Research Topics
| John D StoreySummaryAffiliation: University of Washington Country: USA Publications
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Detail Information
Publications
The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experimentsJohn D Storey
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Biostatistics 8:414-32. 2007..Our proposed microarray method is freely available to academic users in the open-source, point-and-click EDGE software package...
A reanalysis of a published Affymetrix GeneChip control datasetAlan R Dabney
Genome Biol 7:401. 2006..A response to Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16...- Significance analysis of time course microarray experimentsJohn D Storey
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Proc Natl Acad Sci U S A 102:12837-42. 2005..The methodology proposed here has been implemented in the freely distributed and open-source edge software package... - Statistical significance for genomewide studiesJohn D Storey
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Proc Natl Acad Sci U S A 100:9440-5. 2003..Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage... - Multiple locus linkage analysis of genomewide expression in yeastJohn D Storey
Department of Biostatistics, University of Washington, Seattle, Washington, USA
PLoS Biol 3:e267. 2005..In addition, we show that a two-dimensional scan does not truly allow one to test for simultaneous linkage, and the statistical significance measured from this existing method cannot be interpreted among many traits... - Capturing heterogeneity in gene expression studies by surrogate variable analysisJeffrey T Leek
Department of Biostatistics, University of Washington, Seattle, Washington, USA
PLoS Genet 3:1724-35. 2007..We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies... - Calibrating the performance of SNP arrays for whole-genome association studiesKe Hao
Rosetta Inpharmatics, Seattle, Washington, United States of America
PLoS Genet 4:e1000109. 2008.... - A computationally efficient modular optimal discovery procedureSangsoon Woo
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Bioinformatics 27:509-15. 2011..However, their ODP estimator grows quadratically in computational time with respect to the number of genes. Reducing this computational burden is a key step in making the ODP practical for usage in a variety of high-throughput problems... - Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometryLUKAS KALL
Department of Genome Sciences, University of Washington, Seattle, WA, USA
Bioinformatics 24:i42-8. 2008..cult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution... - Gene-expression variation within and among human populationsJohn D Storey
Department of Biostatistics, University of Washington, Seattle, WA 98195 7730, USA
Am J Hum Genet 80:502-9. 2007..These results provide the first insight into how human population structure manifests itself in gene-expression levels and will help guide the search for regulatory quantitative trait loci... - Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysisShameek Biswas
Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA
BMC Bioinformatics 9:244. 2008..In addition, gene expression traits exhibit a complex correlation structure, which is ignored when analyzing traits individually... - EDGE: extraction and analysis of differential gene expressionJeffrey T Leek
Department of Biostatistics, University of Washington, Seattle 98195, USA
Bioinformatics 22:507-8. 2006..EDGE can perform both standard and time course differential expression analysis. The functions are based on newly developed statistical theory and methods. This document introduces the EDGE software package... - Assigning significance to peptides identified by tandem mass spectrometry using decoy databasesLUKAS KALL
Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
J Proteome Res 7:29-34. 2008..We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power... - Relaxed significance criteria for linkage analysisLin Chen
Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA
Genetics 173:2371-81. 2006..A generalized version of the GWER is proposed, called GWERk, that allows one to provide a more liberal balance between true positives and false positives at no additional cost in computation or assumptions... - Mapping the genetic architecture of gene expression in human liverEric E Schadt
Rosetta Inpharmatics, Seattle, Washington, United States of America
PLoS Biol 6:e107. 2008..We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process... - Harnessing naturally randomized transcription to infer regulatory relationships among genesLin S Chen
Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA 98195, USA
Genome Biol 8:R219. 2007..We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network... - A new approach to intensity-dependent normalization of two-channel microarraysAlan R Dabney
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Biostatistics 8:128-39. 2007..We show that CADS removes both dye bias and array-specific effects, and preserves the true differential expression signal for every gene under the assumptions of the model... - Supervised normalization of microarraysBrigham H Mecham
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
Bioinformatics 26:1308-15. 2010..However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date... - The sva package for removing batch effects and other unwanted variation in high-throughput experimentsJeffrey T Leek
Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, USA
Bioinformatics 28:882-3. 2012..The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function... - Genetic interactions between polymorphisms that affect gene expression in yeastRachel B Brem
Program in Computational Biology, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, M2 B876, Seattle, Washington 98109, USA
Nature 436:701-3. 2005..Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions... - Posterior error probabilities and false discovery rates: two sides of the same coinLUKAS KALL
Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
J Proteome Res 7:40-4. 2008..Here, we explain how two types of scores, the q-value and the posterior error probability, are related and complementary to one another... 
Eigen-R2 for dissecting variation in high-dimensional studiesLin S Chen
Lewis Sigler Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
Bioinformatics 24:2260-2. 2008..AVAILABILITY: An R-package eigenR2 is available at http://www.genomine.org/eigenr2/ and will be made publicly available via Bioconductor...- Optimality driven nearest centroid classification from genomic dataAlan R Dabney
Department of Statistics, Texas A and M University, College Station, Texas, United States of America
PLoS ONE 2:e1002. 2007..We apply the proposed method to clinical classification based on gene-expression microarrays, demonstrating that the proposed method can outperform existing nearest centroid classifiers... - In vivo regulation of human skeletal muscle gene expression by thyroid hormoneKarine Clement
Department of Pediatrics and Genetics, Howard Hughes Medical Institute, Beckman Center, Stanford University School of Medicine, Stanford, California 94305, USA
Genome Res 12:281-91. 2002..These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action... - Longitudinal transcriptional analysis of developing neointimal vascular occlusion and pulmonary hypertension in ratsLaszlo T Vaszar
Division of Pulmonary/Critical Care Medicine, Stanford University Medical Center, Stanford, California 94305-5236, USA
Physiol Genomics 17:150-6. 2004..Mast-cell-derived proteases may play a role in regulating the development of neointimal pulmonary vascular occlusion and pulmonary hypertension in response to injury... - Statistical methods for identifying differentially expressed genes in DNA microarraysJohn D Storey
Department of Statistics, Stanford University, Palo Alto, CA, USA
Methods Mol Biol 224:149-57. 2003 - Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiaeYoav Arava
Department of Biochemistry, Stanford University, Stanford, CA 94305-5307, USA
Proc Natl Acad Sci U S A 100:3889-94. 2003..Global analysis revealed an unexpected correlation: Ribosome density decreases with increasing ORF length. Models to account for this surprising observation are discussed... - Precision and functional specificity in mRNA decayYulei Wang
Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305-5307, USA
Proc Natl Acad Sci U S A 99:5860-5. 2002..The results provide strong evidence that precise control of the decay of each mRNA is a fundamental feature of the gene expression program in yeast... - A genome-wide gene expression signature of environmental geography in leukocytes of Moroccan AmazighsYoussef Idaghdour
North Carolina State University, Raleigh, North Carolina, United States of America
PLoS Genet 4:e1000052. 2008.... - On the design and analysis of gene expression studies in human populationsJoshua M Akey
Nat Genet 39:807-8; author reply 808-9. 2007