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).

• CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.

  PMID 41734268 · PMC12987762 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 5 setups

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  CIRCE re-implements the Cicero co-accessibility algorithm in Python, producing near-identical results while running much faster and using far less memory

  bmmc pbmc foetal cis-regulatory enhancer-promoter scalability

• Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap.

  PMID 41650244 · PMC12890892 · Proceedings of the National Academy of Sciences of the United States of America · 2026 · 8 claims · 7 setups

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  SuperMap learns cross-modal feature mappings directly from unpaired multimodal data without requiring paired training data

  pbmc bone skin embryos scrna-seq scnmt-seq asap-seq

• BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.

  PMID 41632825 · PMC12904586 · PLoS computational biology · 2026 · 8 claims · 5 setups

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  BiCLUM consistently outperforms or matches existing integration methods across multiple RNA+ATAC and RNA+protein datasets in visualization and quantitative benchmarks

  pbmc kidney bmmc bmcite scrna-seq cite-seq

• 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

• GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.

  PMID 42108634 · PMC13158126 · Briefings in bioinformatics · 2026 · 6 claims · 5 setups

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  GAMMI consistently outperforms state-of-the-art integration methods (GLUE, Harmony, MIDAS, scMoMaT) in biological conservation and batch correction across five mosaic single-cell multi-omics benchmarks

  pbmc postmortem gse138852 gammi-derived multi-omics scrna-seq differential

• Benchmarking component choices for unpaired single cell RNA and epigenomic integration.

  PMID 41987329 · PMC13192178 · Genome biology · 2026 · 7 claims · 8 setups

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  Gene activity scores (GAS) show limited correlation with actual gene expression but effectively preserve cellular neighborhood structure and support clustering.

  e18 pbmc brain kidney scrna-seq share-seq gas

• Developing a comprehensive database and search tool for single-cell ATAC-seq data.

  PMID 41545440 · PMC12816011 · Scientific reports · 2026 · 5 claims · 6 setups

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  scATAC.Explorer is a curated database containing 39 publicly available scATAC-seq datasets in a consistent format

  hematopoietic drosophila cancer scatac-seq sci-atac-seq sci-car

• Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.

  PMID 42026624 · PMC13238118 · Genome biology · 2026 · 8 claims · 6 setups

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  SEAGALL combines a geometry-regularised autoencoder (GRAE) to embed cells and build a cell-cell graph with a graph attention network (GAT) classifier and GNNExplainer-based XAI to identify features driving cell type/phenotype.

  count same scchip-seq ten count latent classification

• Semi-parametric empirical bayes method for multiplet detection in snATAC-seq with probabilistic multi-omic integration.

  PMID 42054434 · PMC13148828 · PLoS computational biology · 2026 · 8 claims · 5 setups

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  SEBULA models the singlet background directly from observed HCLC (high-coverage locus count) statistics using fragment-level snATAC-seq information, avoiding reliance on synthetic/artificial doublets.

  dogma-seq dogma-seq-derived seven snatac-seq q-q density