An Improved Probabilistic Model for Finding Differential Gene Expression

Finding differentially expressed genes is a fundamental objective of a microarray experiment. Recently proposed method, PPLR, considers the probe-level measurement error and improves accuracy in finding differential gene expression. However, PPLR uses the importance sampling procedure in the E-step of the variational EM algorithm, which leads to less computational efficiency. We modified the original PPLR to obtain an improved model for finding different gene expression. The new model, IPPLR, adds hidden variables to represent the true gene expressions and eliminates the importance sampling in original PPLR. We apply IPPLR on a spike-in data set and a mouse embryo data set. Results show that IPPLR improves accuracy and computational efficiency in finding differential gene expression.