Using anndataR
Robrecht Cannoodt
Luke Zappia
Martin Morgan
Louise Deconinck
Source:vignettes/anndataR.Rmd
anndataR.RmdIntroduction
anndataR
allows users to work with .h5ad files, interact with
AnnData objects and convert to/from
SingleCellExperiment or Seurat objects. This
enables users to move data easily between the different programming
languages and analysis ecosystems needed to perform single-cell data
analysis.
This package builds on our experience developing and using other
interoperability packages and aims to provide a first-class R
AnnData experience.
Relationship to other packages
Existing packages provide similar functionality to anndataR but there are some important differences:
-
zellkonverter
provides conversion of
SingleCellExperimentobjects to/fromAnnDataand reading/writing of.h5adfiles. This is facilitated via reticulate using basilisk to manage Python environments (native reading of.h5adfiles is also possible). In contrast, anndataR provides a native R H5AD interface, removing the need for Python dependencies. Conversion to/fromSeuratobjects is also supported. -
anndata (on
CRAN) is a wrapper around the Python anndata package. It
provides a nicer interface from within R but still requires a Python
environment. anndataR
provides a pure R implementation of the
AnnDatadata structure with different back ends and conversion to more common R objects. - Other interoperability packages typically also require Python dependencies, have limited features or flexibility and/or are not available from a central package repository
There is significant overlap in functionality between these packages. We anticipate that over time they will become more specialised and work better together or be deprecated in favour of anndataR.
Installation
Install anndataR using BiocManager:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("anndataR")Usage
These sections briefly show how to use anndataR.
Read from disk
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
(see vignette("usage_singlecellexperiment")):
sce <- read_h5ad(h5ad_path, as = "SingleCellExperiment")Or as a Seurat object (see
vignette("usage_seurat")):
obj <- read_h5ad(h5ad_path, as = "Seurat")There is also a HDF5-backed AnnData object:
adata <- read_h5ad(h5ad_path, as = "HDF5AnnData")See for interacting with a Python AnnData via reticulate.
Using AnnData objects
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$var[1:5, 1:6]
#> String n_cells_by_counts mean_counts log1p_mean_counts
#> Gene000 String0 44 1.94 1.078410
#> Gene001 String1 42 2.04 1.111858
#> Gene002 String2 43 2.12 1.137833
#> Gene003 String3 41 1.72 1.000632
#> Gene004 String4 42 2.06 1.118415
#> pct_dropout_by_counts total_counts
#> Gene000 12 97
#> Gene001 16 102
#> Gene002 14 106
#> Gene003 18 86
#> Gene004 16 103Subsetting AnnData objects is
covered below. See ?`AnnData-usage`for more details on how
to work with AnnData objects.
Interoperability
Convert the AnnData object to a
SingleCellExperiment object (see
vignette("usage_singlecellexperiment")):
sce <- adata$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 (see vignette("usage_seurat")):
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 (see
vignette("usage_singlecellexperiment")):
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 (see vignette("usage_seurat")):
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'Write to disk
Write an AnnData object to disk:
tmpfile <- tempfile(fileext = ".h5ad")
adata$write_h5ad(tmpfile) # Alternatively, 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)Subsetting AnnData objects
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'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_0.99.5 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_0.54.1 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_2.2.0 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
#> [46] textshaping_1.0.4 progressr_0.17.0 spatstat.sparse_3.1-0
#> [49] polyclip_1.10-7 httr_1.4.7 abind_1.4-8
#> [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
#> [67] Rtsne_0.17 cluster_2.1.8.1 reshape2_1.4.4
#> [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
#> [79] pillar_1.11.1 stringr_1.5.2 spam_2.11-1
#> [82] RcppHNSW_0.6.0 later_1.4.4 splines_4.5.1
#> [85] dplyr_1.1.4 lattice_0.22-7 deldir_2.0-4
#> [88] survival_3.8-3 tidyselect_1.2.1 miniUI_0.1.2
#> [91] pbapply_1.7-4 knitr_1.50 gridExtra_2.3
#> [94] bookdown_0.45 scattermore_1.2 xfun_0.53
#> [97] stringi_1.8.7 UCSC.utils_1.4.0 lazyeval_0.2.2
#> [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
#> [106] uwot_0.2.3 xtable_1.8-4 reticulate_1.43.0
#> [109] systemfonts_1.3.1 jquerylib_0.1.4 Rcpp_1.1.0
#> [112] spatstat.random_3.4-2 globals_0.18.0 png_0.1-8
#> [115] spatstat.univar_3.1-4 parallel_4.5.1 pkgdown_2.1.3
#> [118] ggplot2_4.0.0 dotCall64_1.2 listenv_0.9.1
#> [121] viridisLite_0.4.2 scales_1.4.0 ggridges_0.5.7
#> [124] purrr_1.1.0 crayon_1.5.3 rlang_1.1.6
#> [127] cowplot_1.2.0