Significant Genes Extraction and Analysis of Gene Expression Data Based on Matrix Factorization Techniques_Journal of Biomedical Engineering

Authors：

 KONGWei ¹ , WANGJuan ¹ , MOUXiaoyang ²

1. Information Engineering College, Shanghai Maritime University, Shanghai 201306, China;
2. DNJ Pharma, Rowan University, NJ 08028, USA;

Corresponding author：

KONGWei, Email: weikong@shmtu.edu.cn

Keywords：

matrix factorization; microarray gene expression data; independent component analysis; nonnegative matrix factorization; Alzheimer's disease

DOI：

10.7507/1001-5515.20140124

Video：

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Abstract Full text Figures/Tables Video References Cited by

It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.

Citation： KONGWei, WANGJuan, MOUXiaoyang. Significant Genes Extraction and Analysis of Gene Expression Data Based on Matrix Factorization Techniques. Journal of Biomedical Engineering, 2014, 31(3): 662-670. doi: 10.7507/1001-5515.20140124 Copy

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1. RAY S, BRITSCHGI M, HERBERT C, et al. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins[J]. Nat Med, 2007, 13(11):1359-1362.
2. RAY M, RUAN J, ZHANG W. Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases[J]. Genome Biol, 2008, 9(10):R148.
3. WANG X, CHEN Y, WANG X, et al. Genetic regulatory network analysis for app based on genetical genomics approach[J]. Exp Aging Res, 2010, 36(1):79-93.
4. ZHANG L, JU X, CHENG Y, et al. Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles[J]. BMC Syst Biol, 2011, 5(5):152-156.
5. HORI G, INOUE M, NISHIMURA S, et al. Blind gene classification based on ICA of microarray data[C]//3rd International Conference on Independent Component Analysis and Signal Separation.San Diego, USA:2001:332-336.
6. LIEBERMEISTER W. Linear modes of gene expression determined by independent component analysis[J]. Bioinformatics, 2002, 18(1):51-60.
7. YANG X L, HE Q. Weighted maximum margin criterion method:application to proteomic peptide profile[C]//5th International Conference on Bioinformatics and Biomedical Engineering. Wuhan, China:2011:1-4.
8. EGUIZABAL A, LAUGHNEY A M. ICA-guided delineation of breast cancer pathology[C]//9th IEEE International Symposium on Biomedical Imaging:From Nano to Macro. Barcelona, Spain:2012:1611-1614.
9. SAIDI S A, HOLLAND C M, KREIL D P, et al. Independent component analysis of microarray data in the study of endometrial cancer[J]. Oncogene, 2004, 23(39):6677-6683.
10. ZHOU J, LIN Y Z, CHEN Y H. Ensemble classifiers based on Kernel ICA for cancer data classification[C]//BMEI 2009:Proceedings of the 20092nd International Conference on Biomedical Engineering and Informatics. Tianjin, China:2009:1-5.
11. XU M, PU Y, WANG W. Clean image synthesis and target numerical marching for optical imaging with backscattering light[J]. Biomedical Optics Express, 2011, 2(4):850-857.
12. HAN X X. Nonnegative principal component analysis for cancer molecular pattern discovery[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010, 7(3):537-549.
13. ZHANG W S, EDWARDS A, FAN W, et al. svdPPCS:an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification[J]. BMC Bioinformatics, 2010, 11:338.
14. HAN H, LI X L. Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery[J]. BMC Bioinformatics, 2011, 12(Suppl 1):S7.
15. LEE D D, SEUNG H S. Learning the parts of objects by nonnegative matrix factorization[J]. Nature, 1999, 401:788-791.
16. PASCUAL-MONTANO A. Non-negative matrix factorization in bioinformatics:Towards understanding biological processes[C]//IEEE International Symposium on Circuits and Systems. Seattle, WA:2008:1332-1335.
17. ZHENG C H, NG T Y, ZHANG L, et al. Tumor classification based on non-negative matrix factorization using gene expression data[J]. IEEE Trans Nanobioscience, 2011, 10(2):86-93.
18. CARMONA-SAEZ P, PASCUAL-MARQUI R D, TIRADO F, et al. Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization[J]. BMC Bioinformatics, 2006, 7:78.
19. UBERTI D, CENINI G, BONINI S A, et al. Increased CD44 gene expression in lymphocytes derived from Alzheimer disease patients[J]. Neurodegener Dis, 2010, 7(1-3):143-147.
20. KENCHE V B, BARNHAM K J. Alzheimer's disease&metals:therapeutic opportunities[J]. Br J Pharmacol, 2011, 163(2):211-219.
21. HYVÄRINEN A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Trans Neu Netw, 1999, 10(3):626-634.

Journal of Biomedical Engineering

Significant Genes Extraction and Analysis of Gene Expression Data Based on Matrix Factorization Techniques

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