Closed chenhy-lab closed 9 months ago
Can you provide a complete and reproducible code?
This is the code in the part Cell annotation using single-cell datasets of README.
This error is likely caused by Seurat v5. SCP will undergo compatibility modifications after the official release of Seurat v5.
Thank you very much, I will test it later with Seurat v4.2 or higher.
Hey, thanks for this beautiful package! I have the same issue as described by @chenhy-lab, but I'm using Seurat v4.4.0, not v5. Could you provide a potential solution for this error message? Thanks!
The error is specifically caused by an issue with the SeuratObject package. Currently, it is one version ahead of Seurat on CRAN, with Seurat at version 4.4.0 and SeuratObject at version 5.0.0.
You can install the corresponding version 4 of SeuratObject by remotes::install_version("SeuratObject", version = "4.1.4")
When I encountered this bug, the software version was: Seurat at version 4.1.1 and SeuratObject at version 4.1.0
I found that the message "No cell overlap between new meta data and Seurat object" was first added in version 4.9.9.9027 of SeuratObject. https://github.com/mojaveazure/seurat-object/blame/78f71fee5b0cbe69f669f9a698cb60877eeb8da8/R/seurat.R#L5129 https://github.com/mojaveazure/seurat-object/blob/4a58f01aa30620961e9cb344964699dbc892b317/DESCRIPTION
The error is specifically caused by an issue with the SeuratObject package. Currently, it is one version ahead of Seurat on CRAN, with Seurat at version 4.4.0 and SeuratObject at version 5.0.0.
You can install the corresponding version 4 of SeuratObject by
remotes::install_version("SeuratObject", version = "4.1.3")
Thank you, I downgraded my SeuratObject to v4.1.4 and the error was gone!
Sorry for re-opening this issue but I am stuck here. If I install SeuratObject 4.1.3 or 4.1.4 I get the error:
library(SCP)
Error: package or namespace load failed for ‘SCP’:
object ‘LayerData<-’ is not exported by 'namespace:SeuratObject'
sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Amsterdam
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.4.3 yardstick_1.2.0 workflowsets_1.0.1 workflows_1.1.3
[5] tune_1.1.2 rsample_1.2.0 recipes_1.0.8 parsnip_1.1.1
[9] modeldata_1.2.0 infer_1.0.5 dials_1.2.0 scales_1.3.0
[13] broom_1.0.5 tidymodels_1.1.1 gridExtra_2.3 Nebulosa_1.12.0
[17] tradeSeq_1.16.0 slingshot_2.10.0 TrajectoryUtils_1.10.0 princurve_2.1.6
[21] viridis_0.6.4 viridisLite_0.4.2 monocle3_1.3.4 UCell_2.6.2
[25] stringr_1.5.1 DropletUtils_1.22.0 BiocParallel_1.36.0 scDblFinder_1.16.0
[29] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0 GenomicRanges_1.54.1 GenomeInfoDb_1.38.1
[33] MatrixGenerics_1.14.0 matrixStats_1.1.0 harmony_1.1.0 Rcpp_1.0.11
[37] easyGgplot2_1.0.0.9000 purrr_1.0.2 tibble_3.2.1 patchwork_1.1.3
