The tidyomics ecosystem: Enhancing omic data analyses.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• The increasing volume of omic data poses challenges in data handling and analysis.
• Bioconductor is a community-driven platform for biological data analysis.
• Tidy R programming provides a standardized approach to data organization and manipulation.

Purpose of the Study:

• To introduce the tidyomics software ecosystem, which bridges Bioconductor and the tidy R paradigm.
• To streamline omic data analysis workflows.
• To enhance learning and foster cross-disciplinary collaboration in omics research.

Main Methods:

• Development of the tidyomics software ecosystem.
• Integration of Bioconductor packages within a tidy R framework.
• Application of tidyomics to a large-scale dataset from the Human Cell Atlas.

Main Results:

• Demonstration of tidyomics' effectiveness in analyzing a large omic dataset.
• Successful integration of six data frameworks and ten analysis tools.
• Streamlined omic data analysis and visualization.

Conclusions:

• The tidyomics ecosystem offers a powerful and user-friendly solution for omic data analysis.
• It simplifies complex analyses and promotes reproducible research.
• Encourages broader adoption of advanced analytical methods in biological sciences.