Interpreting single-cell and spatial omics data using deep networks training dynamics
Jonathan Karin,
Reshef Mintz,
Barak Raveh
et al.
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. Here, we show that incongruencies between cells and their input annotations can be identified by analyzing a rich but overlooked source of information: the dif… Show more
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