Molecular Biology Faculty
Director of Center for Statistics and Machine Learning
|Storey Research Lab|
Carl Icahn Lab
Lab (609) 258-1331
Joseph d. capizzi
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
Robinson DG, Wang JY, Storey JD. A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays. Nucleic Acids Res. 2015 Jun 30. [Epub ahead of print]
Song M, Hao W, Storey JD. (2015) Testing for genetic associations in arbitrarily structured populations. Nat Genet. 47:550-4. Pubmed
Robinson DG, Storey JD. (2014) subSeq: Determining appropriate sequencing depth through efficient read subsampling. Bioinformatics. 30:3424-6. Pubmed
Chung NC, Storey JD. (2014) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics. 31:545-54. 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
Mecham BH, Nelson PS, Storey JD. (2010) Supervised normalization of microarrays. Bioinformatics. 26: 1308-15. Pubmed
Kruglyak L, Storey JD. (2009) Cause and express. Nat Biotechnol. 27: 544-45. PubMed
Chen LS, Storey JD. (2008) Eigen-R2 for dissecting variation in high-dimensional studies. Bioinformatics. 24: 2260-62. PubMed
Käll L, Storey JD, MacCoss MJ, Noble WS. (2008) Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. J Prot Res. 7: 29-34. PubMed
Chen LS, Emmert-Streib F, Storey JD. (2007) Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biol. 8: R219. PubMed
Leek JT, Storey JD. (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3: 1724-35. PubMed
Akey JM, Biswas S, Leek JT, Storey JD. (2007) On the design and analysis of expression studies in human populations. Nature Genet. 39: 807-08. PubMed
Storey JD, Dai JY, Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics. 8: 414-32. PubMed
Storey JD, Xiao W, Leek JT, Tompkins RG, Davis RW. (2005) Significance analysis of time course microarray experiments. Proc Natl Acad Sci. 102: 12837-42.PubMed
Brem RB, Storey JD, Whittle J, Kruglyak L. (2005) Genetic interactions between polymorphisms that affect gene expression in yeast. Nature. 436: 701-03. PubMed
Storey JD, Akey JM, Kruglyak L. (2005) Multiple locus linkage analysis of genome-wide expression in yeast. PLoS Biol. 3: 1380-90. PubMed
Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Ann Stat. 31: 2013-35.
Storey JD, Tibshirani R. (2003) Statistical significance for genome-wide studies. Proc Natl Acad Sci. 100: 9440-45. PubMed
Arava Y, Wang Y, Storey JD, Brown PO, Herschlag D. (2003) Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae. Proc Natl Acad Sci. 100: 3889-94. PubMed
Wang Y, Liu CL, Storey JD, Tibshirani RJ, Herschlag D, Brown PO. (2002) Precision and functional specificity in mRNA decay. Proc Natl Acad Sci. 99: 5860-65. PubMed
Efron B, Tibshirani R, Storey JD, Tusher V. (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc. 96: 1151-60.
Storey JD, Siegmund D. (2001) Approximate p-values for local sequence alignments: numerical studies. J Comp Biol. 8: 549-56. PubMed