Article

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

2024-04-10

Abstract excerpt

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. Here, we show that incongruencies between cells and their inp...

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Literature Corpus work: 0651b8f9-9ab7-568b-b27d-66d5da4bd89f
DOI: 10.1101/2024.04.06.588373

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