Closed mossconfuse closed 1 year ago
yuhanH
commented
1 year ago hi @mossconfuse ChromatinAssay is still a v3 assay which cannot be split. V5 specific functions will not work on current ChromatinAssay. You can split assay individually.
ppsn[['RNA']] <- split(ppsn[['RNA']], f = ppsn@meta.data$orig.ident)
mossconfuse
commented
1 year ago Thank you @yuhanH . I am not entirely sure how to reconcile these issues, and because I keep stumbling into new problems with V5, I am trying to revert back to the older Seurat version.
I made a problem for myself though. Previously I had part of my R analysis as a script on our campus server. I used this to load the matrices, build the initial seurat objects with multimodal data (ATAC and RNA), merge objects for 6 samples and to do the linkPeaks assay. It all worked well and I could produce a RData object that I could download to my computer and play with on my PC's R studio. With the latest v5 release, I installed the v5 to my conda environment and tried to build a v5 merged Seurat object but it kept failing.
As I showed in my original example, I still get the error:
Error in validObject(object = .Object) :
invalid class "Seurat" object: all assays must have a key
Calls: merge ... new -> initialize -> initialize -> .local -> validObject
Execution haltedduring the merge.
I have checked my individual objects. They have @assays
> Wt_T00_seurat@assays
$RNA
Assay (v5) data with 33328 features for 2648 cells
First 10 features:
3' rps12, 3' rps12.1, rps7, rps7.1, ndhB, ndhB.1, rps12, rps12.1, trnL, trnL.1
Layers:
counts
$ATAC
ChromatinAssay data with 17333 features for 2648 cells
Variable features: 0
Genome:
Annotation present: TRUE
Motifs present: FALSE
Fragment files: 1 and keys
> Keys(Wt_T00_seurat)
meta.data RNA ATAC
"md_" "rna_" "atac_" I have tried to switch back to the old Seurat:
> options(Seurat.object.assay.version = 'v4')
> remotes::install_version("Seurat", "4.3.0")
Downloading package from url: https://cran.ism.ac.jp/src/contrib/Seurat_4.3.0.tar.gz
* installing *source* package ‘Seurat’ ...
~~~
** testing if installed package keeps a record of temporary installation path
* DONE (Seurat)
> sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /mnt/gpfsA/home/devil/miniconda3/envs/single_cell/lib/libopenblasp-r0.3.21.so
locale:
[1] LC_CTYPE=C 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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] remotes_2.4.2 presto_1.0.0
[3] data.table_1.14.8 devtools_2.4.5
[5] usethis_2.1.6 qlcMatrix_0.9.7
[7] sparsesvd_0.2-1 slam_0.1-50
[9] Matrix_1.5-1 R.utils_2.12.1
[11] R.oo_1.25.0 R.methodsS3_1.8.2
[13] motifmatchr_1.16.0 ggbio_1.42.0
[15] TFBSTools_1.32.0 Rsamtools_2.10.0
[17] GenomicFeatures_1.46.1 AnnotationDbi_1.56.2
[19] Biobase_2.54.0 harmony_1.0
