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BPCells

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.

BPCells provides:

Additionally, BPCells exposes its optimized data processing infrastructure for use in scaling 3rd party single cell tools (e.g. Seurat)

Learn more at our website

R Installation

We recommend installing BPCells directly from github:

remotes::install_github("bnprks/BPCells/r")

Before installing, you must have the HDF5 library installed and accessible on your system. HDF5 can be installed from your choice of package manager.

For Mac and Windows users having trouble installing from github, check our R-universe page for instructions to install pre-built binary packages. These binary packages automatically track the latest github main branch.

<details> <summary>Click here for operating system specific installation information for github-based installs</summary>

Linux

Obtaining the HDF5 dependency is usually pretty straightforward on Linux

Windows

Compiling R packages from source on Windows requires installing R tools for Windows. See Issue #9 for more discussion.

MacOS

For MacOS, installing HDF5 through homebrew seems to be most reliable: brew install hdf5.

Mac-specific troubleshooting:

Supported compilers

In most cases, you will already have an appropriate compiler. BPCells recommends gcc >=9.1, or clang >= 9.0. This corresponds to versions from late-2018 and newer. Older versions may work in some cases so long as they have basic C++17 support, but they are not officially supported.

General Installation troubleshooting

BPCells tries to print informative error messages during compilation to help diagnose the problem. For a more verbose set of information, run Sys.setenv(BPCELLS_DEBUG_INSTALL="true") prior to remotes::install_github("bnprks/BPCells/r"). If you still can't solve the issue with that additional information, feel free to file a Github issue, being sure to use a collapsible section for the verbose installation log.

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Python Installation

BPCells can be directly installed via pip:

python -m pip install bpcells

Contributing

BPCells is an open source project, and we welcome quality contributions. If you are interested in contributing and have experience with C++, along with Python or R, feel free to reach out with ideas you would like to implement yourself. I'm happy to provide pointers for how to get started, my time permitting.

If you are unfamiliar with C++ it will be difficult for you to contribute code, but detailed bug reports with reproducible examples are still a useful way to help out. Github issues are the best forum for this.

If you maintain a single cell analysis package and want to use BPCells to improve your scalability, I'm happy to provide advice. We have had a couple of labs try this so far, with promising success. Email is the best way to get in touch for this (look in the DESCRIPTION file on github for contact info). Python developers welcome, though the full python package will likely not be available until after summer 2023.