Variance stabilization and calibration for microarray data Bioconductor version: Release (2.10) The package implements a method for normalising microarray intensities, both between colours within array, and between arrays. The method uses a robust variant of the maximum-likelihood estimator for the stochastic model of microarray data described in the references (see vignette). The model incorporates data calibration (a.k.a. normalization), a model for the dependence of the variance on the mean intensity, and a variance stabilizing data transformation. Differences between transformed intensities are analogous to ”normalized log-ratios”. However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.

References in zbMATH (referenced in 29 articles )

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  1. Meijer, Rosa J.; Krebs, Thijmen J. P.; Goeman, Jelle J.: Hommel’s procedure in linear time (2019)
  2. Miok, Viktorian; Wilting, Saskia M.; van Wieringen, Wessel N.: Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data (2017)
  3. Tsai, Arthur C.; Liou, Michelle; Simak, Maria; Cheng, Philip E.: On hyperbolic transformations to normality (2017)
  4. Saraiva, Erlandson F.; Louzada, Francisco: A gene-by-gene multiple comparison analysis: a predictive Bayesian approach (2015)
  5. Vilca, Filidor; Rodrigues-Motta, Mariana; Leiva, Víctor: On a variance stabilizing model and its application to genomic data (2013)
  6. Dazard, Jean-Eudes; Rao, J. Sunil: Joint adaptive mean-variance regularization and variance stabilization of high dimensional data (2012)
  7. Hulsman, Marc; Mentink, Anouk; Van Someren, Eugene P.; Dechering, Koen J.; De Boer, Jan; Reinders, Marcel J. T.: Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization (2010) ioport
  8. Zhang, Zhong-Yuan; Li, Tao; Ding, Chris; Ren, Xian-Wen; Zhang, Xiang-Sun: Binary matrix factorization for analyzing gene expression data (2010) ioport
  9. Klein, Hans-Ulrich; Ruckert, Christian; Kohlmann, Alexander; Bullinger, Lars; Thiede, Christian; Haferlach, Torsten; Dugas, Martin: Quantitative comparison of microarray experiments with published leukemia related gene expression signatures (2009) ioport
  10. Lama, Nicola; Boracchi, Patrizia; Biganzoli, Elia: Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data (2009)
  11. Leiva, Víctor; Sanhueza, Antonio; Kelmansky, Diana M.; Martínez, Elena J.: On the glog-normal distribution and its application to the gene expression problem (2009)
  12. Tritchler, David; Parkhomenko, Elena; Beyene, Joseph: Filtering genes for cluster and network analysis (2009) ioport
  13. Ding, Yu; Raghavarao, Damaraju: Hadamard matrix methods in identifying differentially expressed genes from microarray experiments (2008)
  14. Lee, Jae Won; Jhun, Myoungshic; Kim, Jong Young; Lee, JungBok: An optimal choice of window width for LOWESS normalization of microarray data (2008)
  15. Yekutieli, Daniel: False discovery rate control for non-positively regression dependent test statistics (2008)
  16. Froehlich, Holger; Fellmann, Mark; Sueltmann, Holger; Poustka, Annemarie; Beissbarth, Tim: Large scale statistical inference of signaling pathways from rnai and microarray data (2007) ioport
  17. Toedling, Joern; Sklyar, Oleg; Huber, Wolfgang: Ringo - an R/Bioconductor package for analyzing chip-chip readouts (2007) ioport
  18. van de Wiel, Mark A.; Kim, Kyung In: Estimating the false discovery rate using nonparametric deconvolution (2007)
  19. Wu, Zhijin; Irizarry, Rafael A.: A statistical framework for the analysis of microarray probe-level data (2007)
  20. Zhao, Hongya; Yan, Hong: Houghfeature, a novel method for assessing drug effects in three-color cdna microarray experiments (2007) ioport

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