期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 40, 期 8, 页码 723-732出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2010.06.007
关键词
Gene expression; Nonlinear dimensionality reduction; Visualization; Clustering analysis; Cancer tissue
类别
资金
- National Natural Science Foundation of China [60673018]
- National High-Tech Development 863 Program of China [2007AA01Z106]
Gene expression data are the representation of nonlinear interactions among genes and environmental factors. Computing analysis of these data is expected to gain knowledge of gene functions and disease mechanisms. Clustering is a classical exploratory technique of discovering similar expression patterns and function modules. However, gene expression data are usually of high dimensions and relatively small samples, which results in the main difficulty for the application of clustering algorithms. Principal component analysis (PCA) is usually used to reduce the data dimensions for further clustering analysis. While PCA estimates the similarity between expression profiles based on the Euclidean distance, which cannot reveal the nonlinear connections between genes. This paper uses nonlinear dimensionality reduction (NDR) as a preprocessing strategy for feature selection and visualization, and then applies clustering algorithms to the reduced feature spaces. In order to estimate the effectiveness of NDR for capturing biologically relevant structures, the comparative analysis between NOR and PCA is exploited to five real cancer expression datasets. Results show that NOR can perform better than PCA in visualization and clustering analysis of complex gene expression data. (C) 2010 Elsevier Ltd. All rights reserved.
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