
should be used for the estimation.  The model parameters are
non-identifiable with only one sample, i.e. it is not possible to
estimate the ACNE parameter.

It should probably be added to the ACNE method/estimator (somewhere in
the NmfSnpPlm class) to assert that not too few reference samples are
used, instead of returning missing values.

Instead, you should keep all your samples in the same directory and
process the whole data set at once.  You can specify which samples
should be used as the reference set by using argument 'refs' to
NmfSnpPlm, just as above.  For example:

refs <- seq(from=2, to=length(csN), by=2);
nmf <- NmfSnpPlm(csN, mergeStrands=TRUE, refs=refs);

Note that you might use a more clever approach to identify which
samples are normals.  For example, if the normal samples contain the
tag "N", you can do:

refs <- which(sapply(csN, hasTag, "N"));

Hope this solves your problem.


When reporting problems on aroma.affymetrix, make sure 1) to run the latest 
version of the package, 2) to report the output of sessionInfo() and 
traceback(), and 3) to post a complete code example.

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Thanks a lot for your answers. Here is the sessionInfo()

R version 2.11.1 (2010-05-31)

 [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=C              LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] sfit_0.1.9             ACNE_0.4.2             MASS_7.3-7
 [4] aroma.affymetrix_1.7.0 aroma.apd_0.1.7        affxparser_1.20.0
 [7] R.huge_0.2.0           aroma.core_1.7.0       aroma.light_1.18.2
[10] matrixStats_0.2.2      R.rsp_0.4.0            R.cache_0.3.0
[13] R.filesets_0.9.0       digest_0.4.2           R.utils_1.5.3
[16] R.oo_1.7.4             R.methodsS3_1.2.1

loaded via a namespace (and not attached):
[1] tools_2.11.1

Oscar M. Rueda, PhD
Postdoc, Breast Cancer Functional Genomics
Cancer Research UK Cambridge Research Institute
Li Ka Shing Centre
Robinson Way
Cambridge CB2 0RE

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