Colange Maximilien, Appé Guillaume, Meunier Léa, Weill Solène, Johnson W Evan, Nordor Akpéli, Behdenna Abdelkader
Epigene Labs, Paris, France.
Rutgers New Jersey Medical School, Rutgers University, Newark, NJ, USA.
Sci Rep. 2025 May 24;15(1):18104. doi: 10.1038/s41598-025-03376-y.
We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or artificial intelligence (AI). As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. InMoose focuses on providing drop-in replacements for R tools, to ensure consistency and reproducibility between R-based and Python-based pipelines. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis.
我们引入了InMoose，这是一个旨在进行组学数据分析的开源Python环境。我们展示了它在批量转录组数据分析方面的能力。由于其广泛应用，Python已成为生物信息学管道中越来越重要的领域（如数据科学、机器学习或人工智能）事实上的标准。作为一种通用语言，Python还因其通用性和可扩展性而受到认可。InMoose旨在将历来用R编写的前沿工具引入Python生态系统。InMoose专注于为R工具提供直接替代方案，以确保基于R和基于Python的管道之间的一致性和可重复性。第一个开发阶段专注于批量转录组数据，目前的功能包括数据模拟、批次效应校正、差异分析和荟萃分析。
