Tissues & Causal Representation Learning

Through recent technological developments, we can now obtain RNA sequencing data from whole tissue sections without losing cell location information. But the computational methods for analyzing these spatial datasets are still inadequate. In particular, we need methods that can seamlessly integrate images, sequencing data, and 3D coordinates from large-scale spatial transcriptomic studies. To that end, the Eric and Wendy Schmidt Center is developing causal representation learning methods that allow us to integrate different kinds of data to uncover the mechanisms of how tissues are organized in health and disease.

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