Pooling Evidence to Identify Cell Cycle–Regulated Genes

  • Gaolin Zheng
  • Tom Milledge
  • E. Olusegun George
  • Giri Narasimhan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)


Most of the biological studies have embraced statistical approaches to make inferences. It is common to have several independent experiments to test the same null hypothesis. The goal of research on pooling evidence is to combine the results of these tests to ask if there is evidence from the collection of studies to reject the null hypothesis. In this study, we evaluated four different pooling techniques (Fisher, Logit, Stouffer and Liptak) to combine the evidence from independent microarray experiments in order to identify cell cycle-regulated genes. We were able to identify a better set of cell cycle-regulated genes using the pooling techniques based on our benchmark study on budding yeast (Saccharomyces cerevisiae). Our gene ontology study on time series data of both the budding yeast and the fission yeast (Schizosaccharomyces pombe) showed that the GO terms that are related to cell cycle are significantly enriched in the cell cycle-regulated genes identified using pooling techniques.


Fission Yeast Schizosaccharomyces Pombe Sequence Homology Search Gene Ontology Information Logistic Random Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Fisher, R.A.: Statistical Methods for Research Workers, 14th edn. Oliver and Boyd, Edinburgh (1932)MATHGoogle Scholar
  2. 2.
    George, E.O., Mudholkar, G.S.: On the Convolution of Logistic Random Variables. Metrika 30, 1–14 (1983)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Liptak, T.: On the combination of independent tests. Magyar Tud. Akad. Mat. Kutato Int. Kozl 3, 171–197 (1958)Google Scholar
  4. 4.
    Stouffer, S.A., Suchman, E.A., Devinney, L.C., Star, S.A., Williams, R.M.J.: The American Soldier. Adjustment during army life, vol. 1. Princeton University Press, Princeton (1949)Google Scholar
  5. 5.
    Bailey, T.L., Gribskov, M.: Combining evidence using p-values: application to sequence homology searches. Bioinformatics 14(1), 48–54 (1998)CrossRefGoogle Scholar
  6. 6.
    Rustici, G., Mata, J., Kivinen, K., Lio, P., Penkett, C.J., Burns, G., Hayles, J., Brazma, A., Nurse, P., Bahler, J.: Periodic gene expression program of the fission yeast. Cell cycle 36(8), 809–817 (2004)Google Scholar
  7. 7.
    Cho, R.J., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Gabrielian, A., Landsman, D., Lockhart, D., Davis, R.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2, 65–73 (1998)CrossRefGoogle Scholar
  8. 8.
    Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Mol. Biol. Cell 9(12), 3273–3297 (1998)Google Scholar
  9. 9.
    Oliva, A., Rosebrock, A., Ferrezuelo, F., Pyne, S., Chen, H., Skiena, S., Futcher, B., Leatherwood, J.: The Cell Cycle Regulated Genes of Schizosaccharomyces pombe. PLoS Biology 3(7), e225 (2005)CrossRefGoogle Scholar
  10. 10.
    Hartwell, L., Culotti, J., Reid, B.: Genetic control of the cell-division cycle in yeast. I. Detection of mutants. Proc. Nat. Acad. Sci. 66, 352–359 (1970)CrossRefGoogle Scholar
  11. 11.
    Johansson, D., Lindgren, P., Berglund, A.: A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription. Bioinformatics 19(4), 467–473 (2003)CrossRefGoogle Scholar
  12. 12.
    Zhao, L.P., Prentice, R., Breeden, L.: Statistical modeling of large microarray data sets to identify stimulus-response profiles. Proc. Natl. Acad. Sci. 98, 5631–5636 (2001)MATHCrossRefGoogle Scholar
  13. 13.
    Lu, X., Zhang, W., Qin, Z.S., Kwast, K.E., Liu, J.S.: Statistical resynchronization and Bayesian detection of periodically expressed genes. Nucl. Acids Res. 32(2), 447–455 (2004)CrossRefGoogle Scholar
  14. 14.
    Wichert, S., Fokianos, K., Strimmer, K.: Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20(1), 5–20 (2004)CrossRefGoogle Scholar
  15. 15.
    de Lichtenberg, U., Jensen, L.J., Fausboll, A., Jensen, T.S., Bork, P., Brunak, S.: Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics 21(7), 1164–1171 (2005)CrossRefGoogle Scholar
  16. 16.
    Bailey, T.L., Grundy, W.N.: Classifying proteins by family using the product of correlated p-values. In: Proceedings of the Third international conference on computational molecular biology (RECOM 1999) (1999)Google Scholar
  17. 17.
    Zeeberg, B.R., Feng, W., Wang, G., Wang, M.D., Fojo, A.T., Sunshine, M., Narasimhan, S., Kane, D.W., Reinhold, W.C., Lababidi, S., Bussey, K.J., Riss, J., Barrett, J.C., Weinstein, J.N.: GoMiner: A Resource for Biological Interpretation of Genomic and Proteomic Data. Genome Biology 4(4), R28 (2003)CrossRefGoogle Scholar
  18. 18.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289–300 (1995)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gaolin Zheng
    • 1
  • Tom Milledge
    • 1
  • E. Olusegun George
    • 2
  • Giri Narasimhan
    • 1
  1. 1.Bioinformatics Research Group (BioRG), School of Computer ScienceFlorida International UniversityMiamiUSA
  2. 2.Department of Mathematical SciencesUniversity of MemphisMemphisUSA

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