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Theor Biol Med Model. 2019 Dec 23;16(1):20. doi: 10.1186/s12976-019-0117-1.

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

Author information

1
Department of Medical Engineering, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
2
Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210016, Jiangsu, China. zhujunlin_njfh@163.com.
3
Department of Mathematics and Computer, Nanjing Medical University, Nanjing, China.
4
Department of Biomedical Engineering, Nanjing Medical University, Nanjing, China.
5
Department of Medical Engineering, Nanjing First Hospital, Nanjing Medical University, Nanjing, China. njptc_mzz@163.com.
6
Hongbing Jiang, Nanjing Health Information Center, Nanjing, 210016, Jiangsu, China. njptc_mzz@163.com.

Abstract

Variations 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.

KEYWORDS:

Cancer-associated genes; Differentially expressed genes; False discovery rate; RNA-Seq data; Root mean square error; Standard deviation

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