[41] biomaRt_2.58.0 enrichplot_1.22.0 org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1
[45] IRanges_2.36.0 S4Vectors_0.40.2 Biobase_2.62.0 BiocGenerics_0.48.1
[49] clusterProfiler_4.10.0 magrittr_2.0.3 data.table_1.14.8 scCustomize_1.1.3
[53] SeuratObject_4.1.4 Seurat_4.4.0 dplyr_1.1.4 cli_3.6.1
[57] tidyr_1.3.0 ggplot2_3.4.4
loaded via a namespace (and not attached):
[1] igraph_1.5.1 ica_1.0-3 plotly_4.10.3 scater_1.30.0
[5] rematch2_2.1.2 zlibbioc_1.48.0 tidyselect_1.2.0 bit_4.0.5
[9] GPfit_1.0-8 doParallel_1.0.17 clue_0.3-65 lattice_0.22-5
[13] rjson_0.2.21 blob_1.2.4 S4Arrays_1.2.0 parallel_4.3.1
[17] png_0.1-8 ggplotify_0.1.2 goftest_1.2-3 BiocIO_1.12.0
[21] bluster_1.12.0 BiocNeighbors_1.20.0 lhs_1.1.6 uwot_0.1.16
[25] shadowtext_0.1.2 curl_5.1.0 mime_0.12 tidytree_0.4.5
[29] leiden_0.4.3.1 ComplexHeatmap_2.18.0 stringi_1.8.2 backports_1.4.1
[33] XML_3.99-0.16 lubridate_1.9.3 httpuv_1.6.12 paletteer_1.5.0
[37] rappdirs_0.3.3 splines_4.3.1 RcppRoll_0.3.0 mclust_6.0.1
[41] prodlim_2023.08.28 ggraph_2.1.0 sctransform_0.4.1 ggbeeswarm_0.7.2
[45] DBI_1.1.3 terra_1.7-55 HDF5Array_1.30.0 withr_2.5.2
[49] class_7.3-22 xgboost_1.7.6.1 lmtest_0.9-40 tidygraph_1.2.3
[53] rtracklayer_1.62.0 htmlwidgets_1.6.4 fs_1.6.3 ggrepel_0.9.4
[57] SparseArray_1.2.2 cellranger_1.1.0 reticulate_1.34.0 zoo_1.8-12
[61] XVector_0.42.0 timechange_0.2.0 foreach_1.5.2 fansi_1.0.5
[65] grid_4.3.1 timeDate_4022.108 ggtree_3.10.0 rhdf5_2.46.1
[69] R.oo_1.25.0 irlba_2.3.5.1 ggrastr_1.0.2 gridGraphics_0.5-1
[73] ellipsis_0.3.2 lazyeval_0.2.2 yaml_2.3.7 survival_3.5-7
[77] scattermore_1.2 crayon_1.5.2 RcppAnnoy_0.0.21 RColorBrewer_1.1-3
[81] progressr_0.14.0 tweenr_2.0.2 later_1.3.1 ggridges_0.5.4
[85] codetools_0.2-19 GlobalOptions_0.1.2 KEGGREST_1.42.0 Rtsne_0.16
[89] shape_1.4.6 limma_3.58.1 Rsamtools_2.18.0 filelock_1.0.2
[93] DiceDesign_1.9 pkgconfig_2.0.3 xml2_1.3.6 GenomicAlignments_1.38.0
[97] aplot_0.2.2 spatstat.sparse_3.0-3 ape_5.7-1 xtable_1.8-4
[101] plyr_1.8.9 httr_1.4.7 tools_4.3.1 globals_0.16.2
[105] hardhat_1.3.0 beeswarm_0.4.0 nlme_3.1-164 HDO.db_0.99.1
[109] dbplyr_2.4.0 lme4_1.1-35.1 digest_0.6.33 Matrix_1.6-4
[113] furrr_0.3.1 farver_2.1.1 reshape2_1.4.4 ks_1.14.1
[117] yulab.utils_0.1.0 rpart_4.1.23 glue_1.6.2 cachem_1.0.8
[121] BiocFileCache_2.10.1 polyclip_1.10-6 generics_0.1.3 Biostrings_2.70.1
[125] mvtnorm_1.2-4 parallelly_1.36.0 statmod_1.5.0 R.cache_0.16.0
[129] ScaledMatrix_1.10.0 minqa_1.2.6 pbapply_1.7-2 spam_2.10-0
[133] gson_0.1.0 dqrng_0.3.2 utf8_1.2.4 gower_1.0.1
[137] graphlayouts_1.0.2 shiny_1.8.0 lava_1.7.3 GenomeInfoDbData_1.2.11
[141] R.utils_2.12.3 rhdf5filters_1.14.1 RCurl_1.98-1.13 memoise_2.0.1
[145] pheatmap_1.0.12 R.methodsS3_1.8.2 future_1.33.0 RANN_2.6.1
[149] spatstat.data_3.0-3 rstudioapi_0.15.0 cluster_2.1.6 janitor_2.2.0