[21] Rcpp_1.0.10 AnnotationFilter_1.18.0
[23] hdf5r_1.3.7 Signac_1.8.0
[25] SeuratObject_4.9.9.9084 Seurat_4.3.0
[27] forcats_0.5.2 stringr_1.5.0
[29] dplyr_1.1.2 purrr_1.0.1
[31] readr_2.1.3 tidyr_1.3.0
[33] tibble_3.2.1 ggplot2_3.4.2
[35] tidyverse_1.3.2 BSgenome.Ppatens.v3.3_1.0.0
[37] BSgenome_1.62.0 rtracklayer_1.54.0
[39] Biostrings_2.62.0 XVector_0.34.0
[41] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[43] IRanges_2.28.0 S4Vectors_0.32.4
[45] BiocGenerics_0.40.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 scattermore_0.8
[3] GGally_2.1.2 knitr_1.42
[5] bit64_4.0.5 irlba_2.3.5.1
[7] DelayedArray_0.20.0 rpart_4.1.19
[9] KEGGREST_1.34.0 RCurl_1.98-1.9
[11] generics_0.1.3 callr_3.7.3
[13] cowplot_1.1.1 RSQLite_2.2.18
[15] RANN_2.6.1 future_1.32.0
[17] bit_4.0.4 tzdb_0.3.0
[19] spatstat.data_3.0-1 xml2_1.3.3
[21] lubridate_1.9.0 httpuv_1.6.9
[23] SummarizedExperiment_1.24.0 assertthat_0.2.1
[25] DirichletMultinomial_1.36.0 gargle_1.2.1
[27] xfun_0.39 hms_1.1.2
[29] promises_1.2.0.1 fansi_1.0.4
[31] restfulr_0.0.15 progress_1.2.2
[33] caTools_1.18.2 dbplyr_2.2.1
[35] readxl_1.4.1 igraph_1.4.2
[37] DBI_1.1.3 htmlwidgets_1.6.2
[39] reshape_0.8.9 spatstat.geom_3.1-0
[41] googledrive_2.0.0 ellipsis_0.3.2
[43] backports_1.4.1 annotate_1.72.0
[45] biomaRt_2.50.0 deldir_1.0-6
[47] MatrixGenerics_1.6.0 vctrs_0.6.2
[49] ensembldb_2.18.4 ROCR_1.0-11
[51] abind_1.4-5 cachem_1.0.7
[53] withr_2.5.0 progressr_0.13.0
[55] checkmate_2.1.0 sctransform_0.3.5
[57] GenomicAlignments_1.30.0 prettyunits_1.1.1
[59] goftest_1.2-3 cluster_2.1.4
[61] dotCall64_1.0-2 lazyeval_0.2.2
[63] seqLogo_1.60.0 crayon_1.5.2
[65] spatstat.explore_3.1-0 pkgconfig_2.0.3
[67] pkgload_1.3.1 ProtGenerics_1.26.0
[69] nlme_3.1-160 nnet_7.3-18
[71] rlang_1.1.0 globals_0.16.2
[73] lifecycle_1.0.3 miniUI_0.1.1.1
[75] filelock_1.0.2 BiocFileCache_2.2.1
[77] modelr_0.1.9 dichromat_2.0-0.1
[79] cellranger_1.1.0 polyclip_1.10-4
[81] matrixStats_0.63.0 lmtest_0.9-40
[83] graph_1.72.0 zoo_1.8-12
[85] reprex_2.0.2 base64enc_0.1-3
[87] processx_3.8.0 ggridges_0.5.4
[89] googlesheets4_1.0.1 png_0.1-8
[91] viridisLite_0.4.1 rjson_0.2.21
[93] bitops_1.0-7 KernSmooth_2.23-20
[95] spam_2.9-1 blob_1.2.3
[97] parallelly_1.35.0 spatstat.random_3.1-4
[99] jpeg_0.1-9 CNEr_1.30.0
[101] scales_1.2.1 memoise_2.0.1
[103] magrittr_2.0.3 plyr_1.8.8
[105] ica_1.0-3 zlibbioc_1.40.0
[107] compiler_4.1.3 BiocIO_1.4.0
[109] RColorBrewer_1.1-3 fitdistrplus_1.1-8
[111] cli_3.6.1 urlchecker_1.0.1
[113] listenv_0.9.0 ps_1.7.2
[115] patchwork_1.1.2 pbapply_1.7-0
[117] htmlTable_2.4.1 Formula_1.2-4
[119] MASS_7.3-58.1 tidyselect_1.2.0
[121] stringi_1.7.12 yaml_2.3.7
[123] latticeExtra_0.6-30 ggrepel_0.9.3
[125] grid_4.1.3 VariantAnnotation_1.40.0
[127] fastmatch_1.1-3 tools_4.1.3
