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Cluster Analysis and Its Applications to Gene Expression Data

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Bioinformatics and Genome Analysis

Part of the book series: Ernst Schering Research Foundation Workshop ((SCHERING FOUND,volume 38))

Abstract

Technologies for generating high-density arrays of cDNAs and oligonucleotides are developing rapidly, and changing the landscape of biological and biomedical research. They enable, for the first time, a global, simultaneous view on the transcription levels of many thousands of genes, when the cell undergoes specific processes and in certain conditions. For several organisms, the sequences of all genes are available, and thus, transcript levels of the complete gene collection can already be monitored today. The potential of such technologies is tremendous. Monitoring gene expression levels in different developmental stages, tissue types, clinical conditions, and different organisms can help in our understanding of gene function and gene networks, assist in the diagnosis of disease conditions, and reveal the effects of medical treatments. Undoubtedly, other applications will emerge in coming years.

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References

  • Alizadeh AA, Eisen MB, Davis R, Ma C, Lossos I, Rosenwald A, Boldrick J, Warnke R, Levy R, Wilson W, Grever M, Byrd J, Botstein D, Brown PO, Straudt LM (2000) Distinct types of diffuse large B-cell lymphomas identified by gene expression profiling. Nature 403: 503–511

    Article  PubMed  CAS  Google Scholar 

  • Alon U, Barkai N, Notterman DA, Gish G, Ybarra S, Mack D, Levine AJ (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 96: 6745–6750

    Article  PubMed  CAS  Google Scholar 

  • Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6 (314): 28l - 297

    Google Scholar 

  • Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z (2000) Tissue classification with gene expression profiles. J Comput Biol 7 (3/4): 559–583

    Article  PubMed  CAS  Google Scholar 

  • Brazma A, Vilo J (2000) Gene expression data analysis. FEBS Letters 480: 17–24

    Article  PubMed  CAS  Google Scholar 

  • Cho LU, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodica L, Wolfsberg TG et al (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 2: 65–73

    Article  PubMed  CAS  Google Scholar 

  • Clarke PA, George M, Cunningham D, Swift I, Workman P (1999) Analysis of tumor gene expression following chemotherapeutic treatment of patients with bowel cancer In proc. Nature Genetics Microarray Meeting 99, Scottsdale, Arizona, p 39

    Google Scholar 

  • Coller H, Gradori C, Tamayo P, Colbert T, Lander E, Eisenman R, Golub TR (2000) Expression analysis with oligonucleotide reveals that C-Myc regulates genes involved in growth, cell-cycle, signaling and adhesion. Proc Natl Acad Sci USA 97 (7): 3260–3265

    Article  PubMed  CAS  Google Scholar 

  • Cormack RM (1971) A review of classification (with discussion). J Royal Statistical Society, Series A 134: 321–367

    Google Scholar 

  • Dudoit S, Fridlyand J, Speed TP (2000) Comparison of discrimination methods for the classification of tumors using gene expression data. Technical report #576, Dept. of Statistics, university of California, Berkeley

    Google Scholar 

  • Eisen MB, Brown PO (1999) DNA arrays for analysis of gene expression. Methods Enzymol 303: 179–205

    Article  PubMed  CAS  Google Scholar 

  • Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863–14868

    Article  PubMed  CAS  Google Scholar 

  • Even S (1979) Graph Algorithms Computer Science Press, Rockville, Maryland

    Google Scholar 

  • Everitt B (1993) Cluster analysis. Edward Arnold, London, third edition

    Google Scholar 

  • Fodor SP, RP Rua, Huang XC, Pease AC, Holmes CP, Adams CL (1993) Multiplexed biochemical assays with biological chips. Nature 364: 555–556

    Article  PubMed  CAS  Google Scholar 

  • Furey TS, Cristianini N, Duffy N, Bendarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16: 906–914

    Article  PubMed  CAS  Google Scholar 

  • Getz G, Levine E, Domany E, Mang MQ (2000) Super-paramagnetic clustering of yeast gene expression profiles. Physica A279: 457

