Introduces DM deviance residualization for jointly overdispersed count matrices that preserves sparsity, runs in constant time per entry, and generalizes multinomial residuals under a compositional null.
Mauck, Yuhan Hao, Marlon Stoeckius, Peter Smibert, and Rahul Satija
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
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.
MEDAL distills manifold embeddings into autoencoders to enable out-of-sample extension and held-out validation of dimension reduction methods.
scHelix uses explicit gene-level partitioning into Anchors and Variants plus an asymmetric Align-Refine-Fuse dual-stream architecture to improve batch correction in scRNA-seq without over-correcting biological signals.
Two new methods distill implicit regulatory knowledge from single-cell foundation models to enable generalizable gene regulatory network inference on unseen data.
citing papers explorer
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Deviance-style normalization for jointly overdispersed counts
Introduces DM deviance residualization for jointly overdispersed count matrices that preserves sparsity, runs in constant time per entry, and generalizes multinomial residuals under a compositional null.
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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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MEDAL: Manifold Embedding Distillation via Autoencoder Learning
MEDAL distills manifold embeddings into autoencoders to enable out-of-sample extension and held-out validation of dimension reduction methods.
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scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement
scHelix uses explicit gene-level partitioning into Anchors and Variants plus an asymmetric Align-Refine-Fuse dual-stream architecture to improve batch correction in scRNA-seq without over-correcting biological signals.
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Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
Two new methods distill implicit regulatory knowledge from single-cell foundation models to enable generalizable gene regulatory network inference on unseen data.