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Statistics Research Seminar


Double Hierarchical Generalized Linear Models for RNAseq Data: DHGLMseq


Dongseok Choi, Ph.D., Professor, OHSU-PSU School of Public Health, Portland, OR


RNAseq has become the standard technology in gene expression studies in the past few years. It is considered superior to microarrays that used to be the choice of technology in the 2000s. Since RNAseq data are typically summarized as counts per gene for downstream statistical analyses, there have been active developments of statistical models based on negative binomial regression models (NB). To overcome the shortfalls of current NB-based models, we extended the double hierarchical generalized linear models to high dimensional counting data such as RNAseq data and developed an R package for model fitting (DHGLMseq). In addition, we extended Lee and Bjønstad’s false discovery rate (FDR) control for linear mixed models to the high dimensional DHGMLs. In this presentation, we will review a brief history of advancement of statistical methods for RNAseq data and compare their power and false discovery rates by simulations.

Tea and refreshments with faculty and speaker:

3:00pm to 3:45pm in Weniger Room 245


4:00pm in Weniger 149


Monday, October 21, 2019 at 4:00pm to 4:50pm

Weniger Hall, Room 149
103 SW Memorial Place, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic



Community, Faculty and Staff, Student, Alumni, Industry Partner, International, Online




Free and open to the public

College of Agricultural Sciences, Department of Statistics, College of Science
Contact Name

Mary Gardner

Contact Email

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