Differential expression and network inferences through functional data modeling.
Time course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article, we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene-expression profile as a random functional transformation of the scale, amplitude, and phase of a common curve. Inferences about the gene-specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time-transformation functions allow us to examine gene networks while accounting for features of the gene-expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.
Telesca
Donatello
D
University of Texas, M.D. Anderson Cancer Center, Department of Biostatistics, Houston, Texas 77230, USA.
Inoue
Lurdes Y T
LY
Neira
Mauricio
M
Etzioni
Ruth
R
Gleave
Martin
M
Nelson
Colleen
C
D014408:Biomarkers, Tumor / Q000032:analysis*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000032:analysis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D015398:Signal Transduction
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins