See all releases here. The following lists selected improvements.
October 7, 2018: on GitHub and 0.6.11
- :func:`~anndata.AnnData.layers` inspired by .loom files allows their information lossless reading via :func:`~anndata.read_loom`
- initialatization from pandas DataFrames
- iteration over chunks :func:`~anndata.AnnData.chunked_X` and :func:`~anndata.AnnData.chunk_X`
- support for reading zarr files: :func:`~anndata.read_zarr`
May 1, 2018: version 0.6
- compatibility with Seurat converter
- tremendous speedup for :func:`~anndata.AnnData.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
February 9, 2018: version 0.5
- inform about duplicates in :class:`~anndata.AnnData.var_names` and resolve them using :func:`~anndata.AnnData.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: :func:`~anndata.read_umi_tools`
December 23, 2017: version 0.4
- read/write .loom files
- scalability beyond dataset sizes that fit into memory: see this blog post
- :class:`~anndata.AnnData` has a :class:`~anndata.AnnData.raw` attribute that simplifies storing the data matrix when you consider it "raw": see the clustering tutorial