DiSC: Differential Spectral Clustering of Features
This work addresses feature selection for condition differentiation in scientific domains, offering a novel clustering approach that is incremental in improving performance over existing methods.
The paper tackles the problem of selecting clusters of features that differentiate between two conditions, proposing DiSC, a differential spectral clustering method that identifies feature groups with significantly different connectivity across conditions. The authors demonstrate that DiSC outperforms competing methods in uncovering differentiating features on datasets like MNIST, hyperspectral imaging, simulated scRNA-seq, and task fMRI.
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.