4. Approaches and Practical Considerations for the Analysis of Toxicogenomics Data

  1. Darrell R. Boverhof,
  2. B. Bhaskar Gollapudi
  1. Zhenqiang Su1,
  2. Hong Fang1,
  3. Weida Tong2,
  4. Huixiao Hong2,
  5. Roger Perkins2,
  6. Lei Guo2,
  7. Leming Shi2

Published Online: 18 JUL 2011

DOI: 10.1002/9781118001042.ch4

Applications of Toxicogenomics in Safety Evaluation and Risk Assessment

Applications of Toxicogenomics in Safety Evaluation and Risk Assessment

How to Cite

Su, Z., Fang, H., Tong, W., Hong, H., Perkins, R., Guo, L. and Shi, L. (2011) Approaches and Practical Considerations for the Analysis of Toxicogenomics Data, in Applications of Toxicogenomics in Safety Evaluation and Risk Assessment (eds D. R. Boverhof and B. B. Gollapudi), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118001042.ch4

Editor Information

  1. Toxicology and Environmental Research and Consulting, The Dow Chemical Company, Midland, MI, USA

Author Information

  1. 1

    Z-Tech, an ICF International Company, National Center for Toxicological Research, US Food and Drug Adminstration, Jefferson, AR, USA

  2. 2

    National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

Publication History

  1. Published Online: 18 JUL 2011
  2. Published Print: 18 JUL 2011

ISBN Information

Print ISBN: 9780470449820

Online ISBN: 9781118001042

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Keywords:

  • toxicogenomics data analysis - critical step in microarray gene study, identifying differentially expressed genes (DEGs);
  • gene identification methods, and performance assessment - of DEG identification approaches;
  • FC ranking and p-value cutoff, identifying DEGs - nature of data and microarray application, trade-off between sensitivity and specificity

Summary

This chapter contains sections titled:

  • Introduction

  • Concerns on the Reproducibility of Microarray Data

  • Many Different Ways for Identifying Differentially Expressed Genes (DEGs)

  • Assessing the Performance of Gene Identification Methods

  • A Toxicogenomics Case Study

  • Drawback of p-Value Ranking in Analyzing Microarray Data

  • Toward Consensus: Fold-Change Ranking with a Nonstringent p-Value Cutoff

  • Conclusions

  • References