[129] timechange_0.1.1 future.apply_1.10.0
[131] parallel_4.1.3 rstudioapi_0.14
[133] TFMPvalue_0.0.9 foreign_0.8-83
[135] gridExtra_2.3 Rtsne_0.16
[137] BiocManager_1.30.19 digest_0.6.31
[139] rgeos_0.5-9 shiny_1.7.4
[141] pracma_2.4.2 broom_1.0.1
[143] later_1.3.0 OrganismDbi_1.36.0
[145] RcppAnnoy_0.0.20 httr_1.4.5
[147] biovizBase_1.42.0 colorspace_2.1-0
[149] rvest_1.0.3 XML_3.99-0.10
[151] fs_1.6.1 tensor_1.5
[153] reticulate_1.28 splines_4.1.3
[155] RBGL_1.70.0 uwot_0.1.14
[157] RcppRoll_0.3.0 spatstat.utils_3.0-2
[159] sp_1.6-0 sessioninfo_1.2.2
[161] plotly_4.10.1 xtable_1.8-4
[163] jsonlite_1.8.4 poweRlaw_0.70.6
[165] R6_2.5.1 profvis_0.3.7
[167] Hmisc_4.7-1 pillar_1.9.0
[169] htmltools_0.5.5 mime_0.12
[171] glue_1.6.2 fastmap_1.1.1
[173] BiocParallel_1.28.3 codetools_0.2-18
[175] pkgbuild_1.4.0 utf8_1.2.3
[177] lattice_0.20-45 spatstat.sparse_3.0-1
[179] curl_5.0.0 leiden_0.4.3
[181] gtools_3.9.4 interp_1.1-3
[183] GO.db_3.14.0 survival_3.4-0
[185] docopt_0.7.1 munsell_0.5.0
[187] GenomeInfoDbData_1.2.7 haven_2.5.1
[189] reshape2_1.4.4 gtable_0.3.3 I can't figure out why I am stuck with this new issue during the merge or how to overcome/circumvent it. I either have to avoid the new version altogether, or find a way to make my current samples compatible with it. Any help would be appreciated.
UPDATE: I didn't quite make my merged object SeuratV5 compatible yet, but I did figure out why merge didn't work: two packages, Seurat and SeuratObject needed to be downgraded:
remotes::install_version("Seurat", "4.3.0")
remotes::install_version("SeuratObject", "4.1.3")The Seurat V5 vignettes page has a section dedicated to working with multimodal data (Streamlined and multimodal integration), but a better description is needed for users on how to generate and merge objects of multimodal data. The data used in those vignettes are loaded from premade seurat objects.
Gesmira
commented
1 year ago Hi, just adding here that in Seuratv5 you should be able to merge, and do other ChromatinAssay functionalities, if you also update Signac to the v5 version.
remotes::install_github("stuart-lab/signac", "seurat5", quiet = TRUE)
ahoffrichter
commented
1 year ago I encountered a similar problem. I just updated Seurat to version 5 for a different project. But now I apparently can't work with this Seurat object (version 4.1.3.) with gene expression and ATAC data anymore. I also updated Signac as suggested above, but I still get an error message for example when I try subset the object:
Error in checkSlotAssignment(object, name, value) :
assignment of an object of class “S4” is not valid for slot ‘version’ in an object of class “Seurat”; is(value, "package_version") is not TRUEThis is my sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Ventura 13.5.2
