<p align="center">
<br>
<img src="https://github.com/Ran485/STAVER/raw/main/docs/_static/STAVER_logo.svg" width="400"/>
<br>
<h2 align="center">
STAVER: A Standardized Dataset-Based Algorithm for Efficient Variation Reduction
</h2>
<p>
<div align="center">
<a href="#">
<a href="https://github.com/Ran485/STAVER/stargazers">
<img alt="Downloads" src="https://img.shields.io/github/stars/Ran485/STAVER?logo=GitHub&color=red">
</a>
<img src="https://img.shields.io/badge/Python-3.7+-blue">
</a>
<a href="https://github.com/dwyl/esta/issues">
<img alt="PRs welcome" src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat">
</a>
<a href="https://opensource.org/licenses/MIT">
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</div>
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Installation](#installation)
- [Getting Started](#getting-started)
- [Documentation](#documentation)
- [How to Contribute](#how-to-contribute)
- [Contact Us](#contact-us)
- [License](#license)
## Introduction
STAVER is Python library that presents a standardized dataset-based algorithm designed to reduce variation in large-scale data-independent acquisition (DIA) mass spectrometry data. By employing a reference dataset to standardize mass spectrometry signals, STAVER effectively reduces noise and enhances protein quantification accuracy, especially in the context of multi-library search. The effectiveness of STAVER is demonstrated in several large-scale DIA datasets, showing improved identification and quantification of thousands of proteins. STAVER, featuring a modular design, provides flexible compatibility with existing DIA MS data analysis pipelines. The project aims to promote the adoption of multi-library search and improve the quality of DIA proteomics data through the open-source STAVER software package. A comprehensive overview of the research workflow and STAVER algorithm architecture are summarized in the following figure: 
## Installation
You can install ``staver`` package from PyPI by calling the following command:
``` shell
pip install staver
```
You may install from source by cloning the STAVER repo, navigating to the root directory and using one of the following commands ``pip install .``, or ``pip install -e .`` to install in editable mode:
``` shell
# clone the source repo
git clone https://github.com/Ran485/STAVER.git
# install the package in editable mode
pip install .
# or using the following command
pip install -e .
```
You may install additional environmental dependencies:
``` shell
pip install -r requirements_dev.txt
pip install -r requirements.txt
```
## Getting Started
For example code and an introduction to the library, see the Jupyter notebooks in
[tutorials](https://opensource.salesforce.com/STAVER/latest/tutorials.html), and the guided walkthrough
[here](https://opensource.salesforce.com/STAVER/latest/index.html). A straightforward command-line demonstration for a quick start can be discovered in the following block.
```shell
python ./staver_pipeline.py \
--thread_numbers < The CPU worker numbers, Default to [nmax-2] > \
--input < The DIA data input directory > \
--output_peptide < The processed DIA peptide data output directory > \
--output_protein < The processed DIA protein data output directory > \
--count_cutoff_same_libs < Default to 1 > \
--count_cutoff_diff_libs < Default to 2 > \
--proteins_cv_thresh < Default to 0.3 > \
--na_threshold < Default to 0.3 > \
--top_precursor_ions < Default to 3 > \
--file_suffix < Default to "_F1_R1" > \
```
Run the `test-data` in the following block
```shell
python ./staver/staver_pipeline.py \
--thread_numbers 16 \
--input ./staver/data/likai-diann-raw-20/ \
--reference_dataset_path ./data/likai-diann-raw \
--output_peptide ./staver/results/peptides/ \
--output_protein ./staver/results/proteins/ \
--count_cutoff_same_libs 1 \
--count_cutoff_diff_libs 2 \
--peptides_cv_thresh 0.3 \
--proteins_cv_thresh 0.3 \
--na_threshold 0.3 \
--top_precursor_ions 5 \
--file_suffix _F1_R1 \
```
## Documentation
To gain a comprehensive understanding of STAVER's application and to thoroughly appreciate the function and purpose of each parameter, we highly recommend perusing the all-encompassing STAVER [documentation](https://opensource.salesforce.com/STAVER/latest/index.html). This resource provides detailed, step-by-step instructions, accompanied by illustrative examples and clear explanations, equipping users with the knowledge to skillfully navigate and exploit the software's complete potential.
## How to Contribute
We welcome the contribution from the open-source community to improve the library!
To add a new explanation method/feature into the library, please follow the template and steps demonstrated in this
[documentation](https://opensource.salesforce.com/STAVER/latest/staver.html#how-to-contribute).
## Contact Us
If you have any questions, comments or suggestions, please do not hesitate to contact us at 21112030023@m.fudan.edu.cn
## License
The STAVER project licensed under the [MIT License](https://opensource.org/licenses/MIT), granting users open access and the freedom to employ, adapt, and share the software as needed, while preserving the original copyright and license acknowledgements.
=======
History
=======
0.1.0 (2023-03-25)
------------------
* First release on PyPI.
