cvtkpy

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This is a Python package that implements the methods of Buffalo and Coop (2019, 2020) to calculate temporal covariances and convergence correlations.

I have split this repository from http://github.com/vsbuffalo/cvtk, which is the original version of this code used in the analysis of Buffalo and Coop (2020). This way, the original code is preserved exactly for reproducibility purposes. See the cvtk/notebooks for examples from our 2020 PNAS paper on how to do these analyses; this is the most detailed documentation/tutorial currently.

Installation

Currently this Python package is not hosted anywhere. So you will need to install it locally by cloning this repository,

$ git clone https://github.com/vsbuffalo/cvtkpy.git

Then installing,

$ python setup.py install  # must be Python 3

which should also install any dependencies you don't have.

Then, try:

$ python
Python 3.7.2 | packaged by conda-forge | (default, Mar 19 2019, 20:46:22)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import cvtk 
>>> # it works!

Citing

Please cite both Buffalo and Coop (Genetics, 2019) which is the original paper about temporal covariance, and Buffalo and Coop (PNAS, 2020) which is the correct paper to cite for these newer methods. Here are the BibTeX entries:

@ARTICLE{Buffalo2019-qs,
  title    = "The Linked Selection Signature of Rapid Adaptation in Temporal
              Genomic Data",
  author   = "Buffalo, Vince and Coop, Graham",
  journal  = "Genetics",
  volume   =  213,
  number   =  3,
  pages    = "1007--1045",
  month    =  nov,
  year     =  2019,
  language = "en"
}
@ARTICLE{Buffalo2020-my,
  title     = "Estimating the genome-wide contribution of selection to temporal
               allele frequency change",
  author    = "Buffalo, Vince and Coop, Graham",
  journal   = "Proc. Natl. Acad. Sci. U. S. A.",
  publisher = "National Academy of Sciences",
  month     =  aug,
  year      =  2020,
  language  = "en"
}