STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS

By Sandrine Dudoit, Y. H. Yang, M. Callow, Terence P. Speed. 2002

1,619 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d42337ecab378287fbcd60b42e5ceb5b67b6f59a

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Citations in the corpus

Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, methodology): “The (M, A) notation is borrowed from Dudoit et al. (2002), though we use it to describe underlying means rather than statistics (and we use a natural log scale rather than base 2).”
Empirical Bayes Analysis of a Microarray Experiment (2001, methodology): “Multiple comparison techniques, designed to control error rates in thousands of simultaneous hypotheses tests, were explored in Dudoit, Yang, Callow, and Speed (2000).”
WGCNA: an R package for weighted correlation network analysis (2008, methodology): “These functions rely on basic plotting functions provided in R and the packages sma [35] and fields [36].”
From patterns to pathways: gene expression data analysis comes of age (2002, methodology): “The y -axis shows the adjusted p - values using Bonferroni correction (black circles) and Westfall and Young’s permutation-based method 18,20 (blue squares).”
Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. (2001, background): “(3), we drew a so-called M–A plot for the initial investigation where M = log(Cy5/Cy3) represents the log ratio of the two dyes and A = [log(Cy5) + log(Cy3)]/2 is the average logarithmic intensity.”
A Model-Based Background Adjustment for Oligonucleotide Expression Arrays (2004, unknown): “plicitly or explicitly, assumed a multiplicative measurement error model (Dudoit et al., 2002; Newton et al., 2001; Kerr et al., 2000; Wolfinger et al., 2001). The fact, seen in Figure 1, that observed intensity increases linearly with concentration in the original scale but not in the log-scale sug”
Gene selection: a Bayesian variable selection approach (2003, methodology): “Dudoit et al. (2000) have proposed a method for the identification of singly differentially expressed genes by considering a univariate 90 c© Oxford University Press 2003 at Pennsylvania State U niversity on February 27, 2013 http://bioinform atics.oxfordjournals.org/ testing problem for each gene…”
Normalization of cDNA microarray data. (2003, background): “The relationship between dye-bias and intensity can be seen best in an MA-plot, which is a scatterplot of the M-values against the A-values for an array [3].”
Robust computational reconstitution – a new method for the comparative analysis of gene expression in tissues and isolated cell fractions (2006, methodology): “It was chosen to be a 200-genes symmetric moving average instead of the widely used loess procedure [18,38] in order to achieve reasonable computation times.”
Classifying Gene Expression Profiles from Pairwise mRNA Comparisons (2004, unknown): “Detection of marker gene pairs is a problem in feature selection, and plays the same role in our analysis as finding individual marker genes does in more standard methods (Dudoit et al. (2002); Sebastiani et al.”
Multiple Hypothesis Testing in Microarray Experiments (2003, methodology): “The resulting fluorescence intensities were normalized as described in Dudoit et al. (2002).”