Identifying temporally differentially expressed genes through functional principal components analysis.
Time course gene microarray is an important tool to identify genes with differential expressions over time. Traditional analysis of variance (ANOVA) type of longitudinal investigation may not be applicable because of irregular time intervals and possible missingness due to contamination in microarray experiments. Functional principal components analysis is proposed to test hypotheses in the change of the mean curves. A permutation test under a mild assumption is used to make the method more robust. The proposed method outperforms the recently developed extraction of differential gene expression and a 2-way mixed effects ANOVA under reasonable gene expression models in simulation. Real data on transcriptional profiles of blood cells microarray from treated and untreated individuals were used to illustrate this method.
Liu
Xueli
X
Division of Biostatistics, City of Hope, Duarte, CA 91010-3000, USA. xuliu@coh.org
Yang
Mark C K
MC
D000704:Analysis of Variance; D056808:Biostatistics / Q000379:methods; D001773:Blood Cells / Q000378:metabolism; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D009103:Multiple Sclerosis / Q000097:blood / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D013997:Time Factors