The subset of markersPeaks cannot be processed for peakAnnoEnrichment.

[HaixJiang]

After I subset markerspeaks, I enriched motif. Why are there subordinate errors? When I use the overall markerspeaks, I can do it normally.

markersPeaks <- getMarkerFeatures(
    ArchRProj = projHeme2, 
    useMatrix = "PeakMatrix", 
    groupBy = "predictedGroup", #"Clusters", "predictedGroup","Clusters_sub",#需要将标签转移成功后进行
    bias = c("TSSEnrichment", "log10(nFrags)"),
  testMethod = "wilcoxon",
  useGroups = Groups
)

markersPeaks_subset <- markersPeaks[idx, ]

> enrichMotifs <- peakAnnoEnrichment(
    seMarker = markersPeaks_subset,
    ArchRProj = projHeme2,
    peakAnnotation = "Motif",
    cutOff = "FDR <= 0.1 & Log2FC >= 1"
  )
ArchR logging to : ArchRLogs/ArchR-peakAnnoEnrichment-764568468005-Date-2024-06-01_Time-03-15-32.335587.log
If there is an issue, please report to github with logFile!
Error in peakAnnoEnrichment(seMarker = markersPeaks_subset, ArchRProj = projHeme2,  :
  Peaks from seMarker do not match peakSet in ArchRProj!

Attach your log file
ArchR-peakAnnoEnrichment-39b5725a1656d-Date-2024-05-31_Time-23-39-31.018636.log

Session Info

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/JHX68/anaconda3/envs/seurat/lib/libopenblasp-r0.3.21.so;  LAPACK version 3.9.0

Random number generation:
 RNG:     L'Ecuyer-CMRG
 Normal:  Inversion
 Sample:  Rejection

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/New_York
tzcode source: system (glibc)

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

other attached packages:
 [1] nabor_0.5.0                 patchwork_1.2.0.9000
 [3] SeuratObject_4.1.4          Seurat_4.4.0
 [5] magick_2.8.3                dplyr_1.1.4
 [7] org.Gg.eg.db_3.18.0         GenomicFeatures_1.54.1
 [9] AnnotationDbi_1.64.1        rhdf5_2.46.1
[11] SummarizedExperiment_1.32.0 Biobase_2.62.0
[13] MatrixGenerics_1.14.0       Rcpp_1.0.12
[15] Matrix_1.6-5                GenomicRanges_1.54.1
[17] GenomeInfoDb_1.38.0         IRanges_2.36.0
[19] S4Vectors_0.40.1            BiocGenerics_0.48.0
[21] matrixStats_1.2.0           data.table_1.15.4
[23] stringr_1.5.1               plyr_1.8.9
[25] magrittr_2.0.3              ggplot2_3.5.0.9000
[27] gtable_0.3.4                gtools_3.9.5
[29] gridExtra_2.3               ArchR_1.0.2

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22         splines_4.3.1            later_1.3.2
  [4] BiocIO_1.12.0            bitops_1.0-7             filelock_1.0.2
  [7] tibble_3.2.1             polyclip_1.10-6          XML_3.99-0.16.1
 [10] lifecycle_1.0.4          globals_0.16.3           lattice_0.22-5
 [13] MASS_7.3-60.0.1          plotly_4.10.4            yaml_2.3.8
 [16] httpuv_1.6.15            sctransform_0.4.1        sp_2.1-3
 [19] spatstat.sparse_3.0-3    reticulate_1.35.0        cowplot_1.1.3
 [22] pbapply_1.7-2            DBI_1.2.2                RColorBrewer_1.1-3
 [25] abind_1.4-5              zlibbioc_1.48.0          Rtsne_0.17
 [28] purrr_1.0.2              RCurl_1.98-1.12          rappdirs_0.3.3
 [31] GenomeInfoDbData_1.2.11  ggrepel_0.9.5            irlba_2.3.5.1
 [34] listenv_0.9.1            spatstat.utils_3.0-4     goftest_1.2-3
 [37] spatstat.random_3.2-3    fitdistrplus_1.1-11      parallelly_1.37.1
 [40] leiden_0.4.3.1           codetools_0.2-19         DelayedArray_0.28.0
 [43] xml2_1.3.5               tidyselect_1.2.1         BiocFileCache_2.10.1
 [46] spatstat.explore_3.2-7   GenomicAlignments_1.38.0 jsonlite_1.8.8
 [49] progressr_0.14.0         ggridges_0.5.6           survival_3.5-7
 [52] tools_4.3.1              progress_1.2.3           ica_1.0-3
 [55] glue_1.7.0               SparseArray_1.2.0        withr_3.0.0
 [58] fastmap_1.1.1            rhdf5filters_1.14.1      fansi_1.0.6
 [61] digest_0.6.35            R6_2.5.1                 mime_0.12
 [64] colorspace_2.1-0         scattermore_1.2          tensor_1.5
 [67] spatstat.data_3.0-4      biomaRt_2.58.0           RSQLite_2.3.5
 [70] utf8_1.2.4               tidyr_1.3.1              generics_0.1.3
 [73] rtracklayer_1.62.0       prettyunits_1.2.0        httr_1.4.7
 [76] htmlwidgets_1.6.4        S4Arrays_1.2.0           uwot_0.1.16
 [79] pkgconfig_2.0.3          blob_1.2.4               lmtest_0.9-40
 [82] XVector_0.42.0           htmltools_0.5.8.1        scales_1.3.0
 [85] png_0.1-8                reshape2_1.4.4           rjson_0.2.21
 [88] nlme_3.1-163             curl_5.2.1               cachem_1.0.8
 [91] zoo_1.8-12               KernSmooth_2.23-22       miniUI_0.1.1.1
 [94] restfulr_0.0.15          pillar_1.9.0             vctrs_0.6.5
 [97] RANN_2.6.1               promises_1.3.0           dbplyr_2.3.4
[100] xtable_1.8-4             cluster_2.1.4            cli_3.6.2
[103] compiler_4.3.1           Rsamtools_2.18.0         rlang_1.1.3
[106] crayon_1.5.2             future.apply_1.11.2      stringi_1.8.3
[109] deldir_2.0-4             viridisLite_0.4.2        BiocParallel_1.36.0
[112] munsell_0.5.1            Biostrings_2.70.1        lazyeval_0.2.2
[115] spatstat.geom_3.2-9      hms_1.1.3                bit64_4.0.5
[118] future_1.33.2            Rhdf5lib_1.24.0          KEGGREST_1.42.0
[121] shiny_1.8.1.1            ROCR_1.0-11              igraph_2.0.3
[124] memoise_2.0.1            bit_4.0.5

[rcorces]

Hi @HaixJiang! Thanks for using ArchR! Lately, it has been very challenging for me to keep up with maintenance of this package and all of my other
responsibilities as a PI. I have not been responding to issue posts and I have not been pushing updates to the software. We are actively searching to hire
a computational biologist to continue to develop and maintain ArchR and related tools. If you know someone who might be a good fit, please let us know!
In the meantime, your issue will likely go without a reply. Most issues with ArchR right not relate to compatibility. Try reverting to R 4.1 and Bioconductor 3.15.
Newer versions of Seurat and Matrix also are causing issues. Sorry for not being able to provide active support for this package at this time.