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).

• Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.

  PMID 42062553 · PMC13259927 · Nature methods · 2026 · 8 claims · 6 setups

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  Xenium data show strong consistency across patients and technical replicates, with little technical variation between platforms

  ffpe matched xenium xenium single-nucleus immunohistochemistry

• VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.

  PMID 41507434 · PMC12891734 · Communications biology · 2026 · 8 claims · 6 setups

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  VISTA predicts unmeasured gene expression in subcellular spatial transcriptomic data by integrating scRNA-seq and SST through variational inference and geometric deep learning with built-in uncertainty quantification

  brain breast embryo muscle imaging-based sequencing spot-based

• 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

• FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.

  PMID 41839892 · PMC13201544 · Nature communications · 2026 · 8 claims · 6 setups

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  FineST, a bimodal contrastive learning model integrating histology (Virchow2 ViT features) and spatial gene expression, enables nuclei-resolved high-resolution RNA imputation.

  colorectal crc breast he-stained spatial type ccc

• SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.

  PMID 42146899 · PMC13176606 · Computational and structural biotechnology journal · 2026 · 8 claims · 6 setups

  Has reproduction · 74

  SpaGene improves average PCC and SSIM and reduces RMSE compared to 6 baseline methods (SpaGE, gimVI, Tangram, VISTA, spRefine, stDiff) across 8 diverse ST-SC dataset pairs under gene-holdout evaluation.

  pairs nanostring st-sc osmfish_zeisel st-sc 5-fold spatially