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Articles

Interpreting single-cell and spatial omics data using deep networks training dynamics

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By Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan
| biorxiv.org
Abstract Single-cell and spatial genomics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations, or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations based on noisy, sparse, and high-dimensional data.
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