[BioC] MEDIPS

[Paolo Kunderfranco]

Hi Lukas,

Specie is mouse,  4 samples plus 2 input, and ws=100.
The memory fails when I run MEDIPS.meth for differential analysis
between two samples and 1 input.
I bypassed the error as you suggested running 3 MEDIPS.meth with
chromosomes groups

Look forward for the new release!

Cheers,
Paolo



2013/10/9 Lukas Chavez <lukas.chavez.mailings at googlemail.com>:
>
> Hi Paolo,
>
> too bad, you were almost there! When MEDIPS stopped processing due to memory
> limitations, differential coverage was already calculated. Unfortunately,
> the last step, i.e. creating the output result table, was too memory
> intensive for your set up. May I ask how many samples of which species do
> you process and what is the chosen window size? These are the parameters
> that influence memory requirements for the differential coverage analysis.
>
> While I will investigate, how to further reduce memory requirements in
> future versions, I see two immediate possible solutions without reducing
> window and sample sizes: i) migrate your analysis to a cluster with more
> memory, ii) analyze your data in chunks. While I understand that i) is not
> always available, this is what you can try for solution ii):
>
> Update to MEDIPS version >= 1.11.16. This is currently available at
> http://www.bioconductor.org/packages/2.13/bioc/html/MEDIPS.html and will be
> available as version 1.12.0 with the release of Bioconductor 2.13 on October
> 15th 2013. In MEDIPS version >= 1.11.16, the function MEDIPS.meth() has the
> parameter "chr" which allows to process only a set of selected chromosomes.
> In your case, I expect that it will be sufficient to divide the genome into
> two groups of chromosomes. Therefore, you can try running the MEDIPS.meth()
> function twice like:
>
> res = MEDIPS.meth(MSet1 = MSets_groupA, MSet2 = MSets_groupB, chr =
> chr_names(MSets_groupA[[1]])[1:x], ...)
> resB = MEDIPS.meth(MSet1 = MSets_groupA, MSet2 = MSets_groupB, chr =
> chr_names(MSets_groupA[[1]])[x+1:n], ...)
>
> where n is the number of chromosomes included in your MEDIPS Sets and x is
> an arbitrary number that divides your chromosomes into two groups (e.g.
> round(n/2)). Please note that some modeled parameters (e.g. for library size
> normalization) will be slightly different for two subsets of the data
> compared to processing a genome wide table at once.
>
> Finally, you can combine the result tables by
>
> res = rbind(res, res_B)
> rm(res_B)
>
> and continue with a normal workflow.
>
> All the best,
> Lukas
>
>
>
>
>
>
> On Wed, Oct 9, 2013 at 3:33 AM, Paolo Kunderfranco
> <paolo.kunderfranco at gmail.com> wrote:
>>
>> Dear All,
>> I am facing some problems with MEDIPS package,
>> I Think I'am running out of memory once I run the command MEDIPS.meth
>> ................
>> Adjusting p.values for multiple testing...
>> Creating results table...
>> Adding differential coverage results...
>> Error: cannot allocate vector of size 207.9 Mb
>> Inoltre: Warning messages:
>> 1: In unlist(vlist, recursive = FALSE, use.names = FALSE) :
>>   Reached total allocation of 24501Mb: see help(memory.size)
>> 2: In unlist(vlist, recursive = FALSE, use.names = FALSE) :
>>   Reached total allocation of 24501Mb: see help(memory.size)
>> 3: In unlist(vlist, recursive = FALSE, use.names = FALSE) :
>>   Reached total allocation of 24501Mb: see help(memory.size)
>> 4: In unlist(vlist, recursive = FALSE, use.names = FALSE) :
>>   Reached total allocation of 24501Mb: see help(memory.size)
>>
>> Is there a way to avoid this problem?
>>
>> I have a 24 Gb RAM workstation I don't see why it fails...
>>
>> Thanks,
>> Paolo
>>
>>
>> > sessionInfo()
>> R version 3.0.0 (2013-04-03)
>> Platform: x86_64-w64-mingw32/x64 (64-bit)
>>
>> locale:
>> [1] LC_COLLATE=Italian_Italy.1252  LC_CTYPE=Italian_Italy.1252
>> LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C
>> LC_TIME=Italian_Italy.1252
>>
>> attached base packages:
>> [1] parallel  stats     graphics  grDevices utils     datasets
>> methods   base
>>
>> other attached packages:
>> [1] MEDIPS_1.10.0        BiocInstaller_1.10.3 gtools_3.1.0
>> DNAcopy_1.34.0       BSgenome_1.28.0      Biostrings_2.28.0
>> GenomicRanges_1.12.4 IRanges_1.18.1       BiocGenerics_0.6.0
>>
>> loaded via a namespace (and not attached):
>>  [1] biomaRt_2.16.0   bitops_1.0-6     edgeR_3.2.4      limma_3.16.8
>>   RCurl_1.95-4.1   Rsamtools_1.12.4 stats4_3.0.0     tools_3.0.0
>> XML_3.98-1.1     zlibbioc_1.6.0
>
>