A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
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The filter echo generalizes diffusion echoes for visualizing nonlinear filters beyond adaptive smoothing and adds a compression method that cuts storage needs by a factor of 20 to 100.
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A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
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The Filter Echo: A General Tool for Filter Visualisation
The filter echo generalizes diffusion echoes for visualizing nonlinear filters beyond adaptive smoothing and adds a compression method that cuts storage needs by a factor of 20 to 100.