Hello,
I am trying to run Seurat on a fairly large scRNA-Seq experiment, with 16 samples ranging from 1000-10,000 cells.
In my first run of the pipeline, I merged all of the samples into a single Seurat object, like so:
data.combined <- MergeSeurat(object1 = J, object2 = E, add.cell.id1 = "J",
add.cell.id2 = "E", project = "all")
data.combined <- AddSamples(object = data.combined, new.data = F.data, add.cell.id = "F")
...and then followed the tutorial. However, on the scaling step:
data.combined <- ScaleData(object = data.combined, vars.to.regress = c("nUMI"))
I get an error:
Error: vector memory exhausted (limit reached?)
I see that this is associated with running out of RAM with which to do the computation, which isn't surprising given the size of data.combined. How can I overcome this, short of finding a computational cluster to run this on? Due to the large differences in the number of UMIs between the 1000 and 10,000 cells samples, it seems really crucial to run this step on a Seurat object containing all the data, rather than hack together a solution where I run ScaleData on subsets of data and then tack them all together afterwards..
Thank you for your help! I am working on an iMac with 16 GB of RAM. sessionInfo() is:
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-2 gdtools_0.1.7 biomaRt_2.36.1 ggrepel_0.8.0 edgeR_3.22.5
[6] limma_3.36.5 readr_1.1.1 DESeqAid_0.2 DESeq2_1.20.0 SummarizedExperiment_1.10.1
[11] DelayedArray_0.6.6 BiocParallel_1.14.2 matrixStats_0.54.0 Biobase_2.40.0 GenomicRanges_1.32.7
[16] GenomeInfoDb_1.16.0 IRanges_2.14.12 S4Vectors_0.18.3 BiocGenerics_0.26.0 bindrcpp_0.2.2
[21] dplyr_0.7.6 Seurat_2.3.4 Matrix_1.2-14 cowplot_0.9.4 ggplot2_3.0.0
loaded via a namespace (and not attached):
[1] snow_0.4-3 backports_1.1.2 Hmisc_4.1-1 plyr_1.8.4 igraph_1.2.2 lazyeval_0.2.1
[7] splines_3.5.1 digest_0.6.18 foreach_1.4.4 htmltools_0.3.6 lars_1.2 gdata_2.18.0
[13] magrittr_1.5 checkmate_1.8.5 memoise_1.1.0 cluster_2.0.7-1 mixtools_1.1.0 ROCR_1.0-7
[19] annotate_1.58.0 R.utils_2.7.0 svglite_1.2.1 prettyunits_1.0.2 colorspace_1.3-2 blob_1.1.1
[25] crayon_1.3.4 RCurl_1.95-4.11 jsonlite_1.5 genefilter_1.62.0 bindr_0.1.1 survival_2.42-6
[31] zoo_1.8-4 iterators_1.0.10 ape_5.2 glue_1.3.0 gtable_0.2.0 zlibbioc_1.26.0
[37] XVector_0.20.0 kernlab_0.9-27 prabclus_2.2-6 DEoptimR_1.0-8 scales_1.0.0 mvtnorm_1.0-8
[43] DBI_1.0.0 bibtex_0.4.2 Rcpp_0.12.19 metap_1.0 dtw_1.20-1 progress_1.2.0
[49] xtable_1.8-3 htmlTable_1.12 reticulate_1.10 foreign_0.8-71 bit_1.1-14 proxy_0.4-22
[55] mclust_5.4.2 SDMTools_1.1-221 Formula_1.2-3 tsne_0.1-3 htmlwidgets_1.3 httr_1.3.1
[61] gplots_3.0.1 fpc_2.1-11.1 acepack_1.4.1 modeltools_0.2-22 ica_1.0-2 pkgconfig_2.0.2
[67] XML_3.98-1.16 R.methodsS3_1.7.1 flexmix_2.3-14 nnet_7.3-12 locfit_1.5-9.1 tidyselect_0.2.5
[73] labeling_0.3 rlang_0.2.2 reshape2_1.4.3 AnnotationDbi_1.42.1 munsell_0.5.0 tools_3.5.1
[79] RSQLite_2.1.1 ggridges_0.5.1 evaluate_0.12 stringr_1.3.1 yaml_2.2.0 npsurv_0.4-0
[85] knitr_1.20 bit64_0.9-7 fitdistrplus_1.0-11 robustbase_0.93-3 caTools_1.17.1.1 purrr_0.2.5
[91] RANN_2.6.1 pbapply_1.3-4 nlme_3.1-137 R.oo_1.22.0 hdf5r_1.0.1 compiler_3.5.1
[97] rstudioapi_0.8 curl_3.2 png_0.1-7 lsei_1.2-0 statmod_1.4.30 tibble_1.4.2
[103] geneplotter_1.58.0 stringi_1.2.4 lattice_0.20-35 trimcluster_0.1-2.1 pillar_1.3.0 Rdpack_0.10-1
[109] lmtest_0.9-36 data.table_1.11.8 bitops_1.0-6 irlba_2.3.2 gbRd_0.4-11 R6_2.3.0
[115] latticeExtra_0.6-28 KernSmooth_2.23-15 gridExtra_2.3 codetools_0.2-15 MASS_7.3-50 gtools_3.8.1
[121] assertthat_0.2.0 rprojroot_1.3-2 withr_2.1.2 GenomeInfoDbData_1.1.0 hms_0.4.2 diptest_0.75-7
[127] doSNOW_1.0.16 grid_3.5.1 rpart_4.1-13 tidyr_0.8.2 class_7.3-14 rmarkdown_1.10
[133] segmented_0.5-3.0 Rtsne_0.15 base64enc_0.1-3
