%0 Journal Article %1 Liu:2019:Theor-Biol-Med-Model:31865918 %A Liu, C %A Zhou, L %A Wang, Y %A Tian, S %A Zhu, J %A Qin, H %A Ding, Y %A Jiang, H %D 2019 %J Theor Biol Med Model %K false-discovery-rate-correction fulltext rna-seq %N 1 %P 20-20 %R 10.1186/s12976-019-0117-1 %T UGM: a more stable procedure for large-scale multiple testing problems, new solutions to identify oncogene %U https://www.ncbi.nlm.nih.gov/pubmed/31865918 %V 16 %X 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.

@article{Liu:2019:Theor-Biol-Med-Model:31865918, 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.}, added-at = {2020-01-14T19:50:53.000+0100}, author = {Liu, C and Zhou, L and Wang, Y and Tian, S and Zhu, J and Qin, H and Ding, Y and Jiang, H}, biburl = {https://www.bibsonomy.org/bibtex/257af8ecd9896d5f18d441313fce1e21c/marcsaric}, description = {UGM: a more stable procedure for large-scale multiple testing problems, new solutions to identify oncogene. - PubMed - NCBI}, doi = {10.1186/s12976-019-0117-1}, interhash = {59851a22db65c14f600fd889c17923ec}, intrahash = {57af8ecd9896d5f18d441313fce1e21c}, journal = {Theor Biol Med Model}, keywords = {false-discovery-rate-correction fulltext rna-seq}, month = dec, number = 1, pages = {20-20}, pmid = {31865918}, timestamp = {2020-01-14T19:50:53.000+0100}, title = {UGM: a more stable procedure for large-scale multiple testing problems, new solutions to identify oncogene}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31865918}, volume = 16, year = 2019 }