Error while using LoadH5Seurat : "Error in sparseMatrix(i = x[["indices"]][] + 1, p = x[["indptr"]][], x = x[["data"]][], : 'p' must be a nondecreasing vector"

[ellenbouchard]

Hello,

I am trying to use the SeuratDisk tools to read in a .h5ad file and convert it into a Seurat object.

When I use 'Convert' to generate a .h5seurat file, it works, with the following warnings:

Warning message:
“Unknown file type: h5ad”
Warning message:
“'assay' not set, setting to 'RNA'”
Creating h5Seurat file for version 3.1.5.9900

When I then use 'LoadH5Seurat' function on the resulting .h5seurat file, however, I get the following warning and error:

Warning message in sparseMatrix(i = x[["indices"]][] + 1, p = x[["indptr"]][], x = x[["data"]][], :
“NAs introduced by coercion to integer range”
Error in sparseMatrix(i = x[["indices"]][] + 1, p = x[["indptr"]][], x = x[["data"]][], : 'p' must be a nondecreasing vector c(0, ...)
Traceback:

1. LoadH5Seurat("./human_cortex_restricted.h5seurat")
2. LoadH5Seurat.character("./human_cortex_restricted.h5seurat")
3. LoadH5Seurat(file = hfile, assays = assays, reductions = reductions, 
 .     graphs = graphs, neighbors = neighbors, images = images, 
 .     meta.data = meta.data, commands = commands, misc = misc, 
 .     tools = tools, verbose = verbose, ...)
4. LoadH5Seurat.h5Seurat(file = hfile, assays = assays, reductions = reductions, 
 .     graphs = graphs, neighbors = neighbors, images = images, 
 .     meta.data = meta.data, commands = commands, misc = misc, 
 .     tools = tools, verbose = verbose, ...)
5. as.Seurat(x = file, assays = assays, reductions = reductions, 
 .     graphs = graphs, neighbors = neighbors, images = images, 
 .     meta.data = meta.data, commands = commands, misc = misc, 
 .     tools = tools, verbose = verbose, ...)
6. as.Seurat.h5Seurat(x = file, assays = assays, reductions = reductions, 
 .     graphs = graphs, neighbors = neighbors, images = images, 
 .     meta.data = meta.data, commands = commands, misc = misc, 
 .     tools = tools, verbose = verbose, ...)
7. AssembleAssay(assay = assay, file = x, slots = assays[[assay]], 
 .     verbose = verbose)
8. as.matrix(x = assay.group[["data"]])
9. as.matrix.H5Group(x = assay.group[["data"]])
10. as.sparse(x = x, ...)
11. as.sparse.H5Group(x = x, ...)
12. sparseMatrix(i = x[["indices"]][] + 1, p = x[["indptr"]][], x = x[["data"]][], 
  .     dims = h5attr(x = x, which = "dims"))
13. stop("'p' must be a nondecreasing vector c(0, ...)")

The code I'm using is:

Convert("./human_cortex_restricted.h5ad", dest="h5seurat", overwrite=TRUE)
file_seurat<- LoadH5Seurat("./human_cortex_restricted.h5seurat")

Please note that I'm using Seurat V5 but have also tried with the following modifier that did not work:
options(Seurat.object.assay.version = "v3")

The data file I'm using is open source from here:
https://cloud.hiz-saarland.de/s/PirTrKafxwbeQz7

Session info is below:

R version 4.3.3 (2024-02-29)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/groups/AjamiLab/Bouchard/miniconda3/envs/newcondaenv1/lib/libopenblasp-r0.3.27.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       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       

time zone: America/Los_Angeles
tzcode source: system (glibc)

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

other attached packages:
 [1] renv_1.0.11           schard_0.0.1          sceasy_0.0.7         
 [4] anndata_0.7.5.6       SeuratDisk_0.0.0.9021 reticulate_1.38.0    
 [7] DoubletFinder_2.0.4   RColorBrewer_1.1-3    pheatmap_1.0.12      
[10] ggrepel_0.9.5         dplyr_1.1.4           harmony_1.2.0        
[13] Rcpp_1.0.13           glmGamPoi_1.14.3      ggplot2_3.5.1        
[16] gridExtra_2.3         Seurat_5.1.0          SeuratObject_5.0.2   
[19] sp_2.1-4             

