DOI
10.17077/etd.73qfll6a
Document Type
Dissertation
Date of Degree
Fall 2012
Degree Name
PhD (Doctor of Philosophy)
Degree In
Biostatistics
First Advisor
Huang, Jian
Second Advisor
Xing, Yi
First Committee Member
Chaloner, Kathryn
Second Committee Member
Davidson, Beverly L
Third Committee Member
Murray, Jeffrey C
Abstract
Ultra-deep RNA sequencing has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We develop MATS (Multivariate Analysis of Transcript Splicing), a Bayesian statistical framework for flexible hypothesis testing of differential alternative splicing patterns on RNA-Seq data. MATS uses a multivariate uniform prior to model the between-sample correlation in exon splicing patterns, and a Markov chain Monte Carlo (MCMC) method coupled with a simulation-based adaptive sampling procedure to calculate the P value and false discovery rate (FDR) of differential alternative splicing. Importantly, the MATS approach is applicable to almost any type of null hypotheses of interest, providing the flexibility to identify differential alternative splicing events that match a given user-defined pattern. We evaluated the performance of MATS using simulated and real RNA-Seq data sets. In the RNA-Seq analysis of alternative splicing events regulated by the epithelial-specific splicing factor ESRP1, we obtained a high RT-PCR validation rate of 86% for differential alternative splicing events with a MATS FDR of < 10%. Additionally, over the full list of RT-PCR tested exons, the MATS FDR estimates matched well with the experimental validation rate. Our results demonstrate that MATS is an effective and flexible approach for detecting differential alternative splicing from RNA-Seq data.
Keywords
False Discovery Rate, iFDR, MATS, Multivariate Analysis of Transcript Splicing, rMATS, RNA-Seq
Pages
ix, 88 pages
Bibliography
Includes bibliographical references (pages 84-88).
Copyright
Copyright 2012 Shihao Shen
Recommended Citation
Shen, Shihao. "Statistical methods for deep sequencing data." PhD (Doctor of Philosophy) thesis, University of Iowa, 2012.
https://doi.org/10.17077/etd.73qfll6a