Abstract
Microarrays are one of the latest breakthroughs in experimental molecular biology. Thousands of different research groups generate tens of thousands of microarray gene expression profiles based on different tissues, species, and conditions. Combining such vast amount of microarray data sets is an important and yet challenging problem. In this paper, we introduce a “correlation signature” method that allows the coherent interpretation and integration of microarray data across disparate sources. The proposed algorithm first builds, for each gene (row) in a table, a correlation signature that captures the system-wide dependencies existing between the gene and the other genes within the table, and then compares the signatures across the tables for further analysis. We validate our framework with an experimental study using real microarray data sets, the result of which suggests that such an approach can be a viable solution for the microarray data integration and analysis problems.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Xing, E.P., Jordan, M.I., Karp, R.M.: Feature selection for high-dimensional genomic microarray data. In: ICML 2001: Proceedings of the Eighteenth International Conference on Machine Learning, CA, USA, pp. 601–608. Morgan Kaufmann Publishers Inc, San Francisco (2001)
Yu, L., Liu, H.: Redundancy based feature selection for microarray data. In: KDD 2004: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, NY, USA, pp. 737–742. ACM Press, New York (2004)
Achlioptas, D.: Database-friendly random projections. In: PODS 2001: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, NY, USA, pp. 274–281. ACM Press, New York (2001)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC 1998: Proceedings of the thirtieth annual ACM symposium on Theory of computing, NY, USA, pp. 604–613. ACM Press, New York (1998)
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, NY, USA, pp. 245–250. ACM Press, New York (2001)
Papadimitriou, C.H., Tamaki, H., Raghavan, P., Vempala, S.: Latent semantic indexing: a probabilistic analysis. In: PODS 1998: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, NY, USA, pp. 159–168. ACM Press, New York (1998)
Johnson, W.B., Lindenstrauss, J.: Extensions of lipschitz mappings into a hilbert space. Amer. Math. Soc. 26, 189–206 (1984)
Waterston, R.H., et al.: Initial sequencing and comparative analysis of the mouse genome. Nature 420 (2002)
Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Venables, W.N., Smith, D.M.: An Introduction to R. Network Theory Ltd (2002)
Gentleman, Rossini, Dudoit, Hornik: The bioconductor faq (2003), http://www.bioconductor.org/
Pomery, S.L., et al.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415 (2002)
Schena, M., Shalon, D., Davis, R., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary dna microarray. Science 270, 467–470 (1995)
Lockhart, D., Dong, H., Byrne, M., Follettie, M., Gallo, M., Chee, M., Mittmann, M., Wang, C., Kobayashi, M., Horton, H., Brown, E.: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology 14, 1675–1680 (1996)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the International Conference on Intelligent Systems for Molecular Biology (ISMB), San Diego, CA, pp. 93–103 (2000), (data sets are available at) http://arep.med.harvard.edu/biclustering/
Wang, H., Wang, W., Yang, J., Yu, P.: Clustering by pattern similarity in large data sets. In: sigmod (2002)
Kostka, D., Spang, R.: Finding disease specific alternations in the co-expression of genes. Bioinformatics 20, 194–199 (2004)
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D.N.F.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34, 166–176 (2003)
Segal, E., Friedman, N., Koller, D., Regev, A.: A module map showing conditional activity of expression modules in cancer. Nat Genet 36, 1090–1098 (2004)
Gerhold, D., Jensen, R., Gullans, S.: Better therapeutics through microarrays. Nature Genetics 32, 547–551 (2002)
Allocco, D., Kohane, I., Butte, A.: Quantifying the relationship between co-expression, co-regulation and gene function. BMC Bioinformatics 5 (2004)
Stuart, J.M., Segal, E., Koller, D., Kim, S.K.: A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003)
Zhou, X., Kao, M., Huang, H., Wong, A., Nunez-Iglesias, J., Primig, M., Aparicio, O., Finch, C., Morgan, T., Wong, W.: Functional annotation and network reconstruction through cross-platform integration of microarray data. Nature Biotechnology 23 (2005)
Parsons, L., Haque, E., Liu, H.: Subspace clustering for high dimensional data: a review. SIGKDD Explor. Newsl. 6, 90–105 (2004)
Fern, X.Z., Brodley., C.E.: Random projection for high dimensional data clustering: A cluster ensemble approach. In: Machine Learning, Proceedings of the International Conference on (2003)
Buhler, J., Tompa, M.: Finding motifs using random projections. In: RECOMB 2001: Proceedings of the fifth annual international conference on Computational biology, NY, USA, pp. 69–76. ACM Press, New York (2001)
Kurimo, M.: Indexing audio documents by using latent semantic analysis and som. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 363–374 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, J., Yang, J., Xu, W., Chopra, P. (2005). Integrating Heterogeneous Microarray Data Sources Using Correlation Signatures. In: Ludäscher, B., Raschid, L. (eds) Data Integration in the Life Sciences. DILS 2005. Lecture Notes in Computer Science(), vol 3615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11530084_10
Download citation
DOI: https://doi.org/10.1007/11530084_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27967-9
Online ISBN: 978-3-540-31879-8
eBook Packages: Computer ScienceComputer Science (R0)