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).

• 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

  Full-text index only

  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

• 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

• Cell-type annotation with accurate unseen cell-type identification using multiple references.

  PMID 37379341 · PMC10335708 · PLoS computational biology · 2023 · 8 claims · 4 setups

  Has reproduction · 68

  mtANN integrates multiple reference datasets and eight gene selection methods via ensemble learning (multiple deep classification models + majority voting) to improve cell-type annotation accuracy

  pbmc pancreas covid-19 scrna-seq

• DAESC + : high-performance, integrated software for single-cell allele-specific expression data.

  PMID 41851619 · PMC13169709 · BMC bioinformatics · 2026 · 8 claims · 6 setups

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  DAESC+ is a dual-module, end-to-end software package (DAESC-P for preprocessing, DAESC-GPU for differential analysis) for single-cell ASE data

  onek1k endoderm simulated single-cell differential runtime