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Add spatial_simulators v0.2.0 results
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‎spatial_simulators/v0.2.0/dataset_info.json‎

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[
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{
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"name": "brain",
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"label": "Brain",
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"commit": "missing-sha",
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"summary": "10X Visium spatial RNA-seq from adult mouse brain sections paired to single-nucleus RNA-seq",
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"description": "This datasets were generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon.",
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"source_urls": ["https://github.com/BayraktarLab/cell2location"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["homo_sapiens"],
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"authors": [],
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"references": {
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"doi": ["10.1038/s41587-021-01139-4"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 42.6133
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},
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{
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"name": "cortex",
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"label": "Cortex",
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"commit": "missing-sha",
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"summary": "Scripts and source data for image processing, barcode calling, and cell type annotations in a seqFISH+ experiment.",
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"description": "The dataset includes processed image data, cell type annotations with Louvain clusters, gene IDs for transcript locations, and mRNA point locations, with additional data available on Zenodo.",
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"source_urls": ["https://zenodo.org/records/2669683"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["homo_sapiens"],
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"authors": [],
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"references": {
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"doi": ["10.1038/s41586-019-1049-y"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 19.3329
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},
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{
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"name": "pdac",
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"label": "pancreatic ductal adenocarcinomas",
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"commit": "missing-sha",
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"summary": "Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas",
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"description": "We developed a multimodal intersection analysis method combining scRNA-seq with spatial transcriptomics to map and characterize the spatial organization and interactions of distinct cell subpopulations in complex tissues, such as primary pancreatic tumors..",
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"source_urls": ["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111672"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["homo_sapiens"],
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"authors": [],
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"references": {
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"doi": ["10.1101/2020.12.01.407460"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 17.5534
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},
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{
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"name": "olfactorybulb",
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"label": "Olfactorybulb",
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"commit": "missing-sha",
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"summary": "Single-cell and spatial transcriptomic of mouse olfactory bulb",
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"description": "Single-cell and spatial transcriptomic of mouse olfactory bulb",
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"source_urls": ["http://ww1.spatialtranscriptomicsresearch.org/?usid=24&utid=8672855942"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["mus_musculus"],
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"authors": [],
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"references": {
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"doi": ["10.1126/science.aaf2403"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 2.2329
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},
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{
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"name": "fibrosarcoma",
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"label": "Fibrosarcoma",
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"commit": "missing-sha",
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"summary": "Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of Tumor A1 of Tissue 1",
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"description": "Spatial transcriptomics of Tumor A1 of Tissue 1.",
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"source_urls": ["https://github.com/romain-lopez/DestVI-reproducibility"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["mus_musculus"],
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"authors": [],
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"references": {
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"doi": ["10.1038/s41587-022-01272-8"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 27.1576
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},
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{
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"name": "osteosarcoma",
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"label": "Osteosarcoma",
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"commit": "missing-sha",
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"summary": "Spatial profiling of human osteosarcoma cells.",
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"description": "Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression.",
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"source_urls": ["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["homo_sapiens"],
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"authors": [],
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"references": {
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"doi": ["10.1073/pnas.1912459116"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 20.1056
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},
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{
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"name": "gastrulation",
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"label": "Gastrulation",
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"commit": "missing-sha",
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"summary": "single-cell and spatial transcriptomic molecular map of mouse gastrulation",
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"description": "Single-Cell omics Data across Mouse Gastrulation and Highly multiplexed spatially resolved gene expression profiling of Early Organogenesis.",
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"source_urls": ["https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["mus_musculus"],
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"authors": [],
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"references": {
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"doi": ["10.1038/s41587-021-01006-2"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 14.9637
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},
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{
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"name": "prostate",
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"label": "Prostate",
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"commit": "missing-sha",
