pyghdet 0.3.5

Global Hybridization detection using phylogenetic invariants

GHDet: Global Hybridization Detection using Phylogenetic Invariants

GHDet is a python package that can detect if there is any hybridization event given a set of taxa. The main module of this package is called pyghdet (Pythonic Global Hybridization Detection). Using this package, we can test whether there has been any hybridization event among a set of taxa, helping us decide whether or not to perform tree or network analysis.

Installation

Requirements:

• Python 3.6 or above

• Python Modules:

  • Cython

  • Numpy

  • Matplotlib

  • Seaborn

  • Multiprocess

  • math

  • scipy

  • phyde

  • pandas

  • itertools

  • typing

• C++ compiler

# To install dependencies
pip install cython numpy matplotlib seaborn multiprocess math scipy phyde pandas itertools typing

# To install GHDet
pip install pyghdet

Usages

The package has two main modules: comb_species and comb_indiv. The comb_species module performs global hypothesis test to detect if any of the species is a hybrid or not. Similarly, comb_indiv module performs global hypothesis test to detect if there is any hybrid individual in the data. The comb_species and comb_indiv modules has the following arguments:

Arguments
---------

    - infile         <string> : name of the DNA sequence data file.
    - mapfile        <string> : name of the taxon map file.
    - outgroup       <string> : name of the outgroup.
    - nindiv            <int> : number of sampled individuals.
    - ntaxa             <int> : number of sampled taxa/populations.
    - nsites            <int> : number of sampled sites.
    - sus_hyb         <string>: list of suspected hybrid species.
    - alpha            <float>: intended level of significance.
    - ignore_amb_sites <flag> : ignore missing/ambiguous sites.

Examples

Detect if any species is hybrid:

## No suspected hybrid:

  import pyghdet as ghd
  res = ghd.comb_speices("data.txt", "map.txt", "out", 16, 4, 50000)
  res.p_value
  res.detailed


## with suspected hybrid:
  import pyghdet as ghd
  res = ghd.comb_species("data.txt", "map.txt", "out", 16, 4, 50000, ['sp1', 'sp2'])
  res.p_value
  res.detailed

Detect if any individual is hybrid:

## No suspected hybrid:

  import pyghdet as ghd
  res = ghd.comb_indiv("data.txt", "map.txt", "out", 16, 4, 50000)
  res.p_value
  res.detailed


## with suspected hybrid:
  import pyghdet as ghd
  res = ghd.comb_indiv("data.txt", "map.txt", "out", 16, 4, 50000, ['sp1', 'sp2'])
  res.p_value
  res.detailed

Running GHDet from command line

To facilitate analyses using the Python module, two scripts are provided to conduct hybridization detection analyses directly from the command line:

• hdet_species.py: runs a standard hybridization detection analysis on all the species in the data. The output is the p-value of the global test. The script also produce results on individual tests if the global test is significant. One can also provide a list of suspected hybrid, which only test if any of the suspected hybrid species is actually hybrid species or not. evidence for hybridization.

• hdet_indiv.py: tests if any of the individuals in the data is hybrid or not. It is possible to provide a list of suspected hybrid species, then the test will only detect individuals from the provided suspected hybrid species are hybrid or not.

Examples

## species level

hdet_species.py -i data.txt -m map.txt -o out -n 16 -t 4 -s 50000

## individual level

hdet_indiv.py -i data.txt -m map.txt -o out -n 16 -t 4 -s 50000

Performing Combination tests

The package can also be used to perform Combination tests. Three different combination tests have been implemented in the package. The tests are: Cauchy Combination test (CCT), MinP-CCT-MinP (MCM) test, and CCT-MinP-CCT-(CMC) test. These tests can be performed as follows:

import pyghdet as ghd

## CCT with different weights
ghd.cct([0.01,0.05,0.10, 0.53], [1,1,2,1])

## CCT with equal weights
ghd.cct([0.01, 0.10, 0.05, 0.54])

## CMC test
ghd.cmc([0.01,0.05,0.10,0.45])

## MCM test
ghd.mcm([0.01, 0.05, 0.40, 0.33])