Metadata-Version: 2.1
Name: prstools
Version: 0.0.84
Summary: Convenient and powerfull Polygenic Risk Score creation.
Author: Menno Witteveen et al.
Author-email: menno102@hotmail.com
License: MIT License
Project-URL: Home, https://prstools.readthedocs.io/
Project-URL: Repository, https://github.com/mennojw/prstools-release
Keywords: PRS PGS polygenic genomics prediction genetics
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: tqdm
Requires-Dist: h5py
Requires-Dist: ipython
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: joblib
Requires-Dist: pyarrow
Requires-Dist: psutil
Requires-Dist: bed-reader<1.0; python_version < "3.9"
Requires-Dist: bed-reader; python_version >= "3.9"
Provides-Extra: dev
Requires-Dist: nbdev; extra == "dev"
Provides-Extra: full
Requires-Dist: mjwt; extra == "full"
Requires-Dist: pysnptools; extra == "full"
Requires-Dist: seaborn; extra == "full"

# prstools


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

`prstools` is software to create Polygenic Risk Scores (PRS) directly
from the commandline. <br>

It makes PRS generation easier, compared to previous tools, by:

- Super fast reading and matching of sumstats (handles odd formats).
- Rapid creation of the model.
- Automatic generation of the PRS Prediction for target plink files.

All the above, for a real GWAS sumstat, within 30 minutes using only
**1** command. Installation and running the demo example should not take
more than 10 minutes.

**Website:** <https://prstools.readthedocs.io/>

## Install

To install use the following command.

``` sh
pip install -U --prefer-binary prstools
```

For it to work, you should have python3.8 or later installed (`pip` is
included in python3.8+). If the command above does not work directly you
can install using `conda` (or `mamba` if you have that) by running
`conda install "python>=3.9"`. For other install issues please check the
[install
guide](https://prstools.readthedocs.io/en/latest/guides/install_guide.html)
or send us a mail.

## How to use

Immediately after installing prstools, it should be possible to download
& run the demo example (~4mb), by pasting the following into the
commandline:

``` bash
# Makes 'example' dir with data in current path:
prstools downloadutil --pattern example --destdir ./; cd example

# Run the model with example data:
prstools prscs2 --ref ldref_1kg_pop --target target \
                --sst sumstats.tsv --n_gwas 2565 --out ./result 
```

This will run PRS-CS2 and make PRS prediction using example data. For a
more comprehensive introduction and the best and fastest way to get a
PRS for your case, try the [Getting Started
Tutorial](https://prstools.readthedocs.io/en/latest/tutorials/getting_started.html).
<br>

Additionaly, prstools provides documentation through its command-line
interface. Run `prst` to see the available commands, or
`prst prscs2 --help` for options and copy-pastable examples. The same
documention is [available
online](https://prstools.readthedocs.io/en/latest/cli.html) too.

## Tutorial + Video

For more information and a hands-on demonstration of what prstools can
do, have a look at the [Getting Started
Tutorial](https://prstools.readthedocs.io/en/latest/tutorials/getting_started.html).
There is also a [Video](https://youtu.be/BP1zUBFH2l8). The
tutorial+video is a tiny bit older than the current prstools version,
which has more functionality. You can load the tutorial in a free cloud
instance by clicking here: [![Google
Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mennojw/prstools-release/blob/main/docs/tutorials/getting_started.ipynb).

## Contact

For questions and support please send a mail
(menno.j.witteveen@gmail.com).

We are actively developing prstools and feedback by mail or our
[feedback form](https://forms.gle/TnvNyBX6qDy7Vupn9) (with 🏆 lottery)
is much appreciated!
