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  "Description": "The scDblFinder package gathers various methods for the\ndetection and handling of doublets/multiplets in single-cell\nsequencing data (i.e. multiple cells captured within the same\ndroplet or reaction volume). It includes methods formerly found\nin the scran package, the new fast and comprehensive\nscDblFinder method, and a reimplementation of the Amulet\ndetection method for single-cell ATAC-seq.",
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    "getCellPairs",
    "getExpectedDoublets",
    "getFragmentOverlaps",
    "mockDoubletSCE",
    "plotDoubletMap",
    "plotThresholds",
    "propHomotypic",
    "recoverDoublets",
    "scDblFinder",
    "selFeatures",
    "TFIDF"
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      "title": "addDoublets",
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      "title": "clusterStickiness",
      "topics": [
        "clusterStickiness"
      ]
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    {
      "page": "computeDoubletDensity",
      "title": "Compute the density of simulated doublets",
      "topics": [
        "computeDoubletDensity",
        "computeDoubletDensity,ANY-method",
        "computeDoubletDensity,SingleCellExperiment-method",
        "computeDoubletDensity,SummarizedExperiment-method"
      ]
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      "title": "directClassification",
      "topics": [
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      "title": "doubletPairwiseEnrichment",
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      "title": "Detect doublet clusters",
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        "findDoubletClusters,ANY-method",
        "findDoubletClusters,SingleCellExperiment-method",
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      "source": "introduction.Rmd",
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      "title": "Introduction to the scDblFinder package",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "computeDoubletDensity",
        "recoverDoublets",
        "scDblFinder",
        "directDblClassification",
        "findDoubletClusters",
        "Installation",
        "Which method to choose?",
        "Session information"
      ],
      "created": "2020-09-07 17:03:25",
      "modified": "2026-06-01 18:31:05",
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    },
    {
      "source": "scDblFinder.Rmd",
      "filename": "scDblFinder.html",
      "title": "scDblFinder",
      "author": "Pierre-Luc Germain",
      "engine": "knitr::rmarkdown",
      "headings": [
        "scDblFinder",
        "Installation",
        "Usage",
        "Cluster-based approach",
        "Multiple samples",
        "Description of the method",
        "Splitting captures",
        "Reducing and clustering the data",
        "Generating artificial doublets",
        "Examining the k-nearest neighbors (kNN) of each cell",
        "Training a classifier",
        "Thresholding",
        "Doublet origins and enrichments",
        "Some important parameters",
        "Expected proportion of doublets",
        "Number of artificial doublets",
        "Frequently-asked questions",
        "I'm getting way too many doublets called - what's going on?",
        "Should I use the cluster-based doublet generation or not?",
        "The clusters don't make any sense!",
        "'Size factors should be positive' error",
        "Identifying homotypic doublets",
        "What is a sample exactly? Usage with barcoded and 10X Flex data.",
        "How can I make this reproducible?",
        "Can I use this in combination with Seurat or other tools?",
        "How can I call scDblFinder from the command line?",
        "Can this be used with scATACseq data?",
        "Should I run QC cell filtering before or after doublet detection?",
        "What about ambiant RNA decontamination?",
        "Can I combine this method with others?",
        "Session information"
      ],
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      "headings": [
        "tl;dr",
        "Mathematical background",
        "Normalization by library size",
        "Testing for (lack of) intermediacy",
        "Calling doublet clusters",
        "Discussion",
        "Session information"
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      "author": "Aaron Lun",
      "engine": "knitr::rmarkdown",
      "headings": [
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        "Algorithm overview",
        "Size factor handling",
        "Normalization size factors",
        "RNA content size factors",
        "Interactions between them",
        "Doublet score calculations",
        "Session information"
      ],
      "created": "2020-08-04 19:48:56",
      "modified": "2021-04-19 15:59:24",
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