Easyreporting simplifies the implementation of Reproducible Research layers in R software

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Irreproducibility in omics data analysis is a growing concern due to complex computational methods.
• Ensuring the transparency and reproducibility of data analysis is vital to prevent misleading results.

Purpose of the Study:

• To introduce easyreporting, a novel R/Bioconductor package designed to facilitate Reproducible Research (RR).
• To demonstrate how easyreporting can be integrated into reports and tools to enhance analysis transparency.

Main Methods:

• Development of the easyreporting R/Bioconductor package.
• Illustration of RR implementation using a case study on RNA-seq data analysis.
• Showcasing automatic tracing of R functions for tracking analysis steps.

Main Results:

• The easyreporting package provides a framework for implementing RR in data analysis reports.
• Automatic tracing of R functions aids in documenting and reproducing analysis procedures.
• Successful application demonstrated through an RNA-seq data analysis case study.

Conclusions:

• Easyreporting significantly supports the reproducibility of data analysis projects, particularly in bioinformatics.
• The package offers advantages for developing R packages and graphical user interfaces (GUIs) with built-in RR.
• Facilitates tracing complex analysis steps and parameters, crucial for scientific integrity.