Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data
Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis me...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/313747 |
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| author | Li-Ping Tian Li-Zhi Liu Fang-Xiang Wu |
| author_facet | Li-Ping Tian Li-Zhi Liu Fang-Xiang Wu |
| author_sort | Li-Ping Tian |
| collection | DOAJ |
| description | Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results. |
| format | Article |
| id | doaj-art-e401b4f844cb49b8a54889337427fd3f |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e401b4f844cb49b8a54889337427fd3f2025-02-03T05:51:51ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/313747313747Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression DataLi-Ping Tian0Li-Zhi Liu1Fang-Xiang Wu2School of Information, Beijing Wuzi University, No. 1 Fuhe Street, Tongzhou District, Beijing 101149, ChinaDepartment of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, CanadaDepartment of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, CanadaMicroarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results.http://dx.doi.org/10.1155/2014/313747 |
| spellingShingle | Li-Ping Tian Li-Zhi Liu Fang-Xiang Wu Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data The Scientific World Journal |
| title | Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data |
| title_full | Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data |
| title_fullStr | Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data |
| title_full_unstemmed | Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data |
| title_short | Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data |
| title_sort | nonlinear model based analysis methods for time course gene expression data |
| url | http://dx.doi.org/10.1155/2014/313747 |
| work_keys_str_mv | AT lipingtian nonlinearmodelbasedanalysismethodsfortimecoursegeneexpressiondata AT lizhiliu nonlinearmodelbasedanalysismethodsfortimecoursegeneexpressiondata AT fangxiangwu nonlinearmodelbasedanalysismethodsfortimecoursegeneexpressiondata |