Hi.
On Thu, Nov 4, 2010 at 3:11 AM, Oscar Rueda <oscar.ru...@cancer.org.uk> wrote:
> 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:
>
> Hi.
>
> 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
> http://aroma-project.org/publications/)
>
> 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
> have.
>
> 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$byChipType("GenomeWideSNP_6", tags="Full")
>> gi <- getGenomeInformation(cdf)
>> si <- getSnpInformation(cdf)
>> geneinform <- getGenomeInformation(cdf)
>> snpinform <- getSnpInformation(cdf)
FYI, the above 4 lines are just there for assertions to make sure the
annotation files are there before starting.
>> cs.comb<- AffymetrixCelSet$byName("Project1", chipType =
>> "GenomeWideSNP_6",cdf = cdf)
You only need to specify one of 'chipType' and 'cdf' - don't specify
both. If you do:
cs.comb <- AffymetrixCelSet$byName("Project1", cdf=cdf)
you don't have to do the following/below setCdf(cs.comb, cdf) statement.
>> 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)
Instead of explicitly specifying the 'ram' argument here, which will
cause issues if you bring the script somewhere else, you can set an
aroma setting as:
setOption(aromaSettings, "memory/ram", 28);
at the beginning of the script. Actually, you only have to do it once
on the computer if you also save it afterward with
saveAnywhere(aromaSettings). That way you don't have to put in the
script at all, making you script totally transparent to system
properties. See http://aroma-project.org/settings/ for more details.
>> bpn <- BasePositionNormalization(csC, target="zero")
>> csN <- process(bpn, ram=28,verbose=verbose)
Same here.
>> 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)
That completes the allele-specific CRMAv2 pipeline.
>> 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.
So, you are exporting all chip-effect estimates (thetaA, thetaB)
across all samples and per chromosome. First, you are aware that
(thetaA,thetaB) are only defined for SNPs, whereas for non-polymorphic
signals you will only have a value corresponding to the thetaA
position whereas thetaB is NA, correct? BTW, this is also the reason
why we don't label the theta dimension as "A" and "B", because it mean
more than that.
Second, yes, they are on the intensity scale (non-logged) and they are
not ratios.
Third, if you downstream are calculating total signals, theta = thetaA
+ thetaB (for SNPs), and allele B fractions (BAFs), beta =
thetaB/theta, there is a much easier way. Simply use:
data <- extractTotalAndFreqB(ces, units=units);
above instead of extractTheta(ces, units=units);
Moreover, you can export the the Affymetrix-specific chip-effect
estimates to technology-independent (total,fracB) data sets by:
dsList <- exportTotalAndFracB(cesN);
and you'll get a data set containing the total signals (which are
defined for SNPs and non-polymorphic loci) and a data set containing
the BAFs (defined only for SNPs; non-polymorphic loci has NAs). We're
moving toward using this standard everywhere. See how-to page 'Export
total copy numbers and allele B fractions as aroma binary data files':
http://aroma-project.org/howtos/exportTotalAndFracB
Given these, you can then easily extract total and BAF signals (e.g.
http://aroma-project.org/vignettes/tumorboost-highlevel) or write them
to tab-delimited files
(http://aroma-project.org/howtos/writeDataFrame).
Finally, you should also be aware that we are introducing so called
block functions ("blocks"), cf. http://aroma-project.org/blocks/.
This allows you to replace all of the above with:
library("aroma.affymetrix");
cdf <- AffymetrixCdfFile$byChipType("GenomeWideSNP_6", tags="Full");
csR <- AffymetrixCelSet$byName("Project1", cdf=cdf);
dsList <- doASCRMAv2(csR, verbose=verbose);
which gets you the point after exportTotalAndFracB() above, cf.
http://aroma-project.org/vignettes/tumorboost-highlevel
Hope this helps
/Henrik
>
>> 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.
>
> /Henrik
>
> --
> 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.
>
>
> You received this message because you are subscribed to the Google Groups
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>
> Thanks a lot for your answers. Here is the sessionInfo()
>
> R version 2.11.1 (2010-05-31)
> x86_64-pc-linux-gnu
>
> 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=C LC_MESSAGES=en_US.UTF-8
> [7] LC_PAPER=en_US.UTF-8 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] 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
> England
>
>
>
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> --
> 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.
>
>
> You received this message because you are subscribed to the Google Groups
> "aroma.affymetrix" group with website http://www.aroma-project.org/.
> To post to this group, send email to aroma-affymetrix@googlegroups.com
> To unsubscribe and other options, go to http://www.aroma-project.org/forum/
>
--
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.
You received this message because you are subscribed to the Google Groups
"aroma.affymetrix" group with website http://www.aroma-project.org/.
To post to this group, send email to aroma-affymetrix@googlegroups.com
To unsubscribe and other options, go to http://www.aroma-project.org/forum/
