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Regression Models For Multivariate Count Data.

Yiwen Zhang1, Hua Zhou2, Jin Zhou3

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|March 29, 2017
PubMed
Summary
This summary is machine-generated.

New regression models offer a flexible approach for analyzing multivariate count data, overcoming limitations of traditional methods like the multinomial-logit model, especially for RNA-seq data. These models improve accuracy in hypothesis testing and variable selection for complex biological datasets.

Keywords:
Dirichlet-multinomialanalysis of deviancecategorical data analysisgeneralized Dirichlet-multinomialiteratively reweighted Poisson regression (IRPR)negative multinomialreduced rank GLMregularization

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Area of Science:

  • Statistics
  • Bioinformatics
  • Computational Biology

Background:

  • Multivariate count data are common in modern applications, including RNA-sequencing.
  • The standard multinomial-logit model has restrictive mean-variance assumptions, leading to errors with real-world data like RNA-seq.
  • Over-dispersion and complex correlations in count data necessitate more flexible regression models.

Purpose of the Study:

  • To introduce and analyze generalized linear models for multivariate count data with various correlation structures.
  • To address the lack of flexible models in current literature, particularly for non-natural exponential family distributions.
  • To provide a unifying framework for estimation, testing, and variable selection for these advanced models.

Main Methods:

  • Development of generalized linear models accommodating diverse correlation structures for multivariate counts.
  • Application of a unifying framework for statistical inference, including estimation, hypothesis testing, and variable selection.
  • Comparative analysis using both synthetic datasets and real-world RNA-sequencing data.

Main Results:

  • Demonstrated limitations of the multinomial-logit model in handling over-dispersion and correlation in RNA-seq data.
  • Proposed generalized linear models show improved performance and flexibility for multivariate count data.
  • The unifying framework effectively handles estimation, testing, and variable selection for the studied models.

Conclusions:

  • Generalized linear models offer a superior alternative to the multinomial-logit model for analyzing complex multivariate count data.
  • The developed methods are crucial for accurate analysis of RNA-sequencing and similar biological count data.
  • This work provides essential tools for researchers dealing with high-dimensional count-based biological data.