Dear Signac team,
I am encountering issues with the macs2 function that I am unable to solve and I hope that you may be able to provide me with some insights. I saw the other peak calling issues #473, 560, 676 yet could not obtain the information needed.
The code I use is as following:
# General
library(tidyverse)
library(knitr)
library(Cairo)
# Analysis
library(Signac)
library(Seurat)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.NCBI.GRCh38)
library(pastecs)
# Plotting
library(cowplot)
# Prepare Annotations & Genome
# extract gene annotations from EnsDb
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v94)
# rename chromosome names to match cellranger output
#BSgenome.Hsapiens.NCBI.GRCh38.renamed <- renameSeqlevels(BSgenome.Hsapiens.NCBI.GRCh38, value = str_replace(str_replace(seqnames(BSgenome.Hsapiens.NCBI.GRCh38), pattern = "chr", replacement = ""), pattern = "M", replacement = "MT"))
# Load Data & Concatenate Samples
# read preprocessed data
samples <- c("FVRminus","FVRplus")
seurat_list <- list()
for (sample in samples){
seurat_list[[sample]] <- readRDS(paste0("/HOME_DIREC/outs/", sample, "_raw_feature_bc_matrix_filtered_seurat_markedDoublets.rds"))
seurat_list[[sample]] <- subset(seurat_list[[sample]], subset= final_doublets, invert=TRUE)
seurat_list[[sample]]$orig.ident <- sample
}
seurat <- merge(seurat_list[[1]], y=c(seurat_list[[2]]), add.cell.ids = samples, project = "FVR_S5")
saveRDS(seurat, "/HOME_DIREC/scRNA-seq_iPSC_FVR_S5_adata_filtered_rmDoublets_seurat.rds")
saveRDS(seurat_list, "/HOME_DIREC/scRNA-seq_iPSC_FVR_S5_adata_filtered_rmDoublets_list_seurat.rds")
# Joint Peak Calling
DefaultAssay(seurat) <- "ATAC"
peaks <- CallPeaks(seurat, macs2.path = "/ENV/bin/macs2", outdir = "/HOME_DIREC/outs")
This yields the following Info:
INFO :
# Command line: callpeak -t -g 2.7e+09 -f BED --nomodel --extsize 200 --shift -100 -n FVR_S5 --outdir /HOME_DIREC/outs
# ARGUMENTS LIST:
# name = FVR_S5
# format = BED
# ChIP-seq file = ['']
# control file = None
# effective genome size = 2.70e+09
# band width = 300
# model fold = [5, 50]
# qvalue cutoff = 5.00e-02
# The maximum gap between significant sites is assigned as the read length/tag size.
# The minimum length of peaks is assigned as the predicted fragment length "d".
# Larger dataset will be scaled towards smaller dataset.
# Range for calculating regional lambda is: 10000 bps
# Broad region calling is off
# Paired-End mode is off
INFO @ Thu, 31 Mar 2022 19:50:44: #1 read tag files...
INFO @ Thu, 31 Mar 2022 19:50:44: #1 read treatment tags...
Traceback (most recent call last):
File "/ENV/bin/macs2", line 653, in <module>
main()
File "/ENV/bin/macs2", line 51, in main
run( args )
File "/ENV/bin/MACS2/callpeak_cmd.py", line 65, in run
else: (treat, control) = load_tag_files_options (options)
File "/ENV/bin/MACS2/callpeak_cmd.py", line 387, in load_tag_files_options
tp = options.parser(options.tfile[0], buffer_size=options.buffer_size)
File "MACS2/IO/Parser.pyx", line 330, in MACS2.IO.Parser.GenericParser.__init__
File "/usr/lib/python3.8/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/lib/python3.8/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: ''
Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") :
cannot open file '/HOME_DIREC/outs/FVR_S5_peaks.narrowPeak': No such file or directory
Macs2 is installed accordingly:
file.exists("/home/minas/scAnalysis_1.8_R4.1/bin/macs2")
[1] TRUE
My session is as following (sorry for the wall of text):
