Statistical significance for genomewide studies

By John D. Storey, Robert Tibshirani. Proceedings of the National Academy of Sciences of the United States of America, 2003

9,770 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f6dddac967e3347d0cb1b48fec50b5999102fe44

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Citations in the corpus

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision (2016, methodology): “LSA, CoNet and SparCC calculate the P-value through permutation-based approaches, and q-value (Storey and Tibshirani, 2003) and Benjamini–Hochberg Bray-Curtis”
Prediction of individual genetic risk to disease from genome-wide association studies. (2007, methodology): “…be used to select SNPs and to ensure unbiased estimates of effect size of variants contributing to the genomic profile (e.g., Meuwissen et al. 2001; Tibshirani et al. 2002; Storey and Tibshirani 2003; Zollner and Pritchard 2007), and such methods could be adapted to account for LD between the SNPs.”
The effects of human population structure on large genetic association studies (2004, background): “Although appropriate genome-wide significance levels are not yet clea”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, neutral): “Bonferroni correction (Bonferroni, 1935), and if the affected regions are small, they might not even survive false-discovery rate control (Benjamini and Hochberg, 1995; Storey and Tibshirani, 2003).”
A Selective Overview of Variable Selection in High Dimensional Feature Space (2009, methodology): “…data analysis where a two-sample test is used to select signiﬁcant genes between the treatment and control groups (Dudoit, Shaﬀer and Boldrick (2003); Storey and Tibshirani (2003); Fan and Ren (2006); Efron (2007)), feature ranking using a generalized correlation (Hall and Miller (2009a)),…”
Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. (2007, neutral): “[11].”
ChIP-Seq: advantages and challenges of a maturing technology (2009, background): “The statistical significance of enriched sites is generally measured by the false discovery rate (FDR”
CD8+ T-cell responses to different HIV proteins have discordant associations with viral load (2007, methodology): “45 , we used q -values to correct for multiple test”
Applications of Linkage Disequilibrium and Association Mapping in Maize (2009, methodology): “The procedure can be used to estimate a cutoff for a particular FDR (Benjamini and Hochberg 1995) or an FDR for a particular cutoff (Storey 2002; Storey and Tibshirani 2003).”
Patterns of phenotypic plasticity and local adaptation in the wide elevation range of the alpine plant Arabis alpina (2018, methodology): “To control for false discovery rate, the distribution of p-values were further transformed into q-values using the Storey & Tibshirani (2003) algorithm.”
A Downstream Mediator in the Growth Repression Limb of the Jasmonate Pathway[W][OA] (2007, methodology): “This list comprised 77 genes (FDR of 83.7% at P¼ 0.05).”
methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles (2012, methodology): “Following the differential methylation test and calculation of P-values, methylKit will use the sliding linear model (SLIM) method to correct P-values to q-values [31], which corrects for the problem of multiple hypothesis testing [32,33].”
Evolution of Cooperation among Mammalian Carnivores and Its Relevance to Hominin Evolution (2012, background): “Even after correcting for multiple testing (Storey and Tibshirani 2003), fission-fusion and obligately gregarious species had significantly greater cooperation scores than did species living in pairs (table 2).”
A Factor Model Approach to Multiple Testing Under Dependence (2009, methodology): “The BH procedure is improved by estimating m0 according to Storey and Tibshirani (2003)’s method applied to the factor-adjusted p-values.”
mProphet: automated data processing and statistical validation for large-scale SRM experiments (2011, methodology): “The weight of the false population is estimated using the parameters of the null distribution and part of the target data point”
Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, methodology): “We used the method described in Storey and Tibshirani (2003) to estimate π0 on the pooled (maximum) p-values {pj}j=1 over all data-sets obtained during FCI.”