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Add spatial simulators results #367

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7b4afdd
add results
sainirmayi Nov 25, 2024
2bbab46
Merge remote-tracking branch 'origin/main' into feature/no-ref/add-sp…
rcannood Nov 29, 2024
7975720
fix metadata
rcannood Nov 29, 2024
14296f3
Merge remote-tracking branch 'origin/main' into feature/no-ref/add-sp…
rcannood Nov 29, 2024
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update results
rcannood Dec 13, 2024
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102 changes: 102 additions & 0 deletions results/spatial_simulators/data/dataset_info.json
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[
{
"dataset_id": "brain",
"dataset_name": "Brain",
"dataset_summary": "10X Visium spatial RNA-seq from adult mouse brain sections paired to single-nucleus RNA-seq",
"dataset_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.",
"data_reference": "10.1038/s41587-021-01139-4",
"data_url": "https://github.com/BayraktarLab/cell2location",
"date_created": "11-12-2024",
"file_size": 44683309
},
{
"dataset_id": "cortex",
"dataset_name": "Cortex",
"dataset_summary": "Scripts and source data for image processing, barcode calling, and cell type annotations in a seqFISH+ experiment.",
"dataset_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.",
"data_reference": "10.1038/s41586-019-1049-y",
"data_url": "https://zenodo.org/records/2669683",
"date_created": "11-12-2024",
"file_size": 20271969
},
{
"dataset_id": "breast",
"dataset_name": "Breast",
"dataset_summary": "A spatially resolved atlas of human breast cancers",
"dataset_description": "This study presents a spatially resolved transcriptomics analysis of human breast cancers.",
"data_reference": "10.1038/s41588-021-00911-1",
"data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078",
"date_created": "11-12-2024",
"file_size": 23748327
},
{
"dataset_id": "prostate",
"dataset_name": "Prostate",
"dataset_summary": "Spatially resolved gene expression of human protate tissue slices treated with steroid hormones for 8 hours",
"dataset_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.",
"data_reference": "10.1016/j.isci.2021.102640",
"data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159697",
"date_created": "11-12-2024",
"file_size": 20645737
},
{
"dataset_id": "pdac",
"dataset_name": "pancreatic ductal adenocarcinomas",
"dataset_summary": "Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas",
"dataset_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..",
"data_reference": "10.1101/2020.12.01.407460",
"data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111672",
"date_created": "11-12-2024",
"file_size": 18406025
},
{
"dataset_id": "fibrosarcoma",
"dataset_name": "Fibrosarcoma",
"dataset_summary": "Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of Tumor A1 of Tissue 1",
"dataset_description": "Spatial transcriptomics of Tumor A1 of Tissue 1.",
"data_reference": "10.1038/s41587-022-01272-8",
"data_url": "https://github.com/romain-lopez/DestVI-reproducibility",
"date_created": "11-12-2024",
"file_size": 28476822
},
{
"dataset_id": "osteosarcoma",
"dataset_name": "Osteosarcoma",
"dataset_summary": "Spatial profiling of human osteosarcoma cells.",
"dataset_description": "Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression.",
"data_reference": "10.1073/pnas.1912459116",
"data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078",
"date_created": "11-12-2024",
"file_size": 21082263
},
{
"dataset_id": "gastrulation",
"dataset_name": "Gastrulation",
"dataset_summary": "single-cell and spatial transcriptomic molecular map of mouse gastrulation",
"dataset_description": "Single-Cell omics Data across Mouse Gastrulation and Highly multiplexed spatially resolved gene expression profiling of Early Organogenesis.",
"data_reference": "10.1038/s41587-021-01006-2",
"data_url": "https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/",
"date_created": "11-12-2024",
"file_size": 15690550
},
{
"dataset_id": "olfactorybulb",
"dataset_name": "Olfactorybulb",
"dataset_summary": "Single-cell and spatial transcriptomic of mouse olfactory bulb",
"dataset_description": "Single-cell and spatial transcriptomic of mouse olfactory bulb",
"data_reference": "10.1126/science.aaf2403",
"data_url": "http://ww1.spatialtranscriptomicsresearch.org/?usid=24&utid=8672855942",
"date_created": "11-12-2024",
"file_size": 2341323
},
{
"dataset_id": "hindlimbmuscle",
"dataset_name": "Hindlimbmuscle",
"dataset_summary": "Spatial RNA sequencing of regenerating mouse hindlimb muscle",
"dataset_description": "The spatial transcriptomics datasets regenerates mouse muscle tissue generated with the 10x Genomics Visium platform.",
"data_reference": "10.1101/2020.12.01.407460",
"data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161318",
"date_created": "11-12-2024",
"file_size": 20088216
}
]
146 changes: 146 additions & 0 deletions results/spatial_simulators/data/method_info.json
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[
{
"task_id": "methods",
"method_id": "scdesign2",
"method_name": "scDesign2",
"method_summary": "A transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured",
"method_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.\n",
"is_baseline": false,
"references_doi": "10.1186/s13059-021-02367-2",
"references_bibtex": null,
"code_url": "https://github.com/JSB-UCLA/scDesign2",
