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{anndataR} allows users to work with .h5ad files, access various slots in the datasets and convert these files to SingleCellExperiment objects and SeuratObjects, and vice versa.

Check out ?anndataR for a full list of the functions provided by this package.

Installation

Install using:

if (!require("pak", quietly = TRUE)) {
  install.packages("pak")
}
pak::pak("scverse/anndataR")

Usage

Here’s a quick example of how to use {anndataR}.

First, we fetch an example .h5ad file included in the package:

library(anndataR)

h5ad_path <- system.file("extdata", "example.h5ad", package = "anndataR")

Read an h5ad file in memory:

adata <- read_h5ad(h5ad_path)

Read an h5ad file on disk:

adata <- read_h5ad(h5ad_path, as = "HDF5AnnData")

View structure:

adata
#> AnnData object with n_obs × n_vars = 50 × 100
#>     obs: 'Float', 'FloatNA', 'Int', 'IntNA', 'Bool', 'BoolNA', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'leiden'
#>     var: 'String', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
#>     uns: 'Bool', 'BoolNA', 'Category', 'DataFrameEmpty', 'Int', 'IntNA', 'IntScalar', 'Sparse1D', 'String', 'String2D', 'StringScalar', 'hvg', 'leiden', 'log1p', 'neighbors', 'pca', 'rank_genes_groups', 'umap'
#>     obsm: 'X_pca', 'X_umap'
#>     varm: 'PCs'
#>     layers: 'counts', 'csc_counts', 'dense_X', 'dense_counts'
#>     obsp: 'connectivities', 'distances'

Access AnnData slots:

dim(adata$X)
#> [1]  50 100
adata$obs[1:5, 1:6]
#>         Float FloatNA Int IntNA  Bool BoolNA
#> Cell000 42.42     NaN   0    NA FALSE  FALSE
#> Cell001 42.42   42.42   1    42  TRUE     NA
#> Cell002 42.42   42.42   2    42  TRUE   TRUE
#> Cell003 42.42   42.42   3    42  TRUE   TRUE
#> Cell004 42.42   42.42   4    42  TRUE   TRUE
adata$var[1:5, 1:6]
#>          String n_cells_by_counts mean_counts log1p_mean_counts
#> Gene000 String0                44        1.94          1.078410
#> Gene001 String1                42        2.04          1.111858
#> Gene002 String2                43        2.12          1.137833
#> Gene003 String3                41        1.72          1.000632
#> Gene004 String4                42        2.06          1.118415
#>         pct_dropout_by_counts total_counts
#> Gene000                    12           97
#> Gene001                    16          102
#> Gene002                    14          106
#> Gene003                    18           86
#> Gene004                    16          103

Interoperability

Convert the AnnData object to a SingleCellExperiment object:

sce <- adata$as_SingleCellExperiment()
sce
#> class: SingleCellExperiment 
#> dim: 100 50 
#> metadata(18): Bool BoolNA ... rank_genes_groups umap
#> assays(5): data counts csc_counts dense_X dense_counts
#> rownames(100): Gene000 Gene001 ... Gene098 Gene099
#> rowData names(11): String n_cells_by_counts ... dispersions
#>   dispersions_norm
#> colnames(50): Cell000 Cell001 ... Cell048 Cell049
#> colData names(11): Float FloatNA ... log1p_total_counts leiden
#> reducedDimNames(2): X_pca X_umap
#> mainExpName: NULL
#> altExpNames(0):

Convert the AnnData object to a Seurat object:

obj <- adata$as_Seurat()
obj
#> An object of class Seurat 
#> 100 features across 50 samples within 1 assay 
#> Active assay: RNA (100 features, 0 variable features)
#>  5 layers present: counts, data, csc_counts, dense_X, dense_counts
#>  2 dimensional reductions calculated: X_pca, X_umap

Manually create an object

adata <- AnnData(
  X = matrix(rnorm(100), nrow = 10),
  obs = data.frame(
    cell_type = factor(rep(c("A", "B"), each = 5))
  ),
  var = data.frame(
    gene_name = paste0("gene_", 1:10)
  )
)

adata
#> AnnData object with n_obs × n_vars = 10 × 10
#>     obs: 'cell_type'
#>     var: 'gene_name'

