{
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  "Package": "SpaNorm",
  "Title": "Spatially-aware normalisation for spatial transcriptomics data",
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  "Authors@R": "c(\nperson(given = \"Dharmesh D.\",\nfamily = \"Bhuva\",\nrole = c(\"aut\", \"cre\"),\nemail = \"dharmesh.bhuva@adelaide.edu.au\",\ncomment = c(ORCID = \"0000-0002-6398-9157\")),\nperson(given = \"Agus\",\nfamily = \"Salim\",\nrole = c(\"aut\"),\nemail = \"salim.a@unimelb.edu.au\",\ncomment = c(ORCID = \"0000-0003-3999-7701\")),\nperson(given = \"Ahmed\",\nfamily = \"Mohamed\",\nrole = c(\"aut\"),\nemail = \"mohamed.a@wehi.edu.au\",\ncomment = c(ORCID = \"0000-0001-6507-5300\"))\n)",
  "Description": "This package implements the spatially aware library size\nnormalisation algorithm, SpaNorm. SpaNorm normalises out\nlibrary size effects while retaining biology through the\nmodelling of smooth functions for each effect. Normalisation is\nperformed in a gene- and cell-/spot- specific manner, yielding\nlibrary size adjusted data.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "biocViews": "Software, GeneExpression, Transcriptomics, Spatial,\nCellBiology",
  "URL": "https://bhuvalab.github.io/SpaNorm",
  "BugReports": "https://github.com/BhuvaLab/SpaNorm/issues",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-10-21 01:08:42 UTC",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-22 19:41:29 UTC",
    "User": "root"
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  "Author": "Dharmesh D. Bhuva [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6398-9157>),\nAgus Salim [aut] (ORCID: <https://orcid.org/0000-0003-3999-7701>),\nAhmed Mohamed [aut] (ORCID: <https://orcid.org/0000-0001-6507-5300>)",
  "Maintainer": "Dharmesh D. Bhuva <dharmesh.bhuva@adelaide.edu.au>",
  "_user": "bioc",
  "_type": "src",
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  "_expires": "2027-01-30T21:01:42.000Z",
  "_created": "2026-10-22T19:41:29.000Z",
  "_published": "2026-10-22T21:01:45.804Z",
  "_upstream": "https://github.com/bioc/SpaNorm",
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    "checkGPU",
    "diag_mat",
    "dnbinom_gpu",
    "fastSizeFactors",
    "filterGenes",
    "fitNB",
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    "gpuDtypeBytes",
    "hasBadValues",
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    "invert_mat_batched",
    "is_torch_tensor",
    "isPolished",
    "matmul_gpu",
    "mult_vec_mat_gpu",
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    "nbBlockDesign",
    "nbDevianceMoments",
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    "nbMuFloor",
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    "nbNewtonSolverBatch",
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    "nbUnitDeviance",
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    "plotSpatial",
    "polishNB",
    "polishSpaNorm",
    "qlDispersion",
    "qlMomentTable",
    "resetGPUCache",
    "rowSums_gpu",
    "setGPUMemoryBudget",
    "SpaNorm",
    "SpaNormPCA",
    "SpaNormSVG",
    "tcrossprod_gpu",
    "toGPUMatrix",
    "toGPUVector",
    "topSVGs",
    "toRMatrix",
    "tpsBasis"
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      "title": "Human dorsolateral prefrontal cortex (DLPFC) visium sample",
      "object": "HumanDLPFC",
      "file": "HumanDLPFC.rda",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": false
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      "title": "Broadcast-add a vector to a matrix, GPU-aware",
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        "add_vec_mat_gpu"
      ]
    },
    {
      "page": "calculateMu",
      "title": "Compute fitted means from a negative binomial GLM fit",
      "topics": [
        "calculateMu"
      ]
    },
    {
      "page": "checkGPU",
      "title": "Is an accelerated (GPU) torch device in use?",
      "topics": [
        "checkGPU"
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    },
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      "title": "Build a diagonal matrix from a vector, GPU-aware",
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      "topics": [
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    {
      "page": "fastSizeFactors",
      "title": "Filter genes based on expression",
      "topics": [
        "fastSizeFactors",
        "fastSizeFactors,SpatialExperiment-method"
      ]
    },
    {
      "page": "filterGenes",
      "title": "Filter genes based on expression",
      "topics": [
        "filterGenes",
        "filterGenes,Seurat-method",
        "filterGenes,SpatialExperiment-method"
      ]
    },
    {
      "page": "fitNB",
      "title": "Fit a per-gene negative binomial GLM",
      "topics": [
        "fitNB"
      ]
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    },
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      "page": "getGPUMemoryBudget",
      "title": "Determine the accelerator memory budget for blocked fitting",
      "topics": [
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    {
      "page": "gpuDtypeBytes",
      "title": "Bytes per element for the active backend dtype",
