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  "Package": "APL",
  "Type": "Package",
  "Title": "Association Plots",
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  "Authors@R": "c(person(given = \"Clemens\",\nfamily = \"Kohl\",\nrole = c(\"cre\", \"aut\"),\nemail = \"kohl.clemens@gmail.com\"),\nperson(given = \"Elzbieta\",\nfamily = \"Gralinska\",\nrole = c(\"aut\"),\nemail = \"gralinska@molgen.mpg.de\"),\nperson(given = \"Martin\",\nfamily = \"Vingron\",\nrole = c(\"aut\"),\nemail = \"vingron@molgen.mpg.de\"))",
  "Description": "APL is a package developed for computation of Association\nPlots (AP), a method for visualization and analysis of single\ncell transcriptomics data. The main focus of APL is the\nidentification of genes characteristic for individual clusters\nof cells from input data. The package performs correspondence\nanalysis (CA) and allows to identify cluster-specific genes\nusing Association Plots. Additionally, APL computes the\ncluster-specificity scores for all genes which allows to rank\nthe genes by their specificity for a selected cell cluster of\ninterest.",
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  "Collate": "'constructor.R' 'CA.R' 'apl.R' 'convert.R' 'generic_methods.R'\n'import_packages.R' 'plot.R' 'utils-pipe.R'",
  "URL": "https://vingronlab.github.io/APL/",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:58:26 UTC",
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  "Author": "Clemens Kohl [cre, aut],\nElzbieta Gralinska [aut],\nMartin Vingron [aut]",
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    "as.list",
    "ca_3Dplot",
    "ca_biplot",
    "ca_coords",
    "cacomp",
    "cacomp_names",
    "cacomp_slot",
    "check_cacomp",
    "new_cacomp",
    "pick_dims",
    "plot_enrichment",
    "run_APL",
    "runAPL",
    "show",
    "show.cacomp",
    "subset_dims",
    "var_rows"
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  "_help": [
    {
      "page": "apl",
      "title": "Association Plot",
      "topics": [
        "apl"
      ]
    },
    {
      "page": "apl_coords",
      "title": "Calculate Association Plot coordinates",
      "topics": [
        "apl_coords"
      ]
    },
    {
      "page": "apl_ggplot",
      "title": "Plot Association Plot with ggplot",
      "topics": [
        "apl_ggplot"
      ]
    },
    {
      "page": "apl_plotly",
      "title": "Plot Association Plot with plotly",
      "topics": [
        "apl_plotly"
      ]
    },
    {
      "page": "apl_score",
      "title": "Find rows most highly associated with a condition",
      "topics": [
        "apl_score"
      ]
    },
    {
      "page": "apl_topGO",
      "title": "Run Gene overrepresentation analysis with topGO",
      "topics": [
        "apl_topGO"
      ]
    },
    {
      "page": "as.cacomp",
      "title": "Create cacomp object from Seurat/SingleCellExperiment container",
      "topics": [
        "as.cacomp",
        "as.cacomp,cacomp-method",
        "as.cacomp,list-method",
        "as.cacomp,Seurat-method",
        "as.cacomp,SingleCellExperiment-method"
      ]
    },
    {
      "page": "as.list-cacomp-method",
      "title": "Convert cacomp object to list.",
      "topics": [
        "as.list,cacomp-method"
      ]
    },
    {
      "page": "ca_3Dplot",
      "title": "Plot of the first 3D CA projection of the data.",
      "topics": [
        "ca_3Dplot",
        "ca_3Dplot,cacomp-method",
        "ca_3Dplot,Seurat-method",
        "ca_3Dplot,SingleCellExperiment-method"
      ]
    },
    {
      "page": "ca_biplot",
      "title": "Plot of 2D CA projection of the data.",
      "topics": [
        "ca_biplot",
        "ca_biplot,cacomp-method",
        "ca_biplot,Seurat-method",
        "ca_biplot,SingleCellExperiment-method"
      ]
    },
    {
      "page": "ca_coords",
      "title": "Calculate correspondence analysis row and column coordinates.",
      "topics": [
        "ca_coords"
      ]
    },
    {
      "page": "cacomp",
      "title": "Correspondance Analysis",
      "topics": [
        "cacomp",
        "cacomp,dgCMatrix-method",
        "cacomp,matrix-method",
        "cacomp,Seurat-method",
        "cacomp,SingleCellExperiment-method"
      ]
    },
    {
      "page": "cacomp_names",
      "title": "Prints slot names of cacomp object",
      "topics": [
        "cacomp_names"
      ]
    },
    {
      "page": "cacomp_slot",
      "title": "Access slots in a cacomp object",
      "topics": [
        "cacomp_slot"
      ]
    },
    {
      "page": "cacomp-class",
      "title": "An S4 class that contains all elements needed for CA.",
      "topics": [
        "cacomp-class",
        "new_cacomp"
      ]
    },
    {
      "page": "calc_residuals",
      "title": "Calculate residuals for Correspondence analysis",
      "topics": [
        "calc_residuals"
      ]
    },
    {
      "page": "check_cacomp",
      "title": "Check if cacomp object was correctly created.",
      "topics": [
        "check_cacomp"
      ]
    },
    {
      "page": "clip_residuals",
      "title": "Perform clipping of residuals",
      "topics": [
        "clip_residuals"
      ]
    },
    {
      "page": "comp_ft_residuals",
      "title": "Compute Freeman-Tukey residuals",
      "topics": [
        "comp_ft_residuals"
      ]
    },
    {
      "page": "comp_NB_residuals",
      "title": "Compute Negative-Binomial residuals",
      "topics": [
        "comp_NB_residuals"
      ]
    },
    {
      "page": "comp_std_residuals",
      "title": "Compute Standardized Residuals",
      "topics": [
        "comp_std_residuals"
      ]
    },
    {
      "page": "elbow_method",
      "title": "Runs elbow method",
      "topics": [
        "elbow_method"
      ]
    },
    {
      "page": "inertia_rows",
      "title": "Find most variable rows",
      "topics": [
        "inertia_rows"
      ]
    },
    {
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      "title": "Helper function to check if object is empty.",
      "topics": [
        "is.empty"
      ]
    },
    {
      "page": "permutation_cutoff",
      "title": "Calculates permuted association plot coordinates",
      "topics": [
        "permutation_cutoff"
      ]
    },
    {
      "page": "pick_dims",
      "title": "Compute statistics to help choose the number of dimensions",
      "topics": [
        "pick_dims",
        "pick_dims,cacomp-method",
        "pick_dims,Seurat-method",
        "pick_dims,SingleCellExperiment-method"
      ]
    },
    {
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      "title": "Generates plot for results from apl_topGO",
      "topics": [
        "plot_enrichment"
      ]
    },
    {
      "page": "random_direction_cutoff",
      "title": "Random direction association plot coordinates",
      "topics": [
        "random_direction_cutoff"
      ]
    },
    {
      "page": "recompute",
      "title": "Recompute missing values of cacomp object.",
      "topics": [
        "recompute"
      ]
    },
    {
      "page": "rm_zeros",
      "title": "removes 0-only rows and columns in a matrix.",
      "topics": [
        "rm_zeros"
      ]
    },
    {
      "page": "runAPL",
      "title": "Compute and plot Association Plot",
      "topics": [
        "runAPL",
        "runAPL,dgCMatrix-method",
        "runAPL,matrix-method",
        "runAPL,Seurat-method",
        "runAPL,SingleCellExperiment-method",
        "run_APL"
      ]
    },
    {
      "page": "run_cacomp",
      "title": "Internal function for `cacomp`",
      "topics": [
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      ]
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      "page": "scree_plot",
      "title": "Scree Plot",
      "topics": [
        "scree_plot"
      ]
    },
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      "page": "show.cacomp",
      "title": "Prints cacomp object",
      "topics": [
        "show,cacomp-method",
        "show.cacomp"
      ]
    },
    {
      "page": "subset_dims",
      "title": "Subset dimensions of a caobj",
      "topics": [
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      ]
    },
    {
      "page": "var_rows",
      "title": "Find most variable rows",
      "topics": [
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      ]
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      "source": "APL.Rmd",
      "filename": "APL.html",
      "title": "Analyzing data with APL",
      "author": "Elzbieta Gralinska, Clemens Kohl, Martin Vingron",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
        "Changes regarding python dependencies",
        "Preprocessing",
        "Setup",
        "Loading the data",
        "Normalization, PCA & Clustering",
        "Quick start",
        "Step-by-step way of computing Association Plots",
        "Correspondence Analysis",
        "Reducing the number of CA dimensions",
        "Association Plots",
        "Association Plots with the $S_\\alpha$-scores",
        "Visualization of CA",
        "APL and GO enrichment analysis",
        "Session info"
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      "modified": "2024-11-02 21:10:34",
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