scholarly journals A Bayesian Multiple Testing of Detecting Differentially Expressed Genes in Two-sample Comparison Problem

2006 ◽  
Vol 13 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Hyun-Sook Oh ◽  
Wan-Youn Yang
Keyword(s):  
Differentially Expressed Genes ◽  
Multiple Testing ◽  
Differentially Expressed ◽  
Comparison Problem

An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data

2017 ◽  
Vol 15 (05) ◽  
pp. 1750020 ◽  
Author(s):  
Na You ◽  
Xueqin Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Multiple Testing ◽  
Empirical Bayes ◽  
Variance Estimation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Link Function ◽  
Multiple Testing Procedure

The microarray technology is widely used to identify the differentially expressed genes due to its high throughput capability. The number of replicated microarray chips in each group is usually not abundant. It is an efficient way to borrow information across different genes to improve the parameter estimation which suffers from the limited sample size. In this paper, we use a hierarchical model to describe the dispersion of gene expression profiles and model the variance through the gene expression level via a link function. A heuristic algorithm is proposed to estimate the hyper-parameters and link function. The differentially expressed genes are identified using a multiple testing procedure. Compared to SAM and LIMMA, our proposed method shows a significant superiority in term of detection power as the false discovery rate being controlled.

scholarly journals UGM: a more stable procedure for large-scale multiple testing problems, new solutions to identify oncogene

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Chengyou Liu ◽  
Leilei Zhou ◽  
Yuhe Wang ◽  
Shuchang Tian ◽  
Junlin Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
High Throughput ◽  
Multiple Testing ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Type I Error ◽  
Differentially Expressed ◽  
Type I ◽  
Brca1 Brca2

AbstractVariations of gene expression levels play an important role in tumors. There are numerous methods to identify differentially expressed genes in high-throughput sequencing. Several algorithms endeavor to identify distinctive genetic patterns susceptable to particular diseases. Although these processes have been proved successful, the probability that the number of non-differentially expressed genes measured by false discovery rate (FDR) has a large standard deviation, and the misidentification rate (type I error) grows rapidly when the number of genes to be detected become larger. In this study we developed a new method, Unit Gamma Measurement (UGM), accounting for multiple hypotheses test statistics distribution, which could reduce the dependency problem. Simulated expression profile data and breast cancer RNA-Seq data were utilized to testify the accuracy of UGM. The results show that the number of non-differentially expressed genes identified by the UGM is very close to the real-evidence data, and the UGM also has a smaller standard error, range, quartile range and RMS error. In addition, the UGM can be used to screen many breast cancer-associated genes, such as BRCA1, BRCA2, PTEN, BRIP1, etc., provides better accuracy, robustness and efficiency, the method of identification differentially expressed genes in high-throughput sequencing.

cDNA-AFLP Analysis of Differentially Expressed Genes in Tobacco Infected by TMV

2013 ◽  
Vol 38 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Rong-Ping CHEN ◽  
Lie LIU ◽  
Xiu-Qing WAN ◽  
En-Jian QIU ◽  
Chun-Jun WANG ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Aflp Analysis
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