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Identification of Phenotype-Defining Gene Signatures Using the Gene-Pair Matrix Based Clustering

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Data Mining and Bioinformatics (VDMB 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4316))

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Abstract

Mining the “meaningful” clues from vast amount of expression profiling data remains to be challenge for biologists. After all the statistical tests, biologists often struggle deciding how to do next with a large list of genes without any obvious theme of mechanism, partly because most statistical analyses do not incorporate understanding of biological systems before hand. Here, we developed a novel method of “gene –pair difference within a sample” to identify phenotype-defining gene signatures, based on the hypothesis that a biological state is governed by the relative difference among different biological processes. For gene expression, it is relative difference among the genes within a sample (an individual, cell, etc), the highest frequency of occurrences a gene contributing to the within sample difference underline the contributions of genes in defining the biological states. We tested the method on three datasets, and identified the most important gene-pairs to drive the phenotypic differences.

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References

  1. Savage, K.J., Monti, S., Kutok, J.L., Cattoretti, G., Neuberg, D., De Leval, L., et al.: The molecular signature of mediastinal large B-cell lymphoma differs from that of other diffuse large B-cell lymphomas and shares features with classical Hodgkin lymphoma. Blood 102, 3871–3879 (2003)

    Article  Google Scholar 

  2. Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346, 1937–1947 (2002)

    Article  Google Scholar 

  3. Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)

    Article  Google Scholar 

  4. Lossos, I.S., Czerwinski, D.K., Alizadeh, A.A., Wechser, M.A., Tibshirani, R., Botstein, D., et al.: Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N. Engl. J. Med. 350, 1828–1837 (2004)

    Article  Google Scholar 

  5. Barrett, T., Suzek, T.O., Troup, D.B., Wilhite, S.E., Ngau, W.C., Ledoux, P., et al.: NCBI GEO: mining millions of expression profiles–database and tools. Nucleic Acids Res. 33(Database Issue), 562–566 (2005)

    Article  Google Scholar 

  6. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., et al.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS, 0506580102 (2005)

    Google Scholar 

  7. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U.S.A. 95, 14863–14868 (1998)

    Article  Google Scholar 

  8. Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21, 3201–3212 (2005)

    Article  Google Scholar 

  9. Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21, 3201–3212 (2005)

    Article  Google Scholar 

  10. Man, M.Z., Dyson, G., Johnson, K., Liao, B.: Evaluating methods for classifying expression data. J. Biopharm. Stat. 14, 1065–1084 (2004)

    Article  MathSciNet  Google Scholar 

  11. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., et al.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS 2005, 0506580102 (2005)

    Google Scholar 

  12. Oltvai, Z.N., Barabasi, A.L.: Systems biology. Life’s complexity pyramid. Science 298, 763–764 (2002)

    Google Scholar 

  13. Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)

    Article  Google Scholar 

  14. Dezso, Z., Oltvai, Z.N., Barabasi, A.L.: Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae. Genome Res. 13, 2450–2454 (2003)

    Article  Google Scholar 

  15. Wuchty, S., Oltvai, Z.N., Barabasi, A.L.: Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat. Genet. 35, 176–179 (2003)

    Article  Google Scholar 

  16. Fortunel, N.O., Otu, H.H., Ng, H.H., Chen, J., Mu, X., Chevassut, T., et al.: Comment on ‘Stemness’: transcriptional profiling of embryonic and adult stem cells and a stem cell molecular signature. Science 302, 393 (2003)

    Article  Google Scholar 

  17. Liu, W.M., Mei, R., Di, X., Ryder, T.B., Hubbell, E., Dee, S., et al.: Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics 18, 1593–1599 (2002)

    Article  Google Scholar 

  18. Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science %20 270, 467–470 (1995)

    Google Scholar 

  19. Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. %20 346, 1937–1947 (2002)

    Article  Google Scholar 

  20. Monti, S., Savage, K.J., Kutok, J.L., Feuerhake, F., Kurtin, P., Mihm, M., et al.: Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 105, 1851–1861 (2005)

    Article  Google Scholar 

  21. Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., et al.: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. U.S.A. 98, 13790–13795 (2001)

    Article  Google Scholar 

  22. Liu, W.M., Mei, R., Di, X., Ryder, T.B., Hubbell, E., Dee, S., et al.: Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics 18, 1593–1599 (2002)