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"summary": "Spatially resolved gene expression of human protate tissue slices treated with steroid hormones for 8 hours",
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"description": "Spatially resolved gene expression was prepard by dissociated hman prostate tissue to single cells, and collected & prepped for RNA-seq using the Visium Spatial Gene Expression kit.",
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"source_urls": ["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159697"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["homo_sapiens"],
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"authors": [],
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"references": {
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"doi": ["10.1016/j.isci.2021.102640"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 19.6893
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},
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{
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"name": "breast",
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"label": "Breast",
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"commit": "missing-sha",
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"summary": "A spatially resolved atlas of human breast cancers",
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"description": "This study presents a spatially resolved transcriptomics analysis of human breast cancers.",
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"source_urls": ["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["homo_sapiens"],
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"authors": [],
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"references": {
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"doi": ["10.1038/s41588-021-00911-1"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 22.6482
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},
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{
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"name": "hindlimbmuscle",
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"label": "Hindlimbmuscle",
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"commit": "missing-sha",
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"summary": "Spatial RNA sequencing of regenerating mouse hindlimb muscle",
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"description": "The spatial transcriptomics datasets regenerates mouse muscle tissue generated with the 10x Genomics Visium platform.",
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"source_urls": ["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161318"],
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"common_dataset_names": null,
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"modalities": [],
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"organisms": ["mus_musculus"],
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"authors": [],
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"references": {
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"doi": ["10.1101/2020.12.01.407460"],
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"bibtex": []
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},
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"date_created": "11-08-2026",
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"file_size_mb": 19.1576
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}
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]

‎spatial_simulators/v0.2.0/method_info.json‎

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[
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{
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"name": "scdesign2",
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"label": "scDesign2",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured",
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"description": "scDesign2 is a transparent simulator that achieves all three goals (preserving genes, capturing gene correlations, and generating any \nnumber of cells with varying sequencing depths) and generates high-fidelity synthetic data for multiple single-cell gene expression \ncount-based technologies.",
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"type": "method",
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"link_code": "https://github.com/JSB-UCLA/scDesign2",
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"link_documentation": "https://htmlpreview.github.io/?https://github.com/JSB-UCLA/scDesign2/blob/master/vignettes/scDesign2.html",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/scdesign2",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/scdesign2:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1186/s13059-021-02367-2"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "scdesign3_nb",
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"label": "scDesign3 (NB)",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A probabilistic model that unifies the generation and inference for single-cell and spatial omics data",
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"description": "scDesign3 offers a probabilistic model that unifies the generation and inference\nfor single-cell and spatial omics data. The model's interpretable parameters and\nlikelihood enable scDesign3 to generate customized in silico data and unsupervisedly\nassess the goodness-of-fit of inferred cell latent structures (for example, clusters,\ntrajectories and spatial locations).",
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"type": "method",
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"link_code": "https://github.com/SONGDONGYUAN1994/scDesign3",
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"link_documentation": "https://www.bioconductor.org/packages/release/bioc/html/scDesign3.html",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/scdesign3_nb",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/scdesign3_nb:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1038/s41587-023-01772-1"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "scdesign3_poisson",
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"label": "scDesign3 (Poisson)",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A probabilistic model that unifies the generation and inference for single-cell and spatial omics data",
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"description": "scDesign3 offers a probabilistic model that unifies the generation and inference\nfor single-cell and spatial omics data. The model's interpretable parameters and\nlikelihood enable scDesign3 to generate customized in silico data and unsupervisedly\nassess the goodness-of-fit of inferred cell latent structures (for example, clusters,\ntrajectories and spatial locations).",
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"type": "method",
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"link_code": "https://github.com/SONGDONGYUAN1994/scDesign3",
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"link_documentation": "https://www.bioconductor.org/packages/release/bioc/html/scDesign3.html",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/scdesign3_poisson",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/scdesign3_poisson:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1038/s41587-023-01772-1"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "sparsim",
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"label": "SPARsim",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "SPARSim single cell is a count data simulator for scRNA-seq data.",
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"description": "SPARSim is a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. \nIt allows to generate count data that resembles real data in terms of count intensity, variability and sparsity.",
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"type": "method",
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"link_code": "https://gitlab.com/sysbiobig/sparsim",
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"link_documentation": "https://gitlab.com/sysbiobig/sparsim/-/blob/master/vignettes/sparsim.Rmd",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/sparsim",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/sparsim:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1093/bioinformatics/btz752"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "splatter",
