Title
Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments
Campus Units
Statistics, Botany
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
1-2006
Journal or Book Title
Computational Statistics & Data Analysis
Volume
50
Issue
2
First Page
518
Last Page
532
DOI
10.1016/j.csda.2004.09.004
Abstract
Microarray technology has become widespread as a means to investigate gene function and metabolic pathways in an organism. A common experiment involves probing, at each of several time points, the gene expression of experimental units subjected to different treatments. Due to the high cost of microarrays, such experiments may be performed without replication and therefore provide a gene expression measurement of only one experimental unit for each combination of treatment and time point. Though an experiment with replication would provide more powerful conclusions, it is still possible to identify differentially expressed genes and to estimate the number of false positives for a specified rejection region when the data is unreplicated. We present a method for identifying differentially expressed genes in this situation that utilizes polynomial regression models to approximate underlying expression patterns. In the first stage of a two-stage permutation approach, we choose a ‘best’ model at each gene after considering all possible regression models involving treatment effects, terms polynomial in time, and interactions between treatments and polynomial terms. In the second stage, we identify genes whose ‘best’ model differs significantly from the overall mean model as differentially expressed. The number of expected false positives in the chosen rejection region and the overall proportion of differentially expressed genes are both estimated using a method presented by Storey and Tibshirani (2003). For illustration, the proposed method is applied to an Arabidopsis thaliana microarray data set.
Copyright Owner
Elsevier B.V.
Copyright Date
2004
Language
en
File Format
application/pdf
Recommended Citation
DeCook, Rhonda; Nettleton, Dan; Foster, Carol; and Wurtele, Eve S., "Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments" (2006). Statistics Publications. 249.
https://lib.dr.iastate.edu/stat_las_pubs/249
Comments
This is a manuscript of an article published as DeCook, Rhonda, Dan Nettleton, Carol Foster, and Eve S. Wurtele. "Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments." Computational statistics & data analysis 50, no. 2 (2006): 518-532. doi: 10.1016/j.csda.2004.09.004. Posted with permssion.