I am using Diffbind for an ATAC-Seq analysis. My peak caller is MACS2, and here is my sample sheet:

I run Diffbind with the following codes, but it crashed every time on dba.count . it can finished Computing summits... Recentering peaks... Reads will be counted as Paired-end. But have this screen in the end. Does anyone have any idea about this? Thank you so much!

library(BiocParallel)

register(SerialParam())

library(DiffBind)

data <- dba(sampleSheet="sampleSheet.csv")

data <- dba.blacklist(data, blacklist=DBA_BLACKLIST_GRCH38, greylist=FALSE)

data <- dba.count(data)

Error screen:

NA_character_,             NA_character_, NA_character_, NA_character_, NA_character_,             NA_character_, NA_character_, NA_character_, NA_character_,             NA_character_, NA_character_, NA_character_, NA_character_,             NA_character_, NA_character_, NA_character_, NA_character_,             NA_character_, NA_character_, NA_character_, NA_character_,             NA_character_, NA_character_)), elementMetadata = new("DFrame",                 rownames = NULL, nrows = 117227L, elementType = "ANY",                 elementMetadata = NULL, metadata = list(), listData = list()),             elementType = "ANY", metadata = list()), mode = function (features,             reads, ignore.strand = FALSE, inter.feature = TRUE)         {            features <- .removeSharedRegions(features, ignore.strand = ignore.strand)            Union(features, reads, ignore.strand = ignore.strand,                 inter.feature = inter.feature)        }, ignore.strand = TRUE, inter.feature = TRUE, param = new("ScanBamParam",             flag = c(keep0 = 4095L, keep1 = 4095L), simpleCigar = FALSE,             reverseComplement = FALSE, tag = character(0), tagFilter = list(),             what = character(0), which = new("CompressedIRangesList",                 unlistData = new("IRanges", start = integer(0),                   width = integer(0), NAMES = NULL, elementType = "ANY",                   elementMetadata = NULL, metadata = list()),                 elementType = "IRanges", elementMetadata = NULL,                 metadata = list(), partitioning = new("PartitioningByEnd",                   end = integer(0), NAMES = character(0), elementType = "ANY",                   elementMetadata = NULL, metadata = list())),             mapqFilter = 15L), preprocess.reads = NULL), OPTIONS = list(        log = FALSE, stop.on.error = TRUE, as.error = TRUE, timeout = NA_integer_,         force.GC = FALSE, globalOptions = NULL), BPRNGSEED = c(10407L,     897505460L, 209072368L, -936942767L, -474632773L, 911815027L,     1093500380L), GLOBALS = list(), PACKAGES = character(0))
29: do.call(msg$data$fun, msg$data$args)
30: doTryCatch(return(expr), name, parentenv, handler)
31: tryCatchOne(expr, names, parentenv, handlers[[1L]])
32: tryCatchList(expr, classes, parentenv, handlers)
33: tryCatch({    do.call(msg$data$fun, msg$data$args)}, error = function(e) {    list(.error_worker_comm(e, "worker evaluation failed"))})
34: .bpworker_EXEC(msg, bplog(backend$BPPARAM))
35: .recv_any(manager$backend)
36: .recv_any(manager$backend)
37: .manager_recv(manager)
38: .manager_recv(manager)
39: .collect_result(manager, reducer, progress, BPPARAM)
40: .bploop_impl(ITER = ITER, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM,     BPOPTIONS = BPOPTIONS, BPREDO = BPREDO, reducer = reducer,     progress.length = length(redo_index))
41: bploop.lapply(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS,     ...)
42: bploop(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, ...)
43: .bpinit(manager = manager, X = X, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM,     BPOPTIONS = BPOPTIONS, BPREDO = BPREDO)
44: bplapply(X, FUN, ..., BPREDO = BPREDO, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS)
45: bplapply(X, FUN, ..., BPREDO = BPREDO, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS)
46: bplapply(setNames(seq_along(reads), names(reads)), function(i,     FUN, reads, features, mode, ignore.strand, inter.feature,     param, preprocess.reads, ...) {    bf <- reads[[i]]    .countWithYieldSize(FUN, features, bf, mode, ignore.strand,         inter.feature, param, preprocess.reads, ...)}, FUN, reads, features, mode = match.fun(mode), ignore.strand = ignore.strand,     inter.feature = inter.feature, param = param, preprocess.reads = preprocess.reads,     ...)