    Article  CAS  Google Scholar 

  • Golub T, Slonim D, Tamayo P, Huard CM, Caasenbeek JM, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531–537

    Article  PubMed  CAS  Google Scholar 

  • Golumbic MC (1980) Algorithmic graph theory and perfect graphs. Academic Press, New York

    Google Scholar 

  • Hansen P, Jaumard B (1997) Cluster analysis and mathematical programming. Mathemat Program 79: 191–215

    Google Scholar 

  • Hao J, Orlin J (1994) A faster algorithm for finding the minimum cut in a directed graph. J Algorithm 17 (3): 424–446

    Article  Google Scholar 

  • Harkin DP, Bean J, Miklos D, Song Y, Maheswaram V, Oliver J, Haber D (1999) Induction of GADD45 and JNK/SAPK-dependent apoptosis following inducible expression of BRCAl. Cell 97: 575–586

    Article  PubMed  CAS  Google Scholar 

  • Harrington CA, Rosenow C, Retief J (2000) Monitoring gene expression using DNA microarrays. Curr Opin Microbiol 3 (3): 285–291

    Article  PubMed  CAS  Google Scholar 

  • Hartigan JA (1975) Clustering algorithms. John Wiley and Sons

    Google Scholar 

  • Hartuv E, Shamir R (2000) A clustering algorithm based on graph connectivity. Inf. Process Lett 76: 175–181

    Article  Google Scholar 

  • Herwig R, Poustka AJ, Meuller C, Lehrach H, O’Brien J (1999) Large-scale clustering of cDNA-fingerprinting data. Genome Res 9 (11): 1093–1105

    Article  PubMed  CAS  Google Scholar 

  • Heyer LJ, Kruglyak S, Yooseph S (1999) Exploring expression data: identifica- tion and analysis of coexpressed genes. Genome Res 9 (11): 1106–1115

    Article  PubMed  CAS  Google Scholar 

  • Hughes JD, Estep PE, Tavazoie S, Church GM (2000) Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol 296: 1205–1214

    Article  PubMed  CAS  Google Scholar 

  • Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JCF, Trent M, Staudt LM, Hudson J, Boguski MS, Lashkari D, Shalon D, Botstein D, Brown PO (1999) The transcriptional program in the response of human fibroblast to serum. Science 283: 83–87

    Article  PubMed  CAS  Google Scholar 

  • Jelinsky SA, Estep P, Church QM, Samson LD (2000) Regulatory networks revealed by transcriptional profiling of damaged Saccharomyces cerevisiae cells: Rpn4 links base excision repair with proteasomes. MCB 20 (21): 8157–8167

    Article  PubMed  CAS  Google Scholar 

  • Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. Technical report, The Jackson Laboratory

    Google Scholar 

  • Kohonen T (1997) Self-organizing maps. Springer, Berlin

    Book  Google Scholar 

  • Lance GN, Williams WT (1967) A general theory of classification sorting strategies 1 hierarchical systems. Comput J 9: 373–380

    Article  Google Scholar 

  • Lipshutz RJ, Fodor SPA, Gingeras TR, Lockhart DJ (2000) High density synthetic oligonucleotide arrays. Nat Genet Suppl 21: 20–24

    Article  Google Scholar 

  • Livesey FJ, Furukawa T, Steffen MA, Church GM, Cepko CL (2000) Microarray analysis of the transcriptional network controlled by the photoreceptor homeobox gene Crx. Curr Biol 10: 301–310

    Article  PubMed  CAS  Google Scholar 

  • Maleck K, Levine A, Eulgem T, Morgan A, Schmid J, Lawton KA, Dangl JL, Dietrich RA (2000) The transcriptome of Arabidopsis thaliana during systematic acquired resistance. Nat Genet 26: 403–410

    Article  PubMed  CAS  Google Scholar 

  • Marshall A, Hodgson J (1998) DNA chips: an array of possibilities. Nat Biotechnol 16: 27–31

    Article  PubMed  CAS  Google Scholar 

  • Milosavljevic A, Strezoska Z, Zeremski M, Grujic D, Paunesku T, Crkven- jakov R (1995) Clone clustering by hybridization Genomics 27: 83–89

