Signac: RegionStats: Not all seqlevels present in supplied genome, trying to load regions beyond the boundaries of non-circular sequence "chr8"

[Zoeyau531]

First of all, I would like to show my appreciation towards Signac!

I currently ran RegionStats for my single cell atac-seq data, however, I encountered some error.

I am trying to perform this : https://github.com/stuart-lab/signac/blob/HEAD/vignettes/pbmc_multiomic.Rmd

Error and warning are as below:

Purkinje_multiome <- RegionStats(Purkinje_multiome, genome = BSgenome.Mmusculus.UCSC.mm10) Warning in RegionStats.default(object = regions, genome = genome, verbose = verbose, : Not all seqlevels present in supplied genome Error in loadFUN(x, seqname, ranges) : trying to load regions beyond the boundaries of non-circular sequence "chr8"

I used EnsDb.Mmusculus.v79 for the annotation and BSgenome.Mmusculus.UCSC.mm10 as input of RegionStats. Because RegionStats cannot input EnsDb file, also, the vignettes illustrate the use of EnsDb as annotation input and BSgenome as input of RegionStats. I am not sure if this is the problem.

session info: R version 4.4.0 (2024-04-24) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.4 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale: [1] LC_CTYPE=en_HK.UTF-8 LC_NUMERIC=C LC_TIME=en_HK.UTF-8 LC_COLLATE=en_HK.UTF-8
[5] LC_MONETARY=en_HK.UTF-8 LC_MESSAGES=en_HK.UTF-8 LC_PAPER=en_HK.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_HK.UTF-8 LC_IDENTIFICATION=C

time zone: Asia/Hong_Kong tzcode source: system (glibc)

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

other attached packages: [1] BSgenome.Mmusculus.UCSC.mm10_1.4.3 ggdark_0.2.1 lubridate_1.9.3
[4] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[7] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[10] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[13] BSgenome.Mmusculus.UCSC.mm39_1.4.3 BSgenome_1.72.0 rtracklayer_1.64.0
[16] BiocIO_1.14.0 Biostrings_2.72.1 XVector_0.44.0
[19] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.28.0 AnnotationFilter_1.28.0
[22] GenomicFeatures_1.56.0 AnnotationDbi_1.66.0 Biobase_2.64.0
[25] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1 IRanges_2.38.0
[28] S4Vectors_0.42.0 BiocGenerics_0.50.0 Seurat_5.1.0
[31] Signac_1.13.0 SeuratObject_5.0.2 sp_2.1-4

loaded via a namespace (and not attached): [1] RcppAnnoy_0.0.22 splines_4.4.0 later_1.3.2 bitops_1.0-7
[5] polyclip_1.10-6 XML_3.99-0.16.1 fastDummies_1.7.3 lifecycle_1.0.4
[9] globals_0.16.3 lattice_0.22-6 MASS_7.3-60.2 magrittr_2.0.3
[13] plotly_4.10.4 yaml_2.3.8 httpuv_1.6.15 sctransform_0.4.1
[17] spam_2.10-0 spatstat.sparse_3.0-3 reticulate_1.37.0 cowplot_1.1.3
[21] pbapply_1.7-2 DBI_1.2.3 RColorBrewer_1.1-3 abind_1.4-5
[25] zlibbioc_1.50.0 Rtsne_0.17 RCurl_1.98-1.14 GenomeInfoDbData_1.2.12
[29] ggrepel_0.9.5 irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.0-4
[33] goftest_1.2-3 RSpectra_0.16-1 spatstat.random_3.2-3 fitdistrplus_1.1-11
[37] parallelly_1.37.1 leiden_0.4.3.1 codetools_0.2-20 DelayedArray_0.30.1
[41] RcppRoll_0.3.0 tidyselect_1.2.1 UCSC.utils_1.0.0 matrixStats_1.3.0
[45] spatstat.explore_3.2-7 GenomicAlignments_1.40.0 jsonlite_1.8.8 progressr_0.14.0
[49] ggridges_0.5.6 survival_3.6-4 tools_4.4.0 ica_1.0-3
[53] Rcpp_1.0.12 glue_1.7.0 gridExtra_2.3 SparseArray_1.4.8
[57] MatrixGenerics_1.16.0 withr_3.0.0 fastmap_1.2.0 fansi_1.0.6
[61] digest_0.6.35 timechange_0.3.0 R6_2.5.1 mime_0.12
[65] colorspace_2.1-0 scattermore_1.2 tensor_1.5 spatstat.data_3.0-4
[69] RSQLite_2.3.7 utf8_1.2.4 generics_0.1.3 data.table_1.15.4
[73] httr_1.4.7 htmlwidgets_1.6.4 S4Arrays_1.4.1 uwot_0.2.2
[77] pkgconfig_2.0.3 gtable_0.3.5 blob_1.2.4 lmtest_0.9-40
[81] htmltools_0.5.8.1 dotCall64_1.1-1 ProtGenerics_1.36.0 scales_1.3.0
[85] png_0.1-8 rstudioapi_0.16.0 tzdb_0.4.0 reshape2_1.4.4
[89] rjson_0.2.21 nlme_3.1-163 curl_5.2.1 zoo_1.8-12
[93] cachem_1.1.0 KernSmooth_2.23-22 parallel_4.4.0 miniUI_0.1.1.1
[97] restfulr_0.0.15 pillar_1.9.0 grid_4.4.0 vctrs_0.6.5
[101] RANN_2.6.1 promises_1.3.0 xtable_1.8-4 cluster_2.1.6
[105] cli_3.6.2 compiler_4.4.0 Rsamtools_2.20.0 rlang_1.1.3
[109] crayon_1.5.2 future.apply_1.11.2 plyr_1.8.9 stringi_1.8.4
[113] viridisLite_0.4.2 deldir_2.0-4 BiocParallel_1.38.0 munsell_0.5.1
[117] lazyeval_0.2.2 spatstat.geom_3.2-9 Matrix_1.7-0 RcppHNSW_0.6.0
[121] hms_1.1.3 patchwork_1.2.0 bit64_4.0.5 future_1.33.2
[125] KEGGREST_1.44.0 shiny_1.8.1.1 SummarizedExperiment_1.34.0 ROCR_1.0-11
[129] igraph_2.0.3 memoise_2.0.1 fastmatch_1.1-4 bit_4.0.5

