SAKURA: a knowledge-guided approach to recovering important, rare signals from single-cell data
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Dimensionality reduction is routinely applied to single-cell transcriptomic data to improve interpretability, remove noise and redundancy, and enable visualization. Most existing methods aim at preserving the most prominent data properties, which can lead to o... ...
