AR(2) Eigenvalue Modulus as a Measure of Temporal Persistence in Gene Expression: Circadian Hierarchy Emerges from Two Coefficients
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2026
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| _version_ | 1866901352406319104 |
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| author | Whiteside, Michael |
| author_facet | Whiteside, Michael |
| contents | <div> <div> <div> <div> <div> <div> <div> <p>The method fits a simple two-coefficient autoregressive model — AR(2) — to gene expression time series and extracts a single number, the eigenvalue modulus |λ|, which measures how strongly a gene's past expression predicts its future. Without knowing anything about which genes are "clock genes" or "targets," this blind metric recovers the known circadian hierarchy: core clock genes show the highest persistence (median |λ| = 0.647), clock-controlled targets sit in the middle (0.529), and the genome background is lowest (0.496). The paper validates this across 12 mouse tissues, 4 species spanning 400 million years of evolution, and shows the hierarchy collapses when the master clock gene BMAL1 is knocked out. It also demonstrates the method works beyond circadian biology — in immune response data, cancer state-swap experiments, and against standard time-series methods in head-to-head benchmarks. The core argument is that temporal persistence, captured by just two regression coefficients, is a fundamental and underappreciated organising principle in gene expression.</p> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> </div> <div> </div> <div> <div> </div> </div> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19070876 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AR(2) Eigenvalue Modulus as a Measure of Temporal Persistence in Gene Expression: Circadian Hierarchy Emerges from Two Coefficients Whiteside, Michael <div> <div> <div> <div> <div> <div> <div> <p>The method fits a simple two-coefficient autoregressive model — AR(2) — to gene expression time series and extracts a single number, the eigenvalue modulus |λ|, which measures how strongly a gene's past expression predicts its future. Without knowing anything about which genes are "clock genes" or "targets," this blind metric recovers the known circadian hierarchy: core clock genes show the highest persistence (median |λ| = 0.647), clock-controlled targets sit in the middle (0.529), and the genome background is lowest (0.496). The paper validates this across 12 mouse tissues, 4 species spanning 400 million years of evolution, and shows the hierarchy collapses when the master clock gene BMAL1 is knocked out. It also demonstrates the method works beyond circadian biology — in immune response data, cancer state-swap experiments, and against standard time-series methods in head-to-head benchmarks. The core argument is that temporal persistence, captured by just two regression coefficients, is a fundamental and underappreciated organising principle in gene expression.</p> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> </div> <div> </div> <div> <div> </div> </div> <p> </p> |
| title | AR(2) Eigenvalue Modulus as a Measure of Temporal Persistence in Gene Expression: Circadian Hierarchy Emerges from Two Coefficients |
| url | https://doi.org/10.5281/zenodo.19070876 |