| Normalization strategy of microarray gene expression data |
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| 引用本文： | 伍亚舟 张玲 黄明辉 杨梦苏 易东. Normalization strategy of microarray gene expression data[J]. 中国人民解放军军医大学学报, 2007, 22(4): 195-200. DOI: 10.1016/S1000-1948(07)60040-9 |
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| 作者姓名： | 伍亚舟 张玲 黄明辉 杨梦苏 易东 |
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| 作者单位： | [1]Department of Health Statistics, Third Military Medical University, Chongqing 400038, China [2]Shenzhen Institute, City University of HongKong, Shenzhen 518000, China |
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| 摘 要： | Objective： To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods： With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization （TIN） and locally weighted linear regression normalization （LWLRN）. Results： These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion： These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer.
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| 关 键 词： | 微阵列基因表达 数据处理 标准化策略 基因片 |
| 收稿时间： | 2006-07-17 |
| 修稿时间： | 2006-07-172007-06-21 |
Normalization strategy of microarray gene expression data |
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| WU Ya-zhou,ZHANG Ling,HUANG Ming-hui,YANG Meng-su,YI Dong. Normalization strategy of microarray gene expression data[J]. Journal of Medical Colleges of PLA(China), 2007, 22(4): 195-200. DOI: 10.1016/S1000-1948(07)60040-9 |
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| Authors: | WU Ya-zhou ZHANG Ling HUANG Ming-hui YANG Meng-su YI Dong |
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| Affiliation: | 1.Department of Health Statistics, Third Military Medical University, Chongqing 400038, China; 2.Shenzhen Institute, City University of HongKong, Shenzhen 518000, China |
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| Abstract: | Objective To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization (TIN) and locally weighted linear regression normalization (LWLRN). Results These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer. |
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| Keywords: | gene chip normalization factor expression ratio significant difference |
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