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).

• Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

  PMID 41825449 · PMC13030991 · Cell reports methods · 2026 · 7 claims · 3 setups

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  NCLUSION matches the performance of state-of-the-art single-cell clustering techniques with significantly reduced runtime

  brain-large scdesign3-simulated facs-purified scrna-seq simulated

• 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

• Hypergraph representations of single-cell RNA sequencing data for improved cell clustering.

  PMID 41896196 · PMC13070707 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 6 setups

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  Unipartite network projections (e.g. cell/gene co-expression networks) of scRNA-seq data lose higher-order information and are an inefficient, inflated representation of sparse transcriptomic data

  pancreas brain lung simulated scrna-seq simulated

• CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.

  PMID 42059479 · PMC13130071 · Briefings in bioinformatics · 2026 · 7 claims · 5 setups

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  CaHoT-GRN integrates pretrained biological language model embeddings (DNABERT for DNA, ESM for protein) with scRNA-seq expression data to improve GRN inference

  embryonic gene dataset-specific same scrna-seq-based sequence protein-protein

• Leveraging RNA-seq deconvolution to improve complex in vitro model characterization.

  PMID 40701251 · PMC12391696 · The Journal of biological chemistry · 2025 · 8 claims · 6 setups

  Has reproduction · 67

  RNA-seq deconvolution can predict cell type proportions from bulk RNA-seq using scRNA-seq references, offering a useful characterization tool for CIVMs where single-cell methods are impractical

  intestine rodent duodenal neonatal bulk scrna-seq marker

• Robust and efficient annotation of cell states through gene signature scoring.

  PMID 41708334 · PMC12951948 · Genome research · 2026 · 8 claims · 8 setups

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  Established scoring methods (Seurat, SCANPY, UCell, JASMINE) fail to provide robust and comparable score distributions across diverse signatures and experimental conditions, precluding accurate unsupervised cell-state annotation.

  pbmc colorectal esophageal crc scrna-seq cross-implementation signature

• 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

• Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts.

  PMID 41618434 · PMC12930932 · Genome biology · 2026 · 8 claims · 7 setups

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  PEFT enables accurate transfer of Borzoi to new datasets while significantly reducing GPU memory and runtime compared to joint training or full fine-tuning

  gtex encode wi-38 thp-1 bulk eqtl variant-flowfish

• AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.

  PMID 41871930 · PMC13064981 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 3 setups

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  AutoGERN explicitly models regulatory information in the message-passing space via learned link (edge) embeddings, which are scored by a lightweight MLP to infer TF–target interactions.

  seven same scrna-seq-based chip-seq cold-start

• Score Matching for Differential Abundance Testing of Compositional High-Throughput Sequencing Data.

  PMID 41944570 · PMC13055433 · Statistics in medicine · 2026 · 8 claims · 3 setups

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  cosmoDA extends the a-b power interaction model by adding a linear covariate effect on the location vector, enabling differential abundance testing on compositional data with feature interactions.

  simulated cell-type microbial simulated single-cell 16s

• Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.

  PMID 41917934 · PMC13162528 · BMC biology · 2026 · 6 claims · 4 setups

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  scMIB achieves state-of-the-art and more stable clustering performance across diverse scRNA-seq datasets compared with ten baseline methods

  real same splatter-simulated scrna-seq clustering denoising

• Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.

  PMID 41821037 · PMC12983708 · Genome biology · 2026 · 8 claims · 8 setups

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  Horizontal integration of scRNA-seq and snRNA-seq improves cell-type identification

  renal kidney pbmc heart scrna-seq snrna-seq snatac-seq

• 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

• A unified framework for correcting batch effects and integrating multi-omics data.

  PMID 41786846 · PMC13079841 · Scientific reports · 2026 · 7 claims · 6 setups

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  MoDAmix, a four-stage domain adaptation framework (pre-training, single-omics adversarial adaptation, multi-omics adversarial alignment, semi-supervised class alignment), unifies batch correction across multiple omics layers.

  brain aml same single-cell bulk k-means

• 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

• EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.

  PMID 41527854 · PMC12796812 · Briefings in bioinformatics · 2026 · 8 claims · 8 setups

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  EpiXFormer achieves high accuracy (mean AUROC ~0.99) predicting binding sites of both TFs and non-sequence-specific DBPs across 199 DBP-cell type pairs

  dbps tfs common ctcf deep comparative feature-ablation

• 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

• Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from single-cell to spatial resolution.

  PMID 41980945 · PMC13250166 · Nature communications · 2026 · 8 claims · 8 setups

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  Hi-Compass predicts cell-type-specific Hi-C contact maps using only ATAC-seq as cell-type-specific input, plus DNA sequence and a generalized CTCF binding profile

  gm12878 curated bulk training hi-c ctcf matched

• CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing.

  PMID 30744683 · PMC6371477 · Genome biology · 2019 · 8 claims · 5 setups

  Has reproduction · 97

  CellFishing.jl achieves accuracy comparable to state-of-the-art software (scmap-cell) but is markedly faster

  scrna-seq shekhar2016 svd lsh hamming

• ICE: robust detection of cellular senescence from weak single-cell signatures using imputation-based marker refinement.

  PMID 41668152 · PMC12990438 · Genome biology · 2026 · 8 claims · 7 setups

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  Senescence-associated marker genes show weak, non-specific expression across human tissues and cell types compared to canonical tissue/cell-type markers

  tissues pancreas pancreatic brain bulk scrna-seq train