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).

• CellPredX, a computational framework for cross-data type, cross-sample, and cross-protocol cell type annotation through domain adaptation and deep metric learning.

  PMID 41481570 · PMC12758788 · PLoS computational biology · 2026 · 8 claims · 7 setups

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  CellPredX is a unified semi-supervised framework integrating domain adaptation and deep metric learning to align heterogeneous embeddings for cross-modality cell type annotation.

  pbmc fetal pancreas gse149683 matched unmatched cross-protocol

• 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

• Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design.

  PMID 34553212 · PMC8458035 · GigaScience · 2021 · 8 claims · 6 setups

  Has reproduction · 87

  Genetic variation-based demultiplexing tools can be effectively deployed on pooled scRNA-seq experimental designs in cancer tissue (HGSOC and lung adenocarcinoma) despite somatic variation.

  hgsoc lung healthy scrna-seq bulk downstream

• 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

• 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

• 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

• TiRank prioritizes phenotypic niches in tumor microenvironment for clinical biomarker discovery.

  PMID 41689080 · PMC12910759 · Genome medicine · 2026 · 7 claims · 4 setups

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  TiRank is a framework that integrates scRNA-seq, ST, and bulk transcriptomes using an REO-transformation module and multitask transfer learning to align data into a unified embedding space for prioritizing clinically relevant spatial niches

  benchmark pan-cancer gastric independent scrna-seq bulk spatial

• 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

• 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

• TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data.

  PMID 42093817 · PMC13139726 · Computational and structural biotechnology journal · 2026 · 7 claims · 5 setups

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  TDAGENE combines TDA features with a multilayer GAT via gate-controlled fusion to improve GRN inference accuracy

  scrna-seq gene string chip-seq grn persistent prior

• Souporcell3: robust demultiplexing for high-donor single-cell RNA-seq datasets.

  PMID 41808435 · PMC13012599 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 3 setups

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  Souporcell3 can robustly demultiplex pooled scRNA-seq data from up to 64 donors

  pooled scrna-seq doublet genotype-based umap-based

• 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

• Assessment of dispersion metrics for estimating single-cell transcriptional variability.

  PMID 41770747 · PMC12970974 · PLoS computational biology · 2026 · 7 claims · 4 setups

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  The variance-to-mean ratio (VMR/Fano factor) scales approximately linearly with increasing dispersion and is independent of dataset size.

  simulated manivannan lana-elola yuzwa computational scrna-seq

• 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

• 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

• 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

• Benchmarking RNA velocity methods across 17 independent studies.

  PMID 41916302 · PMC13106975 · Cell reports methods · 2026 · 8 claims · 6 setups

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  No single RNA velocity method exhibited superior performance across all accuracy, stability, and usability assessments

  scrna-seq same representative dyngen-simulated velocity cross-method downsampling

• 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

• 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