Flexible empirical Bayes models for differential gene expression.
BACKGROUND
Inference about differential expression is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular for this type of problem. The two most common hierarchical models are the hierarchical Gamma-Gamma (GG) and Lognormal-Normal (LNN) models. However, to facilitate inference, some unrealistic assumptions have been made. One such assumption is that of a common coefficient of variation across genes, which can adversely affect the resulting inference.


RESULTS
In this paper, we extend both the GG and LNN modeling frameworks to allow for gene-specific variances and propose EM based algorithms for parameter estimation. The proposed methodology is evaluated on three experimental datasets: one cDNA microarray experiment and two Affymetrix spike-in experiments. The two extended models significantly reduce the false positive rate while keeping a high sensitivity when compared to the originals. Finally, using a simulation study we show that the new frameworks are also more robust to model misspecification.


BACKGROUND
The R code for implementing the proposed methodology can be downloaded at http://www.stat.ubc.ca/~c.lo/FEBarrays.


BACKGROUND
The supplementary material is available at http://www.stat.ubc.ca/~c.lo/FEBarrays/supp.pdf.
Lo
Kenneth
K
Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, Canada V6T 1Z2. c.lo@stat.ubc.ca
Gottardo
Raphael
R
D000465:Algorithms; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D016015:Logistic Models; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*