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Sep 17, 2013
09/13

by
Pierre Neuvial

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The False Discovery Rate (FDR) is a commonly used type I error rate in multiple testing problems. It is defined as the expected False Discovery Proportion (FDP), that is, the expected fraction of false positives among rejected hypotheses. When the hypotheses are independent, the Benjamini-Hochberg procedure achieves FDR control at any pre-specified level. By construction, FDR control offers no guarantee in terms of power, or type II error. A number of alternative procedures have been developed,...

Source: http://arxiv.org/abs/1003.0747v2

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Sep 18, 2013
09/13

by
Pierre Neuvial

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We investigate the performance of a family of multiple comparison procedures for strong control of the False Discovery Rate ($\mathsf{FDR}$). The $\mathsf{FDR}$ is the expected False Discovery Proportion ($\mathsf{FDP}$), that is, the expected fraction of false rejections among all rejected hypotheses. A number of refinements to the original Benjamini-Hochberg procedure [1] have been proposed, to increase power by estimating the proportion of true null hypotheses, either implicitly, leading to...

Source: http://arxiv.org/abs/0803.2111v2

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Sep 21, 2013
09/13

by
Pierre Neuvial; Etienne Roquain

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We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The "0"-class (null) is assumed to have a known density while the "1"-class (alternative) is obtained from the "0"-class either by translation or by scaling. Furthermore, the "1"-class is assumed to have a small number of elements w.r.t. the "0"-class (sparsity). We focus on densities of the Subbotin family, including Gaussian and Laplace...

Source: http://arxiv.org/abs/1106.6147v3

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Jun 30, 2018
06/18

by
Gilles Blanchard; Pierre Neuvial; Etienne Roquain

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We introduce a general methodology for post hoc inference in a large-scale multiple testing framework. The approach is called " user-agnostic " in the sense that the statistical guarantee on the number of correct rejections holds for any set of candidate items selected by the user (after having seen the data). This task is investigated by defining a suitable criterion, named the joint-family-wise-error rate (JER for short). We propose several procedures for controlling the JER, with a...

Topics: Statistics Theory, Statistics, Mathematics

Source: http://arxiv.org/abs/1703.02307

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Sep 19, 2013
09/13

by
Laurent Jacob; Pierre Neuvial; Sandrine Dudoit

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We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important application of such tests is the detection of differentially expressed genes between two patient populations, as shifts in expression levels are expected to be coherent with the structure of graphs reflecting gene properties such as biological process, molecular function, regulation, or metabolism. For a fixed graph of...

Source: http://arxiv.org/abs/1009.5173v1

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6.0

Jun 30, 2018
06/18

by
Morgane Pierre-Jean; Guillem Rigaill; Pierre Neuvial

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A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. We have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are...

Topics: Applications, Quantitative Methods, Quantitative Biology, Statistics

Source: http://arxiv.org/abs/1402.7203

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Jun 27, 2018
06/18

by
Thomas Picchetti; Julien Chiquet; Mohamed Elati; Pierre Neuvial; Rémy Nicolle; Etienne Birmelé

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In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these alterations do not necessarily induce differential expressions between the subtypes. To answer this question, we propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our model is based on a regulatory process in which all genes are allowed to be...

Topics: Statistics, Applications, Quantitative Biology, Quantitative Methods

Source: http://arxiv.org/abs/1505.05705