Kernel estimation for adjusted p-values in multiple testing
Computational Statistics & Data Analysis
Optimizing design of two-stage experiments for transcriptional profiling
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Floating prioritized subset analysis: A powerful method to detect differentially expressed genes
Computational Statistics & Data Analysis
| Hi-index | 3.84 |
Summary: We consider identifying differentially expressing genes between two patient groups using microarray experiment. We propose a sample size calculation method for a specified number of true rejections while controlling the false discovery rate at a desired level. Input parameters for the sample size calculation include the allocation proportion in each group, the number of genes in each array, the number of differentially expressing genes and the effect sizes among the differentially expressing genes. We have a closed-form sample size formula if the projected effect sizes are equal among differentially expressing genes. Otherwise, our method requires a numerical method to solve an equation. Simulation studies are conducted to show that the calculated sample sizes are accurate in practical settings. The proposed method is demonstrated with a realstudy. Contact: jung005@mc.duke.edu