Skip to main content

Exploiting Dependencies of Patterns in Gene Expression Analysis Using Pairwise Comparisons

  • Conference paper
Bioinformatics Research and Applications (ISBRA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7875))

Included in the following conference series:

Abstract

In using pairwise comparisons to analyze gene expression data, researchers have often treated comparison outcomes independently. We now exploit additional dependencies of comparison outcomes to show that those with a certain property cannot be true patterns of genes’ response to treatments. With this result, we leverage p-values obtained from comparison outcomes to predict true patterns of gene response to treatments. Functional validation of gene lists obtained from our method yielded more and better functional enrichment than those obtained from the conventional approach. Consequently, our method promises to be useful in designing cost-effective experiments with small sample sizes.

The rights of this work are transferred to the extent transferable according to title 17 U.S.C. 105.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. 57(1), 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  2. Benjamini, Y.: Discovering the false discovery rate. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72(4), 405–416 (2010)

    Article  MathSciNet  Google Scholar 

  3. Davidson, A.C., Hinkley, D.V.: Bootstrap methods and their application. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  4. Geman, D., d’Avignon, C., Naiman, D., Winslow, R.: Classifying gene expression profiles from pairwise mrna comparisons. Statistical Applications in Genetics and Molecular Biology 3(article19) (2004)

    Google Scholar 

  5. Glaus, P., Honkela, A., Rattray, M.: Identifying differentially expressed transcripts from rna-seq data with biological variation. Bioinformatics 28(13), 1721–1728 (2012)

    Article  Google Scholar 

  6. Huang, D., Sherman, B., Lempicki, R.: Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nature Protocols 4(1), 44–57 (2008)

    Article  Google Scholar 

  7. Hulshizer, R., Blalock, E.M.: Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data. BMC Bioinformatic 8, 240 (2007)

    Article  Google Scholar 

  8. Lee, M.L., Kuo, F.C., Whitmore, G.A., Sklar, J.: Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cdna hybridization. Prot. Natl. Acad. Sci. 97(18), 9834–9839 (2000)

    Article  MATH  Google Scholar 

  9. Lin, W.J., Hsueh, H.M., Chen, J.J.: Power and sample size estimation in microarray studies. BMC Bioinformatics 11, 48–48 (2010)

    Article  Google Scholar 

  10. Longacre, A., Scott, L., Levine, J.: Linear independence of pairwise comparisons of dna microarray data. J. Bioinform. Comput. Biol. 3(6), 1243–1262 (2005)

    Article  Google Scholar 

  11. Phan, V., George, E.O., Tran, Q.T., Goodwin, S., Bodreddigari, S., Sutter, T.R.: Analyzing microarray data with transitive directed acyclic graphs. Journal of Bioinformatics and Computational Biology 7(1), 135–156 (2009)

    Article  Google Scholar 

  12. Ross, M.S.: Simulation, 3rd edn. Academic Press, San Diego (2002)

    Google Scholar 

  13. Sutter, T.R., He, X.R., Dimitrov, P., Xu, L., Narasimhan, G., George, E.O., Sutter, C.H., Grubbs, C., Savory, R., Stephan-Gueldner, M., Kreder, D., Taylor, M.J., Lubet, R., Patterson, T.A., Kensler, T.W.: Multiple comparisons model-based clustering and ternary pattern tree numerical display of gene response to treatment: procedure and application to the preclinical evaluation of chemopreventive agents. Mol. Cancer Ther. 1(14), 1283–1292 (2002)

    Google Scholar 

  14. Tran, Q.T., Xu, L., Phan, V., Goodwin, S., Rahman, M., Jin, V., Sutter, C.H., Roebuck, B., Kensler, T., George, E.O., Sutter, T.R.: Chemical genomics of cancer chemopreventive dithiolethiones. Carcinogenesis 30(3), 480–486 (2009)

    Article  Google Scholar 

  15. van Iterson, M., ’t Hoen, P.A., Pedotti, P., Hooiveld, G.J., den Dunnen, J.T., van Ommen, G.J., Boer, J.M., Menezes, R.X.: Relative power and sample size analysis on gene expression profiling data. BMC Genomics 10(1), 439 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vo, N.S., Phan, V. (2013). Exploiting Dependencies of Patterns in Gene Expression Analysis Using Pairwise Comparisons. In: Cai, Z., Eulenstein, O., Janies, D., Schwartz, D. (eds) Bioinformatics Research and Applications. ISBRA 2013. Lecture Notes in Computer Science(), vol 7875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38036-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38036-5_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38035-8

  • Online ISBN: 978-3-642-38036-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics