A strategy for meta-analysis of short time series microarray datasets

Many time series microarray experiments have relatively short (less than ten) time points and lack in repeats, weakening the confidence of results. Combining the microarray data from different groups may improve the statistical power of detecting differentially expressed genes. However, few efforts have been taken to combine or compare the time-course array datasets generated by independent groups. Here we demonstrated a suitable strategy for meta-analysis of short time series microarray datasets and implemented this strategy on four published heat shock microarray datasets of Saccharomyces Cerevisiae. We first assessed the significance of each gene in each datasets based on area calculation and the null distribution of the areas. Then the similarity of significance values across datasets was assessed with meta-analysis methods, yielding a set of transient heat shock stress sensitive genes. Following correlation calculation helped us to combine the transformed data at the same time points of each gene. Further bioinformatic investigation showed the significance of our strategy, and also indicated some interesting features of regulatory systems in S. cerevisiae during transient heat stress.