How to overcome issue linked to loading big R object
Entering edit mode
Federica ▴ 10
Last seen 1 day ago

Hi guys,

I am experimenting new bioconductor packages to make quality control of my methylation data.

So, I am consulting the following link:

I used the following commands, but using my data:

# importing raw data with minfi 
   targets<- read.metharray.sheet("D:/RnBeads/RnBeads_files/20210429_QEIC21-03-5_pt12_1-12")

 # reading in the raw data from the IDAT files
   rgSet <- read.metharray.exp(targets=targets)

After running the last command, I found the following message:

Error in h (simpleError (msg, call)):
  error evaluating the 'args' argument when selecting a method for the '' function: it is not possible to allocate a vector of size 16.0 Mb

I supposed that I have to load an object of such a great dimension, considered that I have to load 192 IDAT files each of 8 Mb.

How could I overcome the problem linked to load such a big object in my R environment?

my sessionInfo():

   R Under development (unstable) (2021-02-23 r80032)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

[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] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] minfi_1.37.0                bumphunter_1.33.0           locfit_1.5-9.4              iterators_1.0.13            foreach_1.5.1              
 [6] Biostrings_2.59.2           XVector_0.31.1              SummarizedExperiment_1.21.1 Biobase_2.51.0              MatrixGenerics_1.3.1       
[11] matrixStats_0.58.0          GenomicRanges_1.43.3        GenomeInfoDb_1.27.6         IRanges_2.25.6              S4Vectors_0.29.7           
[16] BiocGenerics_0.37.1        

loaded via a namespace (and not attached):
  [1] nlme_3.1-152              bitops_1.0-6              bit64_4.0.5               filelock_1.0.2            RColorBrewer_1.1-2       
  [6] progress_1.2.2            httr_1.4.2                tools_4.1.0               doRNG_1.8.2               nor1mix_1.3-0            
 [11] utf8_1.1.4                R6_2.5.0                  HDF5Array_1.19.15         DBI_1.1.1                 rhdf5filters_1.3.4       
 [16] tidyselect_1.1.0          prettyunits_1.1.1         base64_2.0                preprocessCore_1.53.1     bit_4.0.4                
 [21] curl_4.3                  compiler_4.1.0            xml2_1.3.2                DelayedArray_0.17.9       rtracklayer_1.51.4       
 [26] readr_1.4.0               quadprog_1.5-8            genefilter_1.73.1         askpass_1.1               rappdirs_0.3.3           
 [31] stringr_1.4.0             digest_0.6.27             Rsamtools_2.7.1           illuminaio_0.33.0         siggenes_1.65.0          
 [36] GEOquery_2.59.0           pkgconfig_2.0.3           scrime_1.3.5              sparseMatrixStats_1.3.6   limma_3.47.8             
 [41] dbplyr_2.1.0              fastmap_1.1.0             rlang_0.4.10              rstudioapi_0.13           RSQLite_2.2.3            
 [46] DelayedMatrixStats_1.13.5 BiocIO_1.1.2              generics_0.1.0            mclust_5.4.7              BiocParallel_1.25.4      
 [51] dplyr_1.0.5               RCurl_1.98-1.2            magrittr_2.0.1            GenomeInfoDbData_1.2.4    Matrix_1.3-2             
 [56] Rcpp_1.0.6                Rhdf5lib_1.13.4           fansi_0.4.2               lifecycle_1.0.0           stringi_1.5.3            
 [61] yaml_2.2.1                MASS_7.3-53.1             zlibbioc_1.37.0           rhdf5_2.35.2              plyr_1.8.6               
 [66] BiocFileCache_1.15.1      grid_4.1.0                blob_1.2.1                crayon_1.4.1              lattice_0.20-41          
 [71] splines_4.1.0             annotate_1.69.0           multtest_2.47.0           GenomicFeatures_1.43.3    hms_1.0.0                
 [76] KEGGREST_1.31.1           beanplot_1.2              pillar_1.5.0              rjson_0.2.20              rngtools_1.5             
 [81] codetools_0.2-18          biomaRt_2.47.4            XML_3.99-0.5              glue_1.4.2                data.table_1.14.0        
 [86] png_0.1-7                 vctrs_0.3.6               tidyr_1.1.2               openssl_1.4.3             purrr_0.3.4              
 [91] reshape_0.8.8             assertthat_0.2.1          cachem_1.0.4              xfun_0.21                 xtable_1.8-4             
 [96] restfulr_0.0.13           survival_3.2-7            tibble_3.0.6              GenomicAlignments_1.27.2  tinytex_0.31             
[101] AnnotationDbi_1.53.1      memoise_2.0.0             ellipsis_0.3.1          
minfi R methylation • 65 views
Entering edit mode
Last seen 18 hours ago
United States

There are a few things you can do.

  1. Add some RAM to your computer
  2. Use a different computer with more RAM.

You might have access to a local computer that has more RAM, or you can fire up a large instance on AWS to do the analysis.

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