Automated cell filtering for single-cell RNA sequencing data.

See our Genome Research paper for details on dropkick's guiding principles and validation.

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dropkick works primarily with scanpy's AnnData objects, and accepts input files in .h5ad or flat (.csv, .tsv) format. It also writes outputs to .h5ad files when called from the terminal.

Installation via pip or from source requires a Fortran compiler (brew install gcc for Mac users, sudo apt install gfortran for Linux users).

Install from PyPI:

pip install dropkick

Or compile from source:

git clone https://github.com/KenLauLab/dropkick.git
cd dropkick
python setup.py install

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dropkick can be run as a command line tool or interactively with the scanpy single-cell analysis suite.

Usage from command line:

dropkick run path/to/counts.h5ad

Output will be saved in a new .h5ad file containing cell probability scores, labels, and model parameters.

You can also run the dropkick.qc module from terminal for a quick look at the total UMI distribution and ambient genes, saved as *_qc.png:

dropkick qc path/to/counts.h5ad

See dropkick_tutorial.ipynb for an interactive walkthrough of the dropkick pipeline and its outputs.

Full documentation is available at KenLauLab.github.io/dropkick.