anndata - Annotated Data¶

Install via pip install anndata or conda install anndata -c bioconda.

Report issues and see the code on GitHub.

AnnData provides a scalable way of keeping track of data and learned annotations. It was initially built for Scanpy (Genome Biology, 2018).

Post v0.6 June 6, 2019¶

convenience accesors obs_vector(), var_vector() for 1d arrays, see here 0.6.21 thanks to I Virshup
compatibility with Scipy >=1.3 by removing IndexMixin dependency, see here 0.6.20 thanks to P Angerer
bug fix for second-indexing into views, see here 0.6.19 thanks to P Angerer
bug fix for reading excel files 0.6.19 thanks to A Wolf
layers inspired by .loom files allows their information lossless reading via read_loom() thanks to S Rybakov
initialization from pandas DataFrames thanks to A Wolf
iteration over chunks chunked_X() and chunk_X() thanks to S Rybakov
support for reading zarr files: read_zarr() thanks to T White
changed default compression to None in write_h5ad() to speed up read and write, disk space use is usually less critical 0.6.16
maintain dtype upon copy 0.6.13 thanks to A Wolf

compatibility with Seurat converter
tremendous speedup for concatenate()
bug fix for deep copy of unstructured annotation after slicing
bug fix for reading HDF5 stored single-category annotations
‘outer join’ concatenation: adds zeros for concatenation of sparse data and nans for dense data
better memory efficiency in loom exports

inform about duplicates in var_names and resolve them using var_names_make_unique()
automatically remove unused categories after slicing
read/write .loom files using loompy 2
fixed read/write for a few text file formats
read UMI tools files: read_umi_tools()

read/write .loom files
scalability beyond dataset sizes that fit into memory: see this blog post
AnnData has a raw attribute, which simplifies storing the data matrix when you consider it raw: see the clustering tutorial