William R. Harman '63 and Mary-Love Harman Professor in Genomics. Professor of Lewis-Sigler Institute for Integrative Genomics. Director, Center for Statistics and Machine Learning.

Areas of Research: Statistical genetics and genomics, population genetics and genomics, applied statistics and data science
Department|Program:
  • Lewis-Sigler Institute for Integrative Genomics

jstorey@princeton.edu
Research Lab
Carl Icahn Laboratory

Faculty Assistant:
Dawn Capizzi
dcapizzi@princeton.edu
609-258-1617
Scholar
Website

Research Focus

My lab develops and applies quantitative methods in genomics. We are particularly focused on functional genomics problems involving high-dimensional data sets, such as that obtained from large-scale genotyping, gene expression monitoring, and mass spectroscopy based proteomics. Because our research deals with large amounts of noisy data, we also develop theory and methods for statistics and machine learning.

This is an especially exciting time for quantitative genomics, as many studies are underway that involve multiple types of large-scale data. For example, we are working on studies involving high-throughput measurements on mRNA expression, protein expression, metabolite levels, protein-DNA binding, chromatin structure, and DNA sequences.

The over-arching goal of our research is to utilize multiple sources of high-throughput genomic data to understand biological regulatory networks and the molecular basis of complex traits. This involves characterizing the "wiring diagram" of the molecular biology of the cell. The ultimate goal is to build a quantitative system for understanding how the hard-wired components of a cell, such as DNA sequence and epigenetic factors, interact with the environment to determine the dynamic molecular behavior of the cell, as manifested in variables such as RNA expression, protein expression, enzymatic activity, and eventually as complex traits.

Specific problems we are working on include:

  • Inferring causal regulatory networks from studies involving high-throughput molecular profiling (e.g., RNA and protein expression) and large-scale genotyping.
  • Decomposing sources of gene expression variation in complex clinical and experimental settings.
  • Understanding the genetic and epigenetic determinants of the gene expression program.
  • Developing quantitative approaches to providing a causal "molecular dissection" of complex traits.
  • Understanding the relationship between evolutionary forces driving natural genetic variation and its effect on variation in expression levels of gene products.
  • Developing new theory and methods for high-dimensional statistical inference, large-scale significance testing, and machine learning

Selected Publications

  • Robinson DG, Storey JD. (2014) subSeq: Determining appropriate sequencing depth through efficient read subsampling. Bioinformatics. Pubmed
  • Chung NC, Storey JD. (2014) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics. Pubmed
  • Kim J, Ghasemzadeh N, Eapen DJ,...Storey JD,...Gibson G. (2014) Gene expression profiles associated with acute myocardial infarction and risk of cardiovascular death. Genome Med. 6: 40. eCollection 2014. Pubmed
  • Marstrand TT, Storey JD. (2014) Identifying and mapping cell-type-specific chromatin programming of gene expression. Proc Natl Acad Sci. 111: E645-54. Pubmed
  • Robinson DG, Chen W, Storey JD, Gresham D. (2013) Design and analysis of bar-seq experiments. G3 (Bethesda). 4: 11-8. Pubmed
  • Chung NC, Storey JD. (2013) Statistical significance of variables driving systematic variation. arXiv:1308.6013 [stat.ME]
  • Jaffe AE, Storey JD, Ji H, Leek JT. (2013) Gene set bagging for estimating replicability of gene set analyses. BMC Bioinformatics. 14: 360. Pubmed
  • Marstrand TT, Storey JD. (2012) Identifying and mapping cell-type specific chromatin programming of gene expression. arXiv:1210.3313 [q-bio.QM]
  • Desai KH, Storey JD. (2012) Cross-dimensional inference of dependent high-dimensional data. J Amer Stat Assoc. 107: 135-151. DOI:10.1080/01621459.2011.645777
  • Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 28: 882-83. Pubmed
  • Xiao W, Mindrinos MN, Seok J,...Storey JD,...Inflammation and Host Response to Injury Large-Scale Collaborative Research Program. (2011) A genomic storm in critically injured humans. J Exp Med. 208: 2581-90. Pbmed
  • Desai KH, Tan CS, Leek JT,...Storey JD,...Inflammation and the Host Response to Injury Large-Scale Collaborative Research Program. (2011) Dissecting inflammatory complications in critically injured patients by within-patient gene expression changes: a longitudinal clinical genomics study. PLoS Med. 8: e1001093. Pubmed
  • Kanodia JS, Kim Y, Tomer R,...Storey JD,...Shvartsman SY. (2011) A computational statistics approach for estimating the spatial range of morphogen gradients. Development. 138: 4867-74. Pubmed
  • Xu W, Seok J, Mindrinos MN,...Storey JD,...Inflammation and Host Response to Injury Large-Scale Collaborative Research Program. (2011) Human transcriptome array for high-throughput clinical studies. Proc Natl Acad Sci 108: 3707-12. Pbmed
  • Woo S, Leek JT, Storey JD. (2011) A computationally efficient modular optimal discovery procedure. Bioinformatics. 27: 509-15. Pubmed

View complete list of Publications.