Session info

sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 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=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: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] anndataR_0.99.0             SingleCellExperiment_1.30.1
#>  [3] SummarizedExperiment_1.38.1 Biobase_2.68.0             
#>  [5] GenomicRanges_1.60.0        GenomeInfoDb_1.44.0        
#>  [7] IRanges_2.42.0              S4Vectors_0.46.0           
#>  [9] BiocGenerics_0.54.0         generics_0.1.4             
#> [11] MatrixGenerics_1.20.0       matrixStats_1.5.0          
#> [13] SeuratObject_5.1.0          sp_2.2-0                   
#> [15] BiocStyle_2.36.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3      jsonlite_2.0.0          magrittr_2.0.3         
#>   [4] spatstat.utils_3.1-3    farver_2.1.2            rmarkdown_2.29         
#>   [7] fs_1.6.6                ragg_1.4.0              vctrs_0.6.5            
#>  [10] ROCR_1.0-11             spatstat.explore_3.4-2  htmltools_0.5.8.1      
#>  [13] S4Arrays_1.8.0          SparseArray_1.8.0       sass_0.4.10            
#>  [16] sctransform_0.4.2       parallelly_1.44.0       KernSmooth_2.23-26     
#>  [19] bslib_0.9.0             htmlwidgets_1.6.4       desc_1.4.3             
#>  [22] ica_1.0-3               plyr_1.8.9              plotly_4.10.4          
#>  [25] zoo_1.8-14              cachem_1.1.0            igraph_2.1.4           
#>  [28] mime_0.13               lifecycle_1.0.4         pkgconfig_2.0.3        
#>  [31] Matrix_1.7-3            R6_2.6.1                fastmap_1.2.0          
#>  [34] GenomeInfoDbData_1.2.14 fitdistrplus_1.2-2      future_1.49.0          
#>  [37] shiny_1.10.0            digest_0.6.37           patchwork_1.3.0        
#>  [40] tensor_1.5              Seurat_5.3.0            RSpectra_0.16-2        
#>  [43] irlba_2.3.5.1           textshaping_1.0.1       progressr_0.15.1       
#>  [46] spatstat.sparse_3.1-0   polyclip_1.10-7         httr_1.4.7             
#>  [49] abind_1.4-8             compiler_4.5.0          bit64_4.6.0-1          
#>  [52] fastDummies_1.7.5       MASS_7.3-65             DelayedArray_0.34.1    
#>  [55] tools_4.5.0             lmtest_0.9-40           httpuv_1.6.16          
#>  [58] future.apply_1.11.3     goftest_1.2-3           glue_1.8.0             
#>  [61] nlme_3.1-168            promises_1.3.2          grid_4.5.0             
#>  [64] Rtsne_0.17              cluster_2.1.8.1         reshape2_1.4.4         
#>  [67] hdf5r_1.3.12            spatstat.data_3.1-6     gtable_0.3.6           
#>  [70] tidyr_1.3.1             data.table_1.17.0       XVector_0.48.0         
#>  [73] spatstat.geom_3.3-6     RcppAnnoy_0.0.22        ggrepel_0.9.6          
#>  [76] RANN_2.6.2              pillar_1.10.2           stringr_1.5.1          
#>  [79] spam_2.11-1             RcppHNSW_0.6.0          later_1.4.2            
#>  [82] splines_4.5.0           dplyr_1.1.4             lattice_0.22-6         
#>  [85] deldir_2.0-4            survival_3.8-3          bit_4.6.0              
#>  [88] tidyselect_1.2.1        miniUI_0.1.2            pbapply_1.7-2          
#>  [91] knitr_1.50              gridExtra_2.3           bookdown_0.43          
#>  [94] scattermore_1.2         xfun_0.52               stringi_1.8.7          
#>  [97] UCSC.utils_1.4.0        lazyeval_0.2.2          yaml_2.3.10            
#> [100] evaluate_1.0.3          codetools_0.2-20        tibble_3.2.1           
#> [103] BiocManager_1.30.25     cli_3.6.5               uwot_0.2.3             
#> [106] xtable_1.8-4            reticulate_1.42.0       systemfonts_1.2.3      
#> [109] jquerylib_0.1.4         Rcpp_1.0.14             spatstat.random_3.3-3  
#> [112] globals_0.18.0          png_0.1-8               spatstat.univar_3.1-3  
#> [115] parallel_4.5.0          pkgdown_2.1.2           ggplot2_3.5.2          
#> [118] dotCall64_1.2           listenv_0.9.1           viridisLite_0.4.2      
#> [121] scales_1.4.0            ggridges_0.5.6          purrr_1.0.4            
#> [124] crayon_1.5.3            rlang_1.1.6             cowplot_1.1.3