Gene set analysis for longitudinal gene expression data.
Bottom Line: Simulation results demonstrate that the proposed method has a greater power than other methods for various data distributions and heteroscedastic correlation structures.This method was used for an IL-2 stimulation study and significantly altered gene sets were identified.The simulation study and the real data application showed that the proposed gene set analysis provides a promising tool for longitudinal microarray analysis.
Affiliation: School of Medicine & Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA. email@example.comShow MeSH
Mentions: First, we conducted a power analysis for the treatment effect. The means of the normal distributions are different between the treatment groups under alternative hypothesis, and the standard deviation of the normal distribution for each gene is randomly generated by a uniform distribution in (0, 3). The mean differences Δ between groups range from 0 to 2.5 to generate the power curves. Thus in each experiment, the logarithm of the mean of treatment group 2 is Δ higher than that of group 1, and that of group 3 is Δ higher than group 2, and so on. The three power curves for NP, LME, and GEE were shown in Figure 1. NP outperformed GEE and NP for all Δ. When Δ = 0.7, NP has 91% power, whereas LME has 60% power and GEE has 70% power.
Affiliation: School of Medicine & Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA. firstname.lastname@example.org