RegionStats mm10

[Dena2021]

Hello,
I am getting an error when running RegionStats on mm10 genome and welcome your advise.

myseurat
An object of class Seurat
407837 features across 18561 samples within 4 assays
Active assay: peaks (173327 features, 173312 variable features)
3 other assays present: RNA, ATAC, SCT
6 dimensional reductions calculated: pca, tsne, umap, lsi, peaks.umap, wnn.umap

myseurat <- RegionStats(myseurat, genome = BSgenome.Mmusculus.UCSC.mm10,assay='peaks')
Each of the 2 combined objects has sequence levels not in the other:

• in 'x': chrM, chr1_GL456210_random, chr1_GL456211_random, chr1_GL456212_random, chr1_GL456213_random, chr1_GL456221_random, chr4_GL456216_random, chr4_GL456350_random, chr4_JH584292_random, chr4_JH584293_random, chr4_JH584294_random, chr4_JH584295_random, chr5_GL456354_random, chr5_JH584296_random, chr5_JH584297_random, chr5_JH584298_random, chr5_JH584299_random, chr7_GL456219_random, chrX_GL456233_random, chrY_JH584300_random, chrY_JH584301_random, chrY_JH584302_random, chrY_JH584303_random, chrUn_GL456239, chrUn_GL456359, chrUn_GL456360, chrUn_GL456366, chrUn_GL456367, chrUn_GL456368, chrUn_GL456370, chrUn_GL456372, chrUn_GL456378, chrUn_GL456379, chrUn_GL456381, chrUn_GL456382, chrUn_GL456383, chrUn_GL456385, chrUn_GL456387, chrUn_GL456389, chrUn_GL456390, chrUn_GL456392, chrUn_GL456393, chrUn_GL456394, chrUn_GL456396, chrUn_JH584304
• in 'y': GL456216.1, GL456221.1, GL456233.1, JH584295.1, JH584304.1
  Make sure to always combine/compare objects based on the same reference
  genome (use suppressWarnings() to suppress this warning).Error in .getOneSeqFromBSgenomeMultipleSequences(x, name, start, NA, width, :
  sequence GL456216.1 not found

Below is my session,
Thanks,
Dena

R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS: /usr/lib64/libblas.so.3.4.2
LAPACK: /apps/RH7U2/gnu/R/4.0.0/lib64/R/lib/libRlapack.so

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C 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 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

other attached packages:
[1] BSgenome.Mmusculus.UCSC.mm10_1.4.0 BSgenome_1.58.0 rtracklayer_1.50.0
[4] Biostrings_2.58.0 XVector_0.30.0 dplyr_1.0.5
[7] rio_0.5.26 patchwork_1.1.1 ggplot2_3.3.3
[10] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.14.1 AnnotationFilter_1.14.0
[13] GenomicFeatures_1.42.3 AnnotationDbi_1.52.0 Biobase_2.50.0
[16] Seurat_4.0.1 Signac_1.2.0 GenomicRanges_1.42.0
[19] GenomeInfoDb_1.26.7 IRanges_2.24.1 S4Vectors_0.28.1
[22] BiocGenerics_0.36.1 SeuratObject_4.0.0

