rcorces
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It is very difficult to address your issue without more information. As outlined in the issue template: Can you reproduce this error with the tutorial dataset? If so, please provide a minimal reproducible example. Have you been able to run these steps of analysis with any other datasets without errors?
Attach your log file ArchR-addReproduciblePeakSet-7d5d6a3f1cf2-Date-2021-04-07_Time-22-26-07.log
Describe the bug projHemeXX <- addReproduciblePeakSet( ArchRProj = projHemeXX, groupBy = "Clusters", pathToMacs2 = "/Share2/home/LanXun/anaconda3/bin/macs2", force = T ) Error is : 2021-04-07 22:26:12 : Batching Peak Calls!, 0.072 mins elapsed. 2021-04-07 22:26:12 : Batch Execution w/ safelapply!, 0 mins elapsed. 2021-04-07 22:30:34 : Identifying Reproducible Peaks!, 4.443 mins elapsed. Error in .safelapply(seq_along(outSummitList), function(i) { : Error Found Iteration 1 : [1] "Error in .logError(e, fn = \".identifyReproduciblePeaks\", info = prefix, : \n Exiting See Error Above\n" <simpleError in .logError(e, fn = ".identifyReproduciblePeaks", info = prefix, errorList = errorList, logFile = logFile): Exiting See Error Above>
To Reproduce To help us optimally address your issue, please try to reproduce this issue using the tutorial hematopoiesis dataset and provide us the command(s) to reproduce your bug. Our first question to you will be "can you reproduce this with the tutorial dataset" so please do this.
Session Info If you do not have a log file because the function that caused the error does not produce one, please paste the output of "sessionInfo()" here. R version 3.5.1 (2018-07-02) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)
Matrix products: default BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
locale: [1] en_US.UTF-8
attached base packages: [1] grid parallel stats4 stats graphics grDevices [7] utils datasets methods base
other attached packages: [1] chromVAR_1.4.1
[2] BSgenome.Mmusculus.UCSC.mm10_1.4.0
[3] chromVARmotifs_0.2.0
[4] motifmatchr_1.4.0
[5] ggrepel_0.8.0
[6] circlize_0.4.10
[7] ComplexHeatmap_1.20.0
[8] dplyr_0.8.0.1
[9] gtable_0.3.0
[10] nabor_0.5.0
[11] xbioc_0.1.17
[12] MuSiC_0.1.1
[13] nnls_1.4
[14] scBio_0.1.6
[15] Rtsne_0.15
[16] uwot_0.1.8
[17] gridExtra_2.3
[18] org.Dr.eg.db_3.7.0
[19] TxDb.Drerio.UCSC.danRer10.refGene_3.4.4 [20] GenomicFeatures_1.34.8
[21] AnnotationDbi_1.44.0
[22] BSgenome.Drerio.UCSC.danRer10_1.4.2
[23] Rsamtools_1.34.1
[24] BSgenome.Hsapiens.UCSC.hg38_1.4.1
[25] BSgenome_1.50.0
[26] rtracklayer_1.42.2
[27] Biostrings_2.50.2
[28] XVector_0.22.0
[29] Seurat_3.2.0
[30] ArchR_1.0.0
[31] magrittr_1.5
[32] rhdf5_2.26.2
[33] Matrix_1.2-14
[34] data.table_1.13.6
[35] SummarizedExperiment_1.12.0
