Data-driven optimization of biomarker panels in highly multiplexed imaging

Multiplexed protein imaging methods provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is currently limited. In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level marker composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set that could not be measured concurrently.