[153] spatstat.utils_3.0-4 hms_1.1.3 fitdistrplus_1.1-11 munsell_0.5.0
[157] cowplot_1.1.1 colorspace_2.1-0 rlang_1.1.2 DelayedMatrixStats_1.24.0
[161] sparseMatrixStats_1.14.0 ipred_0.9-14 dotCall64_1.1-1 ggforce_0.4.1
[165] circlize_0.4.15 scuttle_1.12.0 mgcv_1.9-0 iterators_1.0.14
[169] abind_1.4-5 GOSemSim_2.28.0 treeio_1.26.0 Rhdf5lib_1.24.0
[173] bitops_1.0-7 promises_1.2.1 scatterpie_0.2.1 RSQLite_2.3.3
[177] qvalue_2.34.0 fgsea_1.28.0 DelayedArray_0.28.0 GO.db_3.18.0
[181] compiler_4.3.1 forcats_1.0.0 prettyunits_1.2.0 boot_1.3-28.1
[185] beachmat_2.18.0 listenv_0.9.0 edgeR_4.0.2 BiocSingular_1.18.0
[189] tensor_1.5 MASS_7.3-60 progress_1.2.2 spatstat.random_3.2-2
[193] R6_2.5.1 fastmap_1.1.1 fastmatch_1.1-4 vipor_0.4.5
[197] ROCR_1.0-11 rsvd_1.0.5 nnet_7.3-19 gtable_0.3.4
[201] KernSmooth_2.23-22 miniUI_0.1.1.1 deldir_2.0-2 htmltools_0.5.7
[205] bit64_4.0.5 spatstat.explore_3.2-5 lifecycle_1.0.4 ggprism_1.0.4
[209] nloptr_2.0.3 restfulr_0.0.15 vctrs_0.6.5 spatstat.geom_3.2-7
[213] snakecase_0.11.1 DOSE_3.28.1 scran_1.30.0 ggfun_0.1.3
[217] sp_2.1-2 future.apply_1.11.0 pracma_2.4.4 pillar_1.9.0
[221] prismatic_1.1.1 metapod_1.10.0 locfit_1.5-9.8 jsonlite_1.8.8
[225] GetoptLong_1.0.5
If I update SeuratObject to > version 5.0.0 which is where LayerData function is introduced, I get the same error as above when I run CellColorHeatmap()
No cell overlap between new meta data and Seurat object
And solution seem to be SeuratObject 4.1.4 or 4.1.3 but they don't work as I stated above.
Is this error handed? It still persists with the available data in the package. Can you suggest what is the best way to tackle this? The downgrade is not helping.
Unfortunately I had to go to the source code of many functions it wraps and change the problematic part myself to run it. The authors still need to look at this.
Thanks for responding. Can you share your solution if it is possible? The downgrade does not work and compatibility exists. Also there is no recommendation which version of SCP should be used if Seurat
and SeuratObject
is downgraded.
Unfortunately I had to go to the source code of many functions it wraps and change the problematic part myself to run it. The authors still need to look at this.
sorry to bother, could you please share ur solution about package compatibility? thank you very much! @ayyildizd
pancreas_sub <- RunKNNPredict( srt_query = pancreas_sub, srt_ref = panc8_rename, query_group = "SubCellType", ref_group = "celltype", return_full_distance_matrix = TRUE ) Use the HVF to calculate distance metric. Use 631 features to calculate distance. Detected query data type: log_normalized_counts Detected reference data type: log_normalized_counts Calculate similarity... Use 'raw' method to find neighbors. Predict cell type... 错误: No cell overlap between new meta data and Seurat object