A python package to calculate, analyze, and visualize drug combination synergy and antagonism. Supports multiple models of synergy:
- MuSyC
- Bliss
- Loewe
- Combination Index
- ZIP
- Zimmer
- BRAID
- Schindler
- HSA
If you use, please cite:
Wooten, David J, and Albert, Réka. synergy - A Python library for calculating, analyzing, and visualizing drug combination synergy. (2020) Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa826
Using PIP
pip install synergy
From source
git clone git@github.com:djwooten/synergy.git
cd synergy
pip install -e .You can find extensive documentation and examples at http://synergy.readthedocs.io/
The general usage is the same for most synergy models, though refer to the documentation at http://synergy.readthedocs.io/ for specifics.
For this, I assume you have access to a drug response data set that has (at least) the following columns.
| drug1.conc | drug2.conc | effect |
|---|---|---|
| 0 | 0 | 1 |
| 0 | 0.01 | 0.97 |
| 0 | 0.1 | 0.9 |
| 0 | 1 | 0.7 |
| ... | ... | ... |
from synergy.combination import MuSyC # or any other model
import pandas as pd
df = pd.read_csv("/path/to/your_own_drug_response_data.csv")
# Instantiate the model. Bounds are optional for parametric models, and will be used when fitting to data.
# In this case, imagine that the effect data we have is known to fall between 0 and 1. Further, the given h and alpha
# bounds here are reasonable for most datasets, as changing those parameters has the most impact near `1`.
model = MuSyC(E_bounds=(0, 1), h_bounds=(1e-3, 1e3), alpha_bounds=(1e-3, 1e3))
# Prepare the input data to be fit
d1 = df["drug1.conc"]
d2 = df["drug2.conc"]
E = df["effect"]
# Fit the model (bootstrap_iterations is an option for some models to estimate parameter
model.fit(d1, d2, E, bootstrap_iterations=100)
model.summarize()The last call to model.summarize() will print a table summarizing the synergy findings. This is only available for
parametric synergy models. For example, the table may look like
Parameter | Value | 95% CI | Comparison | Synergy
=====================================================================
beta | 0.261 | (0.175, 0.333) | > 0 | synergistic
alpha12 | 3.54 | (2.67, 4.93) | > 1 | synergistic
alpha21 | 1.29 | (0.845, 2.45) | ~= 1 | additive
gamma12 | 0.947 | (0.762, 1.2) | ~= 1 | additive
gamma21 | 0.722 | (0.487, 1.2) | ~= 1 | additive
Many utilities exist under synergy.utils.plots to create heatmaps, 3D interactive dose-response surfaces, and 3D
interactive isosurfaces. These require intsalling matplotlib for the heatmaps, and plotly for the 3d plots. Many
exmaples can be seen at https://synergy.readthedocs.io/en/latest/models/synergy_models.html.
The basic approach is
from synergy.utils.plots import plot_heatmap, plot_plotly_surface
plot_heatmap(d1, d2, E, title="Dose response surface", fname="heatmap.png")
plot_surface_plotly(d1, d2, E, title="Dose response surface", fname="surface.html")GNU General Public License v3 or later (GPLv3+)