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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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  "License": "GPL-3",
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  "URL": "https://github.com/MarcElosua/SPOTlight",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:58:11 UTC",
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    "Date": "2026-10-31 11:12:02 UTC",
    "User": "root"
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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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    "mockSP",
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    "plotTopicProfiles",
    "runDeconvolution",
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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"
      ]
    },
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      "title": "Synthetic single-cell, mixture and marker data",
      "topics": [
        "data",
        "getMGS",
        "mockSC",
        "mockSP"
      ]
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    {
      "page": "plotCorrelationMatrix",
      "title": "Plot Correlation Matrix",
      "topics": [
        "plotCorrelationMatrix"
      ]
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      "title": "Plot JP(E)G/PNG/Raster/RGB images",
      "topics": [
        "plotImage"
      ]
    },
    {
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      "title": "Plot group interactions",
      "topics": [
        "plotHeatmap",
        "plotInteractions",
        "plotNetwork"
      ]
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      "title": "Plot NMF topic profiles",
      "topics": [
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      ]
    },
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      "title": "Run Deconvolution using NNLS model",
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      "page": "SPOTlight",
      "title": "Deconvolution of mixture using single-cell data",
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      ]
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