Multiple Hypothesis Testing in Microarray Experiments

By Sandrine Dudoit, J. Shaffer, Jennifer C. Boldrick. 2003

1,198 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b16cd25aef1d6663167733a2ac7681a8d7b23990

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In Management Science and Operations Research

In Molecular Biology

Citations in the corpus

Confidence sets for model selection by F -testing (2015, methodology): “Their approach is analogous to some step-down procedures for multiple hypothesis testing (e.g., Dudoit et al. (2003), Lehmann and Romano (2006), Ch. 9, or Romano and Wolf (2005)), as is mentioned in Hansen et al. (2011).”
Balanced Control of Generalized Error Rates (2010, background): “, see Dudoit et al. (2003) for a related discussion. The notion of weak control is not useful for practical and theoretical reasons, and will not be used in the remainder of this paper. A related measure of error control is the average number of false rejections, also known as the per-family error r”
On the Benjamini-Hochberg method (2006, background): “M R2065195 [7] D udoit,S.,Schaffer,J.and Boldrick,J.(2003).”
9) TM4 Microarray Software Suite (2006, background): “options (such as Bonferroni corrections) for multiple testing (Dudoit et al., 2003), as well as false discovery rate (FDR) computations (see later).”
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis (2012, background): “expression level and the responses or target features [41].”
Foundations of Linear and Generalized Linear Models (2015, methodology): “5 Smoothing: For smoothing methods, see Fahrmeir et al. (2013, Chapter 8), Fahrmeir and Tutz (2001, Chapter 5), and Faraway (2006, Chapter 11). Green and Silverman (1993), Hastie et al.”
Multiple Hypothesis Testing by Clustering Treatment Effects (2007, methodology): “The typical multiple testing scenario involves a method of controlling some type I error rate based on gene-specific test statistics (e.g., Dudoit, Shaffer, and Boldrick 2003).”
Large-Scale Simultaneous Hypothesis Testing (2004, methodology): “The central peak is wider here than in Figure 1, with center-width estimates ( δ 0 , σ 0 ) = ( − 0 .”
Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing (2005, background): “As noted by Dudoit, Shaffer, and Boldrick (2003), this distinction is often ignored.”
Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, background): “Both gene-specific permutation-based hypothesis tests, and the nonparametric mixture approach of Efron et al. (2001) are affected adversely by the low complexity of the permutation distribution caused by limited replication.”
The relation between statistical power and inference in fMRI (2017, background): “One important difference is the average power to correctly reject the applicable null hypotheses or the power to correctly reject at least one null hypothesis [25,26].”
A practical guide to methods controlling false discoveries in computational biology (2018, background): “The use of such adjustments for multiple testing has become standard in areas such as genomics [1, 2], neuroimaging [3], proteomics [4], psychology [5, 6], and economics [7].”
Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits (2005, methodology): “Since we are testing for many motifs simultaneously, we applied the step-down min-P-based algorithm for multiple-hypothesis correction [70,71,72].”
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, methodology): “…false discoveries is still an active research area and several new methods have been proposed in last two decades (Benjamini and Hochberg, 1995; Bogdan et al., 2008; Dudoit et al., 2003; Efron, 2007) that improve upon the Bonferroni theorem both by new methodological and theoretical developments.”
Control of the mean number of false discoveries, Bonferroni and stability of multiple testing (2007, methodology): “The Bonferroni method was improved by Holm (1979) who proposed a step-down multiple testing procedure (MTP) that has more power but still controls the FWER at the same level.”
Optimal weighting for false discovery rate control (2008, methodology): “the LSU procedure) are widely used to find the differentially expressed genes while controlling a given type I error rate (see e.g. [ 8 ]).”
PROPERTIES OF HIGHER CRITICISM UNDER STRONG DEPENDENCE (2008, methodology): “Review-type accounts of multiple hypothesis testing include those of Hochberg and Tamhane [13], Pigeot [25], Dudoit, Shaffer and Boldrich [9], Bernhard, Klein and Hommel [3] and Lehmann and Romano ([23], Chapter 9).”