BPcells doesn't work with chromatin Assay #1507
Description
BPCells doesn't wok with chromatin assay
I am working with a big multiomics object. I updated Seurat to Version 5, because I need the support of BPcells to avoid breaking R because of the size of the matrix I am working with.
This works just fine:
reference = readRDS(multiome.dir)
DefaultAssay(reference) <- 'ATAC'
write_matrix_dir(mat = reference[["ATAC"]]$counts, dir = paste0(reference.map.dir, "multiome_counts"))
304316 x 57064 IterableMatrix object with class MatrixDir
Row names: chr1-100006443-100006958, chr1-10001125-10001359 ... chrY-9160959-9161339
Col names: JB_631_627_AAACATGCAAGGTCCT-1, JB_631_627_AAACCGAAGTAACCCG-1 ... QY_2021_2020_GTGGTTAGTCTTACTA-1
Data type: double
Storage order: column major
Queued Operations:
- Load compressed matrix from directory /nfs/lab/projects/Heart_LV/data/reference.map/multiome_counts
counts.mat <- open_matrix_dir(dir = paste0(reference.map.dir, "multiome_counts"))
Then here I get the error:
reference[["ATAC"]]$counts <- counts.mat
Error:
Error in SetAssayData.ChromatinAssay(object = object, slot = layer, new.data = value): Data must be a matrix or sparseMatrix
thoughts?
Here my session info:
R version 4.2.3 (2023-03-15)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS/LAPACK: /home/luca/.conda/envs/seurat5_bpcells/lib/libopenblasp-r0.3.24.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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ggh4x_0.2.6 patchwork_1.1.3
[3] gridExtra_2.3 ggbreak_0.1.2
[5] ggrepel_0.9.3 ggpubr_0.6.0
[7] ggplot2_3.4.3 logr_1.3.4
[9] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.22.0
[11] AnnotationFilter_1.22.0 GenomicFeatures_1.50.4
[13] AnnotationDbi_1.60.2 Biobase_2.58.0
[15] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[17] IRanges_2.32.0 S4Vectors_0.36.2
[19] BiocGenerics_0.44.0 BPCells_0.1.0
[21] SoupX_1.6.2 knitr_1.44
[23] harmony_1.0.3 Rcpp_1.0.11
[25] Signac_1.11.9000 Seurat_4.9.9.9067
[27] SeuratObject_4.9.9.9091 sp_2.1-0
[29] hdf5r_1.3.8 Matrix_1.6-1.1
[31] tidyr_1.3.0 data.table_1.14.8
[33] stringr_1.5.0 dplyr_1.1.3
[35] reticulate_1.32.0
loaded via a namespace (and not attached):
[1] pacman_0.5.1 utf8_1.2.3
[3] spatstat.explore_3.2-3 tidyselect_1.2.0
[5] RSQLite_2.3.1 htmlwidgets_1.6.2
[7] BiocParallel_1.32.6 Rtsne_0.16
[9] munsell_0.5.0 codetools_0.2-19
[11] ica_1.0-3 pbdZMQ_0.3-10
[13] future_1.33.0 miniUI_0.1.1.1
[15] withr_2.5.1 spatstat.random_3.1-6
[17] colorspace_2.1-0 progressr_0.14.0
[19] filelock_1.0.2 uuid_1.1-1
[21] ROCR_1.0-11 ggsignif_0.6.4
[23] tensor_1.5 listenv_0.9.0
[25] MatrixGenerics_1.10.0 repr_1.1.6
[27] GenomeInfoDbData_1.2.9 polyclip_1.10-6
[29] bit64_4.0.5 parallelly_1.36.0
[31] vctrs_0.6.3 generics_0.1.3
[33] xfun_0.40 BiocFileCache_2.6.1
[35] R6_2.5.1 gridGraphics_0.5-1
[37] DelayedArray_0.24.0 bitops_1.0-7
[39] spatstat.utils_3.0-3 cachem_1.0.8
[41] promises_1.2.1 BiocIO_1.8.0
[43] scales_1.2.1 gtable_0.3.4
[45] globals_0.16.2 goftest_1.2-3
[47] spam_2.9-1 rlang_1.1.1
[49] RcppRoll_0.3.0 splines_4.2.3
[51] rstatix_0.7.2 rtracklayer_1.58.0
[53] lazyeval_0.2.2 broom_1.0.5
[55] spatstat.geom_3.2-5 yaml_2.3.7
[57] reshape2_1.4.4 abind_1.4-5
[59] backports_1.4.1 httpuv_1.6.11
[61] tools_4.2.3 ggplotify_0.1.2
[63] ellipsis_0.3.2 RColorBrewer_1.1-3
[65] ggridges_0.5.4 plyr_1.8.9
[67] base64enc_0.1-3 progress_1.2.2
[69] zlibbioc_1.44.0 purrr_1.0.2
[71] RCurl_1.98-1.12 prettyunits_1.2.0
[73] deldir_1.0-9 pbapply_1.7-2
[75] cowplot_1.1.1 zoo_1.8-12
[77] SummarizedExperiment_1.28.0 cluster_2.1.4
[79] fs_1.6.3 magrittr_2.0.3
[81] RSpectra_0.16-1 scattermore_1.2
[83] lmtest_0.9-40 RANN_2.6.1
[85] ProtGenerics_1.30.0 fitdistrplus_1.1-11
[87] matrixStats_1.0.0 hms_1.1.3
[89] mime_0.12 evaluate_0.22
[91] xtable_1.8-4 XML_3.99-0.14
[93] fastDummies_1.7.3 compiler_4.2.3
[95] biomaRt_2.54.1 tibble_3.2.1
[97] KernSmooth_2.23-22 crayon_1.5.2
[99] htmltools_0.5.6.1 ggfun_0.1.3
[101] later_1.3.1 aplot_0.2.2
[103] DBI_1.1.3 dbplyr_2.3.4
[105] MASS_7.3-60 rappdirs_0.3.3
[107] car_3.1-2 cli_3.6.1
[109] dotCall64_1.0-2 igraph_1.5.1
[111] pkgconfig_2.0.3 GenomicAlignments_1.34.1
[113] IRdisplay_1.1 plotly_4.10.2
[115] spatstat.sparse_3.0-2 xml2_1.3.5
[117] XVector_0.38.0 yulab.utils_0.1.0
[119] digest_0.6.33 sctransform_0.4.0
[121] RcppAnnoy_0.0.21 common_1.0.9
[123] spatstat.data_3.0-1 Biostrings_2.66.0
[125] leiden_0.4.3 fastmatch_1.1-4
[127] uwot_0.1.16 restfulr_0.0.15
[129] curl_5.1.0 shiny_1.7.5
[131] Rsamtools_2.14.0 rjson_0.2.21
[133] lifecycle_1.0.3 nlme_3.1-163
[135] jsonlite_1.8.7 carData_3.0-5
[137] viridisLite_0.4.2 fansi_1.0.4
[139] pillar_1.9.0 lattice_0.21-9
[141] KEGGREST_1.38.0 fastmap_1.1.1
[143] httr_1.4.7 survival_3.5-7
[145] glue_1.6.2 png_0.1-8
[147] bit_4.0.5 stringi_1.7.12
[149] blob_1.2.4 RcppHNSW_0.5.0
[151] memoise_2.0.1 IRkernel_1.3.2
[153] irlba_2.3.5.1 future.apply_1.11.0