Analysis of RNA-Seq Data with Excess of Zeros

• Activity Number: 571
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Marcus Nunes*+ and James L Rosenberger
• Companies: Penn State University and Penn State University
• Keywords: RNA-Seq ; Next Generation Sequencing ; Linear Models ; Genomics

Abstract

Next-generation sequencing technologies are revolutionizing the analysis of genomics data. Also called as massively parallel signature sequencing (MPSS), these methods generate large amounts of data through millions of short sequences of genetic code. These short sequences are aligned to a reference genome and the number of occurrences of reads in each gene is counted. These counts are used to assess the digital expression of the genes. However, a large part of these counts are zeros and the conventional generalized linear methods used to test differential expression are not able to deal with this issue. In this work we propose a method that is capable of handling this characteristic of genomic data with many zeros. We show how to apply zero-inflated and hurdle models to large genomic data. To evaluate the performance of our method, we present our simulation results and an application to a real dataset.