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Differential Expression Analysis of ...

Zhang, Yilun.

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Differential Expression Analysis of RNA-Sequencing Data: Existing and Novel Methods.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Differential Expression Analysis of RNA-Sequencing Data: Existing and Novel Methods./
Author:	Zhang, Yilun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	151 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13423296
ISBN:	9781392212349

Differential Expression Analysis of RNA-Sequencing Data: Existing and Novel Methods.
Zhang, Yilun.

Differential Expression Analysis of RNA-Sequencing Data: Existing and Novel Methods. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 151 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2019.

This item must not be sold to any third party vendors.

Tools for analysis of gene expression are central to research in molecular biology. Probably the most important current tool for this analysis is RNA-Seq, which uses next-generation sequencing technology. Arguably, differential expression analysis is the most critical part of analyzing RNA-Seq data. In RNA-Seq, the data from each sample consist of counts of the number of fragments mapped to each gene or exon in the target genome. With the fact that RNA-seq data are usually overdispersed, the negative binomial model stands out with a modeled variance function being a quadratic function of the mean. In the first part of my thesis, I will review some popular methods based on negative binomial distribution, and demonstrate the presence and possible reasons for the inflation of the false positive rate of these methods, including edgeR and DESeq2. In the second part, a novel method, intSEQ, is proposed that is less vulnerable to the false positive problem. The method integrates the joint likelihood function of the negative binomial model and a normal prior for the dispersion parameter on the support of the dispersion parameter. The simulation results show the proposed method has higher power with a controlled false positive rate. In the last part of this thesis, I recommend a pipeline for differential expression analysis of RNA-Seq data, which is encapsulated in an R package, 'intSEQ'.

ISBN: 9781392212349Subjects--Topical Terms:

1002712
Biostatistics.

Differential Expression Analysis of RNA-Sequencing Data: Existing and Novel Methods.
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502 $a Thesis (Ph.D.)--University of California, Davis, 2019.
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520 $a Tools for analysis of gene expression are central to research in molecular biology. Probably the most important current tool for this analysis is RNA-Seq, which uses next-generation sequencing technology. Arguably, differential expression analysis is the most critical part of analyzing RNA-Seq data. In RNA-Seq, the data from each sample consist of counts of the number of fragments mapped to each gene or exon in the target genome. With the fact that RNA-seq data are usually overdispersed, the negative binomial model stands out with a modeled variance function being a quadratic function of the mean. In the first part of my thesis, I will review some popular methods based on negative binomial distribution, and demonstrate the presence and possible reasons for the inflation of the false positive rate of these methods, including edgeR and DESeq2. In the second part, a novel method, intSEQ, is proposed that is less vulnerable to the false positive problem. The method integrates the joint likelihood function of the negative binomial model and a normal prior for the dispersion parameter on the support of the dispersion parameter. The simulation results show the proposed method has higher power with a controlled false positive rate. In the last part of this thesis, I recommend a pipeline for differential expression analysis of RNA-Seq data, which is encapsulated in an R package, 'intSEQ'.
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