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search.json

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     "section": "iSEEpathways",
     "text": "iSEEpathways\n\nFgseaEnrichmentPlot\nGSEA enrichment plot produced by the fgsea package.\n\n\n\nThe FgseaEnrichmentPlot panel class.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nlibrary(iSEE)\nlibrary(fgsea)\nlibrary(iSEEpathways)\n\n# Example data ----\n\nset.seed(1)\nsimulated_data <- simulateExampleData()\n\npathways_list <- simulated_data[[\"pathwaysList\"]]\nfeatures_stat <- simulated_data[[\"featuresStat\"]]\nse <- simulated_data[[\"summarizedexperiment\"]]\n\n# fgsea ----\n\nset.seed(42)\nfgseaRes <- fgsea(pathways = pathways_list,\n  stats    = features_stat,\n  minSize  = 15,\n  maxSize  = 500)\nfgseaRes <- fgseaRes[order(pval), ]\n\n# iSEE / iSEEpathways ---\n\nse <- embedPathwaysResults(fgseaRes, se, name = \"fgsea\", class = \"fgsea\", pathwayType = \"simulated\",\n  pathwaysList = pathways_list, featuresStats = features_stat)\n\napp <- iSEE(se, initial = list(\n  FgseaEnrichmentPlot(ResultName=\"fgsea\", PathwayId = \"pathway_1350\", PanelWidth = 12L)\n))\n\nif (interactive()) {\n  shiny::runApp(app)\n}\n\n\n\n\n\n\nPathwaysTable\nBrowse and filter any table of gene set analysis results embedded in a SummarizedExperiment object.\n\n\n\nThe PathwaysTable panel class.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nlibrary(iSEE)\nlibrary(fgsea)\nlibrary(iSEEpathways)\n\n# Example data ----\n\nset.seed(1)\nsimulated_data <- simulateExampleData()\n\npathways_list <- simulated_data[[\"pathwaysList\"]]\nfeatures_stat <- simulated_data[[\"featuresStat\"]]\nse <- simulated_data[[\"summarizedexperiment\"]]\n\n# fgsea ----\n\nset.seed(42)\nfgseaRes <- fgsea(pathways = pathways_list,\n  stats    = features_stat,\n  minSize  = 15,\n  maxSize  = 500)\nfgseaRes <- fgseaRes[order(pval), ]\n\n# iSEE ---\n\nse <- embedPathwaysResults(fgseaRes, se, name = \"fgsea\", class = \"fgsea\", pathwayType = \"simulated\",\n  pathwaysList = pathways_list, featuresStats = features_stat)\n\napp <- iSEE(se, initial = list(\n  PathwaysTable(ResultName=\"fgsea\", PanelWidth = 12L)\n))\n\nif (interactive()) {\n  shiny::runApp(app)\n}"
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+    "title": "Panels",
+    "section": "iSEEu",
+    "text": "iSEEu\n\nAggregatedDotPlot\nRepresents groups of samples by dots, where colour scales with means assay value and size scales with proportion of non-zero values for selected features.\n\n\n\nThe AggregatedDotPlot panel class.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nlibrary(iSEE)\nlibrary(scRNAseq)\nlibrary(scater)\nlibrary(iSEEu)\n\n# Example data ----\n\nsce <- ReprocessedAllenData(assays=\"tophat_counts\")\n\nsce <- logNormCounts(sce, exprs_values=\"tophat_counts\")\n\n# launch the app itself ----\n\nif (interactive()) {\n  iSEE(\n    sce,\n    initial = list(\n      AggregatedDotPlot(\n        ColumnDataLabel=\"Primary.Type\",\n        CustomRowsText = \"Rorb\\nSnap25\\nFoxp2\",\n        # PanelHeight = 500L,\n        PanelWidth = 12L\n      )\n    )\n  )\n}\n\n\n\n\n\n\nDynamicMarkerTable\nA table that dynamically identifies marker genes for a subset of samples transmitted from another panel. Comparisons are made between the active selection in the transmitting panel and either\n\nall non-selected points, if no saved selections are available, or\neach subset of points in each saved selection.\n\n\n\n\nThe DynamicMarkerTable panel class, alongside a ReducedDimensionPlot panel from which it receives a selection of samples.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nlibrary(iSEE)\nlibrary(iSEEu)\nlibrary(scRNAseq)\nlibrary(scater)\nlibrary(scran)\n\nsce <- ReprocessedAllenData(assays=\"tophat_counts\")\nsce <- logNormCounts(sce, exprs_values=\"tophat_counts\")\nsce <- runPCA(sce, ncomponents=4)\n\nif (interactive()) {\n  iSEE(sce, initial=list(\n    ReducedDimensionPlot(\n      PanelWidth=4L,\n      BrushData = list(\n        lasso = NULL, closed = TRUE,\n        mapping = list(x = \"X\", y = \"Y\"),\n        coord = structure(c(\n          -47.8, -41.9, -14.6, -13.6, -19.1, -27.3, -33.6, -44, -47.8,\n          -23.6, -44.1, -56.4, -46.9, -26.4, -17.4, -6.2, -5.4, -23.6),\n          dim = c(9L, 2L))\n        )\n    ),\n    DynamicMarkerTable(\n      PanelWidth=8L,\n      ColumnSelectionSource=\"ReducedDimensionPlot1\"\n    )\n  ))\n}\n\n\n\n\n\n\nDynamicReducedDimensionPlot\nA dimensionality reduction plot that dynamically recomputes the coordinates for the samples, based on a subset of samples and features transmitted from other panels.