On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data

By Michael A. Newton, Christina Kendziorski, C. Richmond, Frederick R. Blattner, Kam-Wah Tsui. J. Comput. Biol., 2001

810 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/869e99037469fa9caa7b62e913b689812e6767e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data (2013, background): “Genes that are differentially expressed among samples can then be identified by statistical algorithms (see for example [9, 13, 19, 31]).”
A variance-stabilizing transformation for gene-expression microarray data (2002, methodology): “Chen et al. (1997), Ideker et al. (2000), and Newton et al. (2001) have proposed alternative models for the measurement error in microarray data.”
Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes (2008, methodology): “This form facilitates the use of an efficient data augmentation algorithm, while also allowing one to obtain a highly accurate approximation to a logistic regression model by letting σ 2 = π(ν − 2)/3ν and ν = 7.3 (O’Brien and Dunson 2004, and the references therein).”
A comparison of background correction methods for two-colour microarrays (2007, background): “Contact: smyth@wehi.edu.au Supplementary information: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.”
A review of feature selection techniques in bioinformatics (2007, methodology): “Although Gaussian assumptions have dominated the field, other types of parametrical approaches can also be found in the literature, such as regression modelling approaches [117] and Gamma distribution models [85].”
Microarray data analysis: from disarray to consolidation and consensus (2006, background): “FDR is equivalent or related to several other metrics that quantify the confidence we can have that any particular gene is differentially expresse”
Variance stabilization applied to microarray data calibration and to the quantification of differential expression (2002, methodology): “However, it has also become clear that for many data sets that are encountered in practice they are insufficient (e.g. Beißbarth et al. (2000); Hughes et al. (2000); Rocke and Durbin (2001); Newton et al. (2001); Baldi and Long (2001); Baggerly et al. (2001); Theilhaber et al. (2001)).”
Prediction by Supervised Principal Components (2006, methodology): “Identication of the most \signican t" genes is an important problem in microarray analysis; several methods have been proposed for this (Ideker et al. 2000, Kerr et al. 2000, Newton et al. 2001, Tusher et al. 2001b).”
Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. (2007, methodology): “After the signal intensities were averaged across technical replicates, an empirical Bayes method implemented in the bioconductor package EBarrays, was used to estimate the posterior probabilities of the differential expression of genes between the tested conditions (Kendziorski et al., 2003;Newton ”
Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, background): “Another useful and fairly innocuous assumption is the discrete mixture model, which is becoming popular in microarray data analysis (Newton et al., 2001; Efron et al., 2001; Allison et al., 2002; Broët et al., 2002; Lee et al., 2002; Lonnstedt and Speed, 2002; Pan, 2002; Kendziorski et al., 2003; St”
A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments (2002, methodology): “Since then, many more sophisticated statistical methods have been proposed (e.g. Chen et al., 1997; Efron et al., 2000; Ideker et al., 2000; Newton et al., 2001; Tusher et al., 2001; Lin et al., 2001; Pan et al., 2001a).”