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 > >