Open CitricCoR opened 10 months ago
CMK_fc <- p4cNewProfile(str_glue('umi4C_CMK_DS_ANK1_TSS'), scope_5=200000, scope_3=200000)
see above, when trying to run the pipeline, executing this line will result. The dot number just keep increasing (last time I checked it the number of dots reached above 300) after some debug we think the issue may be related to misha https://github.com/tanaylab/misha/blob/e97bde51f020898172d2586e676f7a9796553272/R/compute.R#L285
R version 4.1.1 (2021-08-10) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Ubuntu 22.04.1 LTS Matrix products: default BLAS/LAPACK: /home/jiazr18/.conda/envs/JZR_RS/lib/libopenblasp-r0.3.18.so locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] grid stats4 stats graphics grDevices utils datasets methods base other attached packages: [1] umi4cPackage_0.0.1 misha_4.1.0 zoo_1.8-10 RColorBrewer_1.1-3 [5] VennDiagram_1.7.3 futile.logger_1.4.3 org.Mm.eg.db_3.14.0 GenomicFeatures_1.46.5 [9] AnnotationDbi_1.56.2 clusterProfiler_4.2.0 ChIPpeakAnno_3.28.0 rtracklayer_1.54.0 [13] data.table_1.14.2 Biostrings_2.62.0 XVector_0.34.0 sp_1.5-0 [17] SeuratObject_4.1.0 Seurat_4.1.1 ChIPseeker_1.32.0 DESeq2_1.34.0 [21] DiffBind_3.4.11 SummarizedExperiment_1.24.0 Biobase_2.54.0 MatrixGenerics_1.6.0 [25] matrixStats_0.62.0 GenomicRanges_1.46.1 GenomeInfoDb_1.30.1 IRanges_2.28.0 [29] S4Vectors_0.32.4 BiocGenerics_0.40.0 readxl_1.4.2 forcats_0.5.1 [33] stringr_1.4.0 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 [37] tibble_3.1.7 ggplot2_3.3.6 tidyverse_1.3.2 dplyr_1.0.9 loaded via a namespace (and not attached): [1] ica_1.0-3 apeglm_1.16.0 Rsamtools_2.10.0 [4] foreach_1.5.2 lmtest_0.9-40 crayon_1.5.1 [7] spatstat.core_2.4-2 MASS_7.3-57 nlme_3.1-157 [10] backports_1.4.1 reprex_2.0.1 GOSemSim_2.20.0 [13] rlang_1.0.4 ROCR_1.0-11 irlba_2.3.5 [16] limma_3.50.3 filelock_1.0.2 BiocParallel_1.28.3 [19] rjson_0.2.21 bit64_4.0.5 glue_1.6.0 [22] mixsqp_0.3-43 sctransform_0.3.3 parallel_4.1.1 [25] spatstat.sparse_2.1-1 regioneR_1.26.0 DOSE_3.20.0 [28] spatstat.geom_2.4-0 haven_2.5.2 tidyselect_1.1.2 [31] fitdistrplus_1.1-8 XML_3.99-0.9 GenomicAlignments_1.30.0 [34] xtable_1.8-4 magrittr_2.0.3 cli_3.3.0 [37] zlibbioc_1.40.0 hwriter_1.3.2.1 rstudioapi_0.13 [40] miniUI_0.1.1.1 rpart_4.1.16 GreyListChIP_1.26.0 [43] fastmatch_1.1-3 ensembldb_2.18.4 lambda.r_1.2.4 [46] treeio_1.18.1 shiny_1.7.2 clue_0.3-61 [49] multtest_2.50.0 cluster_2.1.3 caTools_1.18.2 [52] tidygraph_1.2.1 KEGGREST_1.34.0 ggrepel_0.9.1 [55] ape_5.6-2 listenv_0.8.0 png_0.1-7 [58] future_1.27.0 withr_2.5.0 bitops_1.0-7 [61] ggforce_0.3.3 RBGL_1.70.0 plyr_1.8.7 [64] cellranger_1.1.0 AnnotationFilter_1.18.0 coda_0.19-4 [67] pillar_1.8.0 GlobalOptions_0.1.2 gplots_3.1.3 [70] cachem_1.0.6 fs_1.5.2 GetoptLong_1.0.5 [73] vctrs_0.4.1 ellipsis_0.3.2 generics_0.1.3 [76] tools_4.1.1 munsell_0.5.0 