[36] DelayedArray_0.8.0
[37] BiocParallel_1.16.6
[38] matrixStats_0.56.0
[39] Biobase_2.42.0
[40] GenomicRanges_1.34.0
[41] GenomeInfoDb_1.18.2
[42] IRanges_2.16.0
[43] S4Vectors_0.20.1
[44] BiocGenerics_0.28.0
[45] ggplot2_3.3.2
loaded via a namespace (and not attached): [1] R.utils_2.7.0 reticulate_1.16
[3] tidyselect_1.1.0 poweRlaw_0.70.6
[5] RSQLite_2.2.0 htmlwidgets_1.3
[7] munsell_0.5.0 codetools_0.2-16
[9] ica_1.0-2 DT_0.15
[11] future_1.10.0 miniUI_0.1.1.1
[13] withr_2.2.0 colorspace_1.4-1
[15] rstudioapi_0.11 ROCR_1.0-7
[17] tensor_1.5 listenv_0.7.0
[19] labeling_0.3 GenomeInfoDbData_1.2.0
[21] polyclip_1.10-0 MCMCpack_1.4-4
[23] farver_2.0.3 bit64_0.9-7
[25] coda_0.19-2 vctrs_0.3.2
[27] R6_2.4.0 rsvd_0.9
[29] bitops_1.0-6 spatstat.utils_1.17-0
[31] assertthat_0.2.1 promises_1.1.1
[33] scales_1.1.1 npsurv_0.4-0
[35] Cairo_1.5-9 globals_0.12.4
[37] goftest_1.2-2 mcmc_0.9-5
[39] seqLogo_1.48.0 rlang_0.4.7
[41] MatrixModels_0.4-1 GlobalOptions_0.1.2
[43] splines_3.5.1 lazyeval_0.2.1
[45] hexbin_1.27.2 yaml_2.2.0
[47] BiocManager_1.30.10 reshape2_1.4.4
[49] abind_1.4-5 httpuv_1.5.0
[51] tools_3.5.1 ellipsis_0.3.1
[53] gplots_3.0.1 RColorBrewer_1.1-2
[55] ggridges_0.5.1 Rcpp_1.0.5
[57] plyr_1.8.6 progress_1.2.0
[59] zlibbioc_1.28.0 purrr_0.3.4
[61] RCurl_1.98-1.2 prettyunits_1.0.2
[63] rpart_4.1-13 deldir_0.1-22
[65] pbapply_1.4-0 GetoptLong_1.0.2
[67] cowplot_0.9.3 zoo_1.8-4
[69] cluster_2.1.0 SparseM_1.77
[71] lmtest_0.9-36 RANN_2.6.1
[73] fitdistrplus_1.0-11 hms_0.4.2
[75] patchwork_1.0.1 lsei_1.2-0
[77] mime_0.6 xtable_1.8-3
[79] XML_3.98-1.16 shape_1.4.4
[81] compiler_3.5.1 biomaRt_2.38.0
[83] tibble_2.1.1 KernSmooth_2.23-17
[85] crayon_1.3.4 R.oo_1.22.0
[87] htmltools_0.5.1.1 mgcv_1.8-24
[89] later_1.1.0.1 TFBSTools_1.20.0
[91] tidyr_0.8.2 DBI_1.1.0
[93] MASS_7.3-50 rappdirs_0.3.1
[95] readr_1.1.1 cli_2.0.2
[97] R.methodsS3_1.7.1 gdata_2.18.0
[99] igraph_1.2.2 pkgconfig_2.0.2
[101] TFMPvalue_0.0.8 GenomicAlignments_1.18.1
[103] registry_0.5-1 plotly_4.9.2.1
[105] annotate_1.60.0 DirichletMultinomial_1.24.1 [107] pkgmaker_0.31.1 bibtex_0.4.2.2
[109] stringr_1.4.0 digest_0.6.27
[111] pracma_2.1.8 sctransform_0.2.0
[113] RcppAnnoy_0.0.12 CNEr_1.18.1
[115] spatstat.data_1.4-3 leiden_0.3.3
[117] shiny_1.1.0 gtools_3.8.1
[119] quantreg_5.36 rjson_0.2.20
[121] lifecycle_0.2.0 nlme_3.1-137
[123] jsonlite_1.7.1 Rhdf5lib_1.4.3
[125] viridisLite_0.3.0 fansi_0.4.1
[127] pillar_1.4.6 lattice_0.20-35
[129] KEGGREST_1.22.0 GO.db_3.6.0
[131] httr_1.4.2 survival_3.2-3
[133] glue_1.4.1 spatstat_1.64-1
[135] png_0.1-7 bit_1.1-14
[137] stringi_1.3.1 blob_1.2.1
[139] caTools_1.17.1.1 memoise_1.1.0
[141] irlba_2.3.2 future.apply_1.0.1
[143] ape_5.4-1
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