Interpretable discovery of patterns in tabular data via spatially semantic topographic maps

Abstract

Tabular data—rows of samples and columns of sample features—are ubiquitously used across disciplines. Yet the tabular representation makes it difficult to discover underlying associations in the data and thus hinders their analysis and the discovery of useful patterns. Here we report a broadly applicable strategy for unravelling intertwined relationships in tabular data by reconfiguring each data sample into a spatially semantic 2D topographic map, which we refer to as TabMap. A TabMap preserves the original feature values as pixel intensities, with the relationships among the features spatially encoded in the map (the strength of two inter-related features correlates with their distance on the map). TabMap makes it possible to apply 2D convolutional neural networks to extract association patterns in the data to aid data analysis, and offers interpretability by ranking features according to importance. We show the superior predictive performance of TabMap by applying it to 12 datasets across a wide range of biomedical applications, including disease diagnosis, human activity recognition, microbial identification and the analysis of quantitative structure–activity relationships.