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.

中文翻译：

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UGM：针对大规模多重测试问题的更稳定程序，用于鉴定癌基因的新解决方案

基因表达水平的变化在肿瘤中起重要作用。在高通量测序中，有许多方法可以鉴定差异表达的基因。几种算法致力于识别易受特定疾病影响的独特遗传模式。尽管已经证明了这些过程是成功的，但当错误的基因数目达到标准时，通过错误发现率（FDR）测量的非差异表达基因的数量具有较大的标准偏差，并且误识别率（I型错误）迅速增长的可能性。被检测到变得更大。在这项研究中，我们开发了一种新的方法，即单位伽玛测量（UGM），该方法解决了多个假设检验统计信息的分布问题，从而可以减少依赖性问题。模拟的表达谱数据和乳腺癌RNA-Seq数据用于证明UGM的准确性。结果表明，由UGM鉴定的非差异表达基因的数量与真实数据非常接近，并且UGM的标准误差，范围，四分位数范围和RMS误差也较小。此外，UGM可用于筛选许多与乳腺癌相关的基因，例如BRCA1，BRCA2，PTEN，BRIP1等，可提供更高的准确性，鲁棒性和效率，是在高通量测序中鉴定差异表达基因的方法。