RunLNMF error: "memory not mapped" #50
Description
Hi, just want to say thanks for creating RcppML andsinglet packages. They've been a big help and I'm very interested in using them for my analyses, particularly Linked NMF.
Unfortunately I'm currently running into the following issue. Any ideas how to solve this?
Running this below code:
set.seed(123) # for reproducible NMF models
get_pbmc3k_data() %>% NormalizeData %>% RunNMF -> pbmc3k
pbmc3k <- Seurat::RunUMAP(pbmc3k, reduction = "nmf", dims = 1:ncol(pbmc3k@reductions$nmf))
pbmc3k <- RunLNMF(pbmc3k, split.by="cell_type", reduction.use="nmf", link.cutoff=0.7, verbose=FALSE)
Results in the following error:
> pbmc3k <- RunLNMF(pbmc3k, split.by="cell_type", reduction.use="nmf", link.cutoff=0.7, verbose=FALSE)
*** caught segfault ***
address 0x55d9a1419148, cause 'memory not mapped'
Traceback:
1: weight_by_split(A, split.by, length(unique(split.by)))
2: RunLNMF.Seurat(pbmc3k, split.by = "cell_type", reduction.use = "nmf", link.cutoff = 0.7, verbose = FALSE)
3: RunLNMF(pbmc3k, split.by = "cell_type", reduction.use = "nmf", link.cutoff = 0.7, verbose = FALSE)
Possible actions:
1: abort (with core dump, if enabled)
2: normal R exit
3: exit R without saving workspace
4: exit R saving workspace
Session info:
> sessionInfo()
R version 4.3.2 Patched (2024-02-08 r85876)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Rocky Linux 9.2 (Blue Onyx)
Matrix products: default
BLAS: /jhpce/shared/community/core/conda_R/4.3.x/R/lib64/R/lib/libRblas.so
LAPACK: /jhpce/shared/community/core/conda_R/4.3.x/R/lib64/R/lib/libRlapack.so; LAPACK version 3.11.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: US/Eastern
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] cowplot_1.1.3 patchwork_1.2.0
[3] bluster_1.12.0 igraph_2.0.1.9008
[5] RColorBrewer_1.1-3 pheatmap_1.0.12
[7] scran_1.30.2 Matrix_1.6-4
[9] scRNAseq_2.16.0 here_1.0.1
[11] ggspavis_1.8.0 scater_1.30.1
[13] ggplot2_3.4.4 scuttle_1.12.0
[15] SpatialExperiment_1.12.0 SingleCellExperiment_1.24.0
[17] SummarizedExperiment_1.32.0 Biobase_2.62.0
[19] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5
[21] IRanges_2.36.0 S4Vectors_0.40.2
[23] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[25] matrixStats_1.2.0 singlet_0.99.6
[27] RcppEigen_0.3.3.9.4 dplyr_1.1.4
[29] RcppML_0.5.6 SeuratObject_5.0.0
[31] Seurat_4.4.0
loaded via a namespace (and not attached):
[1] ProtGenerics_1.34.0 spatstat.sparse_3.0-3
[3] bitops_1.0-7 httr_1.4.7
[5] tools_4.3.2 sctransform_0.4.1
[7] utf8_1.2.4 R6_2.5.1
[9] lazyeval_0.2.2 uwot_0.1.16
[11] withr_3.0.0 sp_2.1-3
[13] prettyunits_1.2.0 gridExtra_2.3
[15] progressr_0.14.0 cli_3.6.2
[17] spatstat.explore_3.2-5 spatstat.data_3.0-3
