A variance-stabilizing transformation for gene-expression microarray data

By Blythe Durbin, Johanna S. Hardin, Douglas M. Hawkins, David M. Rocke. Intelligent Systems in Molecular Biology, 2002

479 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/383fd5c8ae82d50861f33127e9c2a67cd9c9402a

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Citations in the corpus

GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data (2012, unknown): “In microarray analysis, a well-known variance stabilization method is fold change with offset (or start-log) (Durbin et al. , 2002; Ritchie et al. , 2007; Rocke and Durbin, 2003), which adds an offset before calculating fold changes.”
Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis (2010, methodology): “A common way to check the homoscedasticity of the data is by visualizing the relations between mean and standard deviation [15,16].”
Model-based variance-stabilizing transformation for Illumina microarray data (2008, methodology): “(6) have shown that the variance approaches infinity as the mean approaches zero when a log transformation is applied.”
A Model-Based Background Adjustment for Oligonucleotide Expression Arrays (2004, unknown): “We base our approach on the model proposed by, for example, Durbin et al. (2002). Using replicate array data, Irizarry et al.”
Data-analysis strategies for image-based cell profiling (2017, background): “Redundant features can diminish the signals of other more complementary features that are underrepresented, thus confounding downstream analysis.”
A concave log-like transformation allowing non-positive values (2017, background): “4 Other log-like transformations have been proposed including the generalized log (Durbin et al., 2002), the log modulus (John and Draper, 1980), the log-linear hybrid (Rocke and Durbin, 2003) and the hyperlog (Bagwell, 2005).”
A comparison of background correction methods for two-colour microarrays (2007, background): “ Durbin and Rocke (2004) have previously shown how an additive offset can behave as a simple but effective variance stabilizing transformation.”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, unknown): “Alternative estimators can be found that are more stable than the standard calculation of fold change as the ratio of average observed values for each condition [53-55].”
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment (2004, unknown): “In order to solve the intensity-dependence problem in determined ratios, Huber et al. and Durbin et al. [5, 6 ] produced a new scheme that recognizes the differences in mRNA levels, not in terms of ratios but in terms of the difference values in arsinh functions, which have stability in their statis”
Stochastic Models Inspired by Hybridization Theory for Short Oligonucleotide Arrays (2004, unknown): “A slightly more complicated, additive background multiplicative measurement error (ABME) model has been proposed by, for example, Huber et al. (2002), Cui et al. (2003), and Durbin et al. (2002).”