Application of Improved Locally Linear Embedding Algorithm in Dimensionality Reduction of Cancer Gene Expression Data_Journal of Biomedical Engineering

Authors：

LIUWenyuan ,  WANGChunlei , WANGBaowen , WANGChangwu

College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

WANGChunlei, Email: wcw@ysu.edu.cn

Keywords：

nonlinear dimensionality reduction; locally linear embedding; cancer gene expression data; multiple reconstruction weights

DOI：

10.7507/1001-5515.20140017

Video：

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Cancer gene expression data have the characteristics of high dimensionalities and small samples so it is necessary to perform dimensionality reduction of the data. Traditional linear dimensionality reduction approaches can not find the nonlinear relationship between the data points. In addition, they have bad dimensionality reduction results. Therefore a multiple weights locally linear embedding (LLE) algorithm with improved distance is introduced to perform dimensionality reduction in this study. We adopted an improved distance to calculate the neighbor of each data point in this algorithm, and then we introduced multiple sets of linearly independent local weight vectors for each neighbor, and obtained the embedding results in the low-dimensional space of the high-dimensional data by minimizing the reconstruction error. Experimental result showed that the multiple weights LLE algorithm with improved distance had good dimensionality reduction functions of the cancer gene expression data.

Citation： LIUWenyuan, WANGChunlei, WANGBaowen, WANGChangwu. Application of Improved Locally Linear Embedding Algorithm in Dimensionality Reduction of Cancer Gene Expression Data. Journal of Biomedical Engineering, 2014, 31(1): 85-90. doi: 10.7507/1001-5515.20140017 Copy

1.	GOLUB T R, SLONIM D K, TAMAYO P, et al. Molecular classification of cancer:class discovery and class prediction by gene expression monitoring[J]. Science, 1999, 286(5439):531-537.
2.	ALLISON D B, CUI X Q, PAGE G P, et al. Microarray data analysis:from disarray to consolidation and consensus[J]. Nat Rev Genet, 2006, 7(1):55-65.
3.	RAMASWAMY S, TAMAYO P, RIFKIN R, et al. Multiclass cancer diagnosis using tumor gene expression signatures[J]. Proc Natl Acad Sci U S A, 2001, 98(26):15149-15154.
4.	TENENBAUM J B, DE SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323.
5.	ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326.
6.	ZHANG Z Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. SIAM J Sci Comput, 2005, 26(4):313-338.
7.	DAWSON K, RODRIGUEZ R L, MALYJ W. Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap,a nonlinear algorithm[J]. BMC Bioinformatics, 2005, 6(1):195.
8.	NILSSON J, FIORETOS T, H?GLUND M, et al. Approximate geodesic distances reveal biologically relevant structures in microarray data[J]. Bioinformatics, 2004, 20(6):874-880.
9.	LI X H, SHU L. Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis[J]. Expert Syst Appl, 2009, 36(4):7644-7650.
10.	蔡先发,韦佳,文贵华,等.一种改进距离的局部线性嵌入算法的基因表达谱数据分类[J].生物医学工程学杂志,2011,28(6):1213-1216.
11.	ZHANG Z Y, WANG J. MLLE:Modified locally linear embedding using multiple weights[C]//Advances in Neural Information Processing Systems:v. 19:Proceedings of the 2006 Conference. SCHOLKOPF B, PLATT J, HOFMANN T. Cambridge:MIT Press, 2007:1593-1600.
12.	张骞,盛军.基因芯片技术的发展和应用[J].中国医学科学院学报,2008,30(3):344-347.
13.	SILVA V, TENENBAUM J B. Global versus local methods in nonlinear dimensionality reduction[C]//Advances in Neural Information Processing Systems:v. 15:Proceedings of the 2002 Conference. BECKER B, THRUN S, OBERMAYER K. Cambridge:MIT Press, 2003:721-728.
14.	黄德双.基因表达谱数据挖掘方法研究[M].北京:科学出版社,2009:46-50.
15.	GETZ G E, LEVINE E, DOMANY. Coupled two-way clustering analysis of gene microarray data[J]. Proc Natl Acad Sci, 2000, 97(22):12079-12084.

1. GOLUB T R, SLONIM D K, TAMAYO P, et al. Molecular classification of cancer:class discovery and class prediction by gene expression monitoring[J]. Science, 1999, 286(5439):531-537.
2. ALLISON D B, CUI X Q, PAGE G P, et al. Microarray data analysis:from disarray to consolidation and consensus[J]. Nat Rev Genet, 2006, 7(1):55-65.
3. RAMASWAMY S, TAMAYO P, RIFKIN R, et al. Multiclass cancer diagnosis using tumor gene expression signatures[J]. Proc Natl Acad Sci U S A, 2001, 98(26):15149-15154.
4. TENENBAUM J B, DE SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323.
5. ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326.
6. ZHANG Z Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. SIAM J Sci Comput, 2005, 26(4):313-338.
7. DAWSON K, RODRIGUEZ R L, MALYJ W. Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap,a nonlinear algorithm[J]. BMC Bioinformatics, 2005, 6(1):195.
8. NILSSON J, FIORETOS T, H?GLUND M, et al. Approximate geodesic distances reveal biologically relevant structures in microarray data[J]. Bioinformatics, 2004, 20(6):874-880.
9. LI X H, SHU L. Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis[J]. Expert Syst Appl, 2009, 36(4):7644-7650.
10. 蔡先发,韦佳,文贵华,等.一种改进距离的局部线性嵌入算法的基因表达谱数据分类[J].生物医学工程学杂志,2011,28(6):1213-1216.
11. ZHANG Z Y, WANG J. MLLE:Modified locally linear embedding using multiple weights[C]//Advances in Neural Information Processing Systems:v. 19:Proceedings of the 2006 Conference. SCHOLKOPF B, PLATT J, HOFMANN T. Cambridge:MIT Press, 2007:1593-1600.
12. 张骞,盛军.基因芯片技术的发展和应用[J].中国医学科学院学报,2008,30(3):344-347.
13. SILVA V, TENENBAUM J B. Global versus local methods in nonlinear dimensionality reduction[C]//Advances in Neural Information Processing Systems:v. 15:Proceedings of the 2002 Conference. BECKER B, THRUN S, OBERMAYER K. Cambridge:MIT Press, 2003:721-728.
14. 黄德双.基因表达谱数据挖掘方法研究[M].北京:科学出版社,2009:46-50.
15. GETZ G E, LEVINE E, DOMANY. Coupled two-way clustering analysis of gene microarray data[J]. Proc Natl Acad Sci, 2000, 97(22):12079-12084.

Journal of Biomedical Engineering

Application of Improved Locally Linear Embedding Algorithm in Dimensionality Reduction of Cancer Gene Expression Data

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