Using anndataR
Robrecht Cannoodt
Luke Zappia
Martin Morgan
Louise Deconinck
Source:vignettes/anndataR.Rmd
anndataR.Rmd{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, to = "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$var[1:5, 1:6]
#> String n_cells_by_counts mean_counts log1p_mean_counts
#> Gene000 String0 44 1.94 1.078410
#> Gene001 String1 42 2.04 1.111858
#> Gene002 String2 43 2.12 1.137833
#> Gene003 String3 41 1.72 1.000632
#> Gene004 String4 42 2.06 1.118415
#> pct_dropout_by_counts total_counts
#> Gene000 12 97
#> Gene001 16 102
#> Gene002 14 106
#> Gene003 18 86
#> Gene004 16 103Interoperability
Convert the AnnData object to a SingleCellExperiment object:
sce <- adata$to_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$to_Seurat()
#> Warning: Keys should be one or more alphanumeric characters followed by an
#> underscore, setting key from X_pca to Xpca_
#> Warning: No columnames present in cell embeddings, setting to 'Xpca_1:38'
#> Warning: Keys should be one or more alphanumeric characters followed by an
#> underscore, setting key from X_umap to Xumap_
#> Warning: No columnames present in cell embeddings, setting to 'Xumap_1:2'
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_umapManually 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 4.4.3 (2025-02-28)
#> 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_1.28.1
#> [3] SummarizedExperiment_1.36.0 Biobase_2.66.0
#> [5] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
#> [7] IRanges_2.40.1 S4Vectors_0.44.0
#> [9] BiocGenerics_0.52.0 MatrixGenerics_1.18.1
#> [11] matrixStats_1.5.0 SeuratObject_5.0.2
#> [13] sp_2.2-0 BiocStyle_2.34.0
#>
#> 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_1.6.5 zlibbioc_1.52.0 ragg_1.3.3
#> [10] vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.4-2
#> [13] htmltools_0.5.8.1 S4Arrays_1.6.0 SparseArray_1.6.2
#> [16] sass_0.4.9 sctransform_0.4.1 parallelly_1.43.0
#> [19] KernSmooth_2.23-26 bslib_0.9.0 htmlwidgets_1.6.4
#> [22] desc_1.4.3 ica_1.0-3 plyr_1.8.9
#> [25] plotly_4.10.4 zoo_1.8-13 cachem_1.1.0
#> [28] igraph_2.1.4 mime_0.13 lifecycle_1.0.4
#> [31] pkgconfig_2.0.3 Matrix_1.7-2 R6_2.6.1
#> [34] fastmap_1.2.0 GenomeInfoDbData_1.2.13 fitdistrplus_1.2-2
#> [37] future_1.34.0 shiny_1.10.0 digest_0.6.37
#> [40] colorspace_2.1-1 patchwork_1.3.0 tensor_1.5
#> [43] Seurat_5.2.1 RSpectra_0.16-2 irlba_2.3.5.1
#> [46] textshaping_1.0.0 progressr_0.15.1 spatstat.sparse_3.1-0
#> [49] polyclip_1.10-7 httr_1.4.7 abind_1.4-8
#> [52] compiler_4.4.3 bit64_4.6.0-1 fastDummies_1.7.5
#> [55] MASS_7.3-64 DelayedArray_0.32.0 tools_4.4.3
#> [58] lmtest_0.9-40 httpuv_1.6.15 future.apply_1.11.3
#> [61] goftest_1.2-3 glue_1.8.0 nlme_3.1-167
#> [64] promises_1.3.2 grid_4.4.3 Rtsne_0.17
#> [67] cluster_2.1.8 reshape2_1.4.4 generics_0.1.3
#> [70] hdf5r_1.3.12 spatstat.data_3.1-6 gtable_0.3.6
#> [73] tidyr_1.3.1 data.table_1.17.0 XVector_0.46.0
#> [76] spatstat.geom_3.3-6 RcppAnnoy_0.0.22 stringr_1.5.1
#> [79] ggrepel_0.9.6 RANN_2.6.2 pillar_1.10.2
#> [82] spam_2.11-1 RcppHNSW_0.6.0 later_1.4.2
#> [85] splines_4.4.3 dplyr_1.1.4 lattice_0.22-6
#> [88] deldir_2.0-4 survival_3.8-3 bit_4.6.0
#> [91] tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2
#> [94] knitr_1.50 gridExtra_2.3 bookdown_0.42
#> [97] scattermore_1.2 xfun_0.52 stringi_1.8.7
#> [100] UCSC.utils_1.2.0 lazyeval_0.2.2 yaml_2.3.10
#> [103] evaluate_1.0.3 codetools_0.2-20 tibble_3.2.1
#> [106] BiocManager_1.30.25 cli_3.6.4 uwot_0.2.3
#> [109] xtable_1.8-4 reticulate_1.42.0 systemfonts_1.2.2
#> [112] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.14
#> [115] spatstat.random_3.3-3 globals_0.16.3 png_0.1-8
#> [118] spatstat.univar_3.1-2 parallel_4.4.3 pkgdown_2.1.1
#> [121] ggplot2_3.5.2 dotCall64_1.2 listenv_0.9.1
#> [124] viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6
#> [127] purrr_1.0.4 crayon_1.5.3 rlang_1.1.5
#> [130] cowplot_1.1.3