BPCells 1.0

Contributions welcome :)

BPCells 0.2.1



BPCells 0.2.0

We are finally declaring a new release version, covering a large amount of changes and improvements over the past year. Among the major features here are parallelization options for svds() and matrix_stats(), improved genomic track plots, and runtime CPU feature detection for SIMD code (enables higher performance, more portable builds). Full details of changes below.

This version also comes with a new installation path, which is done in preparation for a future Python package release. (So we can have one folder for R and one for Python, rather than having all the R files sit in the root folder). This is a breaking change and requires a slightly modified installation command.

Thanks to @brgew, @ycli1995, and @Yunuuuu for pull requests that contributed to this release, as well as all users who submitted github issues to help identify and fix bugs.

Breaking changes





BPCells 0.1.0


Note: All operations interoperate with all storage formats. For example, all matrix operations can be applied directly to an AnnData or 10x matrix file. In many cases the bitpacking-compressed formats will provide performance/space advantages, but are not required to use the computations.