{
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  "Package": "levi",
  "Type": "Package",
  "Title": "Landscape Expression Visualization and Network Inference",
  "Version": "1.99.0",
  "Authors@R": "c(person(given = \"Rafael\",\nfamily = \"Pilan\",\nrole = \"aut\",\nemail = \"rafael.pilan@unesp.br\"),\nperson(given = \"Isabelle\",\nfamily = \"Silva\",\nrole = \"ctb\"),\nperson(given = \"Agnes\",\nfamily = \"Takeda\",\nrole = \"ctb\"),\nperson(given = \"Jose\",\nfamily = \"Rybarczyk Filho\",\nrole = c(\"ctb\", \"cre\", \"ths\"),\nemail = \"jose.luiz@unesp.br\",\ncomment = c(ORCID = \"0000-0002-0757-3608\"))\n)",
  "Description": "Projects gene expression onto the plane of a biological\nnetwork and draws it as a landscape: a Gaussian-kernel weighted\nsurface on an absolute scale where 0.5 means no change. Regions\nof the landscape are tested by permutation, either conditional\non the network and layout (node-label shuffling, maximum\nregional mass) or against biological replication (sample-label\npermutation, with blocking). Graph-native tests complement the\nlandscape: threshold-free cluster enhancement on network\ncomponents, Moran and Getis-Ord autocorrelation, Laplacian\nsmoothness, Freedman-Lane residual permutation with covariates\nand degree-preserving rewiring nulls. Pseudobulk workflows for\nsingle-cell RNA-seq permute donors rather than cells. Adapters\nread DESeq2, edgeR, limma and Seurat results,\nSummarizedExperiment objects and STRING networks. A Shiny\ninterface covers the interactive visualization.",
  "URL": "https://bioconductor.org/packages/levi",
  "BugReports": "https://github.com/jrybarczyk/levi/issues",
  "Encoding": "UTF-8",
  "License": "GPL (>= 2)",
  "biocViews": "GeneExpression, Microarray, Sequencing, RNASeq, Network,\nGraphAndNetwork, StatisticalMethod, Software, Visualization,\nGUI, GeneSetEnrichment, DifferentialExpression, SingleCell,\nProteomics",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-09-19 22:48:21 UTC",
  "RemoteUrl": "https://github.com/bioc/levi",
  "RemoteRef": "HEAD",
  "RemoteSha": "ac8c924642aac186de9e2ca9ccc0b8652fd5ab5c",
  "Packaged": {
    "Date": "2026-09-20 06:35:17 UTC",
    "User": "root"
  },
  "Author": "Rafael Pilan [aut],\nIsabelle Silva [ctb],\nAgnes Takeda [ctb],\nJose Rybarczyk Filho [ctb, cre, ths] (ORCID:\n<https://orcid.org/0000-0002-0757-3608>)",
  "Maintainer": "Jose Rybarczyk Filho <jose.luiz@unesp.br>",
  "_user": "bioc",
  "_type": "src",
  "_file": "levi_1.99.0.tar.gz",
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  "_expires": "2026-12-29T06:52:57.000Z",
  "_created": "2026-09-20T06:35:17.000Z",
  "_published": "2026-09-20T06:53:00.010Z",
  "_bioccheck": {
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    "warning": 0,
    "note": 8
  },
  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/bioc/actions/runs/35494372010",
  "_status": "success",
  "_upstream": "https://github.com/bioc/levi",
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    "id": "ac8c924642aac186de9e2ca9ccc0b8652fd5ab5c",
    "author": "jrybarczyk <jose.luiz@unesp.br>",
    "committer": "jrybarczyk <jose.luiz@unesp.br>",
    "message": "Build the TFCE graph once per call instead of at every threshold\n\n.componentsAt() rebuilt the igraph object for each of the 100 integration\nsteps, on both sides, in every permutation draw. The graph is now built\nonce in .tfce() and the components are recomputed only when the active\nset changes between consecutive thresholds. Results are identical; the\nleviSingleCellTFCEInference() example drops from 5.2 s to 0.4 s, under the\n5 s limit of the Bioconductor build report.\n\nCo-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>\n",
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    "leviFromEdgeR",
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    "leviFromExpressionSet",
    "leviFromLimma",
    "leviFromSE",
    "leviFromSeurat",
    "leviFromSTRING",
    "leviGraphClusterInference",
    "leviGraphGetisOrd",
    "leviGraphMoran",
    "leviGraphRewiringInference",
    "leviGraphSpectrum",
    "leviGraphTFCEFreedmanLane",
    "leviGraphTFCEInference",
    "leviGraphTFCERewiring",
    "leviGraphWeightedTopology",
    "leviGrid",
    "leviPseudobulk",
    "leviRegionGenes",
    "leviReplicateInference",
    "leviSave3D",
    "leviSingleCellGraphInference",
    "leviSingleCellInteractionTFCE",
    "leviSingleCellRegionalInference",
    "leviSingleCellTFCEInference",
    "leviSingleCellTopologyInference",
    "LEVIui",
    "readExpColumn"
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      "title": "Bimodal toy dataset",
      "topics": [
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        "bimodal_expression",
        "bimodal_network"
      ]
    },
    {
      "page": "flat_dataset",
      "title": "Flat (null-expression) toy dataset",
      "topics": [
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        "flat_expression",
        "flat_network"
      ]
    },
    {
      "page": "gradient_dataset",
      "title": "Gradient-topology toy dataset",
      "topics": [
        "gradient_dataset",
        "gradient_expression",
        "gradient_network"
      ]
    },
    {
      "page": "hub_dataset",
      "title": "Hub-topology toy dataset",
      "topics": [
        "hub_dataset",
        "hub_expression",
        "hub_network"
      ]
    },
    {
      "page": "levi",
      "title": "levi",
      "topics": [
        "levi"
      ]
    },
    {
      "page": "leviAdjustPathways",
      "title": "Adjust regional LEVI results across pathways or networks",
      "topics": [
        "leviAdjustPathways"
      ]
    },
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      "page": "leviBulkGraphInference",
      "title": "Bulk RNA-seq graph-cluster inference",
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      "title": "leviDiff",
