Evaluating statistical models for overdispersed multi-omics data: a multiplex immunofluorescence case study

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ID: 317439
2026
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Abstract
Multi-omic data analysis poses statistical challenges. We evaluated statistical models for our multiplex immunofluorescence study of T cell subset densities in colorectal cancer. Using 1235 cases, we compared seven models- ordinal logistic regression, Poisson, quasi-Poisson, quadratic negative binomial (NB), linear NB, zero-inflated NB (ZINB) and hurdle NB models- assessing associations with a strong (microsatellite instability, MSI) and a weak (calcium intake) exposure. Simulation studies assessed type I error and power. Effect estimates were generally consistent for the strong exposure (MSI) but varied for the weaker exposure (calcium). Simulations revealed inflated false-positive rates for the Poisson and NB-based models, including quadratic NB, zero-inflated and hurdle, but not for ordinal logistic regression or the linear NB model. The quasi-Poisson model showed modest inflation of low p-values, but the overall p-value distribution remained approximately uniform under the null. Ordinal logistic, linear NB, and quasi-Poisson models achieved the best or near-best power across a range of zero proportions, dispersion levels, and distributions. The ordinal logistic, linear NB, and quasi-Poisson models are useful and robust options for epidemiologic analyses of overdispersed, right-skewed multi-omic data with a nontrivial proportion of zero counts.
Reference Key
openalex_W7164815920 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Claire E. Thomas, Evertine Wesselink, Yasutoshi Takashima, Jeroen R. Huyghe, Daniel D. Buchanan, Robert C Grant, Andressa Dias Costa, Tomotaka Ugai, Shuji Ogino, Jonathan A Nowak, Ulrike Peters, Amanda I Phipps, Li Hsu
Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag127
URL
https://doi.org/10.1093/aje/kwag127
Keywords Keywords not found

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