[19] ggridges_0.5.6 pbapply_1.7-2
[21] Rsamtools_2.18.0 parallelly_1.36.0
[23] limma_3.58.1 RSQLite_2.3.5
[25] generics_0.1.3 BiocIO_1.12.0
[27] ica_1.0-3 spatstat.random_3.2-1
[29] ggbeeswarm_0.7.2 fansi_1.0.6
[31] abind_1.4-5 lifecycle_1.0.4
[33] edgeR_4.0.14 yaml_2.3.8
[35] SparseArray_1.2.3 BiocFileCache_2.10.1
[37] Rtsne_0.17 grid_4.3.2
[39] blob_1.2.4 dqrng_0.3.2
[41] promises_1.2.1 ExperimentHub_2.10.0
[43] crayon_1.5.2 miniUI_0.1.1.1
[45] lattice_0.22-5 beachmat_2.18.0
[47] msigdbr_7.5.1 GenomicFeatures_1.54.3
[49] KEGGREST_1.42.0 magick_2.8.2
[51] metapod_1.10.1 pillar_1.9.0
[53] knitr_1.45 fgsea_1.28.0
[55] rjson_0.2.21 future.apply_1.11.0
[57] codetools_0.2-19 fastmatch_1.1-4
[59] leiden_0.4.3 glue_1.7.0
[61] data.table_1.15.0 vctrs_0.6.5
[63] png_0.1-8 spam_2.10-0
[65] gtable_0.3.4 cachem_1.0.8
[67] xfun_0.42 S4Arrays_1.2.0
[69] mime_0.12 ggside_0.2.3
[71] survival_3.5-7 statmod_1.5.0
[73] interactiveDisplayBase_1.40.0 ellipsis_0.3.2
[75] fitdistrplus_1.1-11 ROCR_1.0-11
[77] nlme_3.1-164 bit64_4.0.5
[79] progress_1.2.3 filelock_1.0.3
[81] RcppAnnoy_0.0.22 rprojroot_2.0.4
[83] irlba_2.3.5.1 vipor_0.4.7
[85] KernSmooth_2.23-22 colorspace_2.1-0
[87] DBI_1.2.1 tidyselect_1.2.0
[89] bit_4.0.5 compiler_4.3.2
[91] curl_5.2.0 BiocNeighbors_1.20.2
[93] xml2_1.3.6 DelayedArray_0.28.0
[95] plotly_4.10.4 rtracklayer_1.62.0
[97] scales_1.3.0 lmtest_0.9-40
[99] rappdirs_0.3.3 stringr_1.5.1
[101] digest_0.6.34 goftest_1.2-3
[103] spatstat.utils_3.0-4 XVector_0.42.0
[105] htmltools_0.5.7 pkgconfig_2.0.3
[107] sparseMatrixStats_1.14.0 ensembldb_2.26.0
[109] dbplyr_2.4.0 fastmap_1.1.1
[111] rlang_1.1.3 htmlwidgets_1.6.4
[113] shiny_1.8.0 DelayedMatrixStats_1.24.0
[115] zoo_1.8-12 jsonlite_1.8.8
[117] BiocParallel_1.36.0 BiocSingular_1.18.0
[119] RCurl_1.98-1.14 magrittr_2.0.3
[121] GenomeInfoDbData_1.2.11 dotCall64_1.1-1
[123] munsell_0.5.0 Rcpp_1.0.12
[125] babelgene_22.9 viridis_0.6.5
[127] reticulate_1.35.0 stringi_1.8.3
[129] zlibbioc_1.48.0 MASS_7.3-60.0.1
[131] AnnotationHub_3.10.0 plyr_1.8.9
[133] parallel_4.3.2 listenv_0.9.1
[135] ggrepel_0.9.5 deldir_2.0-2
[137] Biostrings_2.70.2 splines_4.3.2
[139] tensor_1.5 hms_1.1.3
[141] locfit_1.5-9.8 spatstat.geom_3.2-7
[143] reshape2_1.4.4 biomaRt_2.58.2
[145] ScaledMatrix_1.10.0 BiocVersion_3.18.1
[147] XML_3.99-0.16.1 BiocManager_1.30.22
[149] httpuv_1.6.14 RANN_2.6.1
[151] tidyr_1.3.1 purrr_1.0.2
[153] polyclip_1.10-6 future_1.33.0
[155] scattermore_1.2 rsvd_1.0.5
[157] xtable_1.8-4 AnnotationFilter_1.26.0
[159] restfulr_0.0.15 later_1.3.2
[161] viridisLite_0.4.2 tibble_3.2.1
[163] GenomicAlignments_1.38.2 memoise_2.0.1
[165] beeswarm_0.4.0 AnnotationDbi_1.64.1
[167] cluster_2.1.6 globals_0.16.2