JSM 2011 Online Program

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Abstract Details

Activity Number: 617
Type: Contributed
Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301060
Title: Normalization, Testing, and False Discovery Rate Estimation for RNA-Sequencing Data
Author(s): Jun Li*+ and Daniela Witten and Iain M. Johnstone and Robert Tibshirani
Companies: Stanford University and University of Washington and Stanford University and Stanford University
Address: Department of Statistics, Stanford, CA, 94305,
Keywords: Differential expression ; FDR ; Poisson log linear model ; RNA-Seq ; overdispersion ; score statistic
Abstract:

We discuss the identification of features that are associated with an outcome in RNA-Sequencing and other sequence-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are unsuitable. Moreover, normalization is challenging because different sequencing experiments may generate quite different total numbers of reads. To overcome these difficulties, we use a log linear model and a simple two-step estimation procedure with a new approach to normalization. We derive a novel procedure to estimate the false discovery rate (FDR). Our method can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large datasets. We study the accuracy of our approaches for significance calculation and FDR estimation, and we demonstrate that our method has potential advantages over existing proposals that are based on a Poisson or negative binomial model. In summary, this work provides a pipeline for the significance analysis of sequencing data.


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