Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 411
The study of the network between transcription factors and their targets is important for understanding the complex regulatory mechanisms in a cell. However, due to post-translational modifications the regulator transcription levels (as measured, e.g., by microarray expression arrays) generally provide only little information about the true transcription factor activities (TFAs). Here we propose an approach based on partial least squares (PLS) regression to infer true TFAs from expression data integrated with information from DNA-protein binding experiments (e.g., ChIP). This method is statistically sound also for a small number of samples and enables to detect functional interaction among the transcription factors themselves via the inference of meta-transcription factors. In addition, it allows to identify false positives in ChIP data as well as to predict activation and suppression activities (which is not possible from ChIP data alone). Subsequent to PLS inference, the estimated transcription factor activities may be subject to further analysis such as tests of periodicity or differential regulation. This method overcomes the limitations of previously used approaches, and is illustrated by analyzing expression and ChIP data from Yeast and E. Coli experiments.