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.
Lennon, Kenneth J
6 Pith papers cite this work, alongside 7,845 external citations. Polarity classification is still indexing.
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2026 6representative citing papers
EntroPath defines a free-energy dissimilarity from maximum-entropy random walk path ensembles and proves it converges to squared geodesic distance in the short-time limit via Varadhan's formula.
EpiAwareNet is a prior-guided multi-omic Transformer that uses gene-peak cross-attention for adaptive accessibility aggregation and bulk GRN priors for weak supervision to improve single-cell GRN reconstruction over baselines.
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
A tractable estimator for functional KL divergence provides a coherent way to compare trajectory inference methods and reveal discrepancies in inferred dynamics from snapshot data.
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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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EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning
EntroPath defines a free-energy dissimilarity from maximum-entropy random walk path ensembles and proves it converges to squared geodesic distance in the short-time limit via Varadhan's formula.
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Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
EpiAwareNet is a prior-guided multi-omic Transformer that uses gene-peak cross-attention for adaptive accessibility aggregation and bulk GRN priors for weak supervision to improve single-cell GRN reconstruction over baselines.
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A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
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MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
A tractable estimator for functional KL divergence provides a coherent way to compare trajectory inference methods and reveal discrepancies in inferred dynamics from snapshot data.