{
  "_id": "6a1b01a31d7bb097a0a006f3",
  "Package": "scMultiSim",
  "Title": "Simulation of Multi-Modality Single Cell Data Guided By Gene\nRegulatory Networks and Cell-Cell Interactions",
  "Version": "1.9.0",
  "Authors@R": "c(person(given = \"Hechen\",\nfamily = \"Li\",\nrole = c(\"aut\", \"cre\"),\nemail = \"hli691@gatech.edu\",\ncomment = c(ORCID = \"0000-0003-4907-429X\")),\nperson(given = \"Xiuwei\",\nfamily = \"Zhang\",\nemail = \"zhangxiuwei03@gmail.com\",\nrole = \"aut\"),\nperson(given = \"Ziqi\",\nfamily = \"Zhang\",\nemail = \"ziqi.zhang@gatech.edu\",\nrole = \"aut\"),\nperson(given = \"Michael\",\nfamily = \"Squires\",\nemail = \"squiresmf@gatech.edu\",\nrole = \"aut\"))",
  "Description": "scMultiSim simulates paired single cell RNA-seq, single\ncell ATAC-seq and RNA velocity data, while incorporating\nmechanisms of gene regulatory networks, chromatin accessibility\nand cell-cell interactions. It allows users to tune various\nparameters controlling the amount of each biological factor,\nvariation of gene-expression levels, the influence of chromatin\naccessibility on RNA sequence data, and so on. It can be used\nto benchmark various computational methods for single cell\nmulti-omics data, and to assist in experimental design of\nwet-lab experiments.",
  "License": "Artistic-2.0",
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  "biocViews": "SingleCell, Transcriptomics, GeneExpression, Sequencing,\nExperimentalDesign",
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  "BugReports": "https://github.com/ZhangLabGT/scMultiSim/issues",
  "URL": "https://zhanglabgt.github.io/scMultiSim/",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 13:02:55 UTC",
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  "Packaged": {
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  "Author": "Hechen Li [aut, cre] (ORCID: <https://orcid.org/0000-0003-4907-429X>),\nXiuwei Zhang [aut],\nZiqi Zhang [aut],\nMichael Squires [aut]",
  "Maintainer": "Hechen Li <hli691@gatech.edu>",
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    "run_shiny",
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    "sim_example",
    "sim_example_spatial",
    "sim_true_counts",
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      "title": "this is the density function of log(x+1), where x is the non-zero values for ATAC-SEQ data",
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      "name": "GRN_params_100",
      "title": "100_gene_GRN is a matrix of GRN params consisting of 100 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID",
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      "name": "GRN_params_1139",
      "title": "GRN_params_1139 is a matrix of GRN params consisting of 1139 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID",
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    {
      "page": "add_expr_noise",
      "title": "Add experimental noise to true counts",
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    {
      "page": "add_outliers",
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      "topics": [
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      "title": "Generate cell-type level CCI parameters",
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    {
      "page": "dens_nonzero",
      "title": "this is the density function of log(x+1), where x is the non-zero values for ATAC-SEQ data",
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      "title": "generate a clutter of cells by growing from the center",
      "topics": [
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    {
      "page": "gene_corr_cci",
      "title": "Plot the ligand-receptor correlation summary",
      "topics": [
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    {
      "page": "gene_corr_regulator",
      "title": "Print the correlations between targets of each regulator",
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    {
      "page": "Get_1region_ATAC_correlation",
      "title": "This function gets the average correlation rna seq counts and region effect on genes for genes which are only associated with 1 chromatin region",
      "topics": [
        "Get_1region_ATAC_correlation"
      ]
    },
    {
      "page": "Get_ATAC_correlation",
      "title": "This function gets the average correlation rna seq counts and chromatin region effect on genes",
      "topics": [
        "Get_ATAC_correlation"
      ]
    },
    {
      "page": "GRN_params_100",
      "title": "100_gene_GRN is a matrix of GRN params consisting of 100 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID",
      "topics": [
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    },
    {
      "page": "GRN_params_1139",
      "title": "GRN_params_1139 is a matrix of GRN params consisting of 1139 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID",
      "topics": [
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    {
      "page": "Phyla1",
      "title": "Creating a linear example tree",
      "topics": [
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      "page": "Phyla5",
      "title": "Creating an example tree with 5 tips",
      "topics": [
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    {
      "page": "plot_cell_loc",
      "title": "Plot cell locations",
      "topics": [
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    },
    {
      "page": "plot_gene_module_cor_heatmap",
      "title": "Plot the gene module correlation heatmap",
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      "page": "plot_grid",
      "title": "Plot the CCI grid",
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      "page": "plot_grn",
      "title": "Plot the GRN network",
      "topics": [
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    {
      "page": "plot_phyla",
      "title": "Plot a R phylogenic tree",
      "topics": [
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      "page": "plot_rna_velocity",
      "title": "Plot RNA velocity as arrows on tSNE plot",
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    {
      "page": "plot_tsne",
      "title": "Plot t-SNE visualization of a data matrix",
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    {
      "page": "run_shiny",
      "title": "Launch the Shiny App to configure the simulation",
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      "page": "scmultisim_help",
      "title": "Show detailed documentations of scMultiSim's parameters",
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      "topics": [
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    },
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      "page": "sim_example_spatial",
      "title": "Simulate a small example dataset with 200 cells and the 100-gene GRN, with CCI enabled",
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      "page": "sim_true_counts",
      "title": "Simulate true scRNA and scATAC counts from the parameters",
      "topics": [
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    {
      "page": "spatialGrid-class",
      "title": "The class for spatial grids",
      "topics": [
        ".SpatialGrid",
        "spatialGrid-class"
      ]
    },
    {
      "page": "True2ObservedATAC",
      "title": "Simulate observed ATAC-seq matrix given technical noise and the true counts",
      "topics": [
        "True2ObservedATAC"
      ]
    },
    {
      "page": "True2ObservedCounts",
      "title": "Simulate observed count matrix given technical biases and the true counts",
      "topics": [
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      "title": "Getting Started ",
      "engine": "knitr::knitr",
      "headings": [
        "Installation",
        "Running Simulation",
        "The Shiny app",
        "Add technical noise and batch effect",
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      "created": "2024-08-29 03:39:48",
      "modified": "2024-09-02 22:21:39",
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      "title": "Simulating Multimodal Single-cell Datasets ",
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        "Simulating True Counts",
        "GRN and Differentiation Tree",
        "Omitting the GRN",
        "Running the Simulation",
        "Accessing the Results",
        "Visualizing the Results",
        "Adding Technical Variation and Batch Effect",
        "Adding technical noise",
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