Statistics Department

Topic:

Double Hierarchial Generalized Linear Models for RNAseq Data: DHGLMseq

Abstract:

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.

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