GSA

On testing the significance of sets of genes. This paper discusses the problem of identifying differentially expressed groups of genes from a microarray experiment. The groups of genes are externally defined, for example, sets of gene pathways derived from biological databases. Our starting point is the interesting Gene Set Enrichment Analysis (GSEA) procedure of {it A. Subramanian} et al. [Proc. Natl. Acad. Sci. USA 102, 15545--15550 (2005)]. We study the problem in some generality and propose two potential improvements to GSEA: the maxmean statistic for summarizing gene-sets, and restandardization for more accurate inferences. We discuss a variety of examples and extensions, including the use of gene-set scores for class predictions. We also describe a new $R$ language package GSA that implements our ideas.


References in zbMATH (referenced in 37 articles )

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  1. Wang, Rui; Xu, Xingzhong: A feasible high dimensional randomization test for the mean vector (2019)
  2. Wang, Shulei; Yuan, Ming: Combined hypothesis testing on graphs with applications to gene set enrichment analysis (2019)
  3. Sun, Jiehuan; Herazo-Maya, Jose D.; Huang, Xiu; Kaminski, Naftali; Zhao, Hongyu: Distance-correlation based gene set analysis in longitudinal studies (2018)
  4. Wu, Cen; Zhong, Ping-Shou; Cui, Yuehua: Additive varying-coefficient model for nonlinear gene-environment interactions (2018)
  5. Zhang, Xianyang; Yao, Shun; Shao, Xiaofeng: Conditional mean and quantile dependence testing in high dimension (2018)
  6. Wang, Charlotte; Ruggeri, Fabrizio; Hsiao, Chuhsing K.; Argiento, Raffaele: Bayesian nonparametric clustering and association studies for candidate SNP observations (2017)
  7. Xu, Kai: A new nonparametric test for high-dimensional regression coefficients (2017)
  8. Dong, Kai; Pang, Herbert; Tong, Tiejun; Genton, Marc G.: Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data (2016)
  9. Guo, WenWen; Chen, YongShuai; Cui, HengJian: Robust (U)-type test for high dimensional regression coefficients using refitted cross-validation variance estimation (2016)
  10. Miecznikowski, Jeffrey C.; Gaile, Daniel P.; Chen, Xiwei; Tritchler, David L.: Identification of consistent functional genetic modules (2016)
  11. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Graph centrality based prediction of cancer genes (2016)
  12. Wang, Siyang; Cui, Hengjian: A new test for part of high dimensional regression coefficients (2015)
  13. Fuki, Igar; Brown, Lawrence; Han, Xu; Zhao, Linda: Hunting for significance: Bayesian classifiers under a mixture loss function (2014)
  14. Soneson, Charlotte; Fontes, Magnus: Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings (2014)
  15. Thulin, Måns: A high-dimensional two-sample test for the mean using random subspaces (2014)
  16. Feng, Long; Zou, Changliang; Wang, Zhaojun; Chen, Bin: Rank-based score tests for high-dimensional regression coefficients (2013)
  17. Dazard, Jean-Eudes; Rao, J. Sunil: Joint adaptive mean-variance regularization and variance stabilization of high dimensional data (2012)
  18. Li, Erning; Lim, Johan; Kim, Kyunga; Lee, Shin-Jae: Distribution-free tests of mean vectors and covariance matrices for multivariate paired data (2012)
  19. Li, Jun; Chen, Song Xi: Two sample tests for high-dimensional covariance matrices (2012)
  20. Yu, Donghyeon; Lim, Johan; Liang, Feng; Kim, Kyunga; Kim, Byung Soo; Jang, Woncheol: Permutation test for incomplete paired data with application to cDNA microarray data (2012)

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