      "topics": [
        "leviDiff"
      ]
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      "title": "leviEnrich",
      "topics": [
        "leviEnrich"
      ]
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      "title": "leviFromBioc",
      "topics": [
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      ]
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      "title": "leviFromDESeq2",
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      "title": "leviFromEdgeR",
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      "title": "leviFromEdges",
      "topics": [
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      "title": "leviFromExpressionSet",
      "topics": [
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      "title": "leviFromLimma",
      "topics": [
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      "title": "leviFromSE",
      "topics": [
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      "title": "leviFromSeurat",
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      "title": "leviFromSTRING",
      "topics": [
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      "title": "Graph-connected cluster-mass inference from biological replicates",
      "topics": [
        "leviGraphClusterInference"
      ]
    },
    {
      "page": "leviGraphGetisOrd",
      "title": "Getis-Ord hotspots and coldspots on a biological graph",
      "topics": [
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    {
      "page": "leviGraphMoran",
      "title": "Moran global and local autocorrelation on a biological graph",
      "topics": [
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      "title": "Degree-preserving rewiring test for graph clusters",
      "topics": [
        "leviGraphRewiringInference"
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    {
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      "title": "Laplacian smoothness and graph-Fourier low-frequency energy",
      "topics": [
        "leviGraphSpectrum"
      ]
    },
    {
      "page": "leviGraphTFCEFreedmanLane",
      "title": "Graph TFCE with Freedman-Lane residual permutation",
      "topics": [
        "leviGraphTFCEFreedmanLane"
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    },
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      "title": "Threshold-free graph-cluster enhancement with sample-label inference",
      "topics": [
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      "topics": [
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      "title": "Weighted Moran and Laplacian statistics for scored biological interactions",
      "topics": [
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      "page": "leviGrid",
      "title": "leviGrid",
      "topics": [
        "leviGrid"
      ]
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    {
      "page": "leviPseudobulk",
      "title": "Aggregate single-cell counts to pseudobulks",
      "topics": [
        "leviPseudobulk"
      ]
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    {
      "page": "leviRegionGenes",
      "title": "Attribute a LEVI region to nearby network genes",
      "topics": [
        "leviRegionGenes"
      ]
    },
    {
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      "title": "Regional inference from biological replicates",
      "topics": [
        "leviReplicateInference"
      ]
    },
    {
      "page": "leviSave3D",
      "title": "Save the 3D landscape surface from a chosen viewpoint",
      "topics": [
        "leviSave3D"
      ]
    },
    {
      "page": "leviSingleCellGraphInference",
      "title": "Single-cell pseudobulk graph-cluster inference",
      "topics": [
        "leviSingleCellGraphInference"
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    {
      "page": "leviSingleCellInteractionTFCE",
      "title": "Cell-type-specific condition interaction with joint graph TFCE inference",
      "topics": [
        "leviSingleCellInteractionTFCE"
      ]
    },
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      "page": "leviSingleCellRegionalInference",
      "title": "Joint regional inference for paired single-cell pseudobulks",
      "topics": [
        "leviSingleCellRegionalInference"
      ]
    },
    {
      "page": "leviSingleCellTFCEInference",
      "title": "Cell-type-specific TFCE inference for pseudobulk single-cell RNA-seq",
      "topics": [
        "leviSingleCellTFCEInference"
      ]
    },
    {
      "page": "leviSingleCellTopologyInference",
      "title": "Run Moran, Laplacian/Fourier and rewiring tests per cell type",
      "topics": [
        "leviSingleCellTopologyInference"
      ]
    },
    {
      "page": "LEVIui",
      "title": "LEVIui - Interactive Shiny GUI for levi",
      "topics": [
        "LEVIui"
      ]
    },
    {
      "page": "logfc_dataset",
      "title": "Log2-intensity (logfc-mode) toy dataset",
      "topics": [
        "logfc_dataset",
        "logfc_expression",
        "logfc_network"
      ]
    },
    {
      "page": "multicomp_dataset",
      "title": "Multi-comparison toy dataset",
      "topics": [
        "hub_multicomp_expression",
        "multicomp_dataset"
      ]
    },
    {
      "page": "print.levi_result",
      "title": "Print a levi result",
      "topics": [
        "print.levi_result"
      ]
    },
    {
      "page": "readExpColumn",
      "title": "readExpColumn - Define expression comparisons for levi",
      "topics": [
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      ]
    },
    {
      "page": "sparse_dataset",
      "title": "Sparse-expression toy dataset",
      "topics": [
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        "sparse_expression",
        "sparse_network"
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