{ "_id": "6a1b0c901d7bb097a0a07d82", "Package": "SPOTlight", "Version": "1.16.0", "Type": "Package", "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\"))", "biocViews": "SingleCell, Spatial, StatisticalMethod", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "URL": "https://github.com/MarcElosua/SPOTlight", "BugReports": "https://github.com/MarcElosua/SPOTlight/issues", "Config/pak/sysreqs": "zlib1g-dev", "Repository": "https://bioc-release.r-universe.dev", "Date/Publication": "2026-04-28 12:58:11 UTC", "RemoteUrl": "https://github.com/bioc/SPOTlight", "RemoteRef": "RELEASE_3_23", "RemoteSha": "0a54588cec957dd8bf9275af12272b23cbc7fc8b", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-30 11:02:01 UTC", "User": "root" }, "Author": "Marc Elosua-Bayes [aut, cre],\nZachary DeBruine [aut],\nHelena L. Crowell [aut]", "Maintainer": "Marc Elosua-Bayes ", "MD5sum": "36f73390fe95aa89e7230e85f4355e10", "_user": "bioc-release", "_type": "src", "_file": "SPOTlight_1.16.0.tar.gz", "_fileid": "73445e7edef13d539f4d367a56da91935a468e937a3c93ace20587a208f5ed35", "_filesize": 4256009, "_sha256": "73445e7edef13d539f4d367a56da91935a468e937a3c93ace20587a208f5ed35", "_created": "2026-05-30T11:02:01.000Z", "_published": "2026-05-30T16:13:04.919Z", "_jobs": [ { "job": 78644368253, "time": 264, "config": "bioc-checks", "r": "4.6.0", "check": "NOTE", "artifact": "7307788712" }, { "job": 78644368252, "time": 507, "config": "linux-devel-arm64", "r": "4.7.0", "check": "NOTE", "artifact": "7307757297" }, { "job": 78644368263, "time": 465, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "7307805819" }, { "job": 78644368262, "time": 360, "config": "linux-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "7307744393" }, { "job": 78644368268, "time": 435, "config": 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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 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