Circular Order Aggregation and its Application to Cell-cycle Genes Expressions.

Area of Science:

• Computational Biology
• Data Science
• Bioinformatics

Background:

• Determining the order of events is crucial in many scientific fields.
• Biological processes, like cell cycle gene expression, often exhibit cyclical patterns.
• Existing methods may not effectively aggregate information from heterogeneous data sources to establish circular orders.

Purpose of the Study:

• To introduce and address the novel problem of circular order aggregation.
• To develop computational approaches for finding a circular order of items using angular data from multiple datasets.
• To apply these methods to a biological problem concerning cell cycle gene regulation.

Main Methods:

• Proposed two distinct algorithms for circular order aggregation.
• Utilized pairwise and triplewise information from heterogeneous angular datasets.
• Conducted theoretical analyses and numerical simulations to evaluate the methods.

Main Results:

• Successfully developed and compared two novel approaches for circular order aggregation.
• Demonstrated the applicability of the methods to real-world biological data.
• Provided insights into the order of peak expression for cell cycle genes.

Conclusions:

• Circular order aggregation is a viable approach for sequencing items from diverse data.
• The proposed methods offer effective solutions for this new computational challenge.
• This work provides a computational framework for understanding cyclical biological processes.