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 20 articles )

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  1. 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)
  2. Tsai, Arthur C.; Liou, Michelle; Simak, Maria; Cheng, Philip E.: On hyperbolic transformations to normality (2017)
  3. Saraiva, Erlandson F.; Louzada, Francisco: A gene-by-gene multiple comparison analysis: a predictive Bayesian approach (2015)
  4. Dazard, Jean-Eudes; Rao, J. Sunil: Joint adaptive mean-variance regularization and variance stabilization of high dimensional data (2012)
  5. Zhang, Zhong-Yuan; Li, Tao; Ding, Chris; Ren, Xian-Wen; Zhang, Xiang-Sun: Binary matrix factorization for analyzing gene expression data (2010) ioport
  6. Lama, Nicola; Boracchi, Patrizia; Biganzoli, Elia: Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data (2009)
  7. Leiva, Víctor; Sanhueza, Antonio; Kelmansky, Diana M.; Martínez, Elena J.: On the glog-normal distribution and its application to the gene expression problem (2009)
  8. Ding, Yu; Raghavarao, Damaraju: Hadamard matrix methods in identifying differentially expressed genes from microarray experiments (2008)
  9. Lee, Jae Won; Jhun, Myoungshic; Kim, Jong Young; Lee, JungBok: An optimal choice of window width for LOWESS normalization of microarray data (2008)
  10. Yekutieli, Daniel: False discovery rate control for non-positively regression dependent test statistics (2008)
  11. van de Wiel, Mark A.; Kim, Kyung In: Estimating the false discovery rate using nonparametric deconvolution (2007)
  12. Wu, Zhijin; Irizarry, Rafael A.: A statistical framework for the analysis of microarray probe-level data (2007)
  13. Landgrebe, Jobst; Bretz, Frank; Brunner, Edgar: Efficient design and analysis of two colour factorial microarray experiments (2006)
  14. Lewin, Alex; Richardson, Sylvia; Marshall, Clare; Glazier, Anne; Aitman, Tim: Bayesian modeling of differential gene expression (2006)
  15. Schäfer, Juliane Stephanie: Small-sample analysis and inference of networked dependency structures from complex genomic data. (2005)
  16. Bryan, Jenny: Problems in gene clustering based on gene expression data (2004)
  17. Kauermann, Göran; Eilers, Paul: Modeling microarray data using a threshold mixture model (2004)
  18. McLachlan, G. J.; Khan, N.: On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples (2004)
  19. Wu, Zhijin; Irizarry, Rafael A.; Gentleman, Robert; Martinez-Murillo, Francisco; Spencer, Forrest: A model-based background adjustment for oligonucleotide expression arrays (2004)
  20. Huber, Wolfgang; von Heydebreck, Anja; Sueltmann, Holger; Poustka, Annemarie; Vingron, Martin: Parameter estimation for the calibration and variance stabilization of microarray data (2003)