Purkinje_multiome@assays[["peaks"]]@ranges@seqnames@values [1] 1 2 3 4 5 6 7 8 9 10 11
[12] 12 13 14 15 16 17 18 19 X Y MT
[23] GL456211.1 JH584304.1 GL456212.1 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 X Y MT GL456211.1 GL456212.1 JH584304.1

Purkinje_multiome@assays[["peaks"]]@annotation@seqnames factor-Rle of length 1763965 with 22 runs Lengths: 83099 68561 56688 128222 149106 89406 49376 121281 ... 78167 75582 61589 34433 132985 49365 49256 26 Values : 3 X 16 7 11 6 13 4 ... 10 8 15 18 1 12 19 MT Levels(22): 3 X 16 7 11 6 13 4 9 Y 17 5 2 14 10 8 15 18 1 12 19 MT

BSgenome.Mmusculus.UCSC.mm10 | BSgenome object for Mouse | - organism: Mus musculus | - provider: UCSC | - genome: mm10 | - release date: Sep 2017 | - 239 sequence(s): | 1 2 3
| 4 5 6
| 7 8 9
| 10 11 12
| 13 14 15
| ... ... ...
| MG3683_PATCH MG3686_PATCH MG4209_PATCH
| MG4211_PATCH MG4213_PATCH MMCHR1_CHORI29_IDD5_1
| WSB/EIJ_MMCHR11_CTG1 WSB/EIJ_MMCHR11_CTG2 WSB/EIJ_MMCHR11_CTG3
| PWK/PHJ_MMCHR11_CTG1 PWK/PHJ_MMCHR11_CTG2 PWK/PHJ_MMCHR11_CTG3
| CAST/EI_MMCHR11_CTG4 CAST/EI_MMCHR11_CTG5
| | Tips: call 'seqnames()' on the object to get all the sequence names, call 'seqinfo()' to get the full sequence info, use the '$' | or '[[' operator to access a given sequence, see '?BSgenome' for more information.

Thanks!!!!

[timoast]

Hi @Zoeyau531

The RegionStats() function computes the GC content and length of the peaks in your dataset. To do so, it requires the genome sequence, which is why you need to provide the BSgenome object for your dataset. The BSgenome object needs to have the same chromosome names as your peaks. Here, it looks like your peaks have NCBI-style chromosome naming (1, 2, 3, ...) but you have provided the UCSC genome with different chromosome naming style (chr1, chr2, chr3, ...).

You will need to change the chromosome naming in the BSgenome object to match:

seqlevelsStyle(BSgenome.Mmusculus.UCSC.mm10) <- "NCBI"