tweenr_1.0.2 [79] fgsea_1.20.0 DelayedArray_0.20.0 fastmap_1.1.0 [82] compiler_4.1.1 abind_1.4-5 httpuv_1.6.5 [85] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 plotly_4.10.0 rgeos_0.5-9 [88] GenomeInfoDbData_1.2.7 gridExtra_2.3 InteractionSet_1.22.0 [91] lattice_0.20-45 deldir_1.0-6 utf8_1.2.2 [94] later_1.3.0 BiocFileCache_2.2.1 jsonlite_1.8.0 [97] scales_1.2.0 graph_1.72.0 tidytree_0.3.9 [100] pbapply_1.5-0 genefilter_1.76.0 lazyeval_0.2.2 [103] promises_1.2.0.1 doParallel_1.0.17 latticeExtra_0.6-30 [106] goftest_1.2-3 spatstat.utils_2.3-1 reticulate_1.25 [109] cowplot_1.1.1 Rtsne_0.16 downloader_0.4 [112] BSgenome_1.62.0 uwot_0.1.11 igraph_1.3.1 [115] survival_3.3-1 numDeriv_2016.8-1.1 yaml_2.3.5 [118] plotrix_3.8-2 ashr_2.2-54 SQUAREM_2021.1 [121] htmltools_0.5.3 memoise_2.0.1 BiocIO_1.4.0 [124] locfit_1.5-9.5 graphlayouts_0.8.0 viridisLite_0.4.0 [127] digest_0.6.29 assertthat_0.2.1 mime_0.12 [130] rappdirs_0.3.3 futile.options_1.0.1 emdbook_1.3.12 [133] RSQLite_2.2.14 amap_0.8-18 yulab.utils_0.0.5 [136] future.apply_1.9.0 blob_1.2.3 splines_4.1.1 [139] googledrive_2.0.0 ProtGenerics_1.26.0 RCurl_1.98-1.6 [142] broom_1.0.0 hms_1.1.1 eulerr_6.1.1 [145] modelr_0.1.8 colorspace_2.0-3 shape_1.4.6 [148] aplot_0.1.6 Rcpp_1.0.8.3 RANN_2.6.1 [151] circlize_0.4.15 mvtnorm_1.1-3 enrichplot_1.14.1 [154] fansi_1.0.3 tzdb_0.3.0 truncnorm_1.0-8 [157] parallelly_1.32.1 R6_2.5.1 ggridges_0.5.3 [160] lifecycle_1.0.1 formatR_1.12 ShortRead_1.52.0 [163] curl_4.3.2 googlesheets4_1.0.0 leiden_0.4.2 [166] DO.db_2.9 Matrix_1.4-1 qvalue_2.26.0 [169] RcppAnnoy_0.0.19 iterators_1.0.14 htmlwidgets_1.5.4 [172] polyclip_1.10-0 biomaRt_2.50.3 shadowtext_0.1.2 [175] gridGraphics_0.5-1 ComplexHeatmap_2.10.0 rvest_1.0.2 [178] mgcv_1.8-40 globals_0.15.1 patchwork_1.1.1 [181] spatstat.random_2.2-0 bdsmatrix_1.3-6 progressr_0.10.1 [184] codetools_0.2-18 invgamma_1.1 lubridate_1.8.0 [187] GO.db_3.14.0 gtools_3.9.3 prettyunits_1.1.1 [190] dbplyr_2.2.1 gtable_0.3.0 DBI_1.1.3 [193] ggfun_0.0.6 tensor_1.5 httr_1.4.3 [196] KernSmooth_2.23-20 stringi_1.7.6 progress_1.2.2 [199] reshape2_1.4.4 farver_2.1.1 annotate_1.72.0 [202] viridis_0.6.2 ggtree_3.2.1 xml2_1.3.3 [205] bbmle_1.0.25 systemPipeR_2.0.8 boot_1.3-28 [208] restfulr_0.0.15 interp_1.1-3 geneplotter_1.72.0 [211] ggplotify_0.1.0 scattermore_0.8 bit_4.0.4 [214] scatterpie_0.1.7 jpeg_0.1-9 spatstat.data_2.2-0 [217] ggraph_2.0.5 pkgconfig_2.0.3 gargle_1.2.0
Can you please try to run:
gdb.reload()
and then rerun the above commands?
see above, when trying to run the pipeline, executing this line will result. The dot number just keep increasing (last time I checked it the number of dots reached above 300) after some debug we think the issue may be related to misha https://github.com/tanaylab/misha/blob/e97bde51f020898172d2586e676f7a9796553272/R/compute.R#L285