    CAS  Google Scholar 

  • Mirkin B (1996) Mathematical Classification and Clustering. Kluwer

    Google Scholar 

  • Poustka AJ, Herwig R, Krause A, Hennig S, Meier-Ewert S, Lehrach H (1999) Toward the gene catalogue of sea urchin development: the construction and analysis of an unfertilized egg cDNA library highly normalized by oligonucleotide fingerprinting. Genomics 59: 122–133

    Article  PubMed  CAS  Google Scholar 

  • Ramsay G (1998) DNA chips: state-of-the art. Nat Biotechnol 16: 40–44

    Article  PubMed  CAS  Google Scholar 

  • Roth FP, Hughes JD, Estep PW, Church GM (1998) Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 16: 939–908

    Article  PubMed  CAS  Google Scholar 

  • Schena M (1996) Genome analysis with gene expression microarrays. Bioessays 18: 427–431

    Article  PubMed  CAS  Google Scholar 

  • Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW (1996) Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci USA 93: 10614–10619

    Article  PubMed  CAS  Google Scholar 

  • Shamir R, Sharan R (2001) Algorithmic approaches to clustering gene expression data In: T Jiang, T Smith, Y Xu, MQ Zhang (eds) Current topics in computational biology. MIT Press

    Google Scholar 

  • Sharan R, Shamir R (2000) CLICK: A clustering algorithm with applications to gene expression analysis. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (ISMB), pp 307–316

    Google Scholar 

  • Spellman PT, Sherlock G, Zhang M, Iyer VR, Anders K, Eisen M, Brown PO, Botstein D, Futcher B (1998) Comprehensive identification of cell cycle regulated gene of the yeast Saccharomyces Cerevisia by microarray hybridization. Mol Biol Cell 9: 3273–3297

    PubMed  CAS  Google Scholar 

  • Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Nail Acad Sci USA 96: 2907–2912

    Article  CAS  Google Scholar 

  • Tavazoie S, Hughes J, Campbell M, Cho R, Church GM (1999) Systematic determination of genetic network architecture. Nat Genet 22: 281–285

    Article  PubMed  CAS  Google Scholar 

  • Toronen P, Kolehmainen M, Wong G, Castren E (1999) Analysis of gene expression data using self-organizing maps. FEBS Letters, 451: 142–146

    Article  PubMed  CAS  Google Scholar 

  • Werner T (2001) Target gene identification from expression array data by promoter analysis. Biomol Eng 17: 87–94

    Article  PubMed  CAS  Google Scholar 

  • Xiong M, Jin L, Li W, Boerwinkle E (2000) Computational methods for gene expression based tumor classification. Biotechniques 29: 1264–1270

    PubMed  CAS  Google Scholar 

  • Yeung KY, Haynor DR, Ruzzo WL (2001) Validating clustering for gene expression data. Bioinformatics 17: 309–318

    Article  PubMed  CAS  Google Scholar 

  • Zhang MQ (1999) Large scale gene expression data analysis: a new challenge to computational biologists. Genome Res 9: 681–688

    PubMed  CAS  Google Scholar 

  • Zhao R, Gish K, Yin Y, Notterman D, Hoffman W, Tom E, Mak D, Levine M (2000) Analysis of p53 regulated gene expression patterns using oligonucleotide arrays Genes and Dev. 14: 981–993

    CAS  Google Scholar 

  • Zhu J, Zhang MQ (1999) SCPD: a promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics 15: 607–611

    Article  PubMed  CAS  Google Scholar 

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© 2002 Springer-Verlag Berlin Heidelberg

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Sharan, R., Elkon, R., Shamir, R. (2002). Cluster Analysis and Its Applications to Gene Expression Data. In: Mewes, HW., Seidel, H., Weiss, B. (eds) Bioinformatics and Genome Analysis. Ernst Schering Research Foundation Workshop, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04747-7_5

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  • DOI: https://doi.org/10.1007/978-3-662-04747-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-04749-1

  • Online ISBN: 978-3-662-04747-7

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