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] presto_1.0.0 data.table_1.14.8 Rcpp_1.0.11 GO.db_3.16.0
[5] clusterProfiler_4.6.2 org.Hs.eg.db_3.16.0 AnnotationDbi_1.60.2 Biobase_2.58.0
[9] DT_0.29 openxlsx_4.2.5.2 lubridate_1.9.2 forcats_1.0.0
[13] stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[17] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0
[21] simspec_0.0.0.9000 BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.66.3 rtracklayer_1.58.0
[25] Biostrings_2.66.0 XVector_0.38.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[29] IRanges_2.32.0 S4Vectors_0.36.2 AnnotationHub_3.6.0 BiocFileCache_2.6.1
[33] dbplyr_2.3.3 BiocGenerics_0.44.0 Signac_1.11.9000 Seurat_4.9.9.9060
[37] SeuratObject_4.9.9.9091 sp_2.1-0 patchwork_1.1.3
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 scattermore_1.2 bit64_4.0.5 knitr_1.44 irlba_2.3.5.1
[6] DelayedArray_0.24.0 KEGGREST_1.38.0 RCurl_1.98-1.12 generics_0.1.3 cowplot_1.1.1
[11] RSQLite_2.3.1 shadowtext_0.1.2 RANN_2.6.1 future_1.33.0 bit_4.0.5
[16] tzdb_0.4.0 enrichplot_1.18.4 spatstat.data_3.0-1 httpuv_1.6.11 SummarizedExperiment_1.28.0
[21] viridis_0.6.4 xfun_0.40 hms_1.1.3 evaluate_0.21 promises_1.2.1
[26] fansi_1.0.4 restfulr_0.0.15 igraph_1.5.1 DBI_1.1.3 htmlwidgets_1.6.2
[31] spatstat.geom_3.2-5 ellipsis_0.3.2 RSpectra_0.16-1 deldir_1.0-9 MatrixGenerics_1.10.0
[36] vctrs_0.6.3 ROCR_1.0-11 abind_1.4-5 cachem_1.0.8 withr_2.5.1
[41] ggforce_0.4.1 HDO.db_0.99.1 progressr_0.14.0 sctransform_0.4.0 GenomicAlignments_1.34.1
[46] treeio_1.22.0 goftest_1.2-3 cluster_2.1.4 DOSE_3.24.2 ape_5.7-1
[51] dotCall64_1.0-2 lazyeval_0.2.2 crayon_1.5.2 spatstat.explore_3.2-3 pkgconfig_2.0.3
[56] tweenr_2.0.2 nlme_3.1-163 rlang_1.1.1 globals_0.16.2 lifecycle_1.0.3
[61] miniUI_0.1.1.1 downloader_0.4 filelock_1.0.2 fastDummies_1.7.3 polyclip_1.10-4
[66] RcppHNSW_0.5.0 matrixStats_1.0.0 lmtest_0.9-40 Matrix_1.5-4.1 aplot_0.2.0
[71] zoo_1.8-12 ggridges_0.5.4 png_0.1-8 viridisLite_0.4.2 rjson_0.2.21
[76] bitops_1.0-7 gson_0.1.0 KernSmooth_2.23-22 spam_2.9-1 blob_1.2.4
[81] qvalue_2.30.0 parallelly_1.36.0 spatstat.random_3.1-6 gridGraphics_0.5-1 scales_1.2.1
[86] memoise_2.0.1 magrittr_2.0.3 plyr_1.8.8 ica_1.0-3 zlibbioc_1.44.0
[91] compiler_4.2.2 scatterpie_0.2.1 BiocIO_1.8.0 RColorBrewer_1.1-3 fitdistrplus_1.1-11
[96] Rsamtools_2.14.0 cli_3.6.1 listenv_0.9.0 pbapply_1.7-2 MASS_7.3-60
[101] tidyselect_1.2.0 stringi_1.7.12 yaml_2.3.7 GOSemSim_2.24.0 ggrepel_0.9.3
[106] grid_4.2.2 fastmatch_1.1-4 tools_4.2.2 timechange_0.2.0 future.apply_1.11.0
[111] parallel_4.2.2 rstudioapi_0.15.0 gridExtra_2.3 farver_2.1.1 Rtsne_0.16
[116] ggraph_2.1.0 digest_0.6.33 BiocManager_1.30.22 shiny_1.7.5 BiocVersion_3.16.0
[121] later_1.3.1 RcppAnnoy_0.0.21 httr_1.4.7 colorspace_2.1-0 XML_3.99-0.14
