Abstract
Inferring transcriptional regulatory modules is a useful work for elucidating molecular mechanism. In this paper, we propose a new method for transcriptional regulatory module discovering. The algorithm uses penalized matrix decomposition to model microarray data. Which takes into account the sparse a prior information of transcription factors–gene (TFs–gene) interactions. At the same time, the ChIP-chip data are used as constraints for penalized matrix decomposition of gene expression data. Finally the regulatory modules can be inferred based on the factor matrix. Experiment on yeast dataset shows that our method can identifies more meaningful transcriptional modules relating to specific TFs.
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References
Cavalieri, D., De Filippo, C.: Bioinformatic Methods for Integrating Whole-Genome Expression Results Into Cellular Networks. Drug Discov. Today 10, 727–734 (2005)
The Cancer Genome Atlas Network.: Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012)
Segal, E., et al.: A Module Map Showing Conditional Activity of Expression Modules in Cancer. Nat. Genet. 36, 1090–1098 (2004)
Mukhopadhyay, A., Maulik, U.: Towards Improving Fuzzy Clustering Using Support Vector Machine: Application to Gene Expression Data. Pattern Recognition 42(11), 2744–2763 (2009)
Fernandez, E.A., Balzarini, M.: Improving Cluster Visualization in Self-Organizing Maps: Application in Gene Expression Data Analysis. Computers in Biology and Medicine 37(12), 1677–1689 (2007)
Dueck, D., Morris, Q.D., Frey, B.J.: Multi-way Clustering of Microarray Data Using Probabilistic Sparse Matrix Factorization. Bioinformatics 21(suppl. 1), i144–i151 (2005)
Huang, D.S., Zheng, C.H.: Independent Component Analysis Based Penalized Discriminant Method for Tumor Classification using Gene Expression Data. Bioinformatics 22(15), 1855–1862 (2006)
Zhou, X.J., et al.: Functional Annotation and Network Reconstruction Through Cross-Platform Integration of Microarray Data. Nat. Biotechnol. 23, 238–243 (2005)
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 100, 15522–15527 (2003)
Van den Bulcke, T., Lemmens, K., Van de Peer, Y., Marchal, K.: Inferring Transcriptional Networks by Mining ’Omics’ Data. Current Bioinformatics 1(3), 301–313 (2006)
Bar-Joseph, Z., et al.: Computational Discovery of Gene Modules And Regulatory Networks. Nat. Biotechnol. 21, 1337–1342 (2003)
Chen, G., et al.: Clustering of Genes Into Regulons using Integrated Modeling-COGRIM. Genome Biol. 8, R4 (2007)
Lemmens, K., et al.: Inferring Transcriptional Modules from Chip-Chip, Motif and Microarray Data. Genome Biol. 7, R37 (2006)
Zhang, J., Zheng, C.H., Liu, J.X., Wang, H.: Discovering the Transcriptional Modules using Microarray Data by Penalized Matrix Decomposition. Computers in Biology and Medicine 41(11), 1041–1050 (2011)
Witten, D.M., Tibshirani, R., Hastie, T.: A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis. Biostatistics 10(3), 515–534 (2009)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., et al.: Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Saccharomyces Cerevisiae by Microarray Hybridization. Mol. Biol. Cell 9, 3273–3297 (1998)
Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O.: Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes. Mol. Biol. Cell 11, 4241–4257 (2000)
Troyanskaya, O., Canto’r, M.: Missing Value Estimation Methods for DNA Microarrays. Bioinformatics 17, 520–525 (2001)
Harbison, C.T., et al.: Transcriptional Regulatory Code of a Eukaryotic Genome. Nature 431, 99–104 (2004)
Boyle, E.I., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J.M., Sherlock, G.: GO:TermFinder–Open Source Software for Accessing Gene Ontology Information and Finding Significantly Enriched Gene Ontology Terms Associated With A List Of Genes. Bioinformatics 20(18), 3710–3715 (2004)
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Zheng, CH., Sha, W., Sun, ZL., Zhang, J. (2013). Inferring Transcriptional Modules from Microarray and ChIP-Chip Data Using Penalized Matrix Decomposition. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_29
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DOI: https://doi.org/10.1007/978-3-642-39482-9_29
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