A python API for modeling statistical, high-order epistasis in genotype-phenotype maps. All models follow a Scikit-learn interface, making it easy to integrate epistasis models with other pipelines and software. It includes a plotting module built on matplotlib for visualizing high-order interactions and interactive widgets to simplify complex nonlinear fits.

This package includes APIs for both linear and nonlinear epistasis models, described in this paper, relaxing the assumption of linearity.

A simple example of fitting a data set with a linear epistasis model.

# Import epistasis model
from epistasis.models import EpistasisLinearRegression

# Read data from file and estimate epistasis
model = EpistasisLinearRegression.from_json("dataset.json", order=3)
model.fit()

# Estimate the uncertainty in epistatic coefficients
model.bootstrap_fit()

If analyzing a nonlinear genotype-phenotype map, use NonlinearEpistasisModel (nonlinear least squares regression) to estimate nonlinearity in map:

# Import the nonlinear epistasis model
from epistasis.models import NonlinearEpistasisRegression

# Define a nonlinear function to fit the genotype-phenotype map.
def boxcox(x, lmbda, lmbda2):
    """Fit with a box-cox function to estimate nonlinearity."""
    return ((x-lmbda2)**lmbda - 1 )/lmbda

def reverse_boxcox(y, lmbda, lmbda2):
    "inverse of the boxcox function."
    return (lmbda*y + 1) ** (1/lmbda) + lmbda2

# Read data from file and estimate nonlinearity in dataset.
model = EpistasisNonlinearRegression.from_json("dataset.json",
    function=boxbox,
    reverse=reverse_boxcox,
    order=1,
)

# Give initial guesses for parameters to aid in convergence (not required).
model.fit(lmbda=1, lmbda2=1)

The nonlinear fit also includes Jupyter Notebook widgets to make nonlinear fitting easier.

model.fit_widget(lmbda=(-2,2,.1), lmbda2=(-2,2,.1))

More demos are available as binder notebooks.

To install, clone these repo and run:

python setup.py install

or, if you'd like to soft install for development:

python setup.py develop

This package is still really hacked together. I plan to include examples and clean up some of the plotting/network managing very soon.

Works in Python 2.7+ and Python 3+

Documentation and API reference can be viewed here.

• gpmap: Module for constructing powerful genotype-phenotype map python data-structures.
• Scikit-learn: Simple to use machine-learning algorithms
• Numpy: Python's array manipulation packaged
• Scipy: Efficient scientific array manipulations and fitting.

• matplotlib: Python plotting API.
• ipython: interactive python kernel.
• jupyter notebook: interactive notebook application for running python kernels interactively.
• ipywidgets: interactive widgets in python.

If you use this API for research, please cite this paper.