Hi Henrik,
Thank you, that was helpful.
I run to another problem though. I am trying to perform ExonRmaPlm(csQN,
merge=TRUE) but this produces a following error:
20140226 23:25:33| Identifying CDF cell indices...done
Error in vector("double", nbrOfCells * nbrOfArrays) :
vector size cannot be NA
In addition: Warning message:
In nbrOfCells * nbrOfArrays : NAs produced by integer overflow
20140226 23:28:35| Reading probe intensities from 5622 arrays...done
20140226 23:28:35| Fitting chunk #1 of 1 of 'expression' units (code=1)
with various dimensions...done
20140226 23:28:35| Unit dimension #3 (various dimensions) of 3...done
20140226 23:28:35| Fitting the model by unit dimensions (at least for the
large classes)...done
20140226 23:28:35| Unit type #1 ('expression') of 1...done
20140226 23:28:35| Fitting ExonRmaPlm for each unit type separately...done
20140226 23:28:35|Fitting model of class ExonRmaPlm…done
I testes whether it worked anyway, but the expression is zero across all
arrays when I access it.
Do you know what could be causing the problem?
Best,
Damian
The code I run is below:
library(aroma.affymetrix)
library(aroma.core)
setOption(aromaSettings, "memory/ram", 500.0);
verbose <- Arguments$getVerbose(-8, timestamp=TRUE)
chipType <- "HuEx-1_0-st-v2-core"
cdf <- AffymetrixCdfFile$byChipType(chipType)
#print(cdf)
cs <- AffymetrixCelSet$byName("experiment1", cdf=cdf)
bc <- RmaBackgroundCorrection(cs)
csBC <- process(bc,verbose=verbose)
qn <- QuantileNormalization(csBC, typesToUpdate="pm")
target <- getTargetDistribution(qn, verbose=verbose)
qn <- QuantileNormalization(csBC, typesToUpdate="pm",
targetDistribution=target)
csQN <- process(qn, verbose=verbose)
csPLM <- ExonRmaPlm(csQN, mergeGroups=TRUE)
fit(csPLM, verbose=verbose)
date()
ces <- getChipEffectSet(csPLM)
gExprs <- extractDataFrame(ces, units=1:3, addNames=TRUE)
> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=C LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 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] preprocessCore_1.23.0 aroma.light_1.31.8 matrixStats_0.8.14
[4] aroma.affymetrix_2.11.1 aroma.core_2.11.0 R.devices_2.8.2
[7] R.filesets_2.3.0 R.utils_1.29.8 R.oo_1.17.0
[10] affxparser_1.34.0 R.methodsS3_1.6.1
loaded via a namespace (and not attached):
[1] aroma.apd_0.4.0 base64enc_0.1-1 digest_0.6.4 DNAcopy_1.35.1
[5] PSCBS_0.40.4 R.cache_0.9.2 R.huge_0.6.0 R.rsp_0.9.28
[9] tools_3.0.2
On Thursday, February 20, 2014 1:21:25 PM UTC-5, Henrik Bengtsson wrote:
>
> On Tue, Feb 18, 2014 at 7:30 PM, Damian Plichta
> <damian....@gmail.com <javascript:>> wrote:
> > Thanks, that helped a lot. It took me less than 3 hours to perform the
> > background correction.
> >
> > Now I'm wondering if for the next step, quantile normalization, I could
> do a
> > similar trick. Is there a way to precompute the target empirical
> > distribution based on all arrays and then do the normalization on chunks
> of
> > data (thus in an independent manner)? I can see the option
> > targetDistribution under QuantileNormalization.
>
> # Calculate the target distribution based on *all* arrays [not
> parallalized]
> qn <- QuantileNormalization(dsC, typesToUpdate="pm")
> target <- getTargetDistribution(qn, verbose=verbose)
>
> # Normalize array by array toward the same target distribution [in chucks]
> dsCs <- extract(dsC, 1:100)
> qn <- QuantileNormalization(dsCs, typesToUpdate="pm",
> targetDistribution=target)
> csNs <- process(qn, verbose=verbose)
>
> Hope this helps
>
> /Henrik
>
> >
> > Kind regards,
> >
> > Damian Plichta
> >
> > On Monday, February 17, 2014 4:03:54 PM UTC-5, Henrik Bengtsson wrote:
> >>
> >> Hi.
> >>
> >> On Sun, Feb 16, 2014 at 6:53 PM, Damian Plichta
> >> <damian....@gmail.com> wrote:
> >> > Hi,
> >> >
> >> > I'm processing around 5500 affymetrix exon arrays. The
> >> > RmaBackgroundCorrection() is pretty slow, 1-2 minutes/array. I played
> >> > with
> >> > setOption(aromaSettings, "memory/ram", X) and increased X up to 100
> but
> >> > it
> >> > didn't have any effect on this stage of analysis.
> >>
> >> If you don't notice any difference in processing time by changing
> >> "memory/ram" from the default (1.0) to 100, then the memory is not
> >> your bottleneck.
> >> >
> >> > Any way to speed the process up?
> >>
> >> If you haven't already, make sure to read "How to: Improve processing
> >> time":
> >>
> >> http://aroma-project.org/howtos/ImproveProcessingTime
> >>
> >> If you have access to multiple machines on the same file system, you
> >> can do poor mans parallel processing for the *background correction*,
> >> because each array is corrected independently of the others. You can
> >> do this by processing a subset of arrays per computer, e.g.
> >>
> >> dsR <- AffymetrixCelSet$byName("MyDataSet", chipType="HuEx-1_0-st-v2")
> >> dsR <- extract(dsR, 1:100)
> >> bg <- RmaBackgroundCorrection(dsS)
> >> dsC <- process(bg, verbose=verbose)
> >>
> >> Repeat on another machine with 101:200, and so on.
> >>
> >> When all arrays have been background corrected, you can move back to
> >> your original script - all arrays background corrected are already
> >> saved to file and will therefore not be redone.
> >>
> >> /Henrik
> >>
> >> >
> >> > Kind regards,
> >> >
> >> > Damian Plichta
> >> >
> >> > --
> >> > --
> >> > 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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--
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