See our Genome Research paper for details on dropkick
's guiding principles and validation.
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).
pip install dropkick
git clone https://github.com/KenLauLab/dropkick.git
cd dropkick
python setup.py install
dropkick
can be run as a command line tool or interactively with the scanpy
_ single-cell analysis suite.
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
_.