Journal
BIOSTATISTICS
Volume 10, Issue 4, Pages 667-679Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxp022
Keywords
False discovery rate; Functional hypothesis testing; Functional principal components analysis; Permutation test; Time course gene expression profiles
Funding
- Taiwan National Science Council [NSC 98-2811-M-005-001]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available