phyde 0.2.3

HyDe: hybridization detection using phylogenetic invariants

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Installation

Requirements:

• Python 2.7

• Python Modules:

  • Cython

  • Numpy

  • Scipy

  • Pandas

  • Matplotlib

  • Seaborn

• C++ compiler (g++ >= v4.8)

HyDe is a software package that detects hybridization in phylogenomic data sets using phylogenetic invariants. The primary interface for HyDe is a Python module called phyde (Pythonic Hybridization Detection). phyde provides a suite of tools for performing hypothesis tests on triples of taxa to detect hybridization. It also has built in functions to wrap calls to the pure C++ version of HyDe, hyde_cpp. We have provided a Makefile that will compile the hyde_cpp C++ executable and will then install the phyde Python package using the setup.py file. To ensure that the necessary dependencies are available, we suggest using a Python distribution such as Miniconda.

# To install dependencies
pip install cython numpy scipy pandas matplotlib seaborn

# Clone HyDe repository from GitHub
git clone https://github.com/pblischak/HyDe.git
cd HyDe

# Compile hyde_cpp using `make`
make

# Now install phyde module
python setup.py install

# Test the installation
make test

The phyde module is also hosted on the Python Package Index (PyPI), and can be installed directly using pip.

# Install from PyPI with pip
pip install phyde

Example HyDe analysis in Python

# Import the modules that we'll need
from __future__ import print_function
import phyde as hd

# Run a full HyDe analysis to see if there is any evidence for hybridization.
# This can be done using the run_hyde() function.
# If you're using ipython, type `hd.run_hyde?` to see more information.
res_all = hd.run_hyde("data.txt", "map.txt", "out", 16, 4, 50000)

# res_all is an object of class HydeResult. The main results are stored in this object
# as a dictionary that is called res. We can filter the results using dictionary comprehensions.
# We will filter for significant results with hybridization parameter estimates (gamma)
# between 0 and 1.
filter_res = {k:v for k,v in res_all.res.items() if v['Pvalue'] < 0.025 and v['Gamma'] > 0.0 and v['Gamma'] < 1.0}

# We can now print these results to a file.
outfile = open("hyde-filtered-out.txt", 'wa')
for k,v in filtered_res.items():
  print(k[0], '\t', k[1], '\t', k[2], '\t', sep='', end='', file=outfile)
  for ki,vi in v.items():
    print(ki, ":", vi, '\t', sep='', end='', file=outfile)
  print('\n', end='', file=outfile)
outfile.close()

# Next we'll test each individual in each hybrid population.
# First we have to read the data into Python using the HydeData class
dat = hd.HydeData("data.txt", "map.txt", "out", 16, 4, 50000)

# Then we'll run the tests using the text_individuals() function.
# We'll use a dictionary comprehension again. The significat triples
# are stored at tuples (p1, hyb, p2).
ind_tests = {k: dat.test_individuals(k[0], k[1], k[2]) for k in filter_res.keys()}
print(ind_tests)