 nested clustering is a new computational method for identifying protein-protein interactions from label-free quantitative affinity purification mass spectrometry, APMS, data. This method uses the similarities between the quantitative interaction profiles of proteins to form bait clusters, which identify submatrices of prey proteins that show consistent quantitative association with the bait proteins. Nested clustering does not require specifying the number of bait clusters, making it more robust than other model-based clustering methods. It was tested on two published datasets of human protein-protein interactions derived from APMS experiments, demonstrating its effectiveness in identifying meaningful interactions. Additionally, we discussed some of the challenges associated with interpreting clustering results in the context of APMS data. This article was authored by Haiyang-Wan Choy, Saini Kim, and Claude Jingress, and others. We are article.tv, links in the description below.