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 23 articles )

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  1. Fan, Jianqing; Ke, Yuan; Sun, Qiang; Zhou, Wen-Xin: Farmtest: factor-adjusted robust multiple testing with approximate false discovery control (2019)
  2. Javanmard, Adel; Montanari, Andrea: Online rules for control of false discovery rate and false discovery exceedance (2018)
  3. Sharma, Amit; Hofman, Jake M.; Watts, Duncan J.: Split-door criterion: identification of causal effects through auxiliary outcomes (2018)
  4. Hossain, Ahmed; Beyene, Joseph: Application of skew-normal distribution for detecting differential expression to microRNA data (2015)
  5. 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)
  6. Tong, Tiejun; Feng, Zeny; Hilton, Julia S.; Zhao, Hongyu: Estimating the proportion of true null hypotheses using the pattern of observed (p)-values (2013)
  7. Tong, Tiejun; Jang, Homin; Wang, Yuedong: James-Stein type estimators of variances (2012)
  8. Guerra, Rudy (ed.); Goldstein, Darlene R. (ed.): Meta-analysis and combining information in genetics and genomics. (2010)
  9. Blanchard, Gilles; Roquain, Étienne: Adaptive false discovery rate control under independence and dependence (2009)
  10. Conlon, E. M.; Postier, B. L.; Methé, B. A.; Nevin, K. P.; Lovley, D. R.: Hierarchical Bayesian meta-analysis models for cross-platform microarray studies (2009)
  11. Gavrilov, Yulia; Benjamini, Yoav; Sarkar, Sanat K.: An adaptive step-down procedure with proven FDR control under independence (2009)
  12. Hossain, Ahmed; Beyene, Joseph; Willan, Andrew R.; Hu, Pingzhao: A flexible approximate likelihood ratio test for detecting differential expression in microarray data (2009)
  13. Neuvial, Pierre: Asymptotic properties of false discovery rate controlling procedures under independence (2008)
  14. Wang, Huixia; He, Xuming: An enhanced quantile approach for assessing differential gene expressions (2008)
  15. Moretti, Stefano; Patrone, Fioravante; Bonassi, Stefano: The class of microarray games and the relevance index for genes (2007)
  16. Tibshirani, Robert; Hastie, Trevor: Outlier sums for differential gene expression analysis (2007)
  17. Benjamini, Yoav; Krieger, Abba M.; Yekutieli, Daniel: Adaptive linear step-up procedures that control the false discovery rate (2006)
  18. Cheng, Cheng: An adaptive significance threshold criterion for massive multiple hypotheses testing (2006)
  19. 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)
  20. 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)

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