Bridging the gap between R and Python in bulk transcriptomic data analysis with InMoose

doi:10.1038/s41598-025-03376-y

2025-05-24

DOI：10.1038/s41598-025-03376-y

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我们介绍InMoose，一个面向组学数据分析的开源Python环境。我们展示了其在批量转录组数据分析方面的能力。由于Python的广泛应用，它已成为数据科学、机器学习或人工智能（AI）等日益重要的生物信息学流程领域的事实标准。作为一种通用语言，Python也因其多功能性和可扩展性而备受认可。InMoose旨在将历史上用R编写的先进工具引入Python生态系统。InMoose专注于为R工具提供即插即用的替代方案，以确保基于R和基于Python的流程之间的一致性和可重复性。第一阶段开发专注于批量转录组数据，当前功能涵盖数据模拟、批次效应校正以及差异分析和元分析。

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Scientific Reports

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3.9

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28.3W

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83.5W

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epigene labs

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Rutgers State Univ

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被引数: 296

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