[126] tensor_1.5 reticulate_1.32.0 splines_4.2.2 uwot_0.1.16 yulab.utils_0.0.8
[131] RcppRoll_0.3.0 tidytree_0.4.5 spatstat.utils_3.0-3 graphlayouts_1.0.0 ggplotify_0.1.2
[136] plotly_4.10.2 xtable_1.8-4 jsonlite_1.8.7 ggtree_3.6.2 tidygraph_1.2.3
[141] ggfun_0.1.2 R6_2.5.1 pillar_1.9.0 htmltools_0.5.6 mime_0.12
[146] glue_1.6.2 fastmap_1.1.1 BiocParallel_1.32.6 interactiveDisplayBase_1.36.0 codetools_0.2-19
[151] fgsea_1.24.0 utf8_1.2.3 lattice_0.21-8 spatstat.sparse_3.0-2 curl_5.0.2
[156] leiden_0.4.3 zip_2.3.0 survival_3.5-7 rmarkdown_2.25 munsell_0.5.0
[161] GenomeInfoDbData_1.2.9 reshape2_1.4.4 gtable_0.3.4 
bjstewart1
commented
12 months ago I encountered a similar problem. I just updated Seurat to version 5 for a different project. But now I apparently can't work with this Seurat object (version 4.1.3.) with gene expression and ATAC data anymore. I also updated Signac as suggested above, but I still get an error message for example when I try subset the object:
Error in checkSlotAssignment(object, name, value) : assignment of an object of class “S4” is not valid for slot ‘version’ in an object of class “Seurat”; is(value, "package_version") is not TRUEThis is my sessionInfo()
R version 4.2.2 (2022-10-31) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Ventura 13.5.2 Matrix products: default LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats4 stats graphics grDevices utils datasets methods base other attached packages: [1] presto_1.0.0 data.table_1.14.8 Rcpp_1.0.11 GO.db_3.16.0 [5] clusterProfiler_4.6.2 org.Hs.eg.db_3.16.0 AnnotationDbi_1.60.2 Biobase_2.58.0 [9] DT_0.29 openxlsx_4.2.5.2 lubridate_1.9.2 forcats_1.0.0 [13] stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2 readr_2.1.4 [17] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0 [21] simspec_0.0.0.9000 BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.66.3 rtracklayer_1.58.0 [25] Biostrings_2.66.0 XVector_0.38.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9 [29] IRanges_2.32.0 S4Vectors_0.36.2 AnnotationHub_3.6.0 BiocFileCache_2.6.1 [33] dbplyr_2.3.3 BiocGenerics_0.44.0 Signac_1.11.9000 Seurat_4.9.9.9060 [37] SeuratObject_4.9.9.9091 sp_2.1-0 patchwork_1.1.3 loaded via a namespace (and not attached): [1] rappdirs_0.3.3 scattermore_1.2 bit64_4.0.5 knitr_1.44 irlba_2.3.5.1 [6] DelayedArray_0.24.0 KEGGREST_1.38.0 RCurl_1.98-1.12 generics_0.1.3 cowplot_1.1.1 [11] RSQLite_2.3.1 shadowtext_0.1.2 RANN_2.6.1 future_1.33.0 bit_4.0.5 [16] tzdb_0.4.0 enrichplot_1.18.4 spatstat.data_3.0-1 httpuv_1.6.11 SummarizedExperiment_1.28.0 [21] viridis_0.6.4 xfun_0.40 hms_1.1.3 evaluate_0.21 promises_1.2.1 [26] fansi_1.0.4 restfulr_0.0.15 igraph_1.5.1 DBI_1.1.3 htmlwidgets_1.6.2 [31] spatstat.geom_3.2-5 ellipsis_0.3.2 RSpectra_0.16-1 