Entering edit mode
3.0 years ago
Fede_Santos95
▴
10
Hi guys,
I am experimenting new bioconductor packages to make quality control of my methylation data.
So, I am consulting the following link: https://bioconductor.org/packages/devel/workflows/vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.html#quality-control
I have used the following commands, but using my data:
# importing raw data with minfi
library(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 'do.call' 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
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] 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
I think this question belongs on https://support.bioconductor.org/