47: bplapply(setNames(seq_along(reads), names(reads)), function(i,     FUN, reads, features, mode, ignore.strand, inter.feature,     param, preprocess.reads, ...) {    bf <- reads[[i]]    .countWithYieldSize(FUN, features, bf, mode, ignore.strand,         inter.feature, param, preprocess.reads, ...)}, FUN, reads, features, mode = match.fun(mode), ignore.strand = ignore.strand,     inter.feature = inter.feature, param = param, preprocess.reads = preprocess.reads,     ...)
48: .dispatchBamFiles(features, reads, mode, ignore.strand, inter.feature = inter.feature,     singleEnd = singleEnd, fragments = fragments, param = param,     preprocess.reads = preprocess.reads, ...)
49: .local(features, reads, mode, ignore.strand, ...)
50: GenomicAlignments::summarizeOverlaps(features = intervals, reads = bfl,     ignore.strand = TRUE, singleEnd = singleEnd, mode = mode,     inter.feature = interfeature, fragments = fragments, param = params)
51: GenomicAlignments::summarizeOverlaps(features = intervals, reads = bfl,     ignore.strand = TRUE, singleEnd = singleEnd, mode = mode,     inter.feature = interfeature, fragments = fragments, param = params)
52: assay(GenomicAlignments::summarizeOverlaps(features = intervals,     reads = bfl, ignore.strand = TRUE, singleEnd = singleEnd,     mode = mode, inter.feature = interfeature, fragments = fragments,     param = params))
53: pv.getCountsLowMem(bamfile, intervals, bWithoutDupes, mode, yieldSize,     singleEnd, fragments, scanbamparam, minCount = minCount,     minQC = minMappingQuality, interfeature = interfeature)
54: pv.getCounts(bamfile = countrec$bamfile, intervals = intervals,     insertLength = countrec$insert, bWithoutDupes = bWithoutDupes,     bLowMem = bLowMem, yieldSize = yieldSize, mode = mode, singleEnd = singleEnd,     scanbamparam = scanbamparam, fileType = fileType, summits = summits,     fragments = fragments, minMappingQuality = minMappingQuality,     minCount = minCount, interfeature = interfeature)
55: FUN(X[[i]], ...)
56: lapply(X = S, FUN = FUN, ...)
57: doTryCatch(return(expr), name, parentenv, handler)
58: tryCatchOne(expr, names, parentenv, handlers[[1L]])
59: tryCatchList(expr, classes, parentenv, handlers)
60: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             call <- sys.call(-4L)        dcall <- deparse(call, nlines = 1L)        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }    else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
61: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
62: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
63: FUN(X[[i]], ...)
64: lapply(seq_len(cores), inner.do)
65: mclapply(arglist, fn, ..., mc.preschedule = TRUE, mc.allow.recursive = TRUE)
66: config$lapplyFun(config, params, arglist, fn, ...)
67: dba.parallel.lapply(pv$config, params, todorecs, pv.do_getCounts,     bed, bWithoutDupes = bWithoutDupes, bLowMem, yieldSize, mode,     singleEnd, scanbamparam, readFormat, summits, fragments,     minMappingQuality, minCount, interfeature)
68: pv.counts(res, peaks = res$merged, defaultScore = defaultScore,     bLog = bLog, insertLength = insertLength, bOnlyCounts = TRUE,     bCalledMasks = TRUE, minMaxval = minMaxval, filterFun = filterFun,     bParallel = bParallel, bWithoutDupes = bWithoutDupes, bScaleControl = bScaleControl,     bSignal2Noise = bSignal2Noise, bLowMem = saveLowMem, readFormat = readFormat,     summits = 0, bRecentered = TRUE, minMappingQuality = minMappingQuality,     maxGap = maxGap)
69: pv.counts(DBA, peaks = peaks, minOverlap = minOverlap, defaultScore = score,     bLog = bLog, insertLength = fragmentSize, bOnlyCounts = TRUE,     bCalledMasks = TRUE, minMaxval = filter, bParallel = bParallel,     bUseLast = bUseLast, bWithoutDupes = bRemoveDuplicates, bScaleControl = bScaleControl,     filterFun = filterFun, bLowMem = bUseSummarizeOverlaps, readFormat = readFormat,     summits = summits, minMappingQuality = mapQCth, minCount = minCount,     bSubControl = bSubControl, maxGap = maxGap)
70: dba.count(data)

