BPCells is a package for high performance single cell analysis on RNA-seq and ATAC-seq datasets. It can analyze a 1.3M cell dataset with 2GB of RAM in around 10 minutes (benchmarks). This makes analysis of million-cell datasets practical on a laptop.

The main BPCells interface is in R and the python bindings are still in an experimental phase, so the API is subject to change.

The existing functionality is mainly focused on allowing read/write access to BPCells file formats for integer matrices and scATAC fragments. Future updates will add the data-processing functions present in the R interface (e.g. streaming normalization, PCA, or ATAC-seq peak/tile matrix creation). This will provide Python access to the shared C++ core code.

Notably, plotting functionality is not currently planned for implementation, as it is written primarily in R and relies on R plotting libraries not present in Python. There are a few other helper functions in R BPCells that are implemented in pure R and thus are unlikely to be added in Python in the near future. If any of this functionality is of interest to you, we would welcome your contributions -- you would be able to write most of the code in pure Python. Reach out via github/email if interested.

The current BPCells python bindings provide limited access to BPCells functionality. This mainly focuses on integer matrix slicing / import, and pseudobulk fragment insertion counts. The API is subject to change somewhat once a full-featured port of the BPCells R functionality is completed.

For more information, see our github, python docs, and R docs.