loaded via a namespace (and not attached):
[1] utf8_1.2.1 reticulate_1.20 tidyselect_1.1.1 RSQLite_2.2.7
[5] htmlwidgets_1.5.3 grid_4.0.0 docopt_0.7.1 BiocParallel_1.24.1
[9] Rtsne_0.15 munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[13] future_1.21.0 miniUI_0.1.1.1 withr_2.4.2 colorspace_2.0-0
[17] knitr_1.33 rstudioapi_0.13 ROCR_1.0-11 tensor_1.5
[21] listenv_0.8.0 labeling_0.4.2 MatrixGenerics_1.2.1 slam_0.1-48
[25] GenomeInfoDbData_1.2.4 polyclip_1.10-0 bit64_4.0.5 farver_2.1.0
[29] parallelly_1.25.0 vctrs_0.3.8 generics_0.1.0 xfun_0.22
[33] BiocFileCache_1.14.0 lsa_0.73.2 ggseqlogo_0.1 R6_2.5.0
[37] bitops_1.0-7 spatstat.utils_2.1-0 cachem_1.0.4 DelayedArray_0.16.3
[41] assertthat_0.2.1 promises_1.2.0.1 scales_1.1.1 gtable_0.3.0
[45] globals_0.14.0 goftest_1.2-2 rlang_0.4.11 RcppRoll_0.3.0
[49] splines_4.0.0 lazyeval_0.2.2 spatstat.geom_2.1-0 yaml_2.2.1
[53] reshape2_1.4.4 abind_1.4-5 httpuv_1.6.0 tools_4.0.0
[57] ellipsis_0.3.2 spatstat.core_2.1-2 RColorBrewer_1.1-2 ggridges_0.5.3
[61] Rcpp_1.0.6 plyr_1.8.6 progress_1.2.2.9000 zlibbioc_1.36.0
[65] purrr_0.3.4 RCurl_1.98-1.3 prettyunits_1.1.1 rpart_4.1-15
[69] openssl_1.4.4 deldir_0.2-10 pbapply_1.4-3 cowplot_1.1.1
[73] zoo_1.8-9 SummarizedExperiment_1.20.0 haven_2.4.1 ggrepel_0.9.1
[77] cluster_2.1.2 tinytex_0.31 magrittr_2.0.1 data.table_1.14.0
[81] scattermore_0.7 openxlsx_4.2.3 lmtest_0.9-38 RANN_2.6.1
[85] SnowballC_0.7.0 ProtGenerics_1.22.0 fitdistrplus_1.1-3 matrixStats_0.58.0
[89] hms_1.0.0 mime_0.10 evaluate_0.14 xtable_1.8-4
[93] XML_3.99-0.6 readxl_1.3.1 sparsesvd_0.2 gridExtra_2.3
[97] compiler_4.0.0 biomaRt_2.46.3 tibble_3.1.1 KernSmooth_2.23-18
[101] crayon_1.4.1 htmltools_0.5.1.1 mgcv_1.8-35 later_1.2.0
[105] tidyr_1.1.3 DBI_1.1.1 tweenr_1.0.2 dbplyr_2.1.1
[109] MASS_7.3-54 rappdirs_0.3.3 Matrix_1.3-2 igraph_1.2.6
[113] forcats_0.5.1 pkgconfig_2.0.3 GenomicAlignments_1.26.0 foreign_0.8-81
[117] plotly_4.9.3 spatstat.sparse_2.0-0 xml2_1.3.2 stringr_1.4.0
[121] digest_0.6.27 sctransform_0.3.2 RcppAnnoy_0.0.18 spatstat.data_2.1-0
[125] cellranger_1.1.0 rmarkdown_2.7 leiden_0.3.7 fastmatch_1.1-0
[129] uwot_0.1.10 curl_4.3.1 shiny_1.6.0 Rsamtools_2.6.0
[133] lifecycle_1.0.0 nlme_3.1-152 jsonlite_1.7.2 viridisLite_0.4.0
[137] askpass_1.1 fansi_0.4.2 pillar_1.6.0 lattice_0.20-44
[141] fastmap_1.1.0 httr_1.4.2 survival_3.2-11 glue_1.4.2
[145] zip_2.1.1 qlcMatrix_0.9.7 png_0.1-7 bit_4.0.4
[149] ggforce_0.3.3 stringi_1.5.3 blob_1.2.1 memoise_2.0.0
[153] irlba_2.3.3 future.apply_1.7.0

[bobermayer]

maybe filter out peaks in non-standard chromosomes? works for me

peaks <- grep('^chr',row.names(dge[['Peaks']]),value=TRUE)
sobj[["ATAC"]] <- CreateChromatinAssay(
  counts = dge[['Peaks']][peaks,],
  sep = c(":", "-"),
  fragments = fragpath,
  annotation = annotation
)

[Dena2021]

Thanks, I think I solved this using the function -
keepStandardChromosomes(macspeaks, pruning.mode = "coarse")