Factor analysis with mixture modeling to evaluate coherent patterns in microarray data

Abstract

The computational advances over the last decades have allowed the use of complex models to analyze large data sets. The development of simulation-based methods, such as the Markov chain Monte Carlo(MCMC)method, has contributed to an increased interest in the Bayesian framework as an alternative to work with factor models. Many studies have applied the factor analysis to explore gene expression data with results often outperforming traditional methods for estimating and identifying patterns and metagene groups related to the underlying biology. In this chapter, we present a sparse latent factor model (SLFM) using a mixture prior (sparsity prior) to evaluate the significance of each factor loading; when the loading is significant, the effect of the corresponding factor is detected through patterns displayed along the samples. The SLFM is applied to investigate simulated and real microarray data. The real data sets represent the gene expression for different types of cancer; these include breast, brain, ovarian, and lung tumors. The proposed model can indicate how strong is the observed expression pattern allowing the measurement of the evidence of presence/absence of the gene activity. Finally, we compare the SLFM with two simpler gene detection methods available in the literature. The results suggest that the SLFM outperforms the traditional methods.