{
  "abstract": "Detecting circadian rhythmicity in large-scale transcriptomic data is challenging when sampling is irregular, signals are weak, and expression profiles deviate from sinusoidal shapes. The fixed harmonic form of classical Cosinor-based methods may therefore be poorly suited to asymmetric and temporally complex rhythms. We present CircaFMM, a flexible framework for circadian rhythmicity detection based on the single-component Frequency Modulated Möbius (FMM) model. CircaFMM defines rhythmicity as time dependence induced by the Möbius phase parameter, so that the non-rhythmic null corresponds to a time-invariant mean profile. The resulting non-regular test combines time-density weighting, regularized estimation, and bootstrap calibration of a weighted likelihood-ratio-type statistic. Simulations showed controlled type-I error under irregular sampling and improved power over the classical Cosinor F-test for asymmetric waveforms. Applied to the GTEx-13T postmortem transcriptomic dataset, CircaFMM recovered canonical clock genes and circadian pathways while identifying additional rhythmic candidates with lower signal-to-noise-ratio, greater waveform asymmetry, and non-sinusoidal temporal profiles. CircaFMM is available at https://circafmm.shinyapps.io/circafmm_shinyapp/, with source code at https://github.com/FMMGroupVa/CircaFMM.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.35891",
  "author_total": 3,
  "authors": [
    {
      "name": "Joshua S. Abston"
    },
    {
      "name": "Yolanda Larriba"
    },
    {
      "name": "Itziar Fernández"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a86spr03v8rk8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.35891",
  "fulltext_url": null,
  "id": "a86spr03v8rk8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.35891",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2807|Circadian rhythm and melatonin"
    ]
  ],
  "openalex": "W7224102492",
  "pdf_url": "https://arxiv.org/pdf/2610.35891",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a86spr03v8rk8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:59Z",
      "native_id": "2610.35891",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.35891"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "CircaFMM: A Flexible Statistical Framework for Circadian Rhythmicity Detection in Transcriptomics",
  "title_text": "CircaFMM: A Flexible Statistical Framework for Circadian Rhythmicity Detection in Transcriptomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:59Z",
  "url": "https://research.draconitic.com/paper/a86spr03v8rk8",
  "venue": "arXiv",
  "versions": []
}