NanoStringDiff: A Novel Statistical Method for Differential Expression Analysis Based on NanoString NCounter Data

• Activity Number: 312
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section for Statistical Programmers and Analysts
• Author(s): Hong Wang* and Arnold Stromberg and Chi Wang
• Companies: University of Kentucky and University of Kentucky and University of Kentucky
• Keywords: NanoString nCounter Data ; Negative Binomial Model ; Differential Expression ; Normalization ; Empirical Bayes Shrinkage

Abstract

The NanoString nCounter system is a new and promising technology that enables the digital quantification of multiplexed target RNA molecules. In this talk, we present a novel statistical method to identify differential expression between different treatment groups specifically for NanoString nCounter data. Our method integrates data normalization and differential expression analysis into one negative binomial model, where an Empirical Bayes Shrinkage approach is used to estimate the dispersion parameter in the model. We propose a likelihood ratio test to identify differential expression, and use the Benjamini-Hochberg procedure to calculate false discovery rate. Our simulation results show competitive performance of our method versus existing methods.