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22            splines_4.3.3              
  [3] later_1.3.2                 pbdZMQ_0.3-11              
  [5] bitops_1.0-8                tibble_3.2.1               
  [7] polyclip_1.10-7             fastDummies_1.7.3          
  [9] lifecycle_1.0.4             rprojroot_2.0.4            
 [11] globals_0.16.3              processx_3.8.2             
 [13] lattice_0.22-6              hdf5r_1.3.11               
 [15] MASS_7.3-60                 magrittr_2.0.3             
 [17] plotly_4.10.4               remotes_2.4.2.1            
 [19] httpuv_1.6.15               sctransform_0.4.1          
 [21] spam_2.10-0                 sessioninfo_1.2.2          
 [23] pkgbuild_1.4.2              spatstat.sparse_3.1-0      
 [25] cowplot_1.1.3               pbapply_1.7-2              
 [27] abind_1.4-5                 pkgload_1.3.3              
 [29] zlibbioc_1.48.2             Rtsne_0.17                 
 [31] GenomicRanges_1.54.1        purrr_1.0.2                
 [33] BiocGenerics_0.48.1         RCurl_1.98-1.16            
 [35] rappdirs_0.3.3              GenomeInfoDbData_1.2.11    
 [37] IRanges_2.36.0              S4Vectors_0.40.2           
 [39] irlba_2.3.5.1               listenv_0.9.1              
 [41] spatstat.utils_3.0-5        goftest_1.2-3              
 [43] RSpectra_0.16-1             spatstat.random_3.2-3      
 [45] fitdistrplus_1.2-1          parallelly_1.38.0          
 [47] leiden_0.4.3.1              codetools_0.2-20           
 [49] DelayedArray_0.28.0         tidyselect_1.2.1           
 [51] matrixStats_1.3.0           stats4_4.3.3               
 [53] base64enc_0.1-3             spatstat.explore_3.2-6     
 [55] jsonlite_1.8.8              ellipsis_0.3.2             
 [57] progressr_0.14.0            ggridges_0.5.6             
 [59] survival_3.6-4              tools_4.3.3                
 [61] ica_1.0-3                   glue_1.7.0                 
 [63] SparseArray_1.2.4           here_1.0.1                 
 [65] usethis_2.2.2               MatrixGenerics_1.14.0      
 [67] GenomeInfoDb_1.38.8         IRdisplay_1.1              
 [69] withr_3.0.2                 fastmap_1.2.0              
 [71] rhdf5filters_1.14.1         fansi_1.0.6                
 [73] callr_3.7.3                 digest_0.6.36              
 [75] R6_2.5.1                    mime_0.12                  
 [77] colorspace_2.1-1            scattermore_1.2            
 [79] tensor_1.5                  spatstat.data_3.1-2        
 [81] utf8_1.2.4                  tidyr_1.3.1                
 [83] generics_0.1.3              data.table_1.15.4          
 [85] prettyunits_1.2.0           httr_1.4.7                 
 [87] htmlwidgets_1.6.4           S4Arrays_1.2.1             
 [89] uwot_0.2.2                  pkgconfig_2.0.3            
 [91] gtable_0.3.5                lmtest_0.9-40              
 [93] XVector_0.42.0              htmltools_0.5.8.1          
 [95] profvis_0.3.8               dotCall64_1.1-1            
 [97] scales_1.3.0                Biobase_2.62.0             
 [99] png_0.1-8                   reshape2_1.4.4             
[101] uuid_1.2-1                  curl_5.2.1                 
[103] nlme_3.1-165                rhdf5_2.46.1               
[105] repr_1.1.7                  zoo_1.8-12                 
[107] cachem_1.1.0                stringr_1.5.1              
[109] KernSmooth_2.23-24          parallel_4.3.3             
[111] miniUI_0.1.1.1              desc_1.4.2                 
[113] pillar_1.9.0                grid_4.3.3                 
[115] vctrs_0.6.5                 RANN_2.6.1                 
[117] urlchecker_1.0.1            promises_1.3.0             
[119] xtable_1.8-4                cluster_2.1.6              
[121] evaluate_0.24.0             cli_3.6.3                  
[123] compiler_4.3.3              rlang_1.1.4                
[125] crayon_1.5.3                future.apply_1.11.2        
[127] ps_1.7.5                    getPass_0.2-4              
[129] plyr_1.8.9                  fs_1.6.4                   
[131] stringi_1.8.4               viridisLite_0.4.2          
[133] deldir_2.0-4                assertthat_0.2.1           
[135] munsell_0.5.1               lazyeval_0.2.2             
[137] devtools_2.4.5              spatstat.geom_3.2-9        
[139] Matrix_1.6-5                IRkernel_1.3.2             
[141] RcppHNSW_0.6.0              patchwork_1.2.0            
[143] bit64_4.0.5                 future_1.34.0              
[145] Rhdf5lib_1.24.2             shiny_1.9.1                
[147] SummarizedExperiment_1.32.0 ROCR_1.0-11                
[149] igraph_2.0.3                memoise_2.0.1              
[151] bit_4.0.5          
```

[martina-morchio]

Hi!

I'm having the same issue converting a large h5ad file to seurat, I can obtain the h5seurat file but then it doesn't load with LoadH5Seurat and shows the same output. Did you manage to sort it in the end?
I'm worried there might be an issue with the file as I've tried with other methods (package anndataR) where I can load the file as anndata but the count matrix is empty...

[ellenbouchard]

@martina-morchio

I never was able to get the LoadH5Seurat method working, but I did get anndata working.
Here's a toy example:

data_h <- read_h5ad("./path/to/my/directory/data.h5ad")
data_s <- CreateSeuratObject(counts = t(as.matrix(data_h$X)),
meta.data = data_h$obs,
min.features = 500,
min.cells = 30)