Hi,

I want to try out MMDiff2 for simulated data on one human chromosome (chr19) with around 1000 peaks in both samples, (rather broad peaks like histon marks)

library(MMDiff2)
ExperimentData <- list(genome='BSgenome.Mmusculus.UCSC.mm10',dataDir='.',sampleSheet='mmdiff2.csv')
MetaData <- list('ExpData' = ExperimentData)
MMD <- DBAmmd(MetaData
library('DiffBind')
DBA <- dba(sampleSheet='mmdiff2.csv', minOverlap=3
Peaks <- dba.peakset(DBA, bRetrieve = TRUE)
MMD <- setRegions(MMD,Peaks)
MMD <- getPeakReads(MMD, pairedEnd = FALSE, run.parallel = FALSE)
MMD <- estimateFragmentCenters(MMD)

When running

MMD <- compDists(MMD)

, i get:

checking parameters...
estimating sigma...
pre-computing Kernel matrix...
computing 6 pair-wise distances...
computing distances for S11 vs S12
|=== to 23%
Error: cannot allocate vector of size 40.4 Gb
Execution halted
Warning message:
system call failed: Cannot allocate memory

Which indicates that the tool / R run out of memory, I am using a machine with 128GB memory this should be more than sufficient for comparing data of one chromosome. Is there a way to split this analysis step? or store data on the hard-drive?

sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=de_AT.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=de_AT.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=de_AT.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] MMDiff2_1.10.0       Biobase_2.42.0       Rsamtools_1.34.0    
 [4] Biostrings_2.50.2    XVector_0.22.0       GenomicRanges_1.34.0
 [7] GenomeInfoDb_1.18.1  IRanges_2.16.0       S4Vectors_0.20.1    
[10] BiocGenerics_0.28.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0                  later_0.7.5                
 [3] bindr_0.1.1                 compiler_3.5.2             
 [5] pillar_1.3.1                RColorBrewer_1.1-2         
 [7] plyr_1.8.4                  bitops_1.0-6               
 [9] tools_3.5.2                 zlibbioc_1.28.0            
[11] digest_0.6.18               tibble_2.0.1               
[13] gtable_0.2.0                BSgenome_1.50.0            
[15] lattice_0.20-38             pkgconfig_2.0.2            
[17] rlang_0.3.1                 Matrix_1.2-15              
[19] shiny_1.2.0                 DelayedArray_0.8.0         
[21] bindrcpp_0.2.2              GenomeInfoDbData_1.2.0     
[23] rtracklayer_1.42.1          dplyr_0.7.8                
[25] locfit_1.5-9.1              tidyselect_0.2.5           
[27] grid_3.5.2                  glue_1.3.0                 
[29] R6_2.3.0                    XML_3.98-1.16              
[31] BiocParallel_1.16.5         purrr_0.2.5                
[33] magrittr_1.5                ggplot2_3.1.0              
[35] promises_1.0.1              htmltools_0.3.6            
[37] scales_1.0.0                matrixStats_0.54.0         
[39] GenomicAlignments_1.18.1    assertthat_0.2.0           
[41] SummarizedExperiment_1.12.0 xtable_1.8-3               
[43] mime_0.6                    colorspace_1.4-0           
[45] httpuv_1.4.5.1              RCurl_1.95-4.11            
[47] lazyeval_0.2.1              munsell_0.5.0              
[49] crayon_1.3.4