False Discovery Rate Control With Groups

Hu, James X.; Zhao, Hongyu; Zhou, Harrison H.

doi:10.1198/jasa.2010.tm09329

Cited by 146 publications

(240 citation statements)

References 25 publications

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“…Finally, since we applied M&M to genomic windows genomewide, the genome-wide false discovery rate (FDR) was controlled using the group Benjamini-Hochberg method previously described in Hu et al (2010).…”

Section: Summary Of the Mandm Algorithmmentioning

confidence: 99%

Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm

et al. 2013

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1-15 [Author affiliations appear at the end of the paper.]DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. This includes one of the most widely applied technologies for measuring DNA methylation: methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been unavailable. Here, we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes, and combines MeDIPseq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MREseq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities.

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Section: Summary Of the Mandm Algorithmmentioning

confidence: 99%

Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm

et al. 2013

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“…A typical example is the microarray data of mRNAs from different locations in an organism or from genes that are involved in different biological processes (19,20). Efron (21) recently proposed a method for robust separate FDR estimation for small subgroups in the empirical Bayes framework.…”

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confidence: 99%

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

Qian

2014

Molecular & Cellular Proteomics

122

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In shotgun proteomics, high-throughput mass spectrometry experiments and the subsequent data analysis produce thousands to millions of hypothetical peptide identifications. The common way to estimate the false discovery rate (FDR) of peptide identifications is the target-decoy database search strategy, which is efficient and accurate for large datasets. However, the legitimacy of the target-decoy strategy for protein-modification-centric studies has rarely been rigorously validated. It is often the case that a global FDR is estimated for all peptide identifications including both modified and unmodified peptides, but that only a subgroup of identifications with a certain type of modification is focused on. As revealed recently, the subgroup FDR of modified peptide identifications can differ dramatically from the global FDR at the same score threshold, and thus the former, when it is of interest, should be separately estimated. However, rare modifications often result in a very small number of modified peptide identifications, which makes the direct separate FDR estimation inaccurate because of the inadequate sample size. This paper presents a method called the transferred FDR for accurately estimating the FDR of an arbitrary number of modified peptide identifications. Through flexible use of the empirical data from a targetdecoy database search, a theoretical relationship between the subgroup FDR and the global FDR is made computable. Through this relationship, the subgroup FDR can be predicted from the global FDR, allowing one to avoid an inaccurate direct estimation from a limited amount of data. The effectiveness of the method is demonstrated with both simulated and real mass spectra. Molecular & Cellular

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“…More recently, there has been a greater use of the False Discovery Rate (FDR) (e.g., Hochberg 1995, 1997;Efron 2004;Genovese and Wasserman 2002;Storey 2003;Storey and Tibshirani 2003), and the False Nondiscovery Rate (FNR) (e.g., Craiu and Sun 2008). FDR has been used with correlated data (Benjamini and Yekutieli 2001;Finner et al 2007;Hu et al 2010) and, for spatial data, generalised degrees of freedom and clustering may be used to increase the power of the FDR approach (Benjamini and Heller 2007;Shen et al 2002).…”

Section: Discussionmentioning

confidence: 99%