Discovering the transcriptional modules using microarray data by penalized matrix decomposition

Computers in Biology and Medicine

Volumn 41, Issue 11, 2011, Pages 1041-1050

  Zhang, Jun ^a   Zheng, Chun Hou ^a   Liu, Jin Xing ^b   Wang, Hong Qiang ^c  

^a ANHUI UNIVERSITY   (China)

^b QUFU NORMAL UNIVERSITY   (China)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Clustering; Gene expression data; Penalized matrix decomposition; Transcriptional module

Indexed keywords

BIOLOGICAL NETWORKS; CELLULAR ACTIVITIES; CLUSTER GENES; CLUSTERING; CLUSTERING RESULTS; DATA SETS; EXPRESSION PROFILE; GENE EXPRESSION DATA; MATRIX DECOMPOSITION; META-GENES; MICROARRAY DATA; PENALIZED MATRIX DECOMPOSITION; REGULATORY MECHANISM; SPARSITY CONSTRAINTS; TRANSCRIPTIONAL MODULE; TRANSCRIPTIONAL MODULES;

CLUSTERING ALGORITHMS; MATRIX ALGEBRA;

GENE EXPRESSION;

DNA; MESSENGER RNA; RIBOSOME RNA;

ARTICLE; BINDING SITE; DNA MICROARRAY; GENE EXPRESSION; GENETIC TRANSCRIPTION; HUMAN; HUMAN TISSUE; NONHUMAN; PENALIZED MATRIX DECOMPOSITION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; SPARSE MATRIX FACTORIZATION; STATISTICAL ANALYSIS; YEAST;

ALGORITHMS; COMPUTER SIMULATION; DATABASES, GENETIC; GENE EXPRESSION REGULATION; HUMANS; MODELS, BIOLOGICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; TRANSCRIPTOME;

EID: 82655173784     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2011.09.003     Document Type: Article

References (26)

1. Segal E., et al. A module map showing conditional activity of expression modules in cancer. Nat. Genet. 2004, 36:1090-1098.
2. Eisen M.B., et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 1998, 95:14863-14868.
3. Mukhopadhyay A., Maulik U. Towards improving fuzzy clustering using support vector machine: application to gene expression data. Pattern Recognition 2009, 42(11):2744-2763.
4. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan E., Dmitrovsky E., Sander E.S., Golub T.R. Interpreting patterns of gene expression with self-organizing maps: method and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 1999, 96:2907-2912.
5. Fernandez E.A., Balzarini M. Improving cluster visualization in self-organizing maps: application in gene expression data analysis. Comput. Biol. Med. 2007, 37(12):1677-1689.
6. Lee S.I., Batzoglou S. Application of independent component analysis to microarray. Genome Biol. 2003, 4:R76.
7. Dueck D., Morris Q.D., Frey B.J. Multi-way clustering of microarray data using probabilistic sparse matrix factorization. Bioinformatics 2005, 21(1):i144-i151.
8. Tavazoie S., Hughes J.D., Campbell M.J., Cho R.J., Church G.M. Systematic determination of genetic network architecture. Nat. Genet. 1999, 22:281-285.
9. Zhou X.J., et al. Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat. Biotechnol. 2005, 23:238-243.
10. Zheng C.H., Huang D.S., Kang L., Irwin G., Sun Z. MISEP method for post-nonlinear blind source separation. Neural Comput. 2007, 19(9):2557-2578.
11. Liao J.C., Boscolo R., Yang Y.L., Tran L.M., Sabatti C., Roychowdhury V.P. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. USA 2003, 100:15522-15527.
12. Chang C., Ding Z., Hung Y.S., Fung P. Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 2008, 24(11):1349-1358.
13. Alter O., Brown P.O., Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 1999, 97:10101-10106. (2000).
14. N. Srebro, T. Jaakkola, Sparse Matrix Factorization of Gene Expression Data, MIT Artificial Intelligence Laboratory, Unpublished note, 2001.
15. Li H., Sun Y., Zhan M. The discovery of transcriptional modules by a two-stage matrix decomposition approach. Bioinformatics 2007, 23(4):473-479.
16. Witten D.M., Tibshirani R., Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 2009, 10(3):515-534.
17. Shen H., Huang J. Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 2008, 101:1015-1034.
18. Nogales-Cadenas R., Carmona-Saez P., Vazquez M., Vicente C., Yang X., Tirado F., Carazo J.M., Pascual-Montano A. GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Res. 2009, 10.1093/nar/gkp416.
19. Zheng C.H., Huang D.S., Zhang L., Kong X.Z. Tumor clustering using non-negative matrix factorization with gene selection. IEEE Trans. Inf. Technol. Biomed. 2009, 13(4):599-607.
20. Huang D.S., Zheng C.H. Independent component analysis based penalized discriminant method for tumor classification using gene expression data. Bioinformatics 2006, 22(15):1855-1862.
21. Gasch A.P., Spellman P.T., Kao C.M., Carmel-Harel O., Eisen M.B., Storz G., Botstein D., Brown,Genomic P.O. expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 2000, 11:4241-4257.
22. Hsiao L., Dangond F., Yoshida T., Hong R., Jensen R.V., Misra J., Dilon W., Lee K., Clark K., Harverty P., et al. A Compendium of gene expression in normal human tissues reveals tissue-specific genes and distinct expression patterns of housekeeping genes. Physiol. Genomics 2001, 7:97-104.
23. Troyanskaya O., Canto'r M., Sherlock G., Brown P.O., Hastie T., Tibshirani R., Botstein D., Altman R.B. Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17:520-525.
24. Tasoulis D.K., Plagianakos V.P., Vrahatis M.N. Unsupervised clustering in mRNA expression profiles. Comput. Biol. Med. 2006, 36(10):1126-1142.
25. Khatri P., Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 2005, 21:3587-3595.
26. Kanehisa M., Goto S., Furumichi M., Tanabe M., Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010, 38:D355-D360.