    Article  Google Scholar 

  23. Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science %20 270, 467–470 (1995)

    Article  Google Scholar 

  24. Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. %20 346, 1937–1947 (2002)

    Article  Google Scholar 

  25. Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)

    Article  Google Scholar 

  26. Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. %20 346, 1937–1947 (2002)

    Article  Google Scholar 

  27. Milili, M., Antunes, H., Blanco-Betancourt, C., Nogueiras, A., Santos, E., Vasconcelos, J., et al.: A new case of autosomal recessive agammaglobulinaemia with impaired pre-B cell differentiation due to a large deletion of the IGH locus. European Journal of Pediatrics 161, 479–484 (2002)

    Article  Google Scholar 

  28. Lopez, G.E., Porpiglia, A.S., Hogan, M.B., Matamoros, N., Krasovec, S., Pignata, C., et al.: Clinical and molecular analysis of patients with defects in micro heavy chain gene. Journal of Clinical Investigation 110, 1029–1035 (2002)

    Google Scholar 

  29. Foroni, L., Boehm, T., White, L., Forster, A., Sherrington, P., Liao, X.B., et al.: The rhombotin gene family encode related LIM-domain proteins whose differing expression suggests multiple roles in mouse development. J. Mol. Biol. 226, 747–761 (1992)

    Article  Google Scholar 

  30. Monti, S., Savage, K.J., Kutok, J.L., Feuerhake, F., Kurtin, P., Mihm, M., et al.: Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 105, 1851–1861 (2005)

    Article  Google Scholar 

  31. Cahir-McFarland, E.D., Carter, K., Rosenwald, A., Giltnane, J.M., Henrickson, S.E., Staudt, L.M., et al.: Role of NF-kappa B in cell survival and transcription of latent membrane protein 1-expressing or Epstein-Barr virus latency III-infected cells. Journal of Virology 78, 4108–4119 (2004)

    Article  Google Scholar 

  32. Islam, T.C., Asplund, A.C., Lindvall, J.M., Nygren, L., Liden, J., Kimby, E., et al.: High level of cannabinoid receptor 1, absence of regulator of G protein signalling 13 and differential expression of Cyclin D1 in mantle cell lymphoma. Leukemia 17, 1880–1890 (2003)

    Article  Google Scholar 

  33. Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. %20 346, 1937–1947 (2002)

    Article  Google Scholar 

  34. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., et al.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS 2005, 0506580102 (2005)

    Google Scholar 

  35. Monti, S., Savage, K.J., Kutok, J.L., Feuerhake, F., Kurtin, P., Mihm, M., et al.: Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 105, 1851–1861 (2005)

    Article  Google Scholar 

  36. Bridges, J.P., Wert, S.E., Nogee, L.M., Weaver, T.E.: Expression of a human surfactant protein C mutation associated with interstitial lung disease disrupts lung development in transgenic mice. Journal of Biological Chemistry 278, 52739–52746 (2003)

    Article  Google Scholar 

  37. Vejda, S., Posovszky, C., Zelzer, S., Peter, B., Bayer, E., Gelbmann, D., et al.: Plasma from cancer patients featuring a characteristic protein composition mediates protection against apoptosis. Mol. Cell Proteomics 1, 387–393 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Integrative Biology, Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN, 46285, USA

    Chung-Wein Lee, Shuyu Dan Li, Eric W. Su & Birong Liao

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  1. Chung-Wein Lee
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  2. Shuyu Dan Li
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  3. Eric W. Su
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Editor information

Editors and Affiliations

  1. School of Informatics, Indiana University, 901 E. 10th Street, 47408, Bloomington, IN,  

    Mehmet M. Dalkilic  & Sun Kim  & 

  2. EECS Department, Case Western Reserve Univ., 10900 Euclid Ave, 44106, Cleveland, OH, USA

    Jiong Yang

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© 2006 Springer-Verlag Berlin Heidelberg

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Lee, CW., Li, S.D., Su, E.W., Liao, B. (2006). Identification of Phenotype-Defining Gene Signatures Using the Gene-Pair Matrix Based Clustering. In: Dalkilic, M.M., Kim, S., Yang, J. (eds) Data Mining and Bioinformatics. VDMB 2006. Lecture Notes in Computer Science(), vol 4316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11960669_10

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  • DOI: https://doi.org/10.1007/11960669_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68970-6

  • Online ISBN: 978-3-540-68971-3

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