Name
Abstract | ||
|---|---|---|
Owing to the experimental cost and difficulty in obtaining biological materials, it is essential to consider appropriate sample sizes in microarray studies. With the growing use of the False Discovery Rate (FDR) in microarray analysis, an FDR-based sample size calculation is essential.We describe an approach to explicitly connect the sample size to the FDR and the number of differentially expressed genes to be detected. The method fits parametric models for degree of differential expression using the Expectation-Maximization algorithm.The applicability of the method is illustrated with simulations and studies of a lung microarray dataset. We propose to use a small training set or published data from relevant biological settings to calculate the sample size of an experiment.Code to implement the method in the statistical package R is available from the authors. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1093/bioinformatics/bti519 | Bioinformatics |
Keywords | Field | DocType |
false discovery rate,microarray study,maximization algorithm,fdr-based sample size calculation,relevant biological setting,lung microarray dataset,practical fdr-based sample size,microarray analysis,biological material,sample size,microarray experiment,appropriate sample size | Data mining,False discovery rate,Parametric model,Microarray,Biological materials,Computer science,Microarray analysis techniques,Software,Bioinformatics,Gene expression profiling,Sample size determination | Journal |
Volume | Issue | ISSN |
21 | 15 | 1367-4803 |
Citations | PageRank | References |
10 | 1.21 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhua Hu | 1 | 22 | 3.82 |
| Fei Zou | 2 | 14 | 1.99 |
| Fred A Wright | 3 | 52 | 5.42 |