To identify accurately protein function on a proteome-wide scale requires integrating data within and between high-throughput experiments. High-throughput proteomic datasets often have high rates of errors and thus yield incomplete and contradictory information. In this study, we develop a simple statistical framework using Bayes law to interpret such data and combine information from different high-throughput experiments. In order to illustrate our approach we apply it to two protein complex purification datasets.