Predicting transcription factor activity using prior biological information

Detection of aberrant transcription factor activity is critical to understanding the mechanisms underlying disease pathogenesis. While many approaches have been developed, they often fail to produce models that reflect known biological systems. Here, we present Priori, which utilizes literature-supported regulatory relationship information to predict transcription factor activity. In our work, we evaluated the ability of Priori and 16 other methods to detect aberrant activity from 124 single-gene perturbation experiments. We show that Priori identifies perturbed transcription factors with greater sensitivity and specificity than other methods. Furthermore, our work demonstrates that Priori can be used to discover significant determinants of survival as well as mediators of drug response from breast cancer and leukemia primary patient samples.