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<p>Circadian rhythms are among the key oscillatory functions that rhythmically coordinate many biological processes in living organisms. In this study, we analyze high-throughput gene expression data based on mammalian skin samples collected over the course of multiple days and nights. Our aims are to (1) identify mammalian genes which express circadian patterns that are consistent over days, and (2) cluster such genes that exhibit similar patterns of circadian expression peaking at different phases over a 24-hour period. We extended a recently developed computational framework that we call CIrcular FUnctions (CIFU), which allows representation of gene expression time-course data as flexibly shaped curves, with temporal registration of such curves using a procedure based on Fisher-Rao distance, and followed by model-based curve clustering. Our analysis identified five clusters of genes exhibiting nuanced circadian patterns that were validated against other known clustering approaches. Finally, statistical over-representation analysis was used for testing the identified clusters of their potential regulation by key molecular pathways associated with mammalian immune response, skin cell aging, and circadian rhythm.</p>

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Circadian Gene Expression Analysis Using an Enhanced Circular Functional Framework

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • Brian Wainwright,
  • Erfan Ghasemi,
  • Md. Hasnat Ali,
  • Meghana Ray,
  • Saumyadipta Pyne,
  • S. Rao Jammalamadaka

摘要

Circadian rhythms are among the key oscillatory functions that rhythmically coordinate many biological processes in living organisms. In this study, we analyze high-throughput gene expression data based on mammalian skin samples collected over the course of multiple days and nights. Our aims are to (1) identify mammalian genes which express circadian patterns that are consistent over days, and (2) cluster such genes that exhibit similar patterns of circadian expression peaking at different phases over a 24-hour period. We extended a recently developed computational framework that we call CIrcular FUnctions (CIFU), which allows representation of gene expression time-course data as flexibly shaped curves, with temporal registration of such curves using a procedure based on Fisher-Rao distance, and followed by model-based curve clustering. Our analysis identified five clusters of genes exhibiting nuanced circadian patterns that were validated against other known clustering approaches. Finally, statistical over-representation analysis was used for testing the identified clusters of their potential regulation by key molecular pathways associated with mammalian immune response, skin cell aging, and circadian rhythm.