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A Bayesian Approach to the Analysis of Microarray Datasets using Variational Inference

Background: We present a variational Bayesian approach to inference in a probabilistic model for microarray gene expression data. The algorithmic approach efficiently maximises the probability of the model given the data and provides an unbiased indication of the most probable number of processes or soft clusters in the data. Compared to hierarchical cluster analysis, the method has a number of practical advantages such as an objective assessment of the number of soft clusters in the data, the ability to handle missing values and the ability to provide a confidence measure for process membership. Results: We describe the method and its implementation. We compare the method to a previous variational graphical model, proposed by the authors, and argue that model selection is improved. As examples, we apply the algorithm to microarray datasets for breast cancer, prostate cancer and leukemia. The most detailed application is to breast cancer with a comparative study across 7 microarray datasets. We particularly focus on one subtype indicated by the method (the basaloid subtype) where it delineates a common genetic signature across all these datasets and it suggests a therapeutic target.