Comparisons of Statistical Methods for Detecting Differentially Expressed Genes with RNA-seq Data
Date Issued
2014
Date
2014
Author(s)
Lu, Hung-Ting
Abstract
As the development of next-generation sequencing technologies, RNA-sequencing (RNA-seq) experiment is revolutionizing genomic studies. Compared with the existing microarray technology, RNA-seq usually provides more precise signals. RNA-seq experiment is considered to replace microarray technology when the sequencing cost decreases. Several statistical methods for RNA-seq data analysis have been developed, such as edgeR, DESeq2, baySeq, TSPM, NOISeq, SAMseq, Limma, EBSeq, and PoissonSeq, etc. These nine statistical methods are popular in current RNA-seq data analyses. However, the normalization strategies, the distribution assumptions for reads counts, the statistical methods to detect differentially expressed genes, and the false discovery rate control for the nine methods are different. How to choose a more powerful method is still an open question. In this thesis, we provide a systematic summary and comparison of these nine methods. In addition to Monte-Carlo simulation studies, two real data analyses were also performed. Based on our simulation results, generally Limma and baySeq had the best performance among the nine methods we compared. Moreover, Limma was more computationally feasible than baySeq. Among these nine methods, Limma has more potential to analyze RNA-seq data.
Subjects
次數資料
差異表現基因
蒙地卡羅模擬
次世代定序
核醣核酸定序
Type
thesis
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