Dear everyone,

I'm new to bioinformatics and need to reduce two scRNAseq-Seurat objects to their shared genes and convert the files to the h5ad format for further analysis.

My approach is:

SaveH5Seurat(dat5, filename = "data.h5Seurat")
Convert("data.h5Seurat", dest = "h5ad")

Since the code worked fine for one of the two files I have no clue what the problem is. The second (already pre-processed) exprMatrix.tsv.gz originates from here: https://cells-test.gi.ucsc.edu/?ds=early-brain The Convert()-command is producing an error for the second file. The error message is:

Validating h5Seurat file Adding data from RNA as X Adding counts from
RNA as raw Transfering meta.data to obs Error in self$flush() :
HDF5-API Errors:
     error #000: H5F.c in H5Fflush(): line 677: unable to flush file
         class: HDF5
         major: File accessibility
         minor: Unable to flush data from cache

    error #001: H5VLcallback.c in H5VL_file_specific(): line 3769: file specific failed
        class: HDF5
        major: Virtual Object Layer
        minor: Can't operate on object

    error #002: H5VLcallback.c in H5VL__file_specific(): line 3699: file specific failed
        class: HDF5
        major: Virtual Object Layer
        minor: Can't operate on object

    error #003: H5VLnative_file.c in H5VL__native_file_specific(): line 316: unable to flush mounted file hierarchy
        class: HDF5
        major: File accessibility
        minor: Unable to flush data from cache

    error #004: H5Fmount.c in H5F_flush_mounts(): line 692: unable to flush mounted file hierarchy
        class: HDF5
        major: File accessibility
        minor: Unable to flush data from cache

    error #005:

Thank you so much in advance for any kind of help!

Also, here's my session info:

R version 4.1.2 (2021-11-01)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/rstudio-server/.conda/envs/r4/lib/libopenblasp-r0.3.18.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               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    LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] patchwork_1.1.1             scater_1.22.0               ggplot2_3.3.6               scuttle_1.4.0
 [5] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0 Biobase_2.54.0              GenomicRanges_1.46.1
 [9] GenomeInfoDb_1.30.1         IRanges_2.28.0              S4Vectors_0.32.4            BiocGenerics_0.40.0
[13] MatrixGenerics_1.6.0        matrixStats_0.62.0          data.table_1.14.2           sp_1.5-0
[17] SeuratObject_4.1.0          Seurat_4.1.1                SeuratData_0.2.2            SeuratDisk_0.0.0.9020

loaded via a namespace (and not attached):
  [1] plyr_1.8.7                igraph_1.3.4              lazyeval_0.2.2            splines_4.1.2
  [5] BiocParallel_1.28.3       listenv_0.8.0             scattermore_0.8           digest_0.6.29
  [9] htmltools_0.5.3           viridis_0.6.2             fansi_1.0.3               magrittr_2.0.3
 [13] ScaledMatrix_1.2.0        tensor_1.5                cluster_2.1.2             ROCR_1.0-11
 [17] globals_0.16.0            spatstat.sparse_2.1-1     colorspace_2.0-3          rappdirs_0.3.3
 [21] ggrepel_0.9.1             dplyr_1.0.9               crayon_1.5.1              RCurl_1.98-1.8
 [25] jsonlite_1.8.0            progressr_0.10.1          spatstat.data_2.2-0       survival_3.2-13
 [29] zoo_1.8-10                glue_1.6.2                polyclip_1.10-0           gtable_0.3.0
 [33] zlibbioc_1.40.0           XVector_0.34.0            leiden_0.4.2              DelayedArray_0.20.0
 [37] BiocSingular_1.10.0       future.apply_1.9.0        abind_1.4-5               scales_1.2.0
 [41] spatstat.random_2.2-0     miniUI_0.1.1.1            Rcpp_1.0.9                viridisLite_0.4.0
 [45] xtable_1.8-4              reticulate_1.25           spatstat.core_2.4-4       bit_4.0.4
 [49] rsvd_1.0.5                htmlwidgets_1.5.4         httr_1.4.3                RColorBrewer_1.1-3
 [53] ellipsis_0.3.2            ica_1.0-3                 pkgconfig_2.0.3           uwot_0.1.11
 [57] deldir_1.0-6              utf8_1.2.2                tidyselect_1.1.2          rlang_1.0.4
 [61] reshape2_1.4.4            later_1.3.0               munsell_0.5.0             tools_4.1.2
 [65] cli_3.3.0                 generics_0.1.3            ggridges_0.5.3            stringr_1.4.0
 [69] fastmap_1.1.0             goftest_1.2-3             bit64_4.0.5               fitdistrplus_1.1-8
 [73] purrr_0.3.4               RANN_2.6.1                pbapply_1.5-0             future_1.27.0
 [77] nlme_3.1-155              sparseMatrixStats_1.6.0   mime_0.12                 hdf5r_1.3.5
 [81] compiler_4.1.2            rstudioapi_0.13           beeswarm_0.4.0            plotly_4.10.0
 [85] png_0.1-7                 spatstat.utils_2.3-1      tibble_3.1.8              stringi_1.7.8
 [89] rgeos_0.5-9               lattice_0.20-45           Matrix_1.4-0              vctrs_0.4.1
 [93] pillar_1.8.0              lifecycle_1.0.1           spatstat.geom_2.4-0       lmtest_0.9-40
 [97] RcppAnnoy_0.0.19          BiocNeighbors_1.12.0      cowplot_1.1.1             bitops_1.0-7
[101] irlba_2.3.5               httpuv_1.6.5              R6_2.5.1                  promises_1.2.0.1
[105] KernSmooth_2.23-20        gridExtra_2.3             vipor_0.4.5               parallelly_1.32.1
[109] codetools_0.2-18          MASS_7.3-55               withr_2.5.0               sctransform_0.3.3
[113] GenomeInfoDbData_1.2.7    mgcv_1.8-38               parallel_4.1.2            grid_4.1.2
[117] rpart_4.1.16              beachmat_2.10.0           tidyr_1.2.0               DelayedMatrixStats_1.16.0
[121] Rtsne_0.16                shiny_1.7.2               ggbeeswarm_0.6.0