"documentation_url": "https://htmlpreview.github.io/?https://github.com/JSB-UCLA/scDesign2/blob/master/vignettes/scDesign2.html",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/scdesign2:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/scdesign2",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "methods",
"method_id": "scdesign3",
"method_name": "scDesign3",
"method_summary": "A probabilistic model that unifies the generation and inference for single-cell and spatial omics data",
"method_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).\n",
"is_baseline": false,
"references_doi": "10.1038/s41587-023-01772-1",
"references_bibtex": null,
"code_url": "https://github.com/SONGDONGYUAN1994/scDesign3",
"documentation_url": "https://www.bioconductor.org/packages/release/bioc/html/scDesign3.html",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/scdesign3:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/scdesign3",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "methods",
"method_id": "sparsim",
"method_name": "SPARsim",
"method_summary": "SPARSim single cell is a count data simulator for scRNA-seq data.",
"method_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.\n",
"is_baseline": false,
"references_doi": "10.1093/bioinformatics/btz752",
"references_bibtex": null,
"code_url": "https://gitlab.com/sysbiobig/sparsim",
"documentation_url": "https://gitlab.com/sysbiobig/sparsim/-/blob/master/vignettes/sparsim.Rmd",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/sparsim:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/sparsim",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "methods",
"method_id": "splatter",
"method_name": "Splatter",
"method_summary": "A single cell RNA-seq data simulator based on a gamma-Poisson distribution.",
"method_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.\n",
"is_baseline": false,
"references_doi": "10.1186/s13059-017-1305-0",
"references_bibtex": null,
"code_url": "https://github.com/Oshlack/splatter",
"documentation_url": "https://bioconductor.org/packages/devel/bioc/vignettes/splatter/inst/doc/splatter.html",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/splatter:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/splatter",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "methods",
"method_id": "srtsim",
"method_name": "SRTsim",
"method_summary": "An SRT-specific simulator for scalable, reproducible, and realistic SRT simulations.",
"method_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. \n",
"is_baseline": false,
"references_doi": "10.1186/s13059-023-02879-z",
"references_bibtex": null,
"code_url": "https://github.com/xzhoulab/srtsim",
"documentation_url": "https://xzhoulab.github.io/SRTsim",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/srtsim:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/srtsim",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "methods",
"method_id": "symsim",
"method_name": "symsim",
"method_summary": "Simulating multiple faceted variability in single cell RNA sequencing",
"method_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.\n",
"is_baseline": false,
"references_doi": "10.1038/s41467-019-10500-w",
"references_bibtex": null,
"code_url": "https://github.com/YosefLab/SymSim",
"documentation_url": "https://github.com/YosefLab/SymSim/blob/master/vignettes/SymSimTutorial.Rmd",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/symsim:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/symsim",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "methods",
"method_id": "zinbwave",
"method_name": "zinbwave",
"method_summary": "A general and flexible method for signal extraction from single-cell RNA-seq data",
"method_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.\n",
"is_baseline": false,
"references_doi": "10.1038/s41467-017-02554-5",
"references_bibtex": null,
"code_url": "https://github.com/drisso/zinbwave",
"documentation_url": "https://bioconductor.org/packages/release/bioc/html/zinbwave.html",
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/methods/zinbwave:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/methods/zinbwave",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "control_methods",
"method_id": "positive",
"method_name": "positive",
"method_summary": "A positive control method.",
"method_description": "A positive control method. \n",
"is_baseline": true,
"references_doi": null,
"references_bibtex": null,
"code_url": "https://github.com/openproblems-bio/task_spatial_simulators",
"documentation_url": null,
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/control_methods/positive:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/control_methods/positive",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
},
{
"task_id": "control_methods",
"method_id": "negative",
"method_name": "negative",
"method_summary": "A negative control method.",
"method_description": "A negative control method.\n",
"is_baseline": true,
"references_doi": null,
"references_bibtex": null,
"code_url": "https://github.com/openproblems-bio/task_spatial_simulators",
"documentation_url": null,
"image": "https://ghcr.io/openproblems-bio/task_spatial_simulators/control_methods/negative:build_main",
"implementation_url": "https://github.com/openproblems-bio/task_spatial_simulators/blob/bf1f6d13221baeab459bdadf6993650f03ddb1f2/src/control_methods/negative",
"code_version": "build_main",
"commit_sha": "bf1f6d13221baeab459bdadf6993650f03ddb1f2"
}
]
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