  Selecting subsets of features that differentiate between two conditions is a
key task in a broad range of scientific domains. In many applications, the
features of interest form clusters with similar effects on the data at hand. To
recover such clusters we develop DiSC, a data-driven approach for detecting
groups of features that differentiate between conditions. For each condition,
we construct a graph whose nodes correspond to the features and whose weights
are functions of the similarity between them for that condition. We then apply
a spectral approach to compute subsets of nodes whose connectivity differs
significantly between the condition-specific feature graphs. On the theoretical
front, we analyze our approach with a toy example based on the stochastic block
model. We evaluate DiSC on a variety of datasets, including MNIST,
hyperspectral imaging, simulated scRNA-seq and task fMRI, and demonstrate that
DiSC uncovers features that better differentiate between conditions compared to
competing methods.
