Different, same, or arrhythmic: an empirically informed benchmark of differential rhythmicity methods
- 1Humboldt-Universitaet zu Berlin
Abstract
Circadian rhythms represent a key biological dynamic across all kingdoms of life. These cell-autonomous rhythms generated by a gene-regulatory network regulate processes in a time-of-day-specific manner primarily via transcriptional rhythms in a significant fraction of genes. The controlled experiment paradigm can be used to study causal effects at the molecular level using transcriptomic RNA-seq data even in this dynamic scenario. Multiple tools for the analysis of such data, termed differential rhythmicity (DR) analysis, are available but guidance on method choice remains limited. Here, we present a systematic benchmarking study of 12 different methods from 5 packages using a synthetic model of RNA-seq count data that incorporates both empirically estimated mean-dispersion trends and concomitant differential expression changes. By linking method performance to experimental conditions and analytical goals, we provide practical and simple rules of thumb that will help biologists choose appropriate DR methods and extract reliable biological insight from time series RNA-seq data.
Related
- DiffRhyShiny: A Benchmarking-Informed Platform for Differential Rhythmicity Analysis with Dependence-Aware Evidence Integration
- Differential maturation of mRGC pupillary constriction and circadian rhythmicity in typically developing human infants
- A configuration-resolved benchmark of differential abundance analysis methods for human gut 16S rRNA microbiome data
- Dolycoris baccarum (L.) (Hemiptera: Pentatomidae) Infestation Induced Differential Changes in Plant Physiology: Preference and Damaging Nature to Different Sesame (Sesamum indicum L.) Varieties
- A multidimensional benchmark of confounder-aware differential abundance strategies for single- and multi-cohort microbiome studies
BibTeX
@misc{ananthasubramaniam2026different,
title = {{Different, same, or arrhythmic: an empirically informed benchmark of differential rhythmicity methods}},
author = {B. Ananthasubramaniam},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.21.760649},
url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760649v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Different, same, or arrhythmic: an empirically informed benchmark of differential rhythmicity methods
AU - B. Ananthasubramaniam
PY - 2026
DA - 2026/10/21
PB - bioRxiv
DO - 10.64898/2026.10.21.760649
UR - https://www.biorxiv.org/content/10.64898/2026.10.21.760649v1
AB - Circadian rhythms represent a key biological dynamic across all kingdoms of life. These cell-autonomous rhythms generated by a gene-regulatory network regulate processes in a time-of-day-specific manner primarily via transcriptional rhythms in a significant fraction of genes. The controlled experiment paradigm can be used to study causal effects at the molecular level using transcriptomic RNA-seq data even in this dynamic scenario. Multiple tools for the analysis of such data, termed differential rhythmicity (DR) analysis, are available but guidance on method choice remains limited. Here, we present a systematic benchmarking study of 12 different methods from 5 packages using a synthetic model of RNA-seq count data that incorporates both empirically estimated mean-dispersion trends and concomitant differential expression changes. By linking method performance to experimental conditions and analytical goals, we provide practical and simple rules of thumb that will help biologists choose appropriate DR methods and extract reliable biological insight from time series RNA-seq data.
ER -