Abstract
In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood-based approach to estimating an empirical null distribution to incorporate gene interactions and provide a more accurate false-positive control than the commonly used permutation or theoretical null distribution-based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations and application to lung transplant microarray data, we illustrate the competitive performance of the proposed method.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 347-357 |
| Number of pages | 11 |
| Journal | Journal of Applied Statistics |
| Volume | 40 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2013 |
Bibliographical note
Funding Information:This research was supported in part by NIH grant GM083345 and CA134848. We thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper.
Keywords
- differential expression detection
- empirical Bayes modeling
- empirical null distribution
- false discovery rate
- gene expression data