معرفی شرکت ها
epistasis-0.7.5
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A Python API for estimating statistical high-order epistasis in genotype-phenotype maps.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | epistasis-0.7.5 |
| نام | epistasis |
| نسخه کتابخانه | 0.7.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Zachary R. Sailer |
| ایمیل نویسنده | zachsailer@gmail.com |
| آدرس صفحه اصلی | https://github.com/harmslab/epistasis |
| آدرس اینترنتی | https://pypi.org/project/epistasis/ |
| مجوز | UNLICENSE |
# Epistasis
[](https://gitter.im/harmslab/epistasis?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://beta.mybinder.org/v2/gh/harmslab/epistasis-notebooks/master)
[](http://epistasis.readthedocs.io/?badge=latest)
[](https://github.com/harmslab/epistasis/actions?query=workflow%3A%22Epistasis+Tests%22)
[](https://doi.org/10.5281/zenodo.1215853)
*Python API for estimating statistical, high-order epistasis in genotype-phenotype maps.*
All models follow a *Scikit-learn* interface and thus seamlessly plug in to the PyData ecosystem. For more information about the type of models included in this package,
read our [docs](http://epistasis.readthedocs.io/?badge=latest). You can also read more about the theory behind these models in our [paper](https://doi.org/10.1534/genetics.116.195214).
Finally, if you'd like to test out this package without any installing, try these Jupyter notebooks [here](https://mybinder.org/v2/gh/harmslab/epistasis-notebooks/master) (thank you [Binder](https://mybinder.org/)!).
## Examples
The Epistasis package works best in combinations with GPMap, an API for managing
genotype-phenotype map data. Construct a GenotypePhenotypeMap object and pass it
directly to an epistasis model.
```python
# Import a model and the plotting module
from gpmap import GenotypePhenotypeMap
from epistasis.models import EpistasisLinearRegression
from epistasis.pyplot import plot_coefs
# Genotype-phenotype map data.
wildtype = "AAA"
genotypes = ["ATT", "AAT", "ATA", "TAA", "ATT", "TAT", "TTA", "TTT"]
phenotypes = [0.1, 0.2, 0.4, 0.3, 0.3, 0.6, 0.8, 1.0]
# Create genotype-phenotype map object.
gpm = GenotypePhenotypeMap(wildtype=wildtype,
genotypes=genotypes,
phenotypes=phenotypes)
# Initialize an epistasis model.
model = EpistasisLinearRegression(order=3)
# Add the genotype phenotype map.
model.add_gpm(gpm)
# Fit model to given genotype-phenotype map.
model.fit()
# Plot coefficients (powered by matplotlib).
plot_coefs(model, figsize=(3,5))
```
<img src="docs/img/coef_example.png" width="200">
More examples can be found in these [binder notebooks](https://mybinder.org/v2/gh/harmslab/epistasis-notebooks/master).
## Installation
Epistasis works in Python 3+ (we do not guarantee it will work in Python 2.)
To install the most recent release on PyPi:
```
pip install epistasis
```
To install from source, clone this repo and run:
```
pip install -e .
```
## Documentation
Documentation and API reference can be viewed [here](http://epistasis.readthedocs.io/).
## Dependencies
* [gpmap](https://github.com/harmslab/gpmap): Module for constructing powerful genotype-phenotype map python data-structures.
* [Scikit-learn](http://scikit-learn.org/stable/): Simple to use machine-learning algorithms
* [Numpy](http://www.numpy.org/): Python's array manipulation packaged
* [Scipy](http://www.scipy.org/): Efficient scientific array manipulations and fitting.
* [lmfit](https://lmfit.github.io/lmfit-py/): Non-linear least-squares minimization and curve fitting in Python.
### Optional dependencies
* [matplotlib](): Python plotting API.
* [ipython](): interactive python kernel.
* [jupyter notebook](): interactive notebook application for running python kernels interactively.
* [ipywidgets](): interactive widgets in python.
## Development
We welcome pull requests! If you find a bug, we'd love to have you fix it. If
there is a feature you'd like to add, feel free to submit a
pull request with a description of the addition. We also ask that you write the
appropriate unit-tests for the new feature and add documentation to our Sphinx docs.
To run the tests on this package, make sure you have `pytest` installed and run from the base directory:
```
pytest
```
## Citing
If you use this API for research, please cite this [paper](https://doi.org/10.1534/genetics.116.195214).
You can also cite the software directly:
```
@misc{zachary_sailer_2017_252927,
author = {Zachary Sailer and Mike Harms},
title = {harmslab/epistasis: Genetics paper release},
month = jan,
year = 2017,
doi = {10.5281/zenodo.1215853},
url = {https://doi.org/10.5281/zenodo.1215853}
}
```
نیازمندی
| مقدار | نام |
|---|---|
| - | cython |
| >=1.15.2 | numpy |
| >=0.24.2 | pandas |
| >=0.20.0 | scikit-learn |
| >=1.1.0 | scipy |
| >=2.2.1 | emcee |
| >=0.9.11 | lmfit |
| >=3.0.0 | matplotlib |
| >=0.6.0 | gpmap |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.3.0 | Python |
نحوه نصب
نصب پکیج whl epistasis-0.7.5:
pip install epistasis-0.7.5.whl
نصب پکیج tar.gz epistasis-0.7.5:
pip install epistasis-0.7.5.tar.gz