Dissertation/Thesis Abstract

The author has requested that access to this graduate work be delayed until 2018-05-08. After this date, this graduate work will be available on an open access basis.
gibbSeq: A new Bayesian method for multiple comparisons for RNA-seq data
by Acheampong, Daniel Asare, M.S., New Mexico Institute of Mining and Technology, 2017, 77; 10264015
Abstract (Summary)

Developments in the study of genomes over the last decade using high-throughput sequencing of ribonucleic acid (RNA) have opened a new era of transcriptome analysis. Different statistical methods and software tools have been developed to analyze RNA-sequenced (RNA-seq) data in order to detect genes (features) that are differentially expressed (DE) in an experiment. We developed a new method (gibbSeq) based on log-normal distribution and full Bayesian inference using Gibbs sampling for detecting DE genes. We compare the performance of our method with some existing methods for analyzing RNA-seq data. Using simulated and real biological data, we find that our method performs as well or even better than some of the existing methods, for a wide range of simulated conditions in controlling type I error rate and false discoveries.

Indexing (document details)
Advisor: Makhnin, Oleg
Commitee: Borchers, Brian, Hossain, Anwar
School: New Mexico Institute of Mining and Technology
Department: Mathematics
School Location: United States -- New Mexico
Source: MAI 56/04M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Biostatistics, Statistics
Keywords: Differential expression, Gibbs sampling, High throughput sequencing
Publication Number: 10264015
ISBN: 9781369752571