Please see the documentation for more details, examples, and tutorials.
moments implements methods for demographic history and selection inference
from genetic data, based on diffusion approximations to the allele frequency spectrum.
moments is based on the ∂a∂i open
source package developed by Ryan Gutenkunst.
We largely reuse ∂a∂i's interface but introduced a new simulation engine. This
new method is based on the direct computation of the frequency spectrum without
solving the diffusion system. Consequently we circumvent the numerical PDE
approximations and we get rid of the frequency grids used in ∂a∂i.
moments.LD implements methods for computing linkage disequilibrium statistics and
running multi-population demographic inference using patterns of LD.
If you use moments in your research, please cite:
- Jouganous, J., Long, W., Ragsdale, A. P., & Gravel, S. (2017). Inferring the joint demographic history of multiple populations: beyond the diffusion approximation. Genetics, 206(3), 1549-1567.
If you use moments.LD in your research, please cite:
-
Ragsdale, A. P. & Gravel, S. (2019). Models of archaic admixture and recent history from two-locus statistics. PLoS Genetics, 15(6), e1008204.
-
Ragsdale, A. P. & Gravel, S. (2020). Unbiased estimation of linkage disequilibrium from unphased data. Mol Biol Evol, 37(3), 923-932.
If you use moments.TwoLocus in your research, please cite:
- Ragsdale, A. P. (2021). Can we distinguish modes of selective interactions from linkage disequilibrium? BioRxiv, doi: https://doi.org/10.1101/2021.03.25.437004
moments is developed in Simon Gravel's
group in the Human Genetics
department at McGill University, with with ongoing maintenance and development
by the Gravel Lab and Aaron Ragsdale.
moments now supports python 3. Because python is soon discontinuing support for
python 2, we do not actively ensure that moments remains fully compatable with python
2, and strongly recommend using python 3.
The simplest way to install moments is using pip. Note that numpy and cython
are install requirements, but installing moments directly from the git repository
using pip should install these dependencies automatically:
pip install git+https://github.com/MomentsLD/moments.git
Alternatively, you can clone the git repository
git clone https://github.com/MomentsLD/moments.git
and then from within the moments directory (cd moments), run
pip install numpy, cython, scipy, mpmath
pip install .
If you use conda, moments is available via bioconda:
conda config --add channels bioconda
conda install moments
moments and moments.LD requires a number of dependencies. At a minimum,
these include
-
numpy
-
scipy
-
cython
-
mpmath
Dependencies can be installed using pip. For example to install cython,
pip install cython
Depending on the python distribution you use, it may be useful to add the directory
to cython in your python path.
We also strongly recommend installing ipython.
If you are using conda, all dependencies can be installed by navigating to the moments directory and then running
conda install --file requirements.txt
or, alternatively,
pip install -r requirements.txt
Once dependencies are installed, to install moments, run the following command
in the moments directory:
python setup.py build_ext -i
python setup.py install
You should then be able to import moments in your python scripts. Entering an
ipython or python session, type import moments. More details on installation
can be found in the
documentation.
If, for any reason, you have trouble installing moments after following these
steps, please submit an
issue.
If you use Parsing from moments.LD, which reads vcf files and computes LD
statistics to compare to predictions from moments.LD, you will need to
additionally install
-
hdf5
-
scikit-allel
All changes are detailed in the documentation.