Using anndataR
Robrecht Cannoodt
Luke Zappia
Martin Morgan
Louise Deconinck
Source:vignettes/anndataR.Rmd
anndataR.RmdIntroduction
{anndataR} allows users to work with
.h5ad files, access various slots in the datasets and
convert these files to SingleCellExperiment objects or
Seurat objects, and vice versa. This enables users to move
data easily between the different programming languages and analysis
ecosystems needed to perform single-cell data analysis. This package
differs from {zellkonverter}
because it reads and writes these .h5ad files natively in
R, and allows conversion to and from Seurat objects as well
as SingleCellExperiment.
Installation
Install using either {BiocManager} or from GitHub using {pak}:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("anndataR")
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")By default, a H5AD is read to an in-memory AnnData
object:
adata <- read_h5ad(h5ad_path)It can also be read as a SingleCellExperiment
object:
sce <- read_h5ad(h5ad_path, as = "SingleCellExperiment")Or as a Seurat object:
obj <- read_h5ad(h5ad_path, as = "Seurat")There is also a HDF5-backed AnnData object:
adata <- read_h5ad(h5ad_path, as = "HDF5AnnData")View structure:
adata
#> HDF5AnnData 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'
#> varp: 'test_varp'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_umapConvert a SingleCellExperiment object to an
AnnData object:
adata <- as_AnnData(sce)
adata
#> InMemoryAnnData 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: 'X_pca'
#> layers: 'counts', 'csc_counts', 'dense_X', 'dense_counts', 'X'
#> obsp: 'connectivities', 'distances'
#> varp: 'test_varp'Convert a Seurat object to an AnnData
object:
adata <- as_AnnData(obj)
adata
#> InMemoryAnnData object with n_obs × n_vars = 50 × 100
#> obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', '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'
#> layers: 'counts', 'csc_counts', 'dense_X', 'dense_counts', 'X'
#> obsp: 'connectivities', 'distances'Manually create an AnnData 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
#> InMemoryAnnData object with n_obs × n_vars = 10 × 10
#> obs: 'cell_type'
#> var: 'gene_name'Standard R subsetting with S3 methods
{anndataR} provides standard R subsetting methods
that work with familiar bracket notation. These methods return
AnnDataView objects that provide lazy evaluation for
efficient memory usage:
Basic subsetting
Subset observations (rows) using logical conditions:
# Create some sample data
adata <- AnnData(
X = matrix(rnorm(50), nrow = 10, ncol = 5),
obs = data.frame(
cell_type = factor(rep(c("A", "B", "C"), c(3, 4, 3))),
score = runif(10)
),
var = data.frame(
gene_name = paste0("gene_", 1:5),
highly_variable = c(TRUE, FALSE, TRUE, TRUE, FALSE)
)
)
# Subset to cell type "A"
view1 <- adata[adata$obs$cell_type == "A", ]
view1
#> View of InMemoryAnnData object with n_obs × n_vars = 3 × 5
#> obs: 'cell_type', 'score'
#> var: 'gene_name', 'highly_variable'Subset variables (columns) using logical conditions:
# Subset to highly variable genes
view2 <- adata[, adata$var$highly_variable]
view2
#> View of InMemoryAnnData object with n_obs × n_vars = 10 × 3
#> obs: 'cell_type', 'score'
#> var: 'gene_name', 'highly_variable'Combined subsetting
Subset both observations and variables simultaneously:
# Subset to cell type "A" and highly variable genes
view3 <- adata[adata$obs$cell_type == "A", adata$var$highly_variable]
view3
#> View of InMemoryAnnData object with n_obs × n_vars = 3 × 3
#> obs: 'cell_type', 'score'
#> var: 'gene_name', 'highly_variable'Using different index types
# Numeric indices
view4 <- adata[1:5, 1:3]
view4
#> View of InMemoryAnnData object with n_obs × n_vars = 5 × 3
#> obs: 'cell_type', 'score'
#> var: 'gene_name', 'highly_variable'
# Character names (if available)
rownames(adata) <- paste0("cell_", 1:10)
colnames(adata) <- paste0("gene_", 1:5)
view5 <- adata[c("cell_1", "cell_2"), c("gene_1", "gene_3")]
view5
#> View of InMemoryAnnData object with n_obs × n_vars = 2 × 2
#> obs: 'cell_type', 'score'
#> var: 'gene_name', 'highly_variable'Working with views
