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  "Title": "`SPOTlight`: Spatial Transcriptomics Deconvolution",
  "Description": "`SPOTlight` provides a method to deconvolute spatial\ntranscriptomics spots using a seeded NMF approach along with\nvisualization tools to assess the results. Spatially resolved\ngene expression profiles are key to understand tissue\norganization and function. However, novel spatial\ntranscriptomics (ST) profiling techniques lack single-cell\nresolution and require a combination with single-cell RNA\nsequencing (scRNA-seq) information to deconvolute the spatially\nindexed datasets. Leveraging the strengths of both data types,\nwe developed SPOTlight, a computational tool that enables the\nintegration of ST with scRNA-seq data to infer the location of\ncell types and states within a complex tissue. SPOTlight is\ncentered around a seeded non-negative matrix factorization\n(NMF) regression, initialized using cell-type marker genes and\nnon-negative least squares (NNLS) to subsequently deconvolute\nST capture locations (spots).",
  "Authors@R": "c(\nperson(\"Marc\", \"Elosua-Bayes\", email=\"elosua.marc@gmail.com\", role=c(\"aut\", \"cre\")),\nperson(\"Zachary\", \"DeBruine\", email=\"zacharydebruine@gmail.com\", role=\"aut\"),\nperson(\"Helena L.\", \"Crowell\", email=\"helena@crowell.eu\", role=\"aut\"))",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:58:11 UTC",
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  "Author": "Marc Elosua-Bayes [aut, cre],\nZachary DeBruine [aut],\nHelena L. Crowell [aut]",
  "Maintainer": "Marc Elosua-Bayes <elosua.marc@gmail.com>",
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    "mockSC",
    "mockSP",
    "plotCorrelationMatrix",
    "plotImage",
    "plotInteractions",
    "plotSpatialScatterpie",
    "plotTopicProfiles",
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      "page": "dot-filter",
      "title": ".init_nmf <- function(x, groups, mgs, n_top = NULL, gene_id = \"gene\", group_id = \"cluster\", weight_id = \"weight\") # check validity of input arguments if (is.null(n_top)) n_top <- max(table(mgs[[group_id]])) stopifnot( is.character(gene_id), length(gene_id) == 1, is.character(group_id), length(group_id) == 1, is.character(weight_id), length(weight_id) == 1, c(gene_id, group_id, weight_id) is.numeric(n_top), length(n_top) == 1, round(n_top) == n_top) ng <- nrow(x) nc <- ncol(x) names(ks) <- ks <- unique(groups) # subset 'n_top' features mgs <- split(mgs, mgs[[group_id]]) mgs <- lapply(mgs, function(df) o <- order(df[[weight_id]], decreasing = TRUE) n <- ifelse(nrow(df) < n_top, nrow(df), n_top) df[o, ][seq_len(n), ] ) # subset unique features mgs <- lapply(ks, function(k) g1 <- mgs[[k]][[gene_id]] g2 <- unlist(lapply(mgs[ks != k], `[[`, gene_id)) mgs[[k]][!g1 ) # W is of dimension (#groups)x(#features) with W(i,j) # equal to weight if j is marker for i, and ~0 otherwise W <- vapply(ks, function(k) w <- numeric(ng) + 1e-12 names(w) <- rownames(x) ws <- mgs[[k]][[weight_id]] w[mgs[[k]][[gene_id]]] <- ws return(w) , numeric(ng)) # there is no need to initialize H tp <- paste0(\"topic_\", seq_len(length(ks))) dimnames(W) <- list(rownames(x), tp) return(W) Filter features from expression matrix",
      "topics": [
        ".filter"
      ]
    },
    {
      "page": "data",
      "title": "Synthetic single-cell, mixture and marker data",
      "topics": [
        "data",
        "getMGS",
        "mockSC",
        "mockSP"
      ]
    },
    {
      "page": "plotCorrelationMatrix",
      "title": "Plot Correlation Matrix",
      "topics": [
        "plotCorrelationMatrix"
      ]
    },
    {
      "page": "plotImage",
      "title": "Plot JP(E)G/PNG/Raster/RGB images",
      "topics": [
        "plotImage"
      ]
    },
    {
      "page": "plotInteractions",
      "title": "Plot group interactions",
      "topics": [
        "plotHeatmap",
        "plotInteractions",
        "plotNetwork"
      ]
    },
    {
      "page": "plotSpatialScatterpie",
      "title": "Spatial scatterpie",
      "topics": [
        "plotSpatialScatterpie"
      ]
    },
    {
      "page": "plotTopicProfiles",
      "title": "Plot NMF topic profiles",
      "topics": [
        "plotTopicProfiles"
      ]
    },
    {
      "page": "runDeconvolution",
      "title": "Run Deconvolution using NNLS model",
      "topics": [
        "runDeconvolution"
      ]
    },
    {
      "page": "SPOTlight",
      "title": "Deconvolution of mixture using single-cell data",
      "topics": [
        "SPOTlight"
      ]
    },
    {
      "page": "trainNMF",
      "title": "train NMF model",
      "topics": [
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      ]
    }
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      "source": "SPOTlight_kidney.Rmd",
      "filename": "SPOTlight_kidney.html",
      "title": "Spatial Transcriptomics Deconvolution with SPOTlight",
      "author": "Marc Elosua-Bayes, Helena L. Crowell, Zach DeBruine",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Load packages",
        "Introduction",
        "What is SPOTlight?",
        "Starting point",
        "Getting started",
        "Data description",
        "Loading the data",
        "Workflow",
        "Preprocessing",
        "Feature selection",
        "Variance modelling",
        "Cell Downsampling",
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        "Visualization",
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        "Spatial Correlation Matrix",
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        "Scatterpie",
        "Residuals",
        "Session information"
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      "created": "2022-01-26 17:04:13",
      "modified": "2025-05-26 18:04:27",
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