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

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Publicado en:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 18104
Publicado:
Nature Publishing Group
Materias:
Artificial intelligence
Transcriptomics
Language
Machine learning
Simulation
Bioinformatics
Data analysis
Data science
Ecosystems
Reproducibility
Meta-analysis
Economic
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2045-2322
DOI:10.1038/s41598-025-03376-y
Fuente:Science Database