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS/LAPACK: /opt/openblas/lib/libopenblas_zenp-r0.3.18.dev.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=de_DE.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=de_DE.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.1 pastecs_1.3.21 BSgenome.Hsapiens.NCBI.GRCh38_1.3.1000
[4] BSgenome_1.62.0 rtracklayer_1.54.0 Biostrings_2.62.0
[7] XVector_0.34.0 EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.18.1
[10] AnnotationFilter_1.18.0 GenomicFeatures_1.46.1 AnnotationDbi_1.56.1
[13] Biobase_2.54.0 GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
[16] IRanges_2.28.0 S4Vectors_0.32.0 BiocGenerics_0.40.0
[19] SeuratObject_4.0.2 Seurat_4.0.5 Signac_1.4.0
[22] Cairo_1.5-12.2 knitr_1.36 forcats_0.5.1
[25] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[28] readr_2.0.2 tidyr_1.1.4 tibble_3.1.5
[31] ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.22 tidyselect_1.1.1 RSQLite_2.2.8
[5] htmlwidgets_1.5.4 grid_4.1.1 docopt_0.7.1 BiocParallel_1.28.0
[9] Rtsne_0.15 munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[13] future_1.23.0 miniUI_0.1.1.1 withr_2.4.2 colorspace_2.0-2
[17] filelock_1.0.2 rstudioapi_0.13 ROCR_1.0-11 tensor_1.5
[21] listenv_0.8.0 MatrixGenerics_1.6.0 slam_0.1-48 GenomeInfoDbData_1.2.7
[25] polyclip_1.10-0 bit64_4.0.5 farver_2.1.0 parallelly_1.28.1
[29] vctrs_0.3.8 generics_0.1.1 xfun_0.28 biovizBase_1.42.0
[33] BiocFileCache_2.2.0 lsa_0.73.2 ggseqlogo_0.1 R6_2.5.1
[37] DelayedArray_0.20.0 bitops_1.0-7 spatstat.utils_2.2-0 cachem_1.0.6
[41] assertthat_0.2.1 BiocIO_1.4.0 promises_1.2.0.1 scales_1.1.1
[45] nnet_7.3-16 gtable_0.3.0 globals_0.14.0 goftest_1.2-3
[49] rlang_0.4.12 RcppRoll_0.3.0 splines_4.1.1 lazyeval_0.2.2
[53] dichromat_2.0-0 checkmate_2.0.0 spatstat.geom_2.3-0 broom_0.7.10
[57] yaml_2.2.1 reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[61] backports_1.3.0 httpuv_1.6.3 Hmisc_4.6-0 tools_4.1.1
[65] ellipsis_0.3.2 spatstat.core_2.3-1 RColorBrewer_1.1-2 ggridges_0.5.3
[69] Rcpp_1.0.7 plyr_1.8.6 base64enc_0.1-3 progress_1.2.2
[73] zlibbioc_1.40.0 RCurl_1.98-1.5 prettyunits_1.1.1 rpart_4.1-15
[77] deldir_1.0-6 pbapply_1.5-0 zoo_1.8-9 SummarizedExperiment_1.24.0
[81] haven_2.4.3 ggrepel_0.9.1 cluster_2.1.2 fs_1.5.0
[85] magrittr_2.0.1 data.table_1.14.2 scattermore_0.7 lmtest_0.9-38
[89] reprex_2.0.1 RANN_2.6.1 SnowballC_0.7.0 ProtGenerics_1.26.0
[93] fitdistrplus_1.1-6 matrixStats_0.61.0 hms_1.1.1 patchwork_1.1.1
[97] mime_0.12 xtable_1.8-4 XML_3.99-0.8 jpeg_0.1-9
[101] sparsesvd_0.2 readxl_1.3.1 gridExtra_2.3 compiler_4.1.1
[105] biomaRt_2.50.0 KernSmooth_2.23-20 crayon_1.4.2 htmltools_0.5.2
[109] mgcv_1.8-38 later_1.3.0 tzdb_0.2.0 Formula_1.2-4
[113] lubridate_1.8.0 DBI_1.1.1 tweenr_1.0.2 dbplyr_2.1.1
[117] rappdirs_0.3.3 MASS_7.3-54 boot_1.3-28 Matrix_1.3-4
[121] cli_3.1.0 parallel_4.1.1 igraph_1.2.7 pkgconfig_2.0.3
[125] GenomicAlignments_1.30.0 foreign_0.8-81 plotly_4.10.0 spatstat.sparse_2.0-0
[129] xml2_1.3.2 rvest_1.0.2 VariantAnnotation_1.40.0 digest_0.6.28
[133] sctransform_0.3.2 RcppAnnoy_0.0.19 spatstat.data_2.1-0 cellranger_1.1.0
[137] leiden_0.3.9 fastmatch_1.1-3 htmlTable_2.3.0 uwot_0.1.10
[141] restfulr_0.0.13 curl_4.3.2 shiny_1.7.1 Rsamtools_2.10.0
[145] rjson_0.2.20 lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.7.2
[149] viridisLite_0.4.0 fansi_0.5.0 pillar_1.6.4 lattice_0.20-45
[153] KEGGREST_1.34.0 fastmap_1.1.0 httr_1.4.2 survival_3.2-13
[157] glue_1.4.2 qlcMatrix_0.9.7 png_0.1-7 bit_4.0.4
[161] ggforce_0.3.3 stringi_1.7.5 blob_1.2.2 latticeExtra_0.6-29
[165] memoise_2.0.0 irlba_2.3.3 future.apply_1.8.1
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