synergy 0.1.2.post1

Python package for calculating drug combination synergy

synergy

A python package to calculate, analyze, and visualize drug combination synergy and antagonism. Currently supports multiple models of synergy, including MuSyC, Bliss, Loewe, Combination Index, ZIP, Zimmer, BRAID, Schindler, and HSA.

Installation

Using PIP pip install synergy

Using conda not yet

Using git git clone ...

Example Usage

Parametric Models

Fit to data

from synergy.combination import MuSyC # or BRAID, Zimmer
import pandas as pd

df = pd.read_csv("your_own_drug_response_data.csv")

# bounds are optional, but can help improve fit if you know them
model = MuSyC(E0_bounds=(0,1), E1_bounds=(0,1), E2_bounds=(0,1), E3_bounds=(0,1))
model.fit(df['drug1.conc'], df['drug2.conc'], df['effect'], bootstrap_iterations=100)

Get parameters and confidence intervals

# Note, each synergy model has their own synergy parameters. Read their documentation and publications to understand what they mean.

print(model)
print(model.get_parameter_range(confidence_interval=95).T)

Visualize

# Requires matplotlib
model.plot_colormap(df['drug1.conc'], df['drug2.conc'], xlabel="Drug1", ylabel="Drug2")

# Requires plotly
# scatter_points (optional) expects a pandas dataframe with columns "drug1.conc", "drug2.conc", and "effect"
model.plot_surface_plotly(df['drug1.conc'], df['drug2.conc'], xlabel="Drug1", ylabel="Drug2", zlabel="Effect", fname="plotly.html", scatter_points=df)

Generate synthetic data

from synergy.utils.dose_tools import grid
import numpy as np

model = MuSyC(E0=1, E1=0.6, E2=0.4, E3=0, h1=2, h2=0.8, C1=1e-2, C2=1e-1, oalpha12=2, oalpha21=1, gamma12=2.5, gamma21=0.7)

# Create dose matrix
d1min, d1max = 1e-3, 1
d2min, d2max = 1e-3, 1
npoints1, npoints2 = 8, 8
d1, d2 = grid(d1min, d1max, d2min, d2max, npoints1, npoints2)

E = model.E(d1, d2)
E_noisy = E + 0.1*(2*np.random.rand(len(E))-1) # Add random value from -0.1 to 0.1 to every datapoint

Nonparametric (dose dependent) synergy models

Fit to data

from synergy.combination import Loewe # or Bliss, ZIP, HSA, Schindler, CombinationIndex
import pandas as pd

df = pd.read_csv("your_own_drug_response_data.csv")
model = Loewe()
model.fit(df['drug1.conc'], df['drug2.conc'], df['effect'])

Get synergy values

print(model.synergy) # Will have size equal to d1, d2, and E passed to fit()

Visualize

# Requires matplotlib
model.plot_colormap(df['drug1.conc'], df['drug2.conc'], xlabel="Drug1", ylabel="Drug2")

# Requires plotly
model.plot_surface_plotly(df['drug1.conc'], df['drug2.conc'], xlabel="Drug1", ylabel="Drug2", zlabel="Loewe Synergy", fname="plotly.html")

Requirements

• python >= 3.5
• numpy >= 1.13.0
• scipy >= 0.18.0
• Optional for full plotting functionality
  • matplotlib
  • plotly
  • pandas

Current features

• Calculate two-drug synergy using
  • Parametric
    • MuSyC
    • Zimmer (effective dose model)
    • BRAID
  • Dose-dependent
    • Bliss
    • Loewe
    • Schindler
    • ZIP
    • HSA
    • Combination Index
• Residual bootstrap re-sampling to obtain confidence intervals for parameters of parametric models
• Single drug models
  • Parametric
    • Four-parameter Hill equation
    • Two-parameter Hill equation
    • Median-effect equation
  • Non-parametric
    • Piecewise linear
• Model scoring
  • R-squared
  • Akaike Information Criterion
  • Bayesian Information Criterion
• Visualization
  • Heatmaps
  • 3D Plotly Surfaces
• Synthetic data tools
  • Drug dilutions using grid-based sampling
  • "Sham experiment" simulation

Planned features

• Additional models
  • Parametric
    • GPDI
• Three+ drug combinations (when possible)
  • MuSyC
  • Bliss
  • Loewe / CI
  • HSA
  • Schindler
  • Zimmer (at least incorporating pairwise synergies)
• Visualization
  • Highlight single-drug curves on 3D surface plots
  • matplotlib 3D surface plotting
  • Contour plots for heatmaps
  • Isobolgrams
• Additional dose / experiment design tools
  • Alternative dosing strategies
• Heteroskedastic re-sampling for datasets with >= 3 replicates at each dose
• Parallelization API for fitting high-throughput screen data

License

GNU General Public License v3 or later (GPLv3+)