A new method for analyzing gene expression data
Microarray technique provides a systematic genome-wide approach to solve a wide range of problems such as gene functions, gene regulations, and the disease diagnoses and treatments. A key step in the analysis of gene expression data is to identify biologically relevant groups of genes or tissue samp...
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| Format: | Article |
| Language: | English |
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Zhejiang University Press
2004-09-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
| Online Access: | https://www.academax.com/doi/10.3785/1008-9209.2004.05.0492 |
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| _version_ | 1850044866868805632 |
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| author | PAN Hai-yan ZHU Jun HAN Dan-fu |
| author_facet | PAN Hai-yan ZHU Jun HAN Dan-fu |
| author_sort | PAN Hai-yan |
| collection | DOAJ |
| description | Microarray technique provides a systematic genome-wide approach to solve a wide range of problems such as gene functions, gene regulations, and the disease diagnoses and treatments. A key step in the analysis of gene expression data is to identify biologically relevant groups of genes or tissue samples that have similar expression patterns. However, systematic and stochastic fluctuations are usually involved in microarray experiments<sup>[1]</sup>, so the raw measurements have inherent ‘noise’ within microarray experiments. In current, logarithmic ratios are usually analyzed directly by various clustering methods, which may introduce bias interpretation in identifying groups of genes or samples. In the present study, a new method based on mixed model approaches is proposed for cluster analysis of gene expression data. It is expected to minimize or eliminate inherent ‘noise’ in microarray experiments and to make sure the inputs of cluster analysis are more biologically meaningful. Meanwhile, we present a windows-interface software, called ClusterProject, for gene expression analysis and visualization. |
| format | Article |
| id | doaj-art-442e862c34dc44c5a379769bd5c29c61 |
| institution | DOAJ |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2004-09-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-442e862c34dc44c5a379769bd5c29c612025-08-20T02:54:50ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552004-09-013049249410.3785/1008-9209.2004.05.049210089209A new method for analyzing gene expression dataPAN Hai-yanZHU JunHAN Dan-fuMicroarray technique provides a systematic genome-wide approach to solve a wide range of problems such as gene functions, gene regulations, and the disease diagnoses and treatments. A key step in the analysis of gene expression data is to identify biologically relevant groups of genes or tissue samples that have similar expression patterns. However, systematic and stochastic fluctuations are usually involved in microarray experiments<sup>[1]</sup>, so the raw measurements have inherent ‘noise’ within microarray experiments. In current, logarithmic ratios are usually analyzed directly by various clustering methods, which may introduce bias interpretation in identifying groups of genes or samples. In the present study, a new method based on mixed model approaches is proposed for cluster analysis of gene expression data. It is expected to minimize or eliminate inherent ‘noise’ in microarray experiments and to make sure the inputs of cluster analysis are more biologically meaningful. Meanwhile, we present a windows-interface software, called ClusterProject, for gene expression analysis and visualization.https://www.academax.com/doi/10.3785/1008-9209.2004.05.0492 |
| spellingShingle | PAN Hai-yan ZHU Jun HAN Dan-fu A new method for analyzing gene expression data 浙江大学学报. 农业与生命科学版 |
| title | A new method for analyzing gene expression data |
| title_full | A new method for analyzing gene expression data |
| title_fullStr | A new method for analyzing gene expression data |
| title_full_unstemmed | A new method for analyzing gene expression data |
| title_short | A new method for analyzing gene expression data |
| title_sort | new method for analyzing gene expression data |
| url | https://www.academax.com/doi/10.3785/1008-9209.2004.05.0492 |
| work_keys_str_mv | AT panhaiyan anewmethodforanalyzinggeneexpressiondata AT zhujun anewmethodforanalyzinggeneexpressiondata AT handanfu anewmethodforanalyzinggeneexpressiondata AT panhaiyan newmethodforanalyzinggeneexpressiondata AT zhujun newmethodforanalyzinggeneexpressiondata AT handanfu newmethodforanalyzinggeneexpressiondata |