Skip to main content

SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays

  • Chapter
The Analysis of Gene Expression Data

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

SAM is a computer package for correlating gene expression with an outcome parameter such as treatment, survival time, or diagnostic class. It thresholds an appropriate test statistic and reports the q-value of each test based on a set of sample permutations. SAM works as a Microsoft Excel add-in and has additional features for fold-change thresholding and block permutations. Here, we explain how the SAM methodology works in the context of a general approach to detecting differential gene expression in DNA microarrays. Some recently developed methodology for estimating false discovery rates and q-values has been included in the SAM software, which we summarize here.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

  • Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society, Series B 85: 289–300.

    MathSciNet  Google Scholar 

  • Dudoit S, Yang Y, Callow M, Speed T (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, Statistica Sinica 12:111–139.

    MATH  MathSciNet  Google Scholar 

  • Efron B, Tibshirani RJ (1993). An Introduction to the Bootstrap, Chapman & Hall.

    Google Scholar 

  • Efron B, Tibshirani R, Storey JD, Tusher V (2001). Empirical Bayes analysis of a microarray experiment, Journal of the American Statistical Association 96:1151–1160.

    Article  MATH  MathSciNet  Google Scholar 

  • Newton M, Kendziorski C, Richmond C, Blatter F, Tsui K (2001). On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data, Journal of Computational Biology 8:37–52.

    Article  Google Scholar 

  • Rice JA (1995). Mathematical Statistics and Data Analysis, 2nd ed., Duxbury Press, Belmont, CA.

    MATH  Google Scholar 

  • Romano JP (1989). Bootstrap and randomization tests of some nonparametric hypotheses, Annals of Statistics 17:141–159.

    Article  MATH  MathSciNet  Google Scholar 

  • Storey JD (2001). The positive false discovery rate: A Bayesian interpretation and the q-value. Submitted. Available at http://www.stat.berkeley.edu/~storey/.

  • Storey JD (2002). A direct approach to false discovery rates, Journal of the Royal Statistical Society, Series B 64:479–498.

    Article  MATH  MathSciNet  Google Scholar 

  • Storey JD, Taylor JE, Siegmund D (2002). A unified estimation approach to false discovery rates. Submitted. Available at http://www.stat.berkeley.edu/~storey/.

  • Storey JD, Tibshirani R (2001). Estimating false discovery rates under dependence, with applications to DNA microarrays. Submitted. Available http://www.stat.berkeley.edu/~storey/.

  • Tusher V, Tibshirani R, Chu C (2001). Significance analysis of microarrays applied to transcriptional responses to ionizing radiation, Proceedings of the National Academy of Sciences 98:5116–5121.

    Article  MATH  Google Scholar 

  • Westfall PH, Young SS (1993). Resampling-based Multiple Testing: Examples and Methods for p-value Adjustment, Wiley.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag New York, Inc.

About this chapter

Cite this chapter

Storey, J.D., Tibshirani, R. (2003). SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays. In: Parmigiani, G., Garrett, E.S., Irizarry, R.A., Zeger, S.L. (eds) The Analysis of Gene Expression Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21679-0_12

Download citation

  • DOI: https://doi.org/10.1007/0-387-21679-0_12

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95577-3

  • Online ISBN: 978-0-387-21679-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics