decomPosition of the Hodge Laplacian for inferring trajectOries from floWs of cEll diffeRentiation
PHLOWER module requires a standard computer with enough RAM to support the in-memory operations, high performance computing (HPC) cluster is recommended.
PHLOWER has been tested with the following OS or virtual environment:
- macOS Sonoma 14.5
- Linux 4.18.0/5.15.0
- anaconda 4.12.0
macOS ((install time: 15 minutes)):
- suite-sparse (>=7.8.2)
- graphviz (>=12.1.2)
1. brew install suite-sparse
2. brew install graphviz
Manually install pygraphviz:
export PATH=$(brew --prefix graphviz):$PATH
export CFLAGS="-I $(brew --prefix graphviz)/include"
export LDFLAGS="-L $(brew --prefix graphviz)/lib"
pip install pygraphvizdebian (install time: 15 seconds):
- libsuitesparse-dev (>=1:5.10)
- graphviz (>=2.42.2)
- libgraphviz-dev (>=2.42.2)
1. apt install libsuitesparse-dev
2. apt install graphviz libgraphviz-devconda (install time: 1.5 minutes):
- conda-forge::suitesparse(>=5.10.1)
- graphviz (>=7.1.0)
- pygraphviz (>=1.11)
1. conda install conda-forge::suitesparse
2. conda install conda-forge::python-graphvizWe have tested python version 3.9.0, 3.10.8, 3.10.14, 3.11.0, 3.11.5, 3.12.0.
- python (>=3.9.0)
- numpy (>=1.23.5)
- matplotlib (>=3.9.1)
- seaborn (>=0.13.2)
- networkx (>=2.8.8)
- pydot (>=1.4.2)
- igraph (>=0.10.5)
- scikit-learn (>=1.5.1)
- scipy (>=1.14.0)
- pandas (>=2.2.3)
- plotly (>=5.23.0)
- tqdm (>=4.65.0)
- leidenalg (>= 0.9.1)
- python-louvain (>=0.16)
- colorcet(>=3.0.1)
- umap-learn (>=0.5.5)
- scikit-sparse (>=0.4.8)
- scanpy (>=1.9.3)
- adjustText (>=0.8)
- pygraphviz (>=1.11)
- gudhi (>=3.10.1)
- magic-impute (>=3.0.0)
- anndata (>=0.9.2)
Expect install time on a normal computer(Intel i5-10400 (12) @ 4.300GHz): 2 minutes
pip install phlowerpy
git clone https://github.com/CostaLab/phlower.git
cd phlower
pip install .import phlowerA small scRNA-seq data Fibroblast to Neuron in 5 minutes for a computer(Intel i5-10400 (12) @ 4.300GHz).
10X multiome data Kidney.
Regulators detection example please check Regulators.
The processed data have been deposited at: .
https://github.com/CostaLab/phlower-reproducibility
PHLOWER has been published in Nature Methods: https://doi.org/10.1101/2024.10.01.613179
@article{cheng2025phlower,
title={PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories},
author={Cheng, Mingbo and Jansen, Jitske and Reimer, Katharina C and Grande, Vincent P and Nagai, James S and Li, Zhijian and Kie{\ss}ling, Paul and Grasshoff, Martin and Kuppe, Christoph and Schaub, Michael T and others},
journal={Nature Methods},
pages={1--9},
year={2025},
publisher={Nature Publishing Group US New York}
}