The tidyomics ecosystem: enhancing omic data analyses

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• The rapid expansion of omic datasets necessitates advanced data handling and analysis techniques.
• Existing platforms like Bioconductor offer powerful tools but can present a steep learning curve.
• The tidy R paradigm provides an intuitive framework for data manipulation and organization.

Purpose of the Study:

• To introduce the tidyomics software ecosystem, a novel bridge between Bioconductor and tidy R.
• To simplify and enhance the analysis and integration of complex omic data.
• To foster easier learning and promote interdisciplinary collaboration in biological research.

Main Methods:

• Development of the tidyomics software ecosystem, integrating Bioconductor functionalities within a tidy R framework.
• Application of tidyomics to a large-scale dataset from the Human Cell Atlas.
• Utilizing six distinct data frameworks and ten analysis tools to showcase versatility.

Main Results:

• Demonstrated the effectiveness of tidyomics in analyzing a substantial dataset of 7.5 million peripheral blood mononuclear cells.
• Successfully integrated diverse omic data types and analysis workflows within the tidyomics ecosystem.
• Showcased the platform's capability to handle large-scale biological data efficiently.

Conclusions:

• tidyomics offers a streamlined and accessible approach to omic data analysis.
• The ecosystem effectively bridges established bioinformatics resources with modern data science practices.
• tidyomics has the potential to accelerate biological discovery and interdisciplinary research.