Jeffrey T Leek

Summary

Affiliation: Johns Hopkins Bloomberg School of Public Health
Country: USA

Publications

  1. ncbi request reprint Gene set bagging for estimating the probability a statistically significant result will replicate
    Andrew E Jaffe
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore MD 21205, USA
    BMC Bioinformatics 14:360. 2013
  2. pmc SVAw - a web-based application tool for automated surrogate variable analysis of gene expression studies
    Mehdi Pirooznia
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
    Source Code Biol Med 8:8. 2013
  3. pmc Cooperation between referees and authors increases peer review accuracy
    Jeffrey T Leek
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
    PLoS ONE 6:e26895. 2011
  4. pmc A statistical approach to selecting and confirming validation targets in -omics experiments
    Jeffrey T Leek
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205 2179, USA
    BMC Bioinformatics 13:150. 2012
  5. pmc The sva package for removing batch effects and other unwanted variation in high-throughput experiments
    Jeffrey T Leek
    Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, USA
    Bioinformatics 28:882-3. 2012
  6. pmc Asymptotic conditional singular value decomposition for high-dimensional genomic data
    Jeffrey T Leek
    Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205 2179, USA
    Biometrics 67:344-52. 2011
  7. pmc A computationally efficient modular optimal discovery procedure
    Sangsoon Woo
    Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
    Bioinformatics 27:509-15. 2011
  8. pmc Capturing heterogeneity in gene expression studies by surrogate variable analysis
    Jeffrey T Leek
    Department of Biostatistics, University of Washington, Seattle, Washington, USA
    PLoS Genet 3:1724-35. 2007
  9. ncbi request reprint EDGE: extraction and analysis of differential gene expression
    Jeffrey T Leek
    Department of Biostatistics, University of Washington, Seattle 98195, USA
    Bioinformatics 22:507-8. 2006
  10. pmc A simple and reproducible breast cancer prognostic test
    Luigi Marchionni
    The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, School of Medicine, Baltimore, MD 21231, USA
    BMC Genomics 14:336. 2013

Collaborators

Detail Information

Publications22

  1. ncbi request reprint Gene set bagging for estimating the probability a statistically significant result will replicate
    Andrew E Jaffe
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore MD 21205, USA
    BMC Bioinformatics 14:360. 2013
    ..Gene set bagging involves resampling the original high-throughput data, performing gene-set analysis on the resampled data, and confirming that biological categories replicate in the bagged samples...
  2. pmc SVAw - a web-based application tool for automated surrogate variable analysis of gene expression studies
    Mehdi Pirooznia
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
    Source Code Biol Med 8:8. 2013
    ..Using SVA increases the biological accuracy and reproducibility of gene expression studies by identifying these sources of heterogeneity and correctly accounting for them in the analysis...
  3. pmc Cooperation between referees and authors increases peer review accuracy
    Jeffrey T Leek
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
    PLoS ONE 6:e26895. 2011
    ..016). Our results suggest that increasing cooperation in the peer review process can lead to a decreased risk of reviewing errors...
  4. pmc A statistical approach to selecting and confirming validation targets in -omics experiments
    Jeffrey T Leek
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205 2179, USA
    BMC Bioinformatics 13:150. 2012
    ..But there is no standard approach for selecting and quantitatively evaluating validation targets...
  5. pmc The sva package for removing batch effects and other unwanted variation in high-throughput experiments
    Jeffrey 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...
  6. pmc Asymptotic conditional singular value decomposition for high-dimensional genomic data
    Jeffrey T Leek
    Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205 2179, USA
    Biometrics 67:344-52. 2011
    ....
  7. pmc A computationally efficient modular optimal discovery procedure
    Sangsoon 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...
  8. pmc Capturing heterogeneity in gene expression studies by surrogate variable analysis
    Jeffrey 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...
  9. ncbi request reprint EDGE: extraction and analysis of differential gene expression
    Jeffrey 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...
  10. pmc A simple and reproducible breast cancer prognostic test
    Luigi Marchionni
    The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, School of Medicine, Baltimore, MD 21231, USA
    BMC Genomics 14:336. 2013
    ..As a result, a need has emerged for a template for reproducible development of genomic signatures that incorporates full transparency, data sharing and statistical robustness...
  11. pmc Gene expression anti-profiles as a basis for accurate universal cancer signatures
    Héctor Corrada Bravo
    Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
    BMC Bioinformatics 13:272. 2012
    ..However, progress has been slowed by heterogeneity in cancer profiles and the lack of effective computational prediction tools for this type of data...
  12. pmc The practical effect of batch on genomic prediction
    Hilary S Parker
    Johns Hopkins Bloomberg School of Public Health, USA
    Stat Appl Genet Mol Biol 11:Article 10. 2012
    ....
  13. pmc Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies
    Andrew E Jaffe
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
    Int J Epidemiol 41:200-9. 2012
    ..In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature...
  14. pmc Tackling the widespread and critical impact of batch effects in high-throughput data
    Jeffrey T Leek
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205 2179, USA
    Nat Rev Genet 11:733-9. 2010
    ..We review experimental and computational approaches for doing so...
  15. pmc A general framework for multiple testing dependence
    Jeffrey T Leek
    Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
    Proc Natl Acad Sci U S A 105:18718-23. 2008
    ..This framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology...
  16. pmc Differential expression analysis of RNA-seq data at single-base resolution
    Alyssa C Frazee
    Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
    Biostatistics 15:413-26. 2014
    ..A software implementation of our method is available on github (https://github.com/alyssafrazee/derfinder). ..
  17. pmc Significance analysis and statistical dissection of variably methylated regions
    Andrew E Jaffe
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
    Biostatistics 13:166-78. 2012
    ....
  18. pmc Significance analysis of time course microarray experiments
    John 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...
  19. pmc Cloud-scale RNA-sequencing differential expression analysis with Myrna
    Ben Langmead
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
    Genome Biol 11:R83. 2010
    ..We apply Myrna to the analysis of publicly available data sets and assess the goodness of fit of standard statistical models. Myrna is available from http://bowtie-bio.sf.net/myrna...
  20. pmc The tspair package for finding top scoring pair classifiers in R
    Jeffrey T Leek
    Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
    Bioinformatics 25:1203-4. 2009
    ..Here I describe the R package, tspair, which can be used to quickly identify and assess TSP classifiers for gene expression data. Availability: The R package tspair is freely available from Bioconductor: http://www.bioconductor.org...
  21. ncbi request reprint The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments
    John 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...
  22. pmc ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets
    Alyssa C Frazee
    Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
    BMC Bioinformatics 12:449. 2011
    ..1..