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"label": "Splatter",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A single cell RNA-seq data simulator based on a gamma-Poisson distribution.",
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"description": "The Splat model is a gamma-Poisson distribution used to generate a gene by cell matrix of counts. Mean expression levels for each gene are simulated from a gamma distribution and the Biological Coefficient of Variation is used to enforce a mean-variance trend before counts are simulated from a Poisson distribution.",
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"type": "method",
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"link_code": "https://github.com/Oshlack/splatter",
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"link_documentation": "https://bioconductor.org/packages/release/bioc/vignettes/splatter/inst/doc/splatter.html",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/splatter",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/splatter:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1186/s13059-017-1305-0"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "srtsim",
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"label": "SRTsim",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "An SRT-specific simulator for scalable, reproducible, and realistic SRT simulations.",
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"description": "A key benefit of srtsim is its ability to maintain location-wise and gene-wise SRT count properties and \npreserve spatial expression patterns, enabling evaluation of SRT method performance using synthetic data.",
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"type": "method",
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"link_code": "https://github.com/xzhoulab/srtsim",
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"link_documentation": "https://xzhoulab.github.io/SRTsim",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/srtsim",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/srtsim:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1186/s13059-023-02879-z"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "symsim",
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"label": "symsim",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "Simulating multiple faceted variability in single cell RNA sequencing",
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"description": "SymSim is a simulator for modeling single-cell RNA-Seq data, accounting for three primary sources of variation: intrinsic transcription noise, extrinsic variation from different cell states, \nand technical variation from measurement noise and bias.",
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"type": "method",
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"link_code": "https://github.com/YosefLab/SymSim",
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"link_documentation": "https://github.com/YosefLab/SymSim/blob/master/vignettes/SymSimTutorial.Rmd",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/symsim",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/symsim:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1038/s41467-019-10500-w"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "zinbwave",
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"label": "zinbwave",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A general and flexible method for signal extraction from single-cell RNA-seq data",
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"description": "ZINB-WaVE is a general and flexible zero-inflated negative binomial model, which leads to low-dimensional representations \nof the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data.",
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"type": "method",
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"link_code": "https://github.com/drisso/zinbwave",
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"link_documentation": "https://bioconductor.org/packages/release/bioc/html/zinbwave.html",
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/methods/zinbwave",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/zinbwave:0.2.0",
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"authors": [],
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"references": {
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"doi": ["10.1038/s41467-017-02554-5"],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "positive",
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"label": "positive",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A positive control method.",
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"description": "A positive control method.",
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"type": "control_method",
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"link_code": "https://github.com/openproblems-bio/task_spatial_simulators",
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"link_documentation": null,
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/control_methods/positive",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/control_methods/positive:0.2.0",
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"authors": [],
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"references": {
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"doi": [],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "negative_shuffle",
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"label": "negative_shuffle",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A negative control method which shuffles the input data.",
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"description": "This control method shuffles the input data as a negative control.",
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"type": "control_method",
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"link_code": "https://github.com/openproblems-bio/task_spatial_simulators",
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"link_documentation": null,
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/control_methods/negative_shuffle",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/control_methods/negative_shuffle:0.2.0",
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"authors": [],
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"references": {
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"doi": [],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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},
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{
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"name": "negative_normal",
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"label": "negative_normal",
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"commit": "8a1466a505d6c6aed4cc2b70f078210afb92832c",
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"summary": "A negative control which generates normal distributed data.",
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"description": "This control method generates normally distributed data as a negative control,\nusing a fixed mean of 3 and standard deviation of 1.",
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"type": "control_method",
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"link_code": "https://github.com/openproblems-bio/task_spatial_simulators",
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"link_documentation": null,
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"link_implementation": "https://github.com/openproblems-bio/task_spatial_simulators/blob/8a1466a505d6c6aed4cc2b70f078210afb92832c/src/control_methods/negative_normal",
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"link_container_image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/control_methods/negative_normal:0.2.0",
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"authors": [],
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"references": {
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"doi": [],
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"bibtex": []
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},
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"additional_info": {},
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"version": "0.2.0"
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}
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]

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