Empirical bayes methods and false discovery rates for microarrays

2002 ◽  
Vol 23 (1) ◽  
pp. 70-86 ◽  
Author(s):  
Bradley Efron ◽  
Robert Tibshirani
Keyword(s):  
Empirical Bayes ◽  
False Discovery Rates ◽  
Bayes Methods ◽  
False Discovery ◽  
Empirical Bayes Methods ◽  
Discovery Rates

scholarly journals Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis

BMC Genomics ◽  
2013 ◽  
Vol 14 (Suppl 8) ◽  
pp. S8 ◽  
Author(s):  
Arindom Chakraborty ◽  
Guanglong Jiang ◽  
Malaz Boustani ◽  
Yunlong Liu ◽  
Todd Skaar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Empirical Bayes ◽  
False Discovery Rates ◽  
Bayes Methods ◽  
False Discovery ◽  
Empirical Bayes Methods ◽  
Discovery Rates ◽  
Simultaneous Inferences

scholarly journals False Discovery Rates: A New Deal

2016 ◽  
Author(s):  
Matthew Stephens
Keyword(s):  
Empirical Bayes ◽  
Large Scale ◽  
R Package ◽  
False Discovery Rates ◽  
Interval Estimates ◽  
False Discovery ◽  
Unobserved Effects ◽  
The One ◽  
Significance Measures ◽  
Discovery Rates

AbstractWe introduce a new Empirical Bayes approach for large-scale hypothesis testing, including estimating False Discovery Rates (FDRs), and effect sizes. This approach has two key differences from existing approaches to FDR analysis. First, it assumes that the distribution of the actual (unobserved) effects is unimodal, with a mode at 0. This “unimodal assumption” (UA), although natural in many contexts, is not usually incorporated into standard FDR analysis, and we demonstrate how incorporating it brings many benefits. Specifically, the UA facilitates efficient and robust computation – estimating the unimodal distribution involves solving a simple convex optimization problem – and enables more accurate inferences provided that it holds. Second, the method takes as its input two numbers for each test (an effect size estimate, and corresponding standard error), rather than the one number usually used (p value, or z score). When available, using two numbers instead of one helps account for variation in measurement precision across tests. It also facilitates estimation of effects, and unlike standard FDR methods our approach provides interval estimates (credible regions) for each effect in addition to measures of significance. To provide a bridge between interval estimates and significance measures we introduce the term “local false sign rate” to refer to the probability of getting the sign of an effect wrong, and argue that it is a superior measure of significance than the local FDR because it is both more generally applicable, and can be more robustly estimated. Our methods are implemented in an R package ashr available from http://github.com/stephens999/ashr.

scholarly journals Significance estimation for large scale untargeted metabolomics annotations

2017 ◽  
Author(s):  
Kerstin Scheubert ◽  
Franziska Hufsky ◽  
Daniel Petras ◽  
Mingxun Wang ◽  
Louis-Félix Nothias ◽  
...  
Keyword(s):  
Small Molecules ◽  
Empirical Bayes ◽  
Large Scale ◽  
Estimation Methods ◽  
Scale Analysis ◽  
Reference Library ◽  
False Discovery Rates ◽  
False Discovery ◽  
Large Scale Analysis ◽  
Discovery Rates

AbstractThe annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate. Relying on estimations of false discovery rates, we explore the effect of different spectrum-spectrum match criteria on the number and the nature of the molecules annotated. We show that the spectral matching settings needs to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92% up to +5705%) when compared to a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to define the scoring criteria for large scale analysis of untargeted small molecule data that has been essential in the advancement of large scale proteomics, transcriptomics, and genomics science.

False discovery rates and copy number variation

Biometrika ◽  
2011 ◽  
Vol 98 (2) ◽  
pp. 251-271 ◽  
Author(s):  
Bradley Efron ◽  
Nancy R. Zhang
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
False Discovery Rates ◽  
False Discovery ◽  
Number Variation ◽  
Discovery Rates
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