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PreprintCC BY-NCbioRxiv

DiffRhyShiny: A Benchmarking-Informed Platform for Differential Rhythmicity Analysis with Dependence-Aware Evidence Integration

  • C. Liu
  • L. Liu
  • N. Li
  • Y. Jiang
  • C. Li
  • Y. Liu
  • Y. Fu
  • K. Guo1
  1. 1University of Michigan
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Abstract

Motivation: Differential rhythmicity analysis identifies condition-specific changes in biological oscillations, but diverse algorithmic assumptions complicate method selection. Furthermore, integrating complementary evidence across different tools while accounting for statistical dependence remains an unresolved challenge. Results: We benchmarked seven algorithms across diverse rhythmic scenarios, revealing that optimal selection depends heavily on specific biological variations and experimental designs. To overcome single-algorithm limitations, we introduced Kost's Method, a dependence-aware p-value integration approach. Building on this framework, we developed DiffRhyShiny, an interactive web platform that automates robust differential rhythmicity analysis, consensus evaluation, and visualization. Furthermore, we validated the platform's performance and statistical rigor using both extensive simulated scenarios and real-world transcriptomic datasets. Availability and implementation: DiffRhyShiny is freely available at https://omicsai.shinyapps.io/diffrhyshiny, with the latest source code at https://github.com/liuchuan111/diffrhyshiny. The code and data to reproduce all benchmarking results are archived on Figshare at https://doi.org/10.6084/m9.figshare.33070100.

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Cite

BibTeXRIS
BibTeX
@misc{liu2026diffrhyshiny,
  title = {{DiffRhyShiny: A Benchmarking-Informed Platform for Differential Rhythmicity Analysis with Dependence-Aware Evidence Integration}},
  author = {C. Liu and L. Liu and N. Li and Y. Jiang and C. Li and Y. Liu and Y. Fu and K. Guo},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.07.757323},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.07.757323v1},
  note = {CC BY-NC},
}
RIS
TY  - UNPB
TI  - DiffRhyShiny: A Benchmarking-Informed Platform for Differential Rhythmicity Analysis with Dependence-Aware Evidence Integration
AU  - C. Liu
AU  - L. Liu
AU  - N. Li
AU  - Y. Jiang
AU  - C. Li
AU  - Y. Liu
AU  - Y. Fu
AU  - K. Guo
PY  - 2026
DA  - 2026/10/07
PB  - bioRxiv
DO  - 10.64898/2026.10.07.757323
UR  - https://www.biorxiv.org/content/10.64898/2026.10.07.757323v1
AB  - Motivation: Differential rhythmicity analysis identifies condition-specific changes in biological oscillations, but diverse algorithmic assumptions complicate method selection. Furthermore, integrating complementary evidence across different tools while accounting for statistical dependence remains an unresolved challenge. Results: We benchmarked seven algorithms across diverse rhythmic scenarios, revealing that optimal selection depends heavily on specific biological variations and experimental designs. To overcome single-algorithm limitations, we introduced Kost's Method, a dependence-aware p-value integration approach. Building on this framework, we developed DiffRhyShiny, an interactive web platform that automates robust differential rhythmicity analysis, consensus evaluation, and visualization. Furthermore, we validated the platform's performance and statistical rigor using both extensive simulated scenarios and real-world transcriptomic datasets. Availability and implementation: DiffRhyShiny is freely available at https://omicsai.shinyapps.io/diffrhyshiny, with the latest source code at https://github.com/liuchuan111/diffrhyshiny. The code and data to reproduce all benchmarking results are archived on Figshare at https://doi.org/10.6084/m9.figshare.33070100.
ER  -