\n\n\n\nThe DynamicReducedDimensionPlot panel class, alongside a ReducedDimensionPlot panel from which it receives a selection of samples.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nlibrary(iSEE)\nlibrary(iSEEu)\nlibrary(scRNAseq)\nlibrary(scater)\n\nset.seed(1)\nsce <- ReprocessedAllenData(assays=\"tophat_counts\")\nsce <- logNormCounts(sce, exprs_values=\"tophat_counts\")\nsce <- runPCA(sce, ncomponents=4)\n\nif (interactive()) {\n  iSEE(sce, initial=list(\n    ReducedDimensionPlot(\n      PanelWidth = 6L,\n      BrushData = list(\n        lasso = NULL, closed = TRUE,\n        mapping = list(x = \"X\", y = \"Y\"),\n        coord = structure(c(\n          -47.8, -41.9, -14.6, -13.6, -19.1, -27.3, -33.6, -44, -47.8,\n          -23.6, -44.1, -56.4, -46.9, -26.4, -17.4, -6.2, -5.4, -23.6),\n          dim = c(9L, 2L))\n      )\n    ),\n    DynamicReducedDimensionPlot(\n      PanelWidth = 6L,\n      Assay=\"logcounts\",\n      ColumnSelectionSource=\"ReducedDimensionPlot1\"\n    )\n  ))\n}\n\n\n\n\n\n\nFeatureSetTable\nA table where each row is itself a set of features (i.e., rows) and can be used to transmit such a feature set to another panel.\n\n\n\nThe FeatureSetTable panel class, alongside RowDataPlot and RowDataTable panels to which it transmits a feature set.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nlibrary(iSEE)\nlibrary(iSEEu)\nlibrary(scRNAseq)\nlibrary(scater)\nlibrary(scran)\nlibrary(org.Mm.eg.db)\n\nsce <- LunSpikeInData(location=FALSE)\n\nsce <- logNormCounts(sce)\n\nrowData(sce) <- cbind(rowData(sce), modelGeneVarWithSpikes(sce, \"ERCC\"))\n\ncmds <- createGeneSetCommands(collections=\"GO\",\n  organism=\"org.Mm.eg.db\", identifier=\"ENSEMBL\")\nsce <- registerFeatureSetCommands(sce, cmds)\n\n# Setting up the application.\n\ngst <- FeatureSetTable(\n  Selected = \"GO:0002020\"\n)\n\nrdp <- RowDataPlot(\n  YAxis=\"total\",\n  XAxis=\"Row data\", XAxisRowData=\"mean\",\n  ColorBy=\"Row selection\",\n  RowSelectionSource=\"FeatureSetTable1\"\n)\n\nrdt <- RowDataTable(\n  RowSelectionSource=\"FeatureSetTable1\"\n)\n\nif (interactive()) {\n  iSEE(sce, initial=list(gst, rdp, rdt))\n}\n\n\n\n\n\n\nLogFCLogFCPlot\nPrecursor to the iSEEde::LogFCLogFCPlot class.\n\n\n\n\n\n\nWarning\n\n\n\nDeprecation imminent. Please use iSEEde::LogFCLogFCPlot() instead.\n\n\n\n\n\nThe LogFCLogFCPlot panel class.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\n# Making up some results:\nse <- SummarizedExperiment(matrix(rnorm(10000), 1000, 10))\nrownames(se) <- paste0(\"GENE_\", seq_len(nrow(se)))\nrowData(se)$PValue1 <- runif(nrow(se))\nrowData(se)$LogFC1 <- rnorm(nrow(se))\nrowData(se)$PValue2 <- runif(nrow(se))\nrowData(se)$LogFC2 <- rnorm(nrow(se))\n\nif (interactive()) {\n  iSEE(se, initial=list(\n    LogFCLogFCPlot(\n      PanelWidth = 12L,\n      XAxisRowData=\"LogFC1\", YAxis=\"LogFC2\",\n      XPValueField=\"PValue1\", YPValueField=\"PValue2\"\n    )\n  ))\n}\n\n\n\n\n\n\nMAPlot\nPrecursor to the iSEEde::MAPlot class.\n\n\n\n\n\n\nWarning\n\n\n\nDeprecation imminent. Please use iSEEde::MAPlot() instead.\n\n\n\n\n\nThe MAPlot panel class.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\n# Making up some results:\nse <- SummarizedExperiment(matrix(rnorm(10000), 1000, 10))\nrownames(se) <- paste0(\"GENE_\", seq_len(nrow(se)))\nrowData(se)$PValue <- runif(nrow(se))\nrowData(se)$LogFC <- rnorm(nrow(se))\nrowData(se)$AveExpr <- rnorm(nrow(se))\n\nif (interactive()) {\n  iSEE(se, initial=list(\n    MAPlot(PanelWidth=12L)\n  ))\n}\n\n\n\n\n\n\nMarkdownBoard\nA panel providing an aceEditor that can be used to take notes within the app. Notes should be typed using Markdown syntax, and the panel continuously renders them in HTML format for preview.\n\n\n\nThe MarkdownBoard panel class.\n\n\n\n\n\n\n\n\nReproduce This Output\n\n\n\n\n\n\nif (interactive()) {\n  iSEE(SummarizedExperiment(), initial=list(\n    MarkdownBoard(\n      PanelWidth = 12L,\n      Content = paste0(c(\n        \"# Level 1 header\",\n        \"## Level 2 header\",\n        \"**Bold** and *italic*.\",\n        \"[Link](https://isee.github.io/)\",\n        \"* Bullet point\\n\"),\n        collapse = \"\\n\\n\"\n      )\n    )\n  ))\n}"
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