Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs

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Publication Date: 6/3/2024

Event: https://www.biorxiv.org

Reference: https://www.biorxiv.org/content/10.1101/2024.06.02.596505v1

Authors: Tianci Song, NEC Laboratories America, Inc., University of Minnesota; Eric Cosatto, NEC Laboratories America, Inc.; Gaoyuan Wang, Yale University; Rui Kuang, University of Minnesota; Mark Gerstein, Yale University; Martin Renqiang Min, NEC Laboratories America, Inc.; Jonathan Warrell, NEC Laboratories America, Inc., Yale University

Abstract: Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images, provides a scalable alternative approach to decoding tissue complexity

Publication Link: https://www.biorxiv.org/content/10.1101/2024.06.02.596505v1