synergy 1.0.0

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synergy

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

Citation

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

Installation

Using PIP pip install synergy

From source

git clone [e-mail]:djwooten/synergy.git
cd synergy
pip install -e .

Documentation

You can find extensive documentation and examples at http://synergy.readthedocs.io/

Example Usage

The general usage is the same for most synergy models, though refer to the documentation at http://synergy.readthedocs.io/ for specifics.

Fitting models

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

Visualization

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 http://synergy.readthedocs.io/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")

License

GNU General Public License v3 or later (GPLv3+)