Statistical methods for multi-sample analysis of RNA-SEQ and DNA copy number data / Saran Vardhanabhuti.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Vardhanabhuti, Saran.

Contributor:

Li, Hongzhe, advisor.

University of Pennsylvania.

Language:

English

Subjects (All):

Penn dissertations--Epidemiology and Biostatistics.

Epidemiology and Biostatisticsy--Penn dissertations.

Epidemiology and Biostatistics.

Academic Dissertations as Topic.

Medical Subjects:

Epidemiology and Biostatistics.

Academic Dissertations as Topic.

Local Subjects:

Penn dissertations--Epidemiology and Biostatistics.

Epidemiology and Biostatisticsy--Penn dissertations.

Physical Description:

xxi, 129 pages : illustrations ; 29 cm

Production:

2011.

Summary:

In this dissertation, I developed statistical and computational methods motivated by problems in genomics studies. In particular, the theme was to explore how to improve statistical inference in studies involving multiple samples. The first part of my dissertation, described in Chapter 2, is motivated by the analysis of RNA-Seq data which is being used to study gene and isoform expression generated from the Next Generation Sequencing (NGS) platforms. For this part of the dissertation, we present a Bayesian hierarchical model for multi-sample RNA-Seq data analysis in order to simultaneously estimate isoform-specific expression and to identify differentially expressed isoforms. Our model has the advantage of borrowing information across all samples in estimating expression levels, which can improve the estimates drastically, particularly for low abundance isoforms. Furthermore, our model can easily incorporate sample-specific covariates, which facilitates the isoform-specific differential expression analysis. Simulation studies demonstrated that this Bayesian multi-sample approach can lead to more precise estimates of isoform-specific expression and higher power to detect differential expression by borrowing information across all samples compared to single sample analysis, especially for isoforms of low abundance. We further illustrated our methods using the RNA-Seq data of 10 Yoruban and 10 Caucasian individuals. For the second part of my dissertation, described in Chapter 3, we studied copy number changes in germline and tumor DNA samples. Our approach focuses on the change point detection problem across multiple samples utilizing adapted multi-sample wavelet transformation. We present two approaches to assess the significance of change points: first is a flexible analytic threshold with the ability to control the Type I error rate at a pre-specified level to identify significant shared change points across multiple samples and second, a permutation based method to identify recurrent change points (driver mutations as opposed to random passenger mutations). Simulation and data analysis show that information pooled across samples can help boost detection power compared to single sample analysis particularly in regions with rare proportion of carriers. Examples from germline and tumor DNA copy number data were used to illustrate our approach. In the final chapter, we present a transformation method based on parametric distributions (Poisson and negative bionomial (NB)) for NGS read depth data. The proposed method transforms read depth observations into data that is approximately normal and then applies the LRS method of Jeng et al. (2010) to identify CNVs based on transformed data. We compared the performance of parametric (Poisson and NB) with non-parametric (local median) transformation. Simulation results shows that parametric NB transformation has similar or higher power to detect CNVs compared to local median transformation, particularly when depth of coverage is low. We demonstrated our transformation approach on Hapmap trio NGS data.

Notes:

Adviser: Hongzhe Li.

Thesis (Ph.D. in Epidemiology and Biostatistics) -- University of Pennsylvania, 2011.

Includes bibliographical references.

OCLC:

793488943