Possible actions:
1: abort (with core dump, if enabled)
2: normal R exit
3: exit R without saving workspace
4: exit R saving workspace
Selection:

My system:

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so

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

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] DiffBind_3.6.1              SummarizedExperiment_1.26.1
 [3] Biobase_2.56.0              MatrixGenerics_1.8.0
 [5] matrixStats_0.62.0          GenomicRanges_1.48.0
 [7] GenomeInfoDb_1.32.2         IRanges_2.30.0
 [9] S4Vectors_0.34.0            BiocGenerics_0.42.0
[11] BiocParallel_1.30.3

loaded via a namespace (and not attached):
 [1] bitops_1.0-7             RColorBrewer_1.1-3       numDeriv_2016.8-1.1
 [4] tools_4.2.0              utf8_1.2.2               R6_2.5.1
 [7] irlba_2.3.5              KernSmooth_2.23-20       DBI_1.1.3
[10] colorspace_2.0-3         apeglm_1.18.0            tidyselect_1.1.2
[13] compiler_4.2.0           cli_3.3.0                DelayedArray_0.22.0
[16] rtracklayer_1.56.0       caTools_1.18.2           scales_1.2.0
[19] SQUAREM_2021.1           mvtnorm_1.1-3            mixsqp_0.3-43
[22] stringr_1.4.0            digest_0.6.29            Rsamtools_2.12.0
[25] XVector_0.36.0           jpeg_0.1-9               pkgconfig_2.0.3
[28] htmltools_0.5.2          fastmap_1.1.0            invgamma_1.1
[31] bbmle_1.0.25             limma_3.52.2             BSgenome_1.64.0
[34] htmlwidgets_1.5.4        rlang_1.0.2              BiocIO_1.6.0
[37] generics_0.1.2           hwriter_1.3.2.1          gtools_3.9.2.2
[40] dplyr_1.0.9              RCurl_1.98-1.7           magrittr_2.0.3
[43] GenomeInfoDbData_1.2.8   Matrix_1.4-1             Rcpp_1.0.8.3
[46] munsell_0.5.0            fansi_1.0.3              lifecycle_1.0.1
[49] stringi_1.7.6            yaml_2.3.5               MASS_7.3-57
[52] zlibbioc_1.42.0          gplots_3.1.3             plyr_1.8.7
[55] grid_4.2.0               parallel_4.2.0           ggrepel_0.9.1
[58] bdsmatrix_1.3-6          crayon_1.5.1             lattice_0.20-45
[61] Biostrings_2.64.0        locfit_1.5-9.5           pillar_1.7.0
[64] rjson_0.2.21             systemPipeR_2.2.2        codetools_0.2-18
[67] XML_3.99-0.10            glue_1.6.2               ShortRead_1.54.0
[70] GreyListChIP_1.28.1      latticeExtra_0.6-29      png_0.1-7
[73] vctrs_0.4.1              gtable_0.3.0             purrr_0.3.4
[76] amap_0.8-18              assertthat_0.2.1         ashr_2.2-54
[79] ggplot2_3.3.6            emdbook_1.3.12           restfulr_0.0.15
[82] coda_0.19-4              truncnorm_1.0-8          tibble_3.1.7
[85] GenomicAlignments_1.32.0 ellipsis_0.3.2