Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, ‘Multiscale Graph Correlation’ (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct.

Many immunotherapeutic approaches under development rely on T-cell recognition of cancer-derived peptides bound to human leukocyte antigen molecules on the cell surface. Direct experimental demonstration that such peptides are processed and bound is currently challenging. Here, we describe a method that meets this challenge. The method entailed an optimized immunoprecipitation protocol coupled with two-dimensional chromatography and mass spectrometry. The ability to detect and quantify minute amounts of predefined antigens should be useful both for basic research in tumor immunology and for the development of rationally designed cancer vaccines.