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).

• 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

• GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.

  PMID 41978379 · PMC13076945 · Briefings in bioinformatics · 2026 · 8 claims · 4 setups

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  GDSim, a label-guided diffusion-based deep generative network, can simulate scRNA-seq data that closely reflects the true distribution of original data

  real simulated annotated scrna-seq subtype differential

• scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.

  PMID 41981652 · PMC13188691 · BMC biology · 2026 · 7 claims · 3 setups

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  scDEBGCL is a deep embedding method for scRNA-seq data based on bipartite graph contrastive learning, integrating contrastive learning, graph reconstruction, and ZINB-based data reconstruction losses.

  nine yan dpt scrna-seq trajectory

• Integrating and mapping single-cell transcriptomics across the entire gene expression space.

  PMID 42059480 · PMC13130072 · Briefings in bioinformatics · 2026 · 8 claims · 1 setups

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  scGES is a deep learning framework that corrects batch effects across the entire gene expression space by leveraging information from both HVGs and LVGs

  real scrna-seq

• scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder.

  PMID 41857511 · PMC13122936 · BMC bioinformatics · 2026 · 8 claims · 1 setups

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  scZiva is a novel VAE-based imputation method for scRNA-seq data using a Zero-Inflated Negative Binomial (ZINB) likelihood.

  simulated 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

• ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion.

  PMID 34844559 · PMC8628418 · BMC genomics · 2021 · 8 claims · 8 setups

  Has reproduction · 85

  scLRTC imputes dropout entries closest to the original expression values on simulated datasets, outperforming other state-of-the-art methods by SSE and PCC.

  usoskin pollen zeisel yan scrna-seq umap masking

• OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.

  PMID 41335419 · PMC12766913 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  OTMODE, using an unbalanced Sinkhorn algorithm and Wald test, improves differential feature identification in single-cell multi-omics data

  simulated classical degs naive simulated bulk real

• scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation.

  PMID 41632596 · PMC12866930 · Briefings in bioinformatics · 2026 · 8 claims · 6 setups

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  scGACL, a GAN integrated with multi-scale contrastive learning, is proposed to overcome the over-smoothing problem in scRNA-seq imputation

  simulated real-world scrna-seq scrna-seq clustering gene

• Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing.

  PMID 41503159 · PMC12770975 · NAR genomics and bioinformatics · 2026 · 8 claims · 6 setups

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  Eight prominent imputation methods (MAGIC, SAVER, scVI, DCA, scBiG, kNN-smoothing, scImpute, ALRA) were systematically evaluated for their ability to recover the true non-zero expression fraction using simulated and real-world scRNA-seq data

  basal tracheal airway k562 simulated scrna-seq imputation

• 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

• DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

  PMID 38950178 · PMC11236288 · Bioinformatics (Oxford, England) · 2024 · 8 claims · 2 setups

  Has reproduction · 40

  DeepGSEA is an explainable deep gene set enrichment analysis method built on interpretable, prototype-based neural networks.

  simulated real-world simulation scrna-seq-based

• Enhancing cell subpopulation discovery in cancer by integrating single-cell transcriptome and expressed variants.

  PMID 41647537 · PMC12869734 · Fundamental research · 2026 · 6 claims · 3 setups

  Has reproduction · 68

  scCluster, an end-to-end deep clustering model integrating gene expression and expressed variant (eSNP) features, stratifies cell subpopulations in cancer scRNA-seq data.

  raw real-world expressed gene deep

• Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.

  PMID 41566530 · PMC12906006 · Genome biology · 2026 · 7 claims · 6 setups

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  Pseudobulk and real bulk RNA-seq deconvolution performance differ significantly, and method ranking consistency is lower between pseudobulk and real bulk than within either data type alone

  breast real colon tcga paired pseudobulk bulk

• 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

• scCNMF: an integrated analysis model for paired single-cell RNA sequencing and assay for transposase-accessible chromatin sequencing data leveraging cell similarity and cis-regulatory potential.

  PMID 41800139 · PMC12962131 · PeerJ · 2026 · 7 claims · 2 setups

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  scCNMF is an NMF-based model for vertical integration of paired scRNA-seq and scATAC-seq data that jointly incorporates a cell similarity matrix and a cis-regulatory potential (CRP) matrix

  publicly 10x paired

• 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

• 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

• Prior-guided factorization for reliable imputation of scRNA-seq data.

  PMID 41860953 · PMC13004523 · PLoS computational biology · 2026 · 8 claims · 8 setups

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  scZN models scRNA-seq counts as a mixture of a two-state (Gamma-Poisson/negative binomial) transcriptional bursting process and dropout, formalized via a zero-inflated negative binomial (ZINB) and solved as constrained nonnegative matrix factorization into a cell-to-cell-type assignment matrix and a cell-type expression matrix

  ercc real randomly brain scrna-seq cell-cycle k-nn

• 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