Time series analysis of gene expression and location data

Abstract

We develop a method for integrating time series expression profiles and factor-gene binding data to quantify dynamic aspects of gene regulation. We estimate latencies for transcription activation by explaining time correlations between gene expression profiles through available factor-gene binding information. The resulting aligned expression profiles are subsequently clustered and again combined with binding information to determine groups or subgroups of co-regulated genes. The predictions derived from this approach are consistent with existing results. Our analysis also provides several hypotheses not implicated in previous studies.