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).

• UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles.

  PMID 41520227 · PMC12895075 · Briefings in bioinformatics · 2026 · 7 claims · 4 setups

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  Existing CTS deconvolution methods (e.g., CIBERSORTx, TCA, bMIND, CellDMC, HBI) require cell type proportions that are in practice only estimated, not known, introducing unaccounted uncertainty into CTS inference.

  simulated rosmap onek1k bulk simulation-based cell-type-specific type

• Deconvolving cell-type-specific gene expression profiles from bulk RNA-seq samples.

  PMID 41886524 · PMC13038110 · PLoS computational biology · 2026 · 8 claims · 6 setups

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  BLUE, a U-Net-based deep learning model with dual branches (U-Net for GEPs, MLP for proportions), accurately predicts cell-type proportions and cell-type-specific gene expression profiles from bulk RNA-seq.

  pbmc pancreatic aml scrna-seq-derived real scrna-seq

• DiSCO: deconvoluting spatial transcriptomics via combinatorial optimization with a foundational diffusion model.

  PMID 42101928 · PMC13155122 · Briefings in bioinformatics · 2026 · 7 claims · 2 setups

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  Deconvolution of spatial transcriptomics data can be formulated as a combinatorial optimization (CO) problem of assigning single cells to spatial spots so that aggregated single-cell profiles best approximate observed spot expression

  sccube-simulated real simulation-based spatial

• 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

• A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles.

  PMID 32111252 · PMC7049190 · Genome medicine · 2020 · 8 claims · 8 setups

  Has reproduction · 91

  DeClust is a reference-profile-free deconvolution method that incorporates molecular subtyping directly into the deconvolution process, outputting cohort-level cancer subtype and stromal reference profiles rather than per-individual profiles

  tcga simulated muscle-invasive ccrcc computational simulation-based deconvolution

• Community assessment of methods to deconvolve cellular composition from bulk gene expression.

  PMID 39191725 · PMC11350143 · Nature communications · 2024 · 8 claims · 4 setups

  Has reproduction · 63

  Most deconvolution methods accurately predict coarse-grained immune/stromal cell populations from bulk expression.

  admixtures cancer tumor bulk bulk admixture crowdsourced

• omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.

  PMID 41582216 · PMC12837286 · Genome biology · 2026 · 8 claims · 6 setups

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  omnideconv is an R package providing a unified interface to twelve second-generation deconvolution methods (AutoGeneS, BayesPrism, Bseq-SC, Bisque, CDseq, CIBERSORTx, CPM, DWLS, MOMF, MuSiC, SCDC, Scaden)

  blood-derived pbmc blood spleen bulk scrna-seq pseudo-bulk

• 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

• Digital sorting of complex tissues for cell type-specific gene expression profiles.

  PMID 23497278 · PMC3626856 · BMC bioinformatics · 2013 · 8 claims · 8 setups

  Has reproduction · 85

  The Digital Sorting Algorithm (DSA) deconvolves mixed tissue expression into cell type-specific profiles using only marker genes, without requiring prior knowledge of cell type frequencies or in vitro pure-cell profiles.

  liver im-9 immune deconvolved microarray simulation roc