A mixture model for estimating the local false discovery rate in DNA microarray analysis
| Autor: | Weichung Joe Shih, Jason Liao, Zachariah E. Selvanayagam, Yong Lin |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Statistics and Probability
False discovery rate Uterine Cervical Neoplasms Inference Computational biology Biology Sensitivity and Specificity Biochemistry DNA Microarray Analysis Statistics Humans Computer Simulation False Positive Reactions Genetic Testing Molecular Biology Gene Oligonucleotide Array Sequence Analysis Stochastic Processes Models Statistical Models Genetic Gene Expression Profiling Reproducibility of Results Mixture model Computer Science Applications Gene Expression Regulation Neoplastic Computational Mathematics Computational Theory and Mathematics Multiple comparisons problem Female DNA microarray Artifacts Algorithms Smoothing |
| Zdroj: | Bioinformatics. 20:2694-2701 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/bth310 |
| Popis: | Motivation: Statistical methods based on controlling the false discovery rate (FDR) or positive false discovery rate (pFDR) are now well established in identifying differentially expressed genes in DNA microarray. Several authors have recently raised the important issue that FDR or pFDR may give misleading inference when specific genes are of interest because they average the genes under consideration with genes that show stronger evidence for differential expression. The paper proposes a flexible and robust mixture model for estimating the local FDR which quantifies how plausible each specific gene expresses differentially. Results: We develop a special mixture model tailored to multiple testing by requiring the P-value distribution for the differentially expressed genes to be stochastically smaller than the P-value distribution for the non-differentially expressed genes. A smoothing mechanism is built in. The proposed model gives robust estimation of local FDR for any reasonable underlying P-value distributions. It also provides a single framework for estimating the proportion of differentially expressed genes, pFDR, negative predictive values, sensitivity and specificity. A cervical cancer study shows that the local FDR gives more specific and relevant quantification of the evidence for differential expression that can be substantially different from pFDR. Availability: An R function implementing the proposed model is available at http://www.geocities.com/jg_liao/software |
| Databáze: | OpenAIRE |
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