DiSC: Differential Spectral Clustering of Features

TL;DR

DiSC is a spectral clustering method that identifies feature groups differentiating conditions by analyzing condition-specific feature graphs, effectively uncovering meaningful feature clusters across diverse datasets.

Contribution

We introduce DiSC, a novel spectral clustering approach for detecting feature groups that differentiate conditions, with theoretical analysis and extensive empirical validation.

Findings

DiSC outperforms competing methods in differentiating features across datasets.

Theoretical analysis confirms DiSC's effectiveness under stochastic block model.

DiSC successfully uncovers meaningful feature clusters in real-world data.

Abstract

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.