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).
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Serial Spatial Transcriptomes Reveal Regulatory Transitions in Maize Leaf Development.
PMID 41493197 · PMC13110159 · Plant biotechnology journal · 2026 · 8 claims · 5 setups
An optimised Visium spatial transcriptomics protocol (two-step OCT embedding combined with OCT-Immersion First cryopreservation) preserves high-integrity RNA from fragile plant tissues such as SAM
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Has reproduction
Empowering integrative and collaborative exploration of single-cell and spatial multimodal data with SGS genome browser.
PMID 40233745 · PMC12143324 · Cell genomics · 2025 · 8 claims · 6 setups
SGS is a user-friendly, collaborative, versatile browser for integrative visualization of single-cell and spatial multimodal (scMulti-omics) data
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Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.
PMID 41547856 · PMC12917120 · Nature communications · 2026 · 8 claims · 8 setups
GARDEN identifies and characterizes rare pathogenic cell populations/regions in spatial omics by embedding graph-based dynamic attention into a spatially-aware graph fusion contrastive model
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Full-text index only
MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.
PMID 41650976 · PMC13174227 · Cell genomics · 2026 · 7 claims · 5 setups
MultiSP outperforms existing spatial and single-cell multi-omics integration methods in detecting biologically accurate spatial domains across multiple spatial multi-omics technologies and tissue types
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Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.
PMID 42045691 · PMC13175893 · Nature genetics · 2026 · 6 claims · 7 setups
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