Export all reductions in h5ad

[bellenger-l]

Hello !

I'm currently using your package in order to export my seurat object in h5ad format to work in python.

Here is my code and verbose :

> SaveH5Seurat(object = data_test, 
+              filename = paste0(output_name, ".h5seurat"), 
+              assay = "RNA")
Creating h5Seurat file for version 3.1.5.9900
Adding counts for RNA
Adding data for RNA
No variable features found for RNA
No feature-level metadata found for RNA
Adding data for integrated
Adding scale.data for integrated
Adding variable features for integrated
No feature-level metadata found for integrated
Adding cell embeddings for pca
Adding loadings for pca
Adding projected loadings for pca
Adding standard deviations for pca
Adding JackStraw information for pca
Adding cell embeddings for umap
No loadings for umap
No projected loadings for umap
No standard deviations for umap
No JackStraw data for umap
> Convert(source = paste0(output_name, ".h5seurat"),
+         dest = "h5ad",
+         assay = "RNA")
Validating h5Seurat file
Adding data from RNA as X
Adding counts from RNA as raw
Transfering meta.data to obs
Adding dimensional reduction information for umap (global)

I only retrieve the "umap" reduction. I am working on several umap because I want to evaluate the importance of K in the neighbouring so I have one seurat object with several reduction called : "umapk...". How can I save my Seurat object with all the reductions ?

Is it possible ?

Here is my sessionInfo :

R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS/LAPACK: /opt/rstudio-server_conda/conda/envs/rstudio-server_4.1.0/lib/libopenblasp-r0.3.15.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] tools     parallel  stats     graphics  grDevices utils     datasets  methods  
[9] base     

other attached packages:
[1] SeuratDisk_0.0.0.9019 stringr_1.5.0         ggplot2_3.4.2        
[4] Seurat_4.3.0          SeuratObject_4.1.3    sp_1.6-0             

loaded via a namespace (and not attached):
  [1] Rtsne_0.16             colorspace_2.1-0       deldir_1.0-6          
  [4] ellipsis_0.3.2         ggridges_0.5.4         rstudioapi_0.14       
  [7] spatstat.data_3.0-1    farver_2.1.1           leiden_0.4.3          
 [10] listenv_0.9.0          bit64_4.0.5            ggrepel_0.9.3         
 [13] fansi_1.0.4            codetools_0.2-19       splines_4.1.0         
 [16] knitr_1.42             polyclip_1.10-4        jsonlite_1.8.4        
 [19] ica_1.0-3              cluster_2.1.4          png_0.1-8             
 [22] uwot_0.1.14            shiny_1.7.4            sctransform_0.3.5     
 [25] spatstat.sparse_3.0-1  compiler_4.1.0         httr_1.4.5            
 [28] Matrix_1.5-4           fastmap_1.1.1          lazyeval_0.2.2        
 [31] cli_3.6.1              later_1.3.0            htmltools_0.5.5       
 [34] igraph_1.4.2           gtable_0.3.3           glue_1.6.2            
 [37] RANN_2.6.1             reshape2_1.4.4         dplyr_1.1.2           
 [40] Rcpp_1.0.10            scattermore_0.8        vctrs_0.6.2           
 [43] spatstat.explore_3.1-0 nlme_3.1-162           progressr_0.13.0      
 [46] lmtest_0.9-40          spatstat.random_3.1-4  xfun_0.39             
 [49] globals_0.16.2         mime_0.12              miniUI_0.1.1.1        
 [52] lifecycle_1.0.3        irlba_2.3.5.1          goftest_1.2-3         
 [55] future_1.32.0          MASS_7.3-58.1          zoo_1.8-12            
 [58] scales_1.2.1           promises_1.2.0.1       spatstat.utils_3.0-2  
 [61] RColorBrewer_1.1-3     yaml_2.3.7             reticulate_1.28       
 [64] pbapply_1.7-0          gridExtra_2.3          stringi_1.7.12        
 [67] rlang_1.1.0            pkgconfig_2.0.3        matrixStats_0.63.0    
 [70] evaluate_0.20          lattice_0.21-8         ROCR_1.0-11           
 [73] purrr_1.0.1            tensor_1.5             labeling_0.4.2        
 [76] patchwork_1.1.2        htmlwidgets_1.6.2      bit_4.0.5             
 [79] cowplot_1.1.1          tidyselect_1.2.0       parallelly_1.35.0     
 [82] RcppAnnoy_0.0.20       plyr_1.8.8             magrittr_2.0.3        
 [85] R6_2.5.1               generics_0.1.3         DBI_1.1.3             
 [88] pillar_1.9.0           withr_2.5.0            fitdistrplus_1.1-8    
 [91] survival_3.3-1         abind_1.4-5            tibble_3.2.1          
 [94] future.apply_1.10.0    hdf5r_1.3.5            crayon_1.5.2          
 [97] KernSmooth_2.23-20     utf8_1.2.3             spatstat.geom_3.1-0   
[100] plotly_4.10.1          rmarkdown_2.21         grid_4.1.0            
[103] data.table_1.14.8      digest_0.6.31          xtable_1.8-4          
[106] tidyr_1.3.0            httpuv_1.6.9           munsell_0.5.0         
[109] viridisLite_0.4.1    

Thanks for your time Best Lea