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  "Package": "symphony",
  "Title": "Efficient and Precise Single-Cell Reference Atlas Mapping",
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  "Description": "Implements the Symphony single-cell reference building and\nquery mapping algorithms and additional functions described in\nKang et al\n<https://www.nature.com/articles/s41467-021-25957-x>.",
  "License": "GPL (>= 3)",
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  "Author": "Joyce Kang [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-1962-1291>), Ilya Korsunsky [aut]\n(ORCID: <https://orcid.org/0000-0003-4848-3948>), Soumya\nRaychaudhuri [aut] (ORCID:\n<https://orcid.org/0000-0002-1901-8265>)",
  "Maintainer": "Joyce Kang <joyce.b.kang@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
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    "calcknncorrWithinQuery",
    "calcPerCellMappingMetric",
    "calcPerClusterMappingMetric",
    "evaluate",
    "findVariableGenes",
    "knnPredict",
    "mapQuery",
    "plotReference",
    "rowSDs",
    "runPCAQueryAlone",
    "scaleDataWithStats",
    "vargenes_vst"
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      "name": "pbmcs_exprs_small",
      "title": "Log(CP10k+1) normalized counts matrix (genes by cells) for 10x PBMCs dataset for vignette.",
      "object": "pbmcs_exprs_small",
      "class": [
        "dgCMatrix"
      ],
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      "table": false,
      "tojson": false
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      "name": "pbmcs_meta_small",
      "title": "Metadata for 10x PBMCs dataset for vignette.",
      "object": "pbmcs_meta_small",
      "class": [
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      "fields": [
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        "donor",
        "nUMI",
        "nGene",
        "percent_mito",
        "cell_type",
        "cell_type_broad"
      ],
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      "table": true,
      "tojson": true
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    {
      "page": "buildReference",
      "title": "Function for building a Symphony reference starting from expression matrix",
      "topics": [
        "buildReference"
      ]
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    {
      "page": "buildReferenceFromHarmonyObj",
      "title": "Function for building a Symphony reference from a Harmony object. Useful if you would like your code to be more modular. Note that you must have saved vargenes_means_sds and PCA loadings.",
      "topics": [
        "buildReferenceFromHarmonyObj"
      ]
    },
    {
      "page": "calcknncorr",
      "title": "Calculates the k-NN correlation, which measures how well the sorted ordering of k nearest reference neighbors in a gold standard embedding correlate with the ordering for the same reference cells in an alternative embedding (i.e. from reference mapping). NOTE: it is very important for the order of reference cells (cols) in gold_ref matches that of alt_ref (same for matching columns of gold_query and alt_query).",
      "topics": [
        "calcknncorr"
      ]
    },
    {
      "page": "calcknncorrWithinQuery",
      "title": "Calculates the k-NN correlation within the query cells only, which measures how well the sorted ordering of k nearest query neighbors in a query de novo PCA embedding correlate with the ordering for the cells in the reference mapping embedding.",
      "topics": [
        "calcknncorrWithinQuery"
      ]
    },
    {
      "page": "calcPerCellMappingMetric",
      "title": "Per-cell Confidence Score: Calculates the weighted Mahalanobis distance for the query cells to reference clusters. Returns a vector of distance scores, one per query cell. Higher distance metric indicates less confidence.",
      "topics": [
        "calcPerCellMappingMetric"
      ]
    },
    {
      "page": "calcPerClusterMappingMetric",
      "title": "Per-cluster Confidence Score: Calculates the Mahalanobis distance from user-defined query clusters to their nearest reference centroid after initial projection into reference PCA space. All query cells in a cluster get the same score. Higher distance indicates less confidence. Due to the instability of estimating covariance with small numbers of cells, we do not assign a score to clusters smaller than u * d, where d is the dimensionality of the embedding and u is specified.",
      "topics": [
        "calcPerClusterMappingMetric"
      ]
    },
    {
      "page": "evaluate",
      "title": "Function for evaluating F1 by cell type, adapted from automated cell type identifiaction benchmarking paper (Abdelaal et al. Genome Biology, 2019)",
      "topics": [
        "evaluate"
      ]
    },
    {
      "page": "findVariableGenes",
      "title": "Function to find variable genes using mean variance relationship method",
      "topics": [
        "findVariableGenes"
      ]
    },
    {
      "page": "knnPredict",
      "title": "Predict annotations of query cells from the reference using k-NN method",
      "topics": [
        "knnPredict"
      ]
    },
    {
      "page": "mapQuery",
      "title": "Function for mapping query cells to a Symphony reference",
      "topics": [
        "mapQuery"
      ]
    },
    {
      "page": "pbmcs_exprs_small",
      "title": "Log(CP10k+1) normalized counts matrix (genes by cells) for 10x PBMCs dataset for vignette.",
      "topics": [
        "pbmcs_exprs_small"
      ]
    },
    {
      "page": "pbmcs_meta_small",
      "title": "Metadata for 10x PBMCs dataset for vignette.",
      "topics": [
        "pbmcs_meta_small"
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      "page": "plotReference",
      "title": "Function to plot reference, colored by cell type",
      "topics": [
        "plotReference"
      ]
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    {
      "page": "rowSDs",
      "title": "Calculate standard deviations by row",
      "topics": [
        "rowSDs"
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    },
    {
      "page": "runPCAQueryAlone",
      "title": "Runs a standard PCA pipeline on query (1 batch). Assumes query_exp is already normalized.",
      "topics": [
        "runPCAQueryAlone"
      ]
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      "title": "Scale data with given mean and standard deviations",
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      "title": "Function to find variable genes using variance stabilizing transform (vst) method",
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        "vargenes_vst"
      ]
    }
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        "Installation",
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