TopoMetry systematically learns and evaluates the latent geometry of single-cell data
Reconstructing and investigating the geometry underlying data is a fundamental task in single-cell analysis, yet no unified framework exists for learning, evaluating, and diagnosing representations…
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University of Michigan researchers develop technique to map cell-to-cell communication using spatial transcriptomics
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Discover groundbreaking insights with scMEDAL! Our state-of-the-art framework models batch-invariant and batch-specific effects separately,
scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder
Discover groundbreaking insights with scMEDAL! Our state-of-the-art framework models batch-invariant and batch-specific effects separately,
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