Hello, recently 10x genomics release a new version for Cell ranger ATAC (v2 May3 2021) with improved features. I wanted to re-run previous samples to test if my data would look different in Seurat/Signac. I realized the function CreateChromatinAssay() crashed with error caught segfault - cause 'memory not mapped'. However the same function using the fragment file previously generated through cell ranger atac v1.2 works. I tried to run on linux, macOS, same result. It is unclear whether the issue is coming from R or the fragment file generated by cell ranger atac v2. I also tried on a 10x data set here generated with cellranger atac v2 and CreateChromatinAssay() crashed.
here is the code i am using:
counts <- Read10X_h5(filename = "/path_to/filtered_peak_bc_matrix.h5")
metadata <- read.csv(
file = "/path_to/singlecell.csv",
header = TRUE,
row.names = 1)
chrom_assay <- CreateChromatinAssay(
counts = counts,
sep = c(":", "-"),
genome = 'hg38',
fragments = '/path_to/fragments.tsv.gz',
min.cells = 10,
min.features = 200
#Computing hash
Checking for 4006 cell barcodes
*** caught segfault ***
address (nil), cause 'memory not mapped'
Traceback:
1: validateCells(fragments = filepath, cells = cells, find_n = find_n, max_lines = max.lines, verbose = verbose)
2: ValidateCells(object = frags, verbose = verbose, ...)
3: CreateFragmentObject(path = fragments, cells = cells, validate.fragments = validate.fragments, verbose = verbose, ...)
4: CreateChromatinAssay(counts = counts, sep = c(":", "-"), genome = "hg38", fragments = "/path_to/fragments.tsv.gz", min.cells = 10, min.features = 200)
sessionInfo()
R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] patchwork_1.1.1 ggplot2_3.3.3
[3] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.14.1
[5] AnnotationFilter_1.14.0 GenomicFeatures_1.42.3
[7] AnnotationDbi_1.52.0 Biobase_2.50.0
[9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[11] IRanges_2.24.1 S4Vectors_0.28.1
[13] BiocGenerics_0.36.1 SeuratObject_4.0.0
[15] Seurat_4.0.1 Signac_1.2.0
loaded via a namespace (and not attached):
[1] fastmatch_1.1-0 BiocFileCache_1.14.0
[3] plyr_1.8.6 igraph_1.2.6
[5] lazyeval_0.2.2 splines_4.0.5
[7] BiocParallel_1.24.1 listenv_0.8.0
[9] scattermore_0.7 SnowballC_0.7.0
[11] digest_0.6.27 htmltools_0.5.1.1
[13] fansi_0.4.2 magrittr_2.0.1
[15] memoise_2.0.0 tensor_1.5
[17] cluster_2.1.1 ROCR_1.0-11
[19] globals_0.14.0 Biostrings_2.58.0
[21] matrixStats_0.58.0 docopt_0.7.1
[23] askpass_1.1 spatstat.sparse_2.0-0
[25] prettyunits_1.1.1 colorspace_2.0-1
[27] rappdirs_0.3.3 blob_1.2.1
[29] ggrepel_0.9.1 dplyr_1.0.6
[31] sparsesvd_0.2 crayon_1.4.1
[33] RCurl_1.98-1.3 jsonlite_1.7.2
[35] spatstat.data_2.1-0 survival_3.2-10
[37] zoo_1.8-9 glue_1.4.2
[39] polyclip_1.10-0 gtable_0.3.0
[41] zlibbioc_1.36.0 XVector_0.30.0
[43] leiden_0.3.7 DelayedArray_0.16.3
[45] future.apply_1.7.0 abind_1.4-5
[47] scales_1.1.1 DBI_1.1.1
[49] miniUI_0.1.1.1 Rcpp_1.0.6
[51] progress_1.2.2 viridisLite_0.4.0
[53] xtable_1.8-4 reticulate_1.20
[55] spatstat.core_2.1-2 bit_4.0.4
[57] htmlwidgets_1.5.3 httr_1.4.2
[59] RColorBrewer_1.1-2 ellipsis_0.3.2
[61] ica_1.0-2 XML_3.99-0.6
[63] pkgconfig_2.0.3 farver_2.1.0
[65] dbplyr_2.1.1 ggseqlogo_0.1
[67] uwot_0.1.10 deldir_0.2-10
[69] utf8_1.2.1 tidyselect_1.1.1
[71] rlang_0.4.11 reshape2_1.4.4
[73] later_1.2.0 munsell_0.5.0
[75] tools_4.0.5 cachem_1.0.4
[77] generics_0.1.0 RSQLite_2.2.7
[79] ggridges_0.5.3 stringr_1.4.0
[81] fastmap_1.1.0 goftest_1.2-2
[83] bit64_4.0.5 fitdistrplus_1.1-3
[85] purrr_0.3.4 RANN_2.6.1
[87] pbapply_1.4-3 future_1.21.0
[89] nlme_3.1-152 mime_0.10
[91] slam_0.1-48 RcppRoll_0.3.0
[93] xml2_1.3.2 biomaRt_2.46.3
[95] compiler_4.0.5 rstudioapi_0.13
[97] curl_4.3.1 plotly_4.9.3
[99] png_0.1-7 spatstat.utils_2.1-0
[101] tibble_3.1.1 tweenr_1.0.2
[103] stringi_1.5.3 lattice_0.20-41
[105] ProtGenerics_1.22.0 Matrix_1.3-2
[107] vctrs_0.3.8 pillar_1.6.0
[109] lifecycle_1.0.0 spatstat.geom_2.1-0
[111] lmtest_0.9-38 RcppAnnoy_0.0.18
[113] data.table_1.14.0 cowplot_1.1.1
[115] bitops_1.0-7 irlba_2.3.3
[117] rtracklayer_1.50.0 httpuv_1.6.0
[119] R6_2.5.0 promises_1.2.0.1
[121] KernSmooth_2.23-18 gridExtra_2.3
[123] lsa_0.73.2 parallelly_1.25.0
[125] codetools_0.2-18 MASS_7.3-53.1
[127] assertthat_0.2.1 SummarizedExperiment_1.20.0
[129] openssl_1.4.4 withr_2.4.2
[131] GenomicAlignments_1.26.0 qlcMatrix_0.9.7
[133] sctransform_0.3.2 Rsamtools_2.6.0
[135] GenomeInfoDbData_1.2.4 hms_1.0.0
[137] mgcv_1.8-33 grid_4.0.5
[139] rpart_4.1-15 tidyr_1.1.3
[141] MatrixGenerics_1.2.1 Rtsne_0.15
[143] ggforce_0.3.3 shiny_1.6.0
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4