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).

• SMART: spatial multi-omic aggregation using graph neural networks and metric learning.

  PMID 41896208 · PMC13031631 · Nature communications · 2026 · 8 claims · 5 setups

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  SMART accurately identifies spatial regions of anatomical structures and is compatible with spatial datasets of any type and number of omics layers

  simulated lymph brain spleen simulated spatial multi-section

• Ultra-precision deconvolution of spatial transcriptomics decodes immune heterogeneity and fate-defining programs in tissues.

  PMID 41862467 · PMC13168514 · Nature communications · 2026 · 8 claims · 8 setups

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  UCASpatial is a novel deconvolution algorithm that uses Shannon entropy-based gene weighting combined with weighted non-negative least squares to estimate cell-type composition from spatial transcriptomics data

  simulated colorectal hippocampus ear simulated runtime spatial

• Spider: a flexible and unified framework for simulating spatial transcriptomics data.

  PMID 41237053 · PMC12790819 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 6 setups

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  Spider simulates ST data without requiring real ST data as a reference

  simulated hypothalamus dorsolateral spatial clustering cell-cell

• SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.

  PMID 41955028 · PMC13069903 · Briefings in bioinformatics · 2026 · 8 claims · 1 setups

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  SpaJoint is a transfer-learning-based deconvolution method that integrates scRNA-seq and ST gene expression while accounting for spatial correlation across spots.

  simulated spatial

• Topography-aware optimal transport for alignment of spatial omics data.

  PMID 41916307 · PMC13107060 · Cell reports methods · 2026 · 7 claims · 4 setups

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  TOAST extends the classical FGW objective by adding a spatial coherence term and a neighborhood consistency term to model local spatial organization and molecular heterogeneity.

  simulated dorsolateral simulated multislice ablation

• SA2E: spatial-aware auto-encoder for cell type deconvolution of spatial transcriptomics data.

  PMID 41863296 · PMC13070677 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 5 setups

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  SA2E is a spatial-aware auto-encoder framework for cell-type deconvolution that does not require predefined cell-type biomarkers

  olfactory legacy breast brain simulated real scrna-seq

• SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.

  PMID 41975540 · PMC13185361 · Genome medicine · 2026 · 8 claims · 8 setups

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  SpaPheno integrates spatial transcriptomics with clinically annotated bulk RNA-seq to identify spatially resolved biomarkers predictive of patient outcomes including survival, tumor stage, and immunotherapy response

  cortex liver clear breast spatial elastic

• 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

• RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.

  PMID 41963343 · PMC13247165 · Nature communications · 2026 · 8 claims · 5 setups

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  Existing ST analysis methods (segmentation, deconvolution) systematically omit or mislabel a substantial portion of true molecular expression

  synthetic honey simulation merfish segmentation

• scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.

  PMID 41877186 · PMC13134144 · Genome biology · 2026 · 8 claims · 7 setups

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  scComm infers cell-cell communications at single-cell resolution using a data-adaptive L-R weighting module and supervised contrastive learning (SupCon loss) to distinguish significant CCC events from background noise

  simulated spatial liver single-cell simulated spatial scrna-seq

• Cell neighborhood topology directs rare cell population identification.

  PMID 41912521 · PMC13199379 · Nature communications · 2026 · 8 claims · 8 setups

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  RareQ is a framework that quantifies neighborhood connectivity (Q), a cell-specific measure of kNN-graph cliquishness, to detect rare cell populations from single-cell and spatial omics data

  downsampled pbmc brain simulated scrna-seq cite-seq single-cell

• 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

• scSurv: a deep generative model for single-cell survival analysis.

  PMID 41429574 · PMC12797213 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 6 setups

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  scSurv combines a Cox proportional hazards model with a deep generative model (VAE) of single-cell transcriptomes to estimate individual cellular contributions to clinical outcomes

  simulated tcga melanoma renal scrna-seq bulk single-cell

• 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

• Optimisation of the core subset for the APY approximation of genomic relationships.

  PMID 36418945 · PMC9682752 · Genetics, selection, evolution : GSE · 2022 · 7 claims · 3 setups

  Has reproduction · 85

  APY approximates the full genomic relationship matrix by splitting genotyped animals into a core subset (fully dependent, direct inverse) and a non-core subset (conditionally independent given core), reducing inversion cost.

  simulated real genotyped gblup population

• CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.

  PMID 40739511 · PMC12312500 · BMC biology · 2025 · 8 claims · 7 setups

  Has reproduction · 42

  CanCellCap identifies cancer cells in scRNA-seq data across 13 tissue types, 23 cancer types, and 7 sequencing platforms with 0.977 average accuracy

  cancer testing sequencing unseen scrna-seq spatial

• HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps.

  PMID 41764165 · PMC13065825 · Nature communications · 2026 · 8 claims · 5 setups

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  HRCHY-CytoCommunity is an end-to-end graph neural network framework that jointly identifies multi-level (coarse tissue compartment and fine cellular neighborhood) tissue structures from cell-type spatial maps using differentiable graph pooling, adaptive edge pruning, and consistency/balance regularization.

  healthy colorectal simulated breast codex visium merfish

• Computational modeling demonstrates that glioblastoma cells can survive spatial environmental challenges through exploratory adaptation.

  PMID 31836713 · PMC6911112 · Nature communications · 2019 · 8 claims · 6 setups

  Has reproduction · 100

  Exploratory adaptation (stochastic gene-regulatory network perturbation) explains how GBM cells adapt phenotypically across spatially distinct tumor regions

  patient-derived low-grade tf-gene single-sample bulk differential gene

• 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

• Direct inference of SNP heterozygosity rates and resolution of LOH detection.

  PMID 18052545 · PMC2098867 · PLoS computational biology · 2007 · 6 claims · 7 setups

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  A large proportion of SNPs in dbSNP have high-variance HET rate estimates, limiting their reliability for LOH study design.

  dbsnp theoretical simulated hapmap snp probability sequential