{anndataR}: An R package for working with AnnData objects anndataR logo

{anndataR} aims to make the AnnData format a first-class citizen in the R ecosystem, and to make it easy to work with AnnData files in R, either directly or by converting them to a SingleCellExperiment or Seurat object.

{anndataR} is a an scverse® community project fiscally sponsored by the Chan Zuckerberg Initiative.

Features of {anndataR}

Please report any issues you encounter.

Installation

You can install {anndataR} from Bioconductor using BiocManager:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("anndataR")

Or you can install the development version of {anndataR} from GitHub like so:

# install.packages("pak")
pak::pak("scverse/anndataR")

You will need to install additional dependencies, depending on the task you want to perform.

Alternatively, you can install all suggested dependencies at once:

pak::pak("scverse/anndataR", dependencies = TRUE)

Getting started

The best way to get started with {anndataR} is to explore the package vignettes (available at https://anndataR.scverse.org/articles/).

In order to browse these vignettes locally, you need to build them during installation:

options(pkg.build_vignettes = TRUE)
pak::pak("scverse/anndataR")

Take note that you need all suggested dependencies available, and that building them can take some time.

Citing {anndataR}

If you use {anndataR} in your work, please cite "anndataR improves interoperability between R and Python in single-cell transcriptomics":

citation("anndataR")
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},
  }