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

References in zbMATH (referenced in 16 articles )

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  1. Boca, Simina M.; Céorrada Bravo, Héctor; Caffo, Brian; Leek, Jeffrey T.; Parmigiani, Giovanni: A decision-theory approach to interpretable set analysis for high-dimensional data (2013)
  2. Tong, Tiejun; Jang, Homin; Wang, Yuedong: James-Stein type estimators of variances (2012)
  3. Guerra, Rudy (ed.); Goldstein, Darlene R. (ed.): Meta-analysis and combining information in genetics and genomics. (2010)
  4. Blanchard, Gilles; Roquain, Étienne: Adaptive false discovery rate control under independence and dependence (2009)
  5. Gavrilov, Yulia; Benjamini, Yoav; Sarkar, Sanat K.: An adaptive step-down procedure with proven FDR control under independence (2009)
  6. Hossain, Ahmed; Beyene, Joseph; Willan, Andrew R.; Hu, Pingzhao: A flexible approximate likelihood ratio test for detecting differential expression in microarray data (2009)
  7. Neuvial, Pierre: Asymptotic properties of false discovery rate controlling procedures under independence (2008)
  8. Wang, Huixia; He, Xuming: An enhanced quantile approach for assessing differential gene expressions (2008)
  9. Moretti, Stefano; Patrone, Fioravante; Bonassi, Stefano: The class of microarray games and the relevance index for genes (2007)
  10. Benjamini, Yoav; Krieger, Abba M.; Yekutieli, Daniel: Adaptive linear step-up procedures that control the false discovery rate (2006)
  11. Cheng, Cheng: An adaptive significance threshold criterion for massive multiple hypotheses testing (2006)
  12. Yekutieli, Daniel; Reiner-Benaim, Anat; Benjamini, Yoav; Elmer, Gregory I.; Kafkafi, Neri; Letwin, Noah E.; Lee, Norman H.: Approaches to multiplicity issues in complex research in microarray analysis (2006)
  13. Cui, Xiangqin; Hwang, J.T.Gene; Qiu, Jing; Blades, Natalie J.; Churchill, Gary A.: Improved statistical tests for differential gene expression by shrinking variance components estimates (2005)
  14. Gilbert, Peter B.: A modified false discovery rate multiple-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics (2005)
  15. Müller, Peter; Parmigiani, Giovanni; Robert, Christian; Rousseau, Judith: Optimal sample size for multiple testing: the case of gene expression microarrays (2004)
  16. Parmigiani, Giovanni (ed.); Garrett, Elizabeth S. (ed.); Irizarry, Rafael A. (ed.); Zeger, Scott L. (ed.): The analysis of gene expression data. Methods and software (2003)