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, 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$var[1:5, 1:6]
#> String n_cells_by_counts mean_counts log1p_mean_counts
#> Gene000 String0 44 1.94 1.078410
#> Gene001 String1 42 2.04 1.111858
#> Gene002 String2 43 2.12 1.137833
#> Gene003 String3 41 1.72 1.000632
#> Gene004 String4 42 2.06 1.118415
#> pct_dropout_by_counts total_counts
#> Gene000 12 97
#> Gene001 16 102
#> Gene002 14 106
#> Gene003 18 86
#> Gene004 16 103Interoperability
Convert the AnnData object to a SingleCellExperiment object:
sce <- adata$as_SingleCellExperiment()
sce
#> class: SingleCellExperiment
#> dim: 100 50
#> metadata(18): Bool BoolNA ... rank_genes_groups umap
#> assays(5): counts csc_counts dense_X dense_counts X
#> 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, csc_counts, dense_X, dense_counts, X
#> 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 4.5.1 (2025-06-13)
#> 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_0.1.0.9000 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
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#> [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
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#> [37] shiny_1.10.0 digest_0.6.37 patchwork_1.3.0
#> [40] tensor_1.5.1 Seurat_5.3.0 RSpectra_0.16-2
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#> [52] fastDummies_1.7.5 MASS_7.3-65 DelayedArray_0.34.1
#> [55] tools_4.5.1 lmtest_0.9-40 httpuv_1.6.16
#> [58] future.apply_1.20.0 goftest_1.2-3 glue_1.8.0
#> [61] nlme_3.1-168 promises_1.3.3 grid_4.5.1
#> [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_1.17.6 XVector_0.48.0
#> [73] spatstat.geom_3.4-1 RcppAnnoy_0.0.22 ggrepel_0.9.6
#> [76] RANN_2.6.2 pillar_1.10.2 stringr_1.5.1
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#> [112] globals_0.18.0 png_0.1-8 spatstat.univar_3.1-3
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