Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes\ud normalization, clustering and network analysis of genes.\ud Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and\ud estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The\ud normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger\ud causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger\ud causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting\ud networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000\ud genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new\ud global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.\ud Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum\ud analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency\ud domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray\ud data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by\ud step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of\ud potential interest to Arabidopsis researchers
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