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bioRxiv · 10.1101/2023.08.25.554778

In silico spatial transcriptomic editing at single-cell resolution

Wu, J.·Koelzer, V. H.
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Abstract

MotivationGenerative artificial intelligence (AI) has enabled fundamental breakthroughs in visual content creation by text-guided editing. However, the utility of such generative models remains largely understudied for processing increasingly complex bioimage data. ResultsWe propose to algorithmically edit gene expression data to drive cell-level morphological transitions using Generative Adversarial Networks (GAN) and GAN Inversion models. Leveraging cutting-edge spatial transcriptomic datasets with subcellular in-situ resolution and matched high-content imaging data, we propose an in-silico approach to quantify, model and imitate pathological processes in real-life clinical tissue samples. Availability and implementationThe code and video demo is accessible via https://github.com/CTPLab/In-silico-editing

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Wu, J., Koelzer, V. H.. 2023-08-27. In silico spatial transcriptomic editing at single-cell resolution. https://doi.org/10.1101/2023.08.25.554778

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