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).

• DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks.

  PMID 41663685 · PMC12996496 · Communications biology · 2026 · 7 claims · 6 setups

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  DANST, a deconvolution framework using deep domain adversarial neural networks, achieves superior cell-type deconvolution accuracy compared with existing methods on human and mouse benchmark datasets

  heart cortex visual brain spatial cell-type

• Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.

  PMID 42045691 · PMC13175893 · Nature genetics · 2026 · 6 claims · 7 setups

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  INSPIRE is a deep-learning method that unifies adversarial learning with a GNN-based encoder and integrated NMF to interpretably integrate multiple spatial transcriptomics datasets

  synthetic dorsolateral brain cortex simulated visium spatial

• Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics.

  PMID 41952215 · PMC13174004 · Genome biology · 2026 · 8 claims · 5 setups

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  No prior systematic, quantitative benchmark exists for CCI inference methods specifically developed for spatial transcriptomics across multiple platforms

  synthetic real simulated simulation spot-purity real-data

• Calibrating tissue level PDE models of ligand dynamics using single cell and spatial transcriptomics data.

  PMID 41714655 · PMC13039149 · NPJ systems biology and applications · 2026 · 8 claims · 8 setups

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  scRNA-seq and spatial transcriptomics data provide a rich, underused source of information for calibrating tissue-scale PDE models of ligand dynamics.

  skin combined annotated scrna-seq single-cell spatial cell2location

• Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.

  PMID 41980775 · PMC13198004 · Cell reports methods · 2026 · 7 claims · 4 setups

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  Common DGE analysis methods (Wilcoxon test, unweighted t-test, pseudo-bulk aggregation, SCTransform-style parametrization) rely on unnecessary simplifications and assumptions that are inconsistent with experimental data and cause false findings

  e18 simulated scrna-seq sprna-seq scrna-seq simulation count