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Gene set analysis for longitudinal gene expression data.

Zhang K, Wang H, Bathke AC, Harrar SW, Piepho HP, Deng Y - BMC Bioinformatics (2011)

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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Medicine & Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA. ke.zhang@med.und.edu

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The power curve of NP statistic based on the asymptotic distribution compared to LME and GEE. The empirical powers of the NP statistics for testing of no treatment effect based on the asymptotic distribution compared to LME and GEE are given here. The powers were estimated at level 0.05. Δ is the log-scale mean difference between successive treatment groups.
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Figure 1: The power curve of NP statistic based on the asymptotic distribution compared to LME and GEE. The empirical powers of the NP statistics for testing of no treatment effect based on the asymptotic distribution compared to LME and GEE are given here. The powers were estimated at level 0.05. Δ is the log-scale mean difference between successive treatment groups.

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.


Gene set analysis for longitudinal gene expression data.

Zhang K, Wang H, Bathke AC, Harrar SW, Piepho HP, Deng Y - BMC Bioinformatics (2011)

The power curve of NP statistic based on the asymptotic distribution compared to LME and GEE. The empirical powers of the NP statistics for testing of no treatment effect based on the asymptotic distribution compared to LME and GEE are given here. The powers were estimated at level 0.05. Δ is the log-scale mean difference between successive treatment groups.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3142525&req=5

Figure 1: The power curve of NP statistic based on the asymptotic distribution compared to LME and GEE. The empirical powers of the NP statistics for testing of no treatment effect based on the asymptotic distribution compared to LME and GEE are given here. The powers were estimated at level 0.05. Δ is the log-scale mean difference between successive treatment groups.
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.

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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Medicine & Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA. ke.zhang@med.und.edu

Show MeSH