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Statistics Seminar Series: Double Hierarchical Generalized Linear Models for RNAseq Data: DHGLMseq

When: Wednesday, October 2, 2019
11:00 AM - 12:00 PM  

Where: 2006 Sheridan Road, B02, Evanston, IL 60208 map it

Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students

Contact: Kisa Kowal   847.491.3974

Group: Department of Statistics

Category: Academic


Department of Statistics Fall 2019 Seminar Series

Talk Title: Double Hierarchical Generalized Linear Models for RNAseq Data: DHGLMseq

Speaker: Dongseok Choi – Professor of Biostatistics at Oregon Health & Science University

Time: 11:00am


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.

Location: Department of Statistics, Room B02, 2006 Sheridan Rd, Evanston 60208

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