Department of Clinical Proteomics, Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.
Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad461.
The widespread application of mass spectrometry (MS)-based proteomics in biomedical research increasingly requires robust, transparent, and streamlined solutions to extract statistically reliable insights. We have designed and implemented AlphaPeptStats, an inclusive Python package with currently with broad functionalities for normalization, imputation, visualization, and statistical analysis of label-free proteomics data. It modularly builds on the established stack of Python scientific libraries and is accompanied by a rigorous testing framework with 98% test coverage. It imports the output of a range of popular search engines. Data can be filtered and normalized according to user specifications. At its heart, AlphaPeptStats provides a wide range of robust statistical algorithms such as t-tests, analysis of variance, principal component analysis, hierarchical clustering, and multiple covariate analysis-all in an automatable manner. Data visualization capabilities include heat maps, volcano plots, and scatter plots in publication-ready format. AlphaPeptStats advances proteomic research through its robust tools that enable researchers to manually or automatically explore complex datasets to identify interesting patterns and outliers.
AlphaPeptStats is implemented in Python and part of the AlphaPept framework. It is released under a permissive Apache license. The source code and one-click installers are freely available and on GitHub at https://github.com/MannLabs/alphapeptstats.
基于质谱（MS）的蛋白质组学在生物医学研究中的广泛应用越来越需要强大、透明和简化的解决方案，以提取具有统计学可靠性的见解。我们设计并实现了 AlphaPeptStats，这是一个包含广泛功能的 Python 包，目前具有广泛的功能，可用于无标记蛋白质组学数据的归一化、插补、可视化和统计分析。它在成熟的 Python 科学库堆栈上进行了模块化构建，并附有一个严格的测试框架，具有 98%的测试覆盖率。它导入了一系列流行的搜索引擎的输出。可以根据用户规范对数据进行过滤和归一化。AlphaPeptStats 的核心是提供广泛的强大统计算法，例如 t 检验、方差分析、主成分分析、层次聚类和多协变量分析 - 所有这些都可以自动化进行。数据可视化功能包括热图、火山图和散点图，以出版物准备的格式呈现。AlphaPeptStats 通过其强大的工具推进蛋白质组学研究，使研究人员能够手动或自动探索复杂数据集，以识别有趣的模式和异常值。
AlphaPeptStats 是用 Python 实现的，是 AlphaPept 框架的一部分。它在宽松的 Apache 许可证下发布。源代码和一键安装程序可在 GitHub 上免费获得，网址为 https://github.com/MannLabs/alphapeptstats。