Views maintain access to all original data slots:
# Access dimensions
dim(view3)
#> [1] 3 3
nrow(view3)
#> [1] 3
ncol(view3)
#> [1] 3
# Access names
rownames(view3)
#> [1] "cell_1" "cell_2" "cell_3"
colnames(view3)
#> [1] "gene_1" "gene_3" "gene_4"
# Access data matrices and metadata
view3$X
#> gene_1 gene_3 gene_4
#> cell_1 -0.6905379 0.02229473 0.4543416
#> cell_2 -0.5585420 0.60361101 -0.8552025
#> cell_3 -0.5366633 -0.26265057 -0.2868952
view3$obs
#> cell_type score
#> cell_1 A 0.3492990
#> cell_2 A 0.9473183
#> cell_3 A 0.2161000
view3$var
#> gene_name highly_variable
#> gene_1 gene_1 TRUE
#> gene_3 gene_3 TRUE
#> gene_4 gene_4 TRUE
# Views can be converted to concrete implementations
concrete <- view3$as_InMemoryAnnData()
concrete
#> InMemoryAnnData object with n_obs × n_vars = 3 × 3
#> obs: 'cell_type', 'score'
#> var: 'gene_name', 'highly_variable'Write to disk:
Write an AnnData object to disk:
tmpfile <- tempfile(fileext = ".h5ad")
write_h5ad(adata, tmpfile)Write a SingleCellExperiment object to disk:
tmpfile <- tempfile(fileext = ".h5ad")
write_h5ad(sce, tmpfile)Write a Seurat object to disk:
tmpfile <- tempfile(fileext = ".h5ad")
write_h5ad(obj, tmpfile)Citing {anndataR}
If you use {anndataR} in your work, please cite “anndataR improves interoperability between R and Python in single-cell transcriptomics”:
citation("anndataR")
#> To cite package 'anndataR' in publications use:
#>
#> Deconinck L, Zappia L, Cannoodt R, Morgan M, scverse core, Virshup I,
#> Sang-aram C, Bredikhin D, Seurinck R, Saeys Y (2025). "anndataR
#> improves interoperability between R and Python in single-cell
#> transcriptomics." _bioRxiv_, 2025.08.18.669052.
#> doi:10.1101/2025.08.18.669052
#> <https://doi.org/10.1101/2025.08.18.669052>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {{anndataR} improves interoperability between R and Python in single-cell transcriptomics},
#> author = {Louise Deconinck and Luke Zappia and Robrecht Cannoodt and Martin Morgan and {scverse core} and Isaac Virshup and Chananchida Sang-aram and Danila Bredikhin and Ruth Seurinck and Yvan Saeys},
#> journal = {bioRxiv},
#> year = {2025},
#> pages = {2025.08.18.669052},
#> doi = {10.1101/2025.08.18.669052},
#> }Session info
sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 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_0.99.2 SingleCellExperiment_1.30.1
#> [3] SummarizedExperiment_1.38.1 Biobase_2.68.0
#> [5] GenomicRanges_1.60.0 GenomeInfoDb_1.44.3
#> [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_5.2.0 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_2.0.4
#> [4] spatstat.utils_3.2-0 farver_2.1.2 rmarkdown_2.30
#> [7] fs_1.6.6 ragg_1.5.0 vctrs_0.6.5
#> [10] ROCR_1.0-11 spatstat.explore_3.5-3 htmltools_0.5.8.1
#> [13] S4Arrays_1.8.1 Rhdf5lib_1.30.0 SparseArray_1.8.1
#> [16] rhdf5_2.52.1 sass_0.4.10 sctransform_0.4.2
#> [19] parallelly_1.45.1 KernSmooth_2.23-26 bslib_0.9.0
#> [22] htmlwidgets_1.6.4 desc_1.4.3 ica_1.0-3
#> [25] plyr_1.8.9 plotly_4.11.0 zoo_1.8-14
#> [28] cachem_1.1.0 igraph_2.1.4 mime_0.13
#> [31] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.7-3
#> [34] R6_2.6.1 fastmap_1.2.0 GenomeInfoDbData_1.2.14
#> [37] fitdistrplus_1.2-4 future_1.67.0 shiny_1.11.1
#> [40] digest_0.6.37 patchwork_1.3.2 tensor_1.5.1
#> [43] Seurat_5.3.0 RSpectra_0.16-2 irlba_2.3.5.1
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#> [52] compiler_4.5.1 S7_0.2.0 fastDummies_1.7.5
#> [55] MASS_7.3-65 DelayedArray_0.34.1 tools_4.5.1
#> [58] lmtest_0.9-40 httpuv_1.6.16 future.apply_1.20.0
#> [61] goftest_1.2-3 glue_1.8.0 nlme_3.1-168
#> [64] rhdf5filters_1.20.0 promises_1.3.3 grid_4.5.1
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#> [70] spatstat.data_3.1-8 gtable_0.3.6 tidyr_1.3.1
#> [73] data.table_1.17.8 XVector_0.48.0 spatstat.geom_3.6-0
#> [76] RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
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#> [100] yaml_2.3.10 evaluate_1.0.5 codetools_0.2-20
#> [103] tibble_3.3.0 BiocManager_1.30.26 cli_3.6.5
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