Experiments

Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).

• Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.

  PMID 42026624 · PMC13238118 · Genome biology · 2026 · 8 claims · 6 setups

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  SEAGALL combines a geometry-regularised autoencoder (GRAE) to embed cells and build a cell-cell graph with a graph attention network (GAT) classifier and GNNExplainer-based XAI to identify features driving cell type/phenotype.

  count same scchip-seq ten count latent classification

• Lorentz-regularized interpretable VAE for multi-scale single-cell transcriptomic and epigenomic embeddings.

  PMID 41555918 · PMC12812404 · Frontiers in genetics · 2025 · 7 claims · 5 setups

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  LiVAE, a dual-pathway VAE with Lorentzian geometric regularization between a primary Euclidean pathway and an information-bottleneck pathway, balances local fidelity with global topology coherence in single-cell embeddings

  single-cell same dapp1 benchmarking umap component-wise

• MitoPerturb-Seq identifies gene-specific single-cell responses to mitochondrial DNA depletion and heteroplasmy.

  PMID 41922875 · PMC13095666 · Nature structural & molecular biology · 2026 · 8 claims · 6 setups

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  MitoPerturb-Seq combines pooled CRISPR–Cas9 screening (CROP-seq) with 10x Genomics multiome (scATAC-seq + scRNA-seq) to simultaneously profile mtDNA sequence/copy number/heteroplasmy and the nuclear transcriptome/chromatin accessibility in single heteroplasmic cells

  cas9-expressing heteroplasmic tfam-ko pooled tap-seq hybridization-capture-based

• iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.

  PMID 41775921 · PMC13066597 · Communications biology · 2026 · 8 claims · 5 setups

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  iAODE combines a ZINB-likelihood VAE, a latent Neural ODE, low-weight KL regularization, and an interpretable reconstruction (irecon) bottleneck to learn generative, temporally continuous latent spaces for scATAC-seq.

  simulated real small simulated scatac-seq

• Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.

  PMID 41869863 · PMC13170260 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups

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  Among ten/eleven evaluated single-cell FMs, scGPT, Geneformer, and CellFM are the top models considering both performance and user accessibility

  scrna-seq unpaired single-cell scgpt batch multi-omic cell-type