Empirical Bayes analysis of unreplicated microarray data |
| |
Authors: | HyungJun Cho Jaewoo Kang Jae K Lee |
| |
Institution: | (1) Department of Statistics, Korea University, Seoul, South Korea;(2) Department of Biostatistics, Korea University, Seoul, South Korea;(3) Department of Computer Science and Engineering, Korea University, Seoul, South Korea;(4) Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA |
| |
Abstract: | Because of the high costs of microarray experiments and the availability of only limited biological materials, microarray
experiments are often performed with a small number of replicates. Investigators, therefore, often have to perform their experiments
with low replication or without replication. However, the heterogeneous error variability observed in microarray experiments
increases the difficulty in analyzing microarray data without replication. No current analysis techniques are practically
applicable to such microarray data analysis. We here introduce a statistical method, the so-called unreplicated heterogeneous
error model (UHEM) for the microarray data analysis without replication. This method is possible by utilizing many adjacent-intensity
genes for estimating local error variance after nonparametric elimination of differentially expressed genes between different
biological conditions. We compared the performance of UHEM with three empirical Bayes prior specification methods: between-condition
local pooled error, pseudo standard error, or adaptive standard error-based HEM. We found that our unreplicated HEM method
is effective for the microarray data analysis when replication of an array experiment is impractical or prohibited. |
| |
Keywords: | Microarray data Empirical Bayes Markov chain Monte Carlo No replication |
本文献已被 SpringerLink 等数据库收录! |
|