Unable to subset chromatinassay by features like in vignette "Analyzing PBMC scATAC-seq" when other assays are present #1847
Description
In the Signac vignette "Analyzing PBMC scATAC-seq", there is a step in which we filter out those features falling in chromosome scaffolds or other sequences instead of the standard chromosomes:
peaks.keep <- seqnames(granges(pbmc)) %in% standardChromosomes(granges(pbmc))
pbmc <- pbmc[as.vector(peaks.keep), ]
I have no problem running that step when the seurat object only include a single assat "peaks". However, if the seurat object contains other assays like "RNA" (for example, the one generated in another vignette "Joint RNA and ATAC analysis: 10x multiomic"), this step will fail, and produce an error:
> pbmc2
An object of class Seurat
204947 features across 11211 samples within 3 assays
Active assay: ATAC (143887 features, 143887 variable features)
2 layers present: counts, data
2 other assays present: RNA, SCT
2 dimensional reductions calculated: pca, lsi
> pbmc2[as.vector(peaks.keep), ]
Error: None of the features provided found in this assay
As have been mentioned in issue #315, this only happens to the features, but not the cells.
> pbmc2[, 1]
An object of class Seurat
204947 features across 1 samples within 3 assays
Active assay: ATAC (143887 features, 143887 variable features)
2 layers present: counts, data
2 other assays present: RNA, SCT
2 dimensional reductions calculated: pca, lsi
The pbmc2 is generated as detailed in the vignette "Joint RNA and ATAC analysis: 10x multiomic", and the only modification is that I used another .h5 and fragment files from 10X genomics. I guess maybe assay RNA or SCT is causing this issue? I would really appreciate it if anyone knows the reason behind it.
I'm using the latest release of Signac (1.14.0, which should provide support for seurat v5) and Seurat 5.0.1. I would really appreciate if anyone knows the reason behind this issue.
Here is the session info:
> sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 8
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el8-x86_64/lib/libopenblas_skylakexp-r0.3.13.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] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratDisk_0.0.0.9021 ggplot2_3.4.4 biovizBase_1.46.0
[4] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.66.3 rtracklayer_1.58.0
[7] Biostrings_2.66.0 XVector_0.38.0 EnsDb.Hsapiens.v86_2.99.0
[10] ensembldb_2.22.0 AnnotationFilter_1.22.0 GenomicFeatures_1.50.4
[13] AnnotationDbi_1.60.2 Biobase_2.58.0 GenomicRanges_1.50.2
[16] GenomeInfoDb_1.34.9 IRanges_2.32.0 S4Vectors_0.36.2
[19] BiocGenerics_0.44.0 Seurat_5.0.1 SeuratObject_5.0.2
[22] sp_1.6-0 Signac_1.14.9001
loaded via a namespace (and not attached):
[1] utf8_1.2.2 spatstat.explore_3.2-7 reticulate_1.24 tidyselect_1.2.0
[5] RSQLite_2.3.1 htmlwidgets_1.6.2 grid_4.2.0 BiocParallel_1.32.6
[9] Rtsne_0.16 munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[13] future_1.25.0 miniUI_0.1.1.1 withr_2.5.0 spatstat.random_3.2-3
[17] colorspace_2.0-3 progressr_0.10.0 filelock_1.0.2 knitr_1.42
[21] rstudioapi_0.14 ROCR_1.0-11 tensor_1.5 listenv_0.8.0
[25] MatrixGenerics_1.10.0 GenomeInfoDbData_1.2.9 polyclip_1.10-0 bit64_4.0.5
[29] parallelly_1.31.1 vctrs_0.6.1 generics_0.1.3 xfun_0.38
[33] BiocFileCache_2.6.1 R6_2.5.1 hdf5r_1.3.11 bitops_1.0-7
[37] spatstat.utils_3.0-5 cachem_1.0.6 DelayedArray_0.24.0 promises_1.2.0.1
[41] BiocIO_1.8.0 scales_1.3.0 nnet_7.3-17 gtable_0.3.0
[45] globals_0.14.0 goftest_1.2-3 spam_2.8-0 rlang_1.1.2
[49] RcppRoll_0.3.1 splines_4.2.0 lazyeval_0.2.2 dichromat_2.0-0.1
[53] checkmate_2.1.0 spatstat.geom_3.2-9 yaml_2.3.5 reshape2_1.4.4
[57] abind_1.4-5 backports_1.4.1 httpuv_1.6.5 Hmisc_5.1-0
[61] tools_4.2.0 ellipsis_0.3.2 RColorBrewer_1.1-3 ggridges_0.5.3
[65] Rcpp_1.0.11 plyr_1.8.7 base64enc_0.1-3 progress_1.2.2
[69] zlibbioc_1.44.0 purrr_1.0.1 RCurl_1.98-1.12 prettyunits_1.1.1
[73] rpart_4.1.16 deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[77] zoo_1.8-11 SummarizedExperiment_1.28.0 ggrepel_0.9.3 cluster_2.1.3
[81] tinytex_0.38 magrittr_2.0.3 data.table_1.14.4 RSpectra_0.16-1
[85] scattermore_1.2 lmtest_0.9-40 RANN_2.6.1 ProtGenerics_1.30.0
[89] fitdistrplus_1.1-8 matrixStats_1.1.0 evaluate_0.15 hms_1.1.3
[93] patchwork_1.1.1 mime_0.12 xtable_1.8-4 XML_3.99-0.9
[97] fastDummies_1.7.3 gridExtra_2.3 compiler_4.2.0 biomaRt_2.54.1
[101] tibble_3.2.1 KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.8.1
[105] later_1.3.0 Formula_1.2-4 tidyr_1.3.0 DBI_1.1.2
[109] dbplyr_2.3.2 MASS_7.3-58.3 rappdirs_0.3.3 Matrix_1.6-4
[113] cli_3.6.2 parallel_4.2.0 dotCall64_1.0-1 igraph_2.0.3
[117] pkgconfig_2.0.3 GenomicAlignments_1.34.1 foreign_0.8-82 plotly_4.10.0
[121] spatstat.sparse_3.0-3 xml2_1.3.3 VariantAnnotation_1.44.1 stringr_1.5.0
[125] digest_0.6.29 sctransform_0.4.1 RcppAnnoy_0.0.19 spatstat.data_3.0-4
[129] rmarkdown_2.21 leiden_0.3.10 fastmatch_1.1-3 htmlTable_2.4.0
[133] uwot_0.1.14 restfulr_0.0.15 curl_4.3.2 shiny_1.7.1
[137] Rsamtools_2.14.0 rjson_0.2.21 lifecycle_1.0.4 nlme_3.1-157
[141] jsonlite_1.8.7 viridisLite_0.4.0 fansi_1.0.3 pillar_1.9.0
[145] lattice_0.20-45 KEGGREST_1.38.0 fastmap_1.2.0 httr_1.4.5
[149] survival_3.3-1 glue_1.6.2 png_0.1-7 bit_4.0.4
[153] stringi_1.7.6 blob_1.2.3 RcppHNSW_0.4.1 memoise_2.0.1
[157] dplyr_1.1.2 irlba_2.3.3 future.apply_1.9.0