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
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"description": "<p align=\"center\">\n <br>\n <img src=\"https://github.com/Ran485/STAVER/raw/main/docs/_static/STAVER_logo.svg\" width=\"400\"/>\n <br>\n <h2 align=\"center\">\n STAVER: A Standardized Dataset-Based Algorithm for Efficient Variation Reduction\n </h2>\n<p>\n\n\n<div align=\"center\">\n <a href=\"#\">\n <a href=\"https://github.com/Ran485/STAVER/stargazers\">\n <img alt=\"Downloads\" src=\"https://img.shields.io/github/stars/Ran485/STAVER?logo=GitHub&color=red\">\n </a>\n <img src=\"https://img.shields.io/badge/Python-3.7+-blue\">\n </a>\n <a href=\"https://github.com/dwyl/esta/issues\">\n <img alt=\"PRs welcome\" src=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat\">\n </a>\n <a href=\"https://opensource.org/licenses/MIT\">\n <img alt=\"DOI\" src=\"https://img.shields.io/badge/License-MIT-yellow.svg\">\n </a>\n</div>\n\n## Table of Contents\n- [Table of Contents](#table-of-contents)\n- [Introduction](#introduction)\n- [Installation](#installation)\n- [Getting Started](#getting-started)\n- [Documentation](#documentation)\n- [How to Contribute](#how-to-contribute)\n- [Contact Us](#contact-us)\n- [License](#license)\n\n\n## Introduction\n\nSTAVER is Python library that presents a standardized dataset-based algorithm designed to reduce variation in large-scale data-independent acquisition (DIA) mass spectrometry data. By employing a reference dataset to standardize mass spectrometry signals, STAVER effectively reduces noise and enhances protein quantification accuracy, especially in the context of multi-library search. The effectiveness of STAVER is demonstrated in several large-scale DIA datasets, showing improved identification and quantification of thousands of proteins. STAVER, featuring a modular design, provides flexible compatibility with existing DIA MS data analysis pipelines. The project aims to promote the adoption of multi-library search and improve the quality of DIA proteomics data through the open-source STAVER software package. A comprehensive overview of the research workflow and STAVER algorithm architecture are summarized in the following figure: \n\n## Installation\n\nYou can install ``staver`` package from PyPI by calling the following command: \n``` shell\npip install staver\n```\nYou may install from source by cloning the STAVER repo, navigating to the root directory and using one of the following commands ``pip install .``, or ``pip install -e .`` to install in editable mode:\n``` shell\n# clone the source repo\ngit clone https://github.com/Ran485/STAVER.git\n\n# install the package in editable mode\npip install .\n\n# or using the following command\npip install -e .\n```\nYou may install additional environmental dependencies:\n\n``` shell\npip install -r requirements_dev.txt\npip install -r requirements.txt\n```\n\n## Getting Started\n\nFor example code and an introduction to the library, see the Jupyter notebooks in\n[tutorials](https://opensource.salesforce.com/STAVER/latest/tutorials.html), and the guided walkthrough\n[here](https://opensource.salesforce.com/STAVER/latest/index.html). A straightforward command-line demonstration for a quick start can be discovered in the following block.\n\n```shell\npython ./staver_pipeline.py \\\n --thread_numbers < The CPU worker numbers, Default to [nmax-2] > \\\n --input < The DIA data input directory > \\\n --output_peptide < The processed DIA peptide data output directory > \\\n --output_protein < The processed DIA protein data output directory > \\\n --count_cutoff_same_libs < Default to 1 > \\\n --count_cutoff_diff_libs < Default to 2 > \\\n --proteins_cv_thresh < Default to 0.3 > \\\n --na_threshold < Default to 0.3 > \\\n --top_precursor_ions < Default to 3 > \\\n --file_suffix < Default to \"_F1_R1\" > \\\n```\nRun the `test-data` in the following block\n```shell\npython ./staver/staver_pipeline.py \\\n --thread_numbers 16 \\\n --input ./staver/data/likai-diann-raw-20/ \\\n --reference_dataset_path ./data/likai-diann-raw \\\n --output_peptide ./staver/results/peptides/ \\\n --output_protein ./staver/results/proteins/ \\\n --count_cutoff_same_libs 1 \\\n --count_cutoff_diff_libs 2 \\\n --peptides_cv_thresh 0.3 \\\n --proteins_cv_thresh 0.3 \\\n --na_threshold 0.3 \\\n --top_precursor_ions 5 \\\n --file_suffix _F1_R1 \\\n```\n\n## Documentation\nTo gain a comprehensive understanding of STAVER's application and to thoroughly appreciate the function and purpose of each parameter, we highly recommend perusing the all-encompassing STAVER [documentation](https://opensource.salesforce.com/STAVER/latest/index.html). This resource provides detailed, step-by-step instructions, accompanied by illustrative examples and clear explanations, equipping users with the knowledge to skillfully navigate and exploit the software's complete potential.\n\n## How to Contribute\nWe welcome the contribution from the open-source community to improve the library!\n\nTo add a new explanation method/feature into the library, please follow the template and steps demonstrated in this \n[documentation](https://opensource.salesforce.com/STAVER/latest/staver.html#how-to-contribute).\n\n## Contact Us\nIf you have any questions, comments or suggestions, please do not hesitate to contact us at 21112030023@m.fudan.edu.cn\n\n## License\nThe STAVER project licensed under the [MIT License](https://opensource.org/licenses/MIT), granting users open access and the freedom to employ, adapt, and share the software as needed, while preserving the original copyright and license acknowledgements.\n\n\n=======\nHistory\n=======\n\n0.1.0 (2023-03-25)\n------------------\n\n* First release on PyPI.\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n",
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