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  1. Home
  3. Evaluating Statistical Models For Overdispersed Multi-omics Data: A Multiplex Immunofluorescence Case Study.
  1. Home
  3. Evaluating Statistical Models For Overdispersed Multi-omics Data: A Multiplex Immunofluorescence Case Study.

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

Claire E Thomas1, Evertine Wesselink2, Yasutoshi Takashima3

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States.

American Journal of Epidemiology
|June 15, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Ordinal logistic, linear negative binomial (NB), and quasi-Poisson models are robust for analyzing multi-omic data in cancer research. These models effectively handle overdispersed and zero-inflated data, crucial for accurate T cell subset density analysis.

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Cancer Research

Background:

  • Multi-omic data analysis presents significant statistical challenges, particularly in complex biological systems like colorectal cancer.
  • T cell subset densities are critical biomarkers in cancer immunology, but their analysis requires robust statistical methods.

Purpose of the Study:

  • To evaluate and compare the performance of seven statistical models for analyzing multiplex immunofluorescence T cell subset density data in colorectal cancer.
  • To identify the most suitable models for handling overdispersed and zero-inflated multi-omic data in epidemiological studies.

Main Methods:

  • A comparative analysis of seven statistical models: ordinal logistic regression, Poisson, quasi-Poisson, quadratic negative binomial (NB), linear NB, zero-inflated NB (ZINB), and hurdle NB.
  • Assessment of model performance using a large dataset (1235 cases) of colorectal cancer multiplex immunofluorescence data.
  • Simulation studies to evaluate type I error rates and statistical power across various data distributions and zero proportions.

    • Main Results:

    • Poisson and NB-based models (quadratic, ZINB, hurdle) exhibited inflated false-positive rates.
    • Ordinal logistic regression and linear NB models showed no significant inflation of false positives.
    • Ordinal logistic, linear NB, and quasi-Poisson models demonstrated superior or near-optimal statistical power across different data scenarios, including varying zero proportions and dispersion levels.

    Conclusions:

    • Ordinal logistic, linear NB, and quasi-Poisson models are recommended as robust and effective statistical tools for analyzing overdispersed, right-skewed multi-omic data with a substantial proportion of zero counts.
    • These models provide reliable effect estimates, particularly for weaker exposures, and maintain appropriate statistical properties for epidemiological research in cancer.
    • The findings offer valuable guidance for researchers in bioinformatics and cancer epidemiology dealing with complex high-dimensional biological data.