Package: dia-staver
STAVER: A Standardized Dataset-Based Algorithm for Efficient Variation ReductionTable of ContentsTable of ContentsIntroductionInstallationGetting StartedDocumentationHow to ContributeContact UsLicenseIntroductionSTAVER 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:InstallationYou can installstaverpackage from PyPI by calling the following command:pipinstallstaverYou may install from source by cloning the STAVER repo, navigating to the root directory and using one of the following commandspip install ., orpip install -e .to install in editable mode:# clone the source repogitclonehttps://github.com/Ran485/STAVER.git# install the package in editable modepipinstall.# or using the following commandpipinstall-e.You may install additional environmental dependencies:pipinstall-rrequirements_dev.txt
pipinstall-rrequirements.txtGetting StartedFor example code and an introduction to the library, see the Jupyter notebooks intutorials, and the guided walkthroughhere. A straightforward command-line demonstration for a quick start can be discovered in the following block.python./staver_pipeline.py\--thread_numbers<TheCPUworkernumbers,Defaultto[nmax-2]>\--input<TheDIAdatainputdirectory>\--output_peptide<TheprocessedDIApeptidedataoutputdirectory>\--output_protein<TheprocessedDIAproteindataoutputdirectory>\--count_cutoff_same_libs<Defaultto1>\--count_cutoff_diff_libs<Defaultto2>\--proteins_cv_thresh<Defaultto0.3>\--na_threshold<Defaultto0.3>\--top_precursor_ions<Defaultto3>\--file_suffix<Defaultto"_F1_R1">\Run thetest-datain the following blockpython./staver/staver_pipeline.py\--thread_numbers16\--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_libs1\--count_cutoff_diff_libs2\--peptides_cv_thresh0.3\--proteins_cv_thresh0.3\--na_threshold0.3\--top_precursor_ions5\--file_suffix_F1_R1\DocumentationTo 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 STAVERdocumentation. 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 ContributeWe 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 thisdocumentation.Contact UsIf you have any questions, comments or suggestions, please do not hesitate to contact us at21112030023@m.fudan.edu.cnLicenseThe STAVER project licensed under theMIT License, granting users open access and the freedom to employ, adapt, and share the software as needed, while preserving the original copyright and license acknowledgements.=======
History0.1.0 (2023-03-25)First release on PyPI.CreditsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template... _Cookiecutter:https://github.com/audreyr/cookiecutter.. _audreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage