Statistical methods for whole transcriptome sequencing : from bulk tissue to single cells / Cheng Jia.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Jia, Cheng, author.

Contributor:

Li, Mingyao, degree supervisor, degree committee member.

Li, Hongzhe, degree supervisor, degree committee member.

Himes, Blanca E., degree committee member.

Zhang, Nancy R. (Nancy Ruonan), degree committee member.

University of Pennsylvania. Department of Epidemiology and Biostatistics, degree granting institution.

Language:

English

Subjects (All):

Penn dissertations--Epidemiology and biostatistics.

Epidemiology and biostatistics--Penn dissertations.

Local Subjects:

Penn dissertations--Epidemiology and biostatistics.

Epidemiology and biostatistics--Penn dissertations.

Physical Description:

xviii,209 leaves : illustrations ; 29 cm

Production:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2017.

Summary:

RNA-Sequencing (RNA-Seq) has enabled detailed unbiased profiling of whole transcriptomes with incredible throughput. Recent technological breakthroughs have pushed back the frontiers of RNA expression measurement to single-cell level (scRNA-Seq). With both bulk and single-cell RNA-Seq analyses, modeling of the noise structure embedded in the data is crucial for drawing correct inference. In this dissertation, I developed a series of statistical methods to account for the technical variations specific in RNA-Seq experiments in the context of isoform- or gene- level differential expression analyses. In the first part of my dissertation, I developed MetaDiff (https://github.com/jiach/MetaDiff ), a random-effects meta-regression model, that allows the incorporation of uncertainty in isoform expression estimation in isoform differential expression analysis. This framework was further extended to detect splicing quantitative trait loci with RNA-Seq data. In the second part of my dissertation, I developed TASC (Toolkit for Analysis of Single-Cell data; https://github.com/scrna-seq/TASC), a hierarchical mixture model, to explicitly adjust for cell-to-cell technical differences in scRNA-Seq analysis using an empirical Bayes approach. This framework can be adapted to perform differential gene expression analysis. In the third part of my dissertation, I developed, TASC-B, a method extended from TASC to model transcriptional bursting- induced zero-inflation. This model can identify and test for the difference in the level of transcriptional bursting. Compared to existing methods, these new tools that I developed have been shown to better control the false discovery rate in situations where technical noise cannot be ignored. They also display superior power in both our simulation studies and real world applications.

Notes:

Ph. D. University of Pennsylvania 2017.

Department: Epidemiology and Biostatistics.

Supervisor: Mingyao Li; Hongzhe Li.

Includes bibliographical references.

OCLC:

1334941765