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DiSC: Differential Spectral Clustering of Features
Ram Dyuthi Sristi · Gal Mishne · Ariel Jaffe

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #408

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 pattern 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.

Author Information

Ram Dyuthi Sristi (University of California, San Diego, University of California, San Diego)
Gal Mishne (UC San Diego)
Ariel Jaffe (Hebrew University of Jerusalem)

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