A Significance Method for Time Course Microarray Experiments Applied to Two Human Studies

John Storey, Jeffrey Leek, Wenzhong Xiao, James Dai and Ron Davis
Additional contact information
John Storey: University of Washington
Jeffrey Leek: University of Washington
Wenzhong Xiao: Stanford University
James Dai: University of Washington
Ron Davis: Stanford University

No 1065, UW Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: A common goal in microarray experiments is to identify genes that are differentially expressed among two or more biological conditions. There is currently no standard methodology for detecting differential expression in time course studies. However, it is clear that monitoring the behavior of gene expression over time is important and will be a common experimental design in the future. Here we present a general statistical significance method for detecting temporal differential expression that can be applied to the typical types of comparisons and sampling schemes. We apply this method to two studies that we have carried out on humans. The goal of one study is to identify genes showing temporal differential expression between controls and endotoxin-treated individuals, and the other is to identify genes that show aging effects in the kidney. Genes identified in both studies corroborate previous findings and also provide novel insights. This methodology has been implemented in the freely distributed EDGE software package.

Keywords: aging; differential expression; endotoxin; expression arrays; time series; q-values (search for similar items in EconPapers)
Date: 2004-09-03
Note: oai:bepress.com:uwbiostat-1065
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.bepress.com/cgi/viewcontent.cgi?article=1065&context=uwbiostat (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bep:uwabio:1065

Access Statistics for this paper

More papers in UW Biostatistics Working Paper Series from Berkeley Electronic Press
Bibliographic data for series maintained by Christopher F. Baum ().