SPATAData: UKF313T download lacks spatial annotations present in SPATA2 example object #11
Description
Hello SPATAData maintainers,
I think there is an inconsistency for the sample UKF313T: the SPATA2 inbuilt example object contains spatial annotations, but the SPATAData-downloaded object for the same sample does not.
What I see
When I load the inbuilt SPATA2 example object, spatial annotation IDs are present:
library(SPATA2)
library(tidyverse)
obj <- loadExampleObject("UKF313T", process = TRUE, meta = TRUE)
getSpatAnnIds(obj)
# [1] "necrotic_area" "necrotic_center" "necrotic_edge" "necrotic_edge2" "necrotic_edge2_transgr"However, when I download the same sample via SPATAData and load it from the saved RDS file, the object contains no spatial annotations:
library(SPATA2)
library(SPATAData)
library(tidyverse)
obj <- SPATAData::downloadSpataObject(sample_name = "UKF313T", file = "./UKF313T.RDS")
obj <- loadSpataObject(directory_spata = "./UKF313T.RDS")
getSpatAnnIds(obj)
# character(0)Expected vs actual
- Expected: The downloaded UKF313T object includes the same spatial annotations (or at least the necrosis-related annotations) that are present in the SPATA2 example UKF313T object.
- Actual:
getSpatAnnIds()returnscharacter(0)for the SPATAData-downloaded UKF313T object.
It is somewhat confusing that the SPATA2 tutorial here https://themilolab.github.io/SPATA2/articles/using-spatial-annotations.html demonstrates spatial annotations for UKF313T as part of the example workflow, while these annotations are not available when loading the same sample via the standard SPATAData download and loadSpataObject().
Question
Is this intended (i.e., the SPATA2 example object ships with extra tutorial annotations that are not distributed via SPATAData), or is UKF313T missing annotation content in SPATAData?
Best regards,
Daniel
> sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=fi_FI.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=fi_FI.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=fi_FI.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=fi_FI.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Helsinki
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.5 forcats_1.0.1 stringr_1.6.0 dplyr_1.2.0
[5] purrr_1.2.1 readr_2.1.6 tidyr_1.3.2 tibble_3.3.1
[9] ggplot2_4.0.2 tidyverse_2.0.0 SPATAData_1.0.0 SPATA2_3.1.4
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.23 splines_4.4.1
[3] later_1.4.5 bitops_1.0-9
[5] polyclip_1.10-7 fastDummies_1.7.5
[7] lifecycle_1.0.5 tcltk_4.4.1
[9] globals_0.19.0 lattice_0.22-9
[11] MASS_7.3-65 magrittr_2.0.4
[13] openxlsx_4.2.8.1 plotly_4.12.0
[15] rmarkdown_2.30 httpuv_1.6.16
[17] otel_0.2.0 Seurat_5.4.0
[19] sctransform_0.4.3 zip_2.3.3
[21] spam_2.11-3 sp_2.2-0
[23] spatstat.sparse_3.1-0 reticulate_1.44.1
[25] cowplot_1.2.0 pbapply_1.7-4
[27] RColorBrewer_1.1-3 abind_1.4-8
[29] zlibbioc_1.52.0 Rtsne_0.17
[31] GenomicRanges_1.58.0 msigdbr_25.1.1
[33] BiocGenerics_0.52.0 RCurl_1.98-1.17
[35] tweenr_2.0.3 reactable_0.4.5
[37] GenomeInfoDbData_1.2.13 IRanges_2.40.1
[39] S4Vectors_0.44.0 ggrepel_0.9.6
[41] irlba_2.3.7 listenv_0.10.0
[43] spatstat.utils_3.2-1 units_1.0-0
[45] goftest_1.2-3 RSpectra_0.16-2
[47] spatstat.random_3.4-4 fitdistrplus_1.2-6
[49] parallelly_1.46.1 svglite_2.2.2
[51] codetools_0.2-20 DelayedArray_0.32.0
[53] ggforce_0.5.0 xml2_1.5.2
[55] tidyselect_1.2.1 UCSC.utils_1.2.0
[57] farver_2.1.2 matrixStats_1.5.0
[59] stats4_4.4.1 spatstat.explore_3.7-0
[61] jsonlite_2.0.0 progressr_0.18.0
[63] ggridges_0.5.7 survival_3.8-6
[65] systemfonts_1.3.1 dbscan_1.2.4
[67] tools_4.4.1 ica_1.0-3
[69] Rcpp_1.1.1 glue_1.8.0
[71] gridExtra_2.3 SparseArray_1.6.2
[73] xfun_0.56 MatrixGenerics_1.18.1
[75] GenomeInfoDb_1.42.3 EBImage_4.48.0
[77] withr_3.0.2 fastmap_1.2.0
[79] digest_0.6.39 timechange_0.4.0
[81] R6_2.6.1 mime_0.13
[83] textshaping_1.0.4 scattermore_1.2
[85] tensor_1.5.1 jpeg_0.1-11
[87] spatstat.data_3.1-9 generics_0.1.4
[89] data.table_1.18.2.1 httr_1.4.7
[91] htmlwidgets_1.6.4 S4Arrays_1.6.0
[93] uwot_0.2.4 pkgconfig_2.0.3
[95] gtable_0.3.6 lmtest_0.9-40
[97] S7_0.2.1 SingleCellExperiment_1.28.1
[99] XVector_0.46.0 htmltools_0.5.9
[101] dotCall64_1.2 fftwtools_0.9-11
[103] SeuratObject_5.3.0 scales_1.4.0
[105] kableExtra_1.4.0 Biobase_2.66.0
[107] png_0.1-8 spatstat.univar_3.1-6
[109] knitr_1.51 rstudioapi_0.18.0
[111] tzdb_0.5.0 reshape2_1.4.5
[113] visNetwork_2.1.4 nlme_3.1-168
[115] curl_7.0.0 zoo_1.8-15
[117] KernSmooth_2.23-26 parallel_4.4.1
[119] miniUI_0.1.2 concaveman_1.2.0
[121] pillar_1.11.1 grid_4.4.1
[123] vctrs_0.7.1 RANN_2.6.2
[125] promises_1.5.0 xtable_1.8-4
[127] cluster_2.1.8.2 evaluate_1.0.5
[129] cli_3.6.5 locfit_1.5-9.12
[131] compiler_4.4.1 rlang_1.1.7
[133] crayon_1.5.3 hypeR_2.8.2
[135] future.apply_1.20.1 confuns_1.0.3
[137] plyr_1.8.9 stringi_1.8.7
[139] viridisLite_0.4.3 deldir_2.0-4
[141] babelgene_22.9 assertthat_0.2.1
[143] lazyeval_0.2.2 tiff_0.1-12
[145] spatstat.geom_3.7-0 V8_8.0.1
[147] Matrix_1.7-4 RcppHNSW_0.6.0
[149] hms_1.1.4 patchwork_1.3.2
[151] future_1.69.0 shiny_1.12.1
[153] SummarizedExperiment_1.36.0 ROCR_1.0-12
[155] igraph_2.2.1