      "topics": [
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      "page": "HumanDLPFC",
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      "topics": [
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    },
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      "topics": [
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      "title": "Is an object a torch tensor?",
      "topics": [
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    },
    {
      "page": "isPolished",
      "title": "Does a SpaNormFit carry a converged per-gene polish?",
      "topics": [
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      "title": "Broadcast-multiply a vector and a matrix, GPU-aware",
      "topics": [
        "mult_vec_mat_gpu"
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      "page": "nbBlockDesign",
      "title": "A design with a per-group block, in compact form",
      "topics": [
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        "dim.nbBlockDesign",
        "dimnames.nbBlockDesign",
        "nbBlockDesign",
        "print.nbBlockDesign"
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      "title": "First two moments of the NB unit deviance, by direct summation",
      "topics": [
        "nbDevianceMoments"
      ]
    },
    {
      "page": "nbUnitDeviance",
      "title": "NB unit deviance",
      "topics": [
        "nbUnitDeviance"
      ]
    },
    {
      "page": "plotCovariate",
      "title": "Diagnostic plot of predicted expression for a covariate",
      "topics": [
        "plotCovariate"
      ]
    },
    {
      "page": "plotSpatial",
      "title": "Plot spatial transcriptomic annotations per spot",
      "topics": [
        "plotSpatial"
      ]
    },
    {
      "page": "polishNB",
      "title": "Converge every gene of a shared negative binomial fit to its own optimum",
      "topics": [
        "polishNB"
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    },
    {
      "page": "polishSpaNorm",
      "title": "Converge each gene of a SpaNorm fit to its own optimum",
      "topics": [
        "polishSpaNorm",
        "polishSpaNorm,Seurat-method",
        "polishSpaNorm,SpatialExperiment-method"
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    {
      "page": "qlDispersion",
      "title": "Per-gene quasi-likelihood dispersion",
      "topics": [
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      "page": "qlMomentTable",
      "title": "A shared table of NB deviance moments",
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      "page": "resetGPUCache",
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      "topics": [
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    },
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      "page": "setGPUMemoryBudget",
      "title": "Set the accelerator memory budget for blocked fitting",
      "topics": [
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      "page": "SpaNorm",
      "title": "Spatially-dependent normalisation for spatial transcriptomics data",
      "topics": [
        "SpaNorm",
        "SpaNorm,Seurat-method",
        "SpaNorm,SpatialExperiment-method"
      ]
    },
    {
      "page": "SpaNormFit",
      "title": "An S4 class to store a SpaNorm model fit",
      "topics": [
        "$,SpaNormFit-method",
        "SpaNormFit",
        "SpaNormFit-class"
      ]
    },
    {
      "page": "SpaNormPCA",
      "title": "GLM-based (SpaNorm) PCA",
      "topics": [
        "SpaNormPCA",
        "SpaNormPCA,Seurat-method",
        "SpaNormPCA,SpatialExperiment-method"
      ]
    },
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      "page": "SpaNormSVG",
      "title": "Model-based spatially variable gene (SVG) calling",
      "topics": [
        "SpaNormSVG",
        "SpaNormSVG,Seurat-method",
        "SpaNormSVG,SpatialExperiment-method"
      ]
    },
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      "title": "Tensor-aware 'tcrossprod'",
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        "tcrossprod_gpu"
      ]
    },
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      "page": "toGPUMatrix",
      "title": "Convert a matrix to a torch tensor on the active backend",
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        "toGPUMatrix"
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      "page": "toGPUVector",
      "title": "Convert a vector to a torch tensor on the active backend",
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        "toGPUVector"
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    },
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      "title": "Export top SVG results to a data frame",
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        "topSVGs"
      ]
    },
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      "page": "toRMatrix",
      "title": "Convert a torch tensor (or R object) to a base R matrix",
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      "title": "Thin-plate-style tensor spline basis over spatial coordinates",
      "topics": [
        "tpsBasis"
      ]
    }
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