On Mon, Jul 29, 2013 at 7:45 PM, ying chen <njs...@gmail.com> wrote:
> Hi Henrik,
> Thanks a lot for the help!
> Sorry I have more questions. I am following "How to: Calculate total copy
> number ratios from total (non-polymorphic) signals" and "Vignette: Total
> copy-number segmentation (non-paired CBS)", but I am not sure if I do it
> correctly.
> I have two SNP6 datasets Tumor and HapMap270 and I want to use HpaMap270 as
> reference to go all the way to CBS step. So I do the following steps
> respectively.
> > ds1 <- doCRMAv2("HapMap270", chipType="GenomeWideSNP_6,Full")
> > ds2 <- doCRMAv2("Tumor", chipType="GenomeWideSNP_6,Full")
> After that, I do
> > dataSet <- "HapMap270"
> > tags <- "ACC,ra,-XY,BPN,-XY,AVG,A+B,FLN,-XY"
> > chipType <- "GenomeWideSNP_6"
> > dsN <- AromaUnitTotalCnBinarySet$byName(dataSet, tags=tags,
> chipType=chipType)
> > dataSet <- "Tumor"
> > tags <- "ACC,ra,-XY,BPN,-XY,AVG,A+B,FLN,-XY"
> > chipType <- "GenomeWideSNP_6"
> > dsT <- AromaUnitTotalCnBinarySet$byName(dataSet, tags=tags,
> chipType=chipType)
> > dfR <- getAverageFile(dsN) # ?
> > dsTR <- exportTotalCnRatioSet(dsT, ref=dfR) # ?
> Would the above two steps work? My question is how to go ahead from here to
> do CBS and will sm <- CbsModel(dsTR) work?
Skip the exportTotalCnRatioSet() call, and instead use:
sm <- CbsModel(dsTR, ref=dfR, tags="HapMapRef");
The 'tags' is just to add an informative tag to the output data set.
/Henrik
> Thanks again for your help!
> Sean
>
>
>
>
> On Sun, Jul 28, 2013 at 4:28 PM, Henrik Bengtsson
> <henrik.bengts...@aroma-project.org> wrote:
>>
>> Hi.
>>
>> On Fri, Jul 26, 2013 at 8:02 AM, sean nj <njs...@gmail.com> wrote:
>> > Hi guys,
>> >
>> > I have a question regarding how to calculate raw copy numbers using
>> > common
>> > reference instead of average of all samples of the study. Basically I
>> > want
>> > to use average of HapMap270 samples as reference for all further copy
>> > number
>> > calculations.
>> >
>> > I have a bunch HapMap270 snp6 cel files and I followed Vignette:
>> > Estimation
>> > of total copy numbers using the CRMA v2 method (10K-CytoScanHD) to Step
>> > 5 -
>> > Calculation of raw copy numbers, and generated ceR and saved it as a
>> > RData
>> > file ceR.Rdata.
>>
>> It's important to understand that almost all objects in the Aroma
>> framework are basically "pointers" to external files. For instance,
>> your 'ceR', which I assume you've got from something like ceR <-
>> getAverageFile(ces), is referring to the file with pathname
>> getPathname(ceR). More below...
>>
>> >
>> > My first question is, how to use this data for any future copy number
>> > analysis? My guess is that instead of calculating the ceR from the
>> > sample
>> > set I can just load the ceR.RData file I saved and use it. Right?
>>
>> First of all, please note that when do ceR <- getAverageFile(ces) on
>> the same data set 'ces', the result is already available on file and
>> it will be quickly found and returned. In other words, it will not
>> recalcuate the averages again [unless you do ceR <-
>> getAverageFile(ces, force=TRUE)].
>>
>> However, I do understand that you may not want to have to keep a large
>> 'ces' data set around, when you're only interested in the pooled
>> average. In that case, I would copy the file containing the "average"
>> to a new data set. Currently, this is not straightforward in Aroma
>> (I'll think about something), but you can do the following:
>>
>> # Calculate the pooled average
>> > ceR <- getAverageFile(cesN);
>>
>> # Copy this file to plmData/HapMap270,pooled/GenomeWideSNP_6/, e.g.
>> > filename <- getFilename(ceR);
>> > filename
>> [1] ".average-intensities-median-mad,d03faaf8b707a97c4e43381b1a5d1ef2.CEL"
>> > rootPath <- getParent(getPath(cesN), depth=2L);
>> > dataSet <- "HapMap270,pooled";
>> > chipType <- getChipType(ceR, fullname=FALSE);
>> > path <- file.path(rootPath, dataSet, chipType);
>> > path
>> [1] "plmData/HapMap270,pooled/GenomeWideSNP_6"
>> > mkdirs(path);
>> > copyFile(getPathname(ceR), file.path(path, filename));
>>
>> With this done, you can then grab this pooled reference as:
>>
>> > library("aroma.affymetrix")
>> > path <- "plmData/HapMap270,pooled/GenomeWideSNP_6";
>> > filename <-
>> > ".average-intensities-median-mad,d03faaf8b707a97c4e43381b1a5d1ef2.CEL";
>> > ceR <- CnChipEffectFile(filename, path=path);
>>
>> Note, when you save 'ceR', you are basically saving the reference to
>> the file. Yes, you can load it later, but make sure not to move it,
>> otherwise you'll get some type of "file not found" error.
>>
>> > saveObject(ceR, "HapMap270,GenomeWideSNP_6,reference.Rdata");
>>
>> If already saved, and file not moved, you can then do:
>>
>> > library("aroma.affymetrix");
>> > ceR <- loadObject("HapMap270,GenomeWideSNP_6,reference.Rdata");
>>
>> All this is very ad hoc (=non-aroma style), and as I said, I'll see if
>> I can come up with a cleaner solution for storing and retrieving
>> pooled averages.
>>
>> >
>> > My second question is, how to go ahead from there to calculate the
>> > relative
>> > copy numbers for all unit from all samples? The two examples given in
>> > the
>> > Vignette are for one unit from one sample and for a few unit on
>> > chromosome 2
>> > for one sample. What is the function to retrieve all units on all
>> > chromosomes instead of units <- getUnitsOnChromosome(gi, chromosome=2,
>> > region=c(81,86)*1e6)?
>>
>> You can set 'units' to NULL to retrieve all loci, i.e. no need to use
>> getUnitsOnChromosome(). FYI, units <- NULL will give the same data as
>> with units <- 1:nbrOfUnits(gi).
>>
>> > And what is the function to retrieve all samples
>> > instead of ce <- getFile(cesN, indexOf(cesN, "NA06985"))?
>>
>> Hmm... not clear what you mean. All samples are in 'cesN', and you do
>> need to iterate over them somehow. Is this what you're looking for?
>>
>> for (ii in seq_along(cesN)) {
>> ce <- getFile(cesN, ii)
>> ...
>> }
>>
>> Or are you asking how to extract the data from all samples? Then you can
>> do:
>>
>> theta <- extractTheta(cesN, units=units)
>>
>> but be careful because that loads a lot of data into memory.
>>
>> Hope this helps,
>>
>> Henrik
>>
>> >
>> > Thanks a lot for the help,
>> >
>> > Sean
>> >
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>
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