welch-lab / liger

R package for integrating and analyzing multiple single-cell datasets
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Error on makeFeatureMatrix #296

Closed minsu1794 closed 9 months ago

minsu1794 commented 11 months ago

Hello I`m trying the tutorial "Joint definition of cell types from single-cell gene expression and chromatin accessibility data" to make scATAC to makeFeatureMatrix function to calculate accessibility counts for gene body and promoter individually. Once I run this line gene.counts <- makeFeatureMatrix(genes.bc, barcodes)

I get an error accordingly: **** caught segfault address 0x7f9443e3a000, cause 'memory not mapped' Error: no more error handlers available (recursive errors?); invoking 'abort' restart****

Not sure why this error occurred.

This is my sessionInfo(): R version 4.3.2 (2023-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 18.04.6 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so; LAPACK version 3.7.1

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: America/Chicago tzcode source: system (glibc)

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] rliger_1.0.1 Matrix_1.6-3

loaded via a namespace (and not attached): [1] bit_4.0.5 gtable_0.3.4 dplyr_1.1.4 compiler_4.3.2 [5] tidyselect_1.2.0 Rcpp_1.0.11 FNN_1.1.3.2 parallel_4.3.2 [9] scales_1.3.0 lattice_0.22-5 ica_1.0-3 ggplot2_3.4.4 [13] R6_2.5.1 plyr_1.8.9 generics_0.1.3 patchwork_1.1.3 [17] iterators_1.0.14 ggrepel_0.9.4 tibble_3.2.1 Rtsne_0.16 [21] munsell_0.5.0 pillar_1.9.0 rlang_1.1.2 utf8_1.2.4 [25] hdf5r_1.3.8 bit64_4.0.5 doParallel_1.0.17 cli_3.6.1 [29] magrittr_2.0.3 foreach_1.5.2 grid_4.3.2 irlba_2.3.5.1 [33] cowplot_1.1.1 mclust_6.0.1 lifecycle_1.0.4 vctrs_0.6.5 [37] glue_1.6.2 scattermore_1.2 codetools_0.2-19 fansi_1.0.5 [41] colorspace_2.1-0 pkgconfig_2.0.3

mvfki commented 11 months ago

Hi, thanks for trying out!

If I understand correctly, the steps "Stage I: 1~5" only demonstrate how users should generate a mapped-to-gene matrix from the ATAC fragment data. We didn't share the input data for these steps after all. The R code that users can run by themselves includes: The very first block after "For convenience...", the step starting from "6.", and everything after that. If you do have your own ATAC data, you can follow the workflow we demonstrated from 1. to 5. and see if it works.

Best, LIGER team