Estimating the proportion of true null hypotheses, with application to DNA microarray data

By Mette Langaas, Bo Henry Lindqvist, Egil Ferkingstad. 2005

311 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/68232dd7ebbcb8a58072be064b211f9a098508bb

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Citations in the corpus

limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “Other methods are the histogram method of (48,49), the convex decreasing density estimate of (50) and a very simple estimate based on averaging the P -values.”
fdrtool: a versatile R package for estimating local and tail area-based false discovery rates (2008, background): “More formally, consider an observed test statistic y 0 designed such that a small y indicates an ‘uninteresting’ null case, and conversely, a large y an ‘interesting’ alternative case.”
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, unknown): “Even when not on the boundary, the estimator for pj is likely to be sensitive to the particular form of the prior distribution assumed for gj and possibly also to dependence between the genes ( Ferkingstad et al, 2003 ).”
Estimation of a two‐component mixture model with applications to multiple testing (2012, methodology): “See Storey (2002), Black (2004), Langaas et al. (2005), Benjamini et al. (2006), and Donoho and Jin (2004) for a discussion on the importance of efficient estimation of α0 and some proposed estimators.”
Size, power and false discovery rates (2007, background): “form of the zero assumption is required because the two-class model (2.2) is unidentiﬁable in the absence of strong parametric assumptions on f 1. A healthy literature exists on estimating p 0, as in [21] and [29], all of which relies on the zero assumption [mostly working with p-values rather than ”
Controlling the familywise error rate with plug‐in estimator for the proportion of true null hypotheses (2009, methodology): “Langaas et al. (2005) reviewed and investigated methods for estimating π0 and choosing the tuning parameter λ with application to DNA microarray data.”
Estimating the number of true null hypotheses from a histogram of p values (2006, methodology): “The iterative procedure proposed by Mosig et al. (2001) and developed in this article as a noniterative procedure offers a simple and relatively effective method for estimating the number of true null hypotheses when conducting many tests.”
Correlation and Large-Scale Simultaneous Significance Testing (2007, unknown): “(2005), Owen (2005), Dudoit, van der Laan, and Pollard (2004), and Hsu (1993, 1996).”
BMC Bioinformatics (2005, unknown): “Careful simulation studies on the comparison of performance of π 0 estimation methods are done by Black [9] and Langaas et al. [10].”
NetCoMi: network construction and comparison for microbiome data in R (2020, unknown): “This algorithm provides a force-directed layout aimed at high readability of the network by placing pairs of nodes with a high D o w n l oaded f r o m h tt p s :/ / a c ade m i c . oup . c o m / b i b / ad v an c e - a r t i c l e / do i / 10 .”
Covariate‐assisted ranking and screening for large‐scale two‐sample inference (2019, methodology): “We start with a description of a classical estimator (Schweder and Spjøtvoll, 1982; Storey, 2002) for π1; see Langaas et al. (2005) for an detailed discussion of various extensions.”