deldir_1.0-9 MatrixGenerics_1.10.0 [36] vctrs_0.6.3 ROCR_1.0-11 abind_1.4-5 cachem_1.0.8 withr_2.5.1 [41] ggforce_0.4.1 HDO.db_0.99.1 progressr_0.14.0 sctransform_0.4.0 GenomicAlignments_1.34.1 [46] treeio_1.22.0 goftest_1.2-3 cluster_2.1.4 DOSE_3.24.2 ape_5.7-1 [51] dotCall64_1.0-2 lazyeval_0.2.2 crayon_1.5.2 spatstat.explore_3.2-3 pkgconfig_2.0.3 [56] tweenr_2.0.2 nlme_3.1-163 rlang_1.1.1 globals_0.16.2 lifecycle_1.0.3 [61] miniUI_0.1.1.1 downloader_0.4 filelock_1.0.2 fastDummies_1.7.3 polyclip_1.10-4 [66] RcppHNSW_0.5.0 matrixStats_1.0.0 lmtest_0.9-40 Matrix_1.5-4.1 aplot_0.2.0 [71] zoo_1.8-12 ggridges_0.5.4 png_0.1-8 viridisLite_0.4.2 rjson_0.2.21 [76] bitops_1.0-7 gson_0.1.0 KernSmooth_2.23-22 spam_2.9-1 blob_1.2.4 [81] qvalue_2.30.0 parallelly_1.36.0 spatstat.random_3.1-6 gridGraphics_0.5-1 scales_1.2.1 [86] memoise_2.0.1 magrittr_2.0.3 plyr_1.8.8 ica_1.0-3 zlibbioc_1.44.0 [91] compiler_4.2.2 scatterpie_0.2.1 BiocIO_1.8.0 RColorBrewer_1.1-3 fitdistrplus_1.1-11 [96] Rsamtools_2.14.0 cli_3.6.1 listenv_0.9.0 pbapply_1.7-2 MASS_7.3-60 [101] tidyselect_1.2.0 stringi_1.7.12 yaml_2.3.7 GOSemSim_2.24.0 ggrepel_0.9.3 [106] grid_4.2.2 fastmatch_1.1-4 tools_4.2.2 timechange_0.2.0 future.apply_1.11.0 [111] parallel_4.2.2 rstudioapi_0.15.0 gridExtra_2.3 farver_2.1.1 Rtsne_0.16 [116] ggraph_2.1.0 digest_0.6.33 BiocManager_1.30.22 shiny_1.7.5 BiocVersion_3.16.0 [121] later_1.3.1 RcppAnnoy_0.0.21 httr_1.4.7 colorspace_2.1-0 XML_3.99-0.14 [126] tensor_1.5 reticulate_1.32.0 splines_4.2.2 uwot_0.1.16 yulab.utils_0.0.8 [131] RcppRoll_0.3.0 tidytree_0.4.5 spatstat.utils_3.0-3 graphlayouts_1.0.0 ggplotify_0.1.2 [136] plotly_4.10.2 xtable_1.8-4 jsonlite_1.8.7 ggtree_3.6.2 tidygraph_1.2.3 [141] ggfun_0.1.2 R6_2.5.1 pillar_1.9.0 htmltools_0.5.6 mime_0.12 [146] glue_1.6.2 fastmap_1.1.1 BiocParallel_1.32.6 interactiveDisplayBase_1.36.0 codetools_0.2-19 [151] fgsea_1.24.0 utf8_1.2.3 lattice_0.21-8 spatstat.sparse_3.0-2 curl_5.0.2 [156] leiden_0.4.3 zip_2.3.0 survival_3.5-7 rmarkdown_2.25 munsell_0.5.0 [161] GenomeInfoDbData_1.2.9 reshape2_1.4.4 gtable_0.3.4
same problem
I have multimodal (RNA and ATAC seq) data. I want to make use of the tools offered in seurat v5, but just recreating or converting my data in the new v5 format is not straightforward.
Trying to recreate my merged object using the same code I used previously runs into an error:
Here is the code to create and merge the seurats (This worked with previous seurat versions):
I have also considered switching my current seurat object to the v5 format using split:
But it does the split with just the current active assay? How do I ensure splitting of all four assays present in my object? Or am I misunderstanding something? Do I have to run
splitreiteratively for each assay in the object?