Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships.CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.Fundamental functions of living cells are controlled by regulatory relations between genes and proteins. Most cell types respond to changes in their environment (e.g. drug treatments or disease causing mutations) by altering their transcriptional patterns. More than a decade ago, it became possible to measure snapshots of all transcript levels in a given tissue sample using microarray technology. Since then, advances in technology have multiplied and the cost of experiments has decreased significantly. As a consequence, cell lines, animal models as well as clinical subjects in drug trials or in the general population [1] have been characterized on a molecular level. One crucial problem in such studies is the detection of active key regulators; i.e