I have been trying to convert my Giotto object to Seurat using the function giottoToSeuratV5(). However I get the following error:
fov_s <- giottoToSeuratV5(fov_join)
Error in (function (cl, name, valueClass) :
‘meta.data’ is not a slot in class “Assay”
The function works perfectly for me while using the example for this function in the documentation
g <- GiottoData::loadGiottoMini("visium")
read Giotto object
read Giotto feature information
read Giotto spatial information
3.1 read Giotto spatial shape information
cell_spatInfo_spatVector.shp
cell
3.2 read Giotto spatial centroid information
cell
3.3 read Giotto spatial overlap information
No overlaps were found, overlap loading will be
skipped
read Giotto image information
no external python path was provided,
but a giotto python environment was found and
will be used
Warning message:
This giotto object was created in a newer version of
GiottoClass (v0.3.2)
giottoToSeuratV5(g)
An object of class Seurat
634 features across 624 samples within 1 assay
Active assay: rna (634 features, 0 variable features)
3 layers present: counts, data, scale.data
6 dimensional reductions calculated: pca, custom_pca, umap, custom_umap, tsne, spatial
2 images present: alignment, image
Warning messages:
1: Keys should be one or more alphanumeric characters followed by an underscore, setting key from custompca to custompca_
2: Keys should be one or more alphanumeric characters followed by an underscore, setting key from customumap to customumap_
However while trying the same with the example cosmx dataset I get the same error:
g <- GiottoData::loadGiottoMini("cosmx")
read Giotto object
read Giotto feature information
/home/guruiz/R/x86_64-pc-linux-gnu-library/4.4/GiottoData/Mini_datasets/CosMx/CosMxObject/Features/neg_probe_feature_spatVector.shp
neg_probe
/home/guruiz/R/x86_64-pc-linux-gnu-library/4.4/GiottoData/Mini_datasets/CosMx/CosMxObject/Features/rna_feature_spatVector.shp
rna
read Giotto spatial information
3.1 read Giotto spatial shape information
cell_spatInfo_spatVector.shp
cell
3.2 read Giotto spatial centroid information
cell
3.3 read Giotto spatial overlap information
neg_probe_cell_spatInfo_spatVectorOverlaps.shp
rna_cell_spatInfo_spatVectorOverlaps.shp
poly_ID feat_ID feat_ID_uniq
cell and neg_probe
poly_ID feat_ID feat_ID_uniq
cell and rna
read Giotto image information
no external python path was provided,
but a giotto python environment was found and
will be used
giottoToSeuratV5(g)
Error in (function (cl, name, valueClass) :
‘meta.data’ is not a slot in class “Assay”
I guess then it is a problem of the function itself with CosMX data rather than a problem with my data.
I have been trying to convert my Giotto object to Seurat using the function giottoToSeuratV5(). However I get the following error:
fov_s <- giottoToSeuratV5(fov_join)
The function works perfectly for me while using the example for this function in the documentation
g <- GiottoData::loadGiottoMini("visium")
giottoToSeuratV5(g)
However while trying the same with the example cosmx dataset I get the same error:
g <- GiottoData::loadGiottoMini("cosmx")
giottoToSeuratV5(g)
I guess then it is a problem of the function itself with CosMX data rather than a problem with my data.
Could you please provide any solution to this?
Thank you very much.
sessionInfo()
> sessionInfo() R version 4.4.1 (2024-06-14) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.1 LTS Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C time zone: Europe/Zurich tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] sp_2.1-4 ggplot2_3.5.1 GiottoUtils_0.1.8 Giotto_4.0.8 [5] GiottoClass_0.3.1 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 jsonlite_1.8.8 magrittr_2.0.3 [4] spatstat.utils_3.0-5 magick_2.8.3 farver_2.1.2 [7] zlibbioc_1.50.0 vctrs_0.6.5 ROCR_1.0-11 [10] spatstat.explore_3.2-7 terra_1.7-78 htmltools_0.5.8.1 [13] S4Arrays_1.4.1 SparseArray_1.4.8 sctransform_0.4.1 [16] parallelly_1.37.1 KernSmooth_2.23-20 htmlwidgets_1.6.4 [19] ica_1.0-3 plyr_1.8.9 plotly_4.10.4 [22] zoo_1.8-12 igraph_2.0.3 mime_0.12 [25] lifecycle_1.0.4 pkgconfig_2.0.3 rsvd_1.0.5 [28] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0 [31] magic_1.6-1 MatrixGenerics_1.16.0 fitdistrplus_1.2-1 [34] future_1.33.2 shiny_1.8.1.1 digest_0.6.35 [37] colorspace_2.1-0 patchwork_1.2.0 S4Vectors_0.42.0 [40] Seurat_5.1.0 tensor_1.5 RSpectra_0.16-1 [43] irlba_2.3.5.1 beachmat_2.20.0 labeling_0.4.3 [46] progressr_0.14.0 spatstat.sparse_3.1-0 fansi_1.0.6 [49] polyclip_1.10-6 httr_1.4.7 abind_1.4-5 [52] compiler_4.4.1 withr_3.0.0 backports_1.5.0 [55] BiocParallel_1.38.0 fastDummies_1.7.3 MASS_7.3-55 [58] rappdirs_0.3.3 DelayedArray_0.30.1 gtools_3.9.5 [61] GiottoVisuals_0.2.3 tools_4.4.1 lmtest_0.9-40 [64] httpuv_1.6.15 future.apply_1.11.2 goftest_1.2-3 [67] glue_1.7.0 dbscan_1.1-12 nlme_3.1-155 [70] promises_1.3.0 grid_4.4.1 checkmate_2.3.1 [73] Rtsne_0.17 cluster_2.1.2 reshape2_1.4.4 [76] generics_0.1.3 spatstat.data_3.1-2 gtable_0.3.5 [79] tidyr_1.3.1 data.table_1.15.4 BiocSingular_1.20.0 [82] ScaledMatrix_1.12.0 utf8_1.2.4 XVector_0.44.0 [85] spatstat.geom_3.2-9 BiocGenerics_0.50.0 RcppAnnoy_0.0.22 [88] ggrepel_0.9.5 RANN_2.6.1 pillar_1.9.0 [91] stringr_1.5.1 spam_2.10-0 RcppHNSW_0.5.0 [94] later_1.3.2 splines_4.4.1 dplyr_1.1.4 [97] lattice_0.20-45 survival_3.2-13 deldir_2.0-4 [100] tidyselect_1.2.1 pbapply_1.7-2 miniUI_0.1.1.1 [103] gridExtra_2.3 IRanges_2.38.0 scattermore_1.2 [106] stats4_4.4.1 Biobase_2.62.0 matrixStats_1.3.0 [109] stringi_1.8.4 lazyeval_0.2.2 codetools_0.2-18 [112] GiottoData_0.2.13 tibble_3.2.1 colorRamp2_0.1.0 [115] cli_3.6.2 uwot_0.2.2 geometry_0.4.7 [118] xtable_1.8-4 reticulate_1.38.0 munsell_0.5.1 [121] Rcpp_1.0.12 spatstat.random_3.2-3 globals_0.16.3 [124] png_0.1-8 parallel_4.4.1 dotCall64_1.1-1 [127] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0 [130] ggridges_0.5.6 SeuratObject_5.0.2 leiden_0.4.3.1 [133] purrr_1.0.2 crayon_1.5.2 rlang_1.1.4 [136] cowplot_1.1.3