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).

• Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap.

  PMID 41650244 · PMC12890892 · Proceedings of the National Academy of Sciences of the United States of America · 2026 · 8 claims · 7 setups

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  SuperMap learns cross-modal feature mappings directly from unpaired multimodal data without requiring paired training data

  pbmc bone skin embryos scrna-seq scnmt-seq asap-seq

• Determination of complete chromosomal haplotypes by bulk DNA sequencing.

  PMID 33957932 · PMC8101039 · Genome biology · 2021 · 8 claims · 8 setups

  Has reproduction · 97

  A hierarchical computational strategy that first builds high-confidence local haplotype blocks from long-range/linked-read linkage and then concatenates them into whole-chromosome haplotypes using Hi-C contacts

  rpe-1 na12878 aneuploid linked-read hi-c bulk

• CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.

  PMID 41231245 · PMC13053056 · Cancer research · 2026 · 7 claims · 7 setups

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  CanSig is a comprehensive benchmarking tool for evaluating computational methods that identify shared transcriptional signatures in cancer from scRNA-seq data

  neftel kim scrna-seq same scrna-seq ground-truth robustness

• PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.

  PMID 41673899 · PMC12998178 · Genome biology · 2026 · 7 claims · 8 setups

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  PreTSA dramatically reduces computational time and memory versus GAM (Monocle, TSCAN) and PseudotimeDE for identifying temporally variable genes (TVGs) while producing highly similar results

  simulated null pbmc pancreas pseudotime tvg false-positive

• sCellST predicts single-cell gene expression from H& E images.

  PMID 41513659 · PMC12858858 · Nature communications · 2026 · 7 claims · 6 setups

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  sCellST is a weakly supervised (Multiple Instance Learning) deep learning framework that predicts single-cell gene expression from H&E images alone, trained using paired spatial transcriptomics (Visium) and H&E slides

  ovarian kidney prostate breast simulation spot-level nucleus