A semi-parametric approach for mixture models: Application to local false discovery rate estimation
Computational Statistics & Data Analysis
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
A Likelihood Ratio Test for Differential Metabolic Profiles in Multiple Intensity Measurements
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
A mixture model approach for the analysis of small exploratory microarray experiments
Computational Statistics & Data Analysis
A robust unified approach to analyzing methylation and gene expression data
Computational Statistics & Data Analysis
A GMM-IG framework for selecting genes as expression panel biomarkers
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
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Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets. Availability: An R-program for implementing the approach is freely available at http://www.maths.uq.edu.au/~gjm/ Contact: gjm@maths.uq.edu.au Supplementary information: http://www.maths.uq.edu.au/~gjm/bioinf061supp_data.pdf