## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup, message = FALSE--------------------------------------------------- library(SingleCellExperiment) library(TabulaMurisSenisData) library(ggplot2) ## ----------------------------------------------------------------------------- tmp <- TabulaMurisSenisBulk(infoOnly = TRUE) tms_bulk <- TabulaMurisSenisBulk() tms_bulk ## ----------------------------------------------------------------------------- table(colData(tms_bulk)$organ) ## ----------------------------------------------------------------------------- listTabulaMurisSenisTissues(dataset = "Droplet") ## ----------------------------------------------------------------------------- tmp <- TabulaMurisSenisDroplet(tissues = "All", infoOnly = TRUE) tms_droplet <- TabulaMurisSenisDroplet(tissues = "All") tms_droplet ## ----------------------------------------------------------------------------- # tissue colors tissue_cols <- c(Pancreas = "#3182bd", Thymus = "#31a354", Trachea = "#636363", Bladder = "#637939", Lung = "#7b4173", Large_Intestine = "#843c39", Fat = "#969696", Tongue = "#a1d99b", Mammary_Gland = "#ce6dbd", Limb_Muscle = "#d6616b", Marrow = "#de9ed6", Skin = "#e6550d", Liver = "#e7969c", Heart_and_Aorta = "#e7ba52", Kidney = "#e7cb94", Spleen = "#fd8d3c") # get dataset with all tissues se <- tms_droplet$All se # prepare data set for ggplot ds <- as.data.frame(reducedDim(se, "UMAP")) ds <- cbind(ds, tissue = colData(se)$tissue) head(ds) # plot ggplot(ds, aes(x = UMAP1, y = UMAP2, color = tissue)) + geom_point(size = 0.05) + scale_color_manual(values = tissue_cols) + theme_classic() + guides(colour = guide_legend(override.aes = list(size = 5))) ## ----------------------------------------------------------------------------- listTabulaMurisSenisTissues(dataset = "FACS") ## ----------------------------------------------------------------------------- tmp <- TabulaMurisSenisFACS(tissues = "All", infoOnly = TRUE) tms_facs <- TabulaMurisSenisFACS(tissues = "All") tms_facs ## ----------------------------------------------------------------------------- # tissue colors tissue_cols <- c(Skin = "#e6550d", Pancreas = "#3182bd", Limb_Muscle = "#d6616b", Heart = "#e7ba52", Spleen = "#fd8d3c", Diaphragm = "#8c6d31", Trachea = "#636363", Tongue = "#a1d99b", Thymus = "#31a354", `Brain_Non-Myeloid` = "#cedb9c", Brain_Myeloid = "#b5cf6b", Bladder = "#637939", Large_Intestine = "#843c39", BAT = "#9c9ede", GAT = "#bd9e39", MAT = "#a55194", SCAT = "#6baed6", Lung = "#7b4173", Liver = "#e7969c", Marrow = "#de9ed6", Kidney = "#e7cb94", Aorta = "#393b79", Mammary_Gland = "#ce6dbd") # get dataset with all tissues se <- tms_facs$All se # prepare data set for ggplot ds <- as.data.frame(reducedDim(se, "UMAP")) ds <- cbind(ds, tissue = colData(se)$tissue) head(ds) # plot ggplot(ds, aes(x = UMAP1, y = UMAP2, color = tissue)) + geom_point(size = 0.05) + scale_color_manual(values = tissue_cols) + theme_classic() + guides(colour = guide_legend(override.aes = list(size = 5))) ## ----------------------------------------------------------------------------- sce_all_facs <- TabulaMurisSenisFACS(tissues = "All", processedCounts = TRUE)$All sce_all_facs ## ----------------------------------------------------------------------------- assayNames(sce_all_facs) reducedDimNames(sce_all_facs) ## ----include=FALSE------------------------------------------------------------ library(iSEE) ## ----launchisee, eval=FALSE--------------------------------------------------- # library(iSEE) # iSEE(sce_all_facs) ## ----------------------------------------------------------------------------- initial <- list() ################################################################################ # (Compact) Settings for Reduced dimension plot 1 ################################################################################ initial[["ReducedDimensionPlot1"]] <- new( "ReducedDimensionPlot", DataBoxOpen = TRUE, Type = "UMAP", VisualBoxOpen = TRUE, ColorBy = "Feature name", ColorByFeatureName = "Col1a1", ColorByFeatureSource = "RowDataTable1", ColorByFeatureDynamicSource = FALSE ) ################################################################################ # (Compact) Settings for Row data table 1 ################################################################################ initial[["RowDataTable1"]] <- new( "RowDataTable", Selected = "Col1a1", Search = "Col1" ) ################################################################################ # (Compact) Settings for Feature assay plot 1 ################################################################################ initial[["FeatureAssayPlot1"]] <- new( "FeatureAssayPlot", DataBoxOpen = TRUE, Assay = "logcounts", XAxis = "Column data", XAxisColumnData = "tissue", YAxisFeatureName = "Col1a1", YAxisFeatureSource = "RowDataTable1" ) ## ----eval=FALSE--------------------------------------------------------------- # initial <- list() # # ################################################################################ # # Settings for Reduced dimension plot 1 # ################################################################################ # # initial[["ReducedDimensionPlot1"]] <- new("ReducedDimensionPlot", Type = "UMAP", XAxis = 1L, YAxis = 2L, # FacetRowByColData = "FACS.selection", FacetColumnByColData = "FACS.selection", # ColorByColumnData = "FACS.selection", ColorByFeatureNameAssay = "logcounts", # ColorBySampleNameColor = "#FF0000", ShapeByColumnData = "FACS.selection", # SizeByColumnData = "n_genes", FacetRowBy = "None", FacetColumnBy = "None", # ColorBy = "Feature name", ColorByDefaultColor = "#000000", # ColorByFeatureName = "Col1a1", ColorByFeatureSource = "RowDataTable1", # ColorByFeatureDynamicSource = FALSE, ColorBySampleName = "A10_B000497_B009023_S10.mm10-plus-0-0", # ColorBySampleSource = "---", ColorBySampleDynamicSource = FALSE, # ShapeBy = "None", SizeBy = "None", SelectionAlpha = 0.1, # ZoomData = numeric(0), BrushData = list(), VisualBoxOpen = TRUE, # VisualChoices = "Color", ContourAdd = FALSE, ContourColor = "#0000FF", # PointSize = 1, PointAlpha = 1, Downsample = FALSE, DownsampleResolution = 200, # CustomLabels = FALSE, CustomLabelsText = "A10_B000497_B009023_S10.mm10-plus-0-0", # FontSize = 1, LegendPointSize = 1, LegendPosition = "Bottom", # HoverInfo = TRUE, LabelCenters = FALSE, LabelCentersBy = "FACS.selection", # LabelCentersColor = "#000000", VersionInfo = list(iSEE = structure(list( # c(2L, 5L, 1L)), class = c("package_version", "numeric_version" # ))), PanelId = c(ReducedDimensionPlot = 1L), PanelHeight = 500L, # PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---", # ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE, # ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE, # ColumnSelectionRestrict = FALSE, SelectionHistory = list()) # # ################################################################################ # # Settings for Row data table 1 # ################################################################################ # # initial[["RowDataTable1"]] <- new("RowDataTable", Selected = "Col1a1", Search = "Col1", SearchColumns = c("", # "", "", "", ""), HiddenColumns = character(0), VersionInfo = list( # iSEE = structure(list(c(2L, 5L, 1L)), class = c("package_version", # "numeric_version"))), PanelId = c(RowDataTable = 1L), PanelHeight = 500L, # PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---", # ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE, # ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE, # ColumnSelectionRestrict = FALSE, SelectionHistory = list()) # # ################################################################################ # # Settings for Feature assay plot 1 # ################################################################################ # # initial[["FeatureAssayPlot1"]] <- new("FeatureAssayPlot", Assay = "logcounts", XAxis = "Column data", # XAxisColumnData = "tissue", XAxisFeatureName = "0610005C13Rik", # XAxisFeatureSource = "---", XAxisFeatureDynamicSource = FALSE, # YAxisFeatureName = "Col1a1", YAxisFeatureSource = "RowDataTable1", # YAxisFeatureDynamicSource = FALSE, FacetRowByColData = "FACS.selection", # FacetColumnByColData = "FACS.selection", ColorByColumnData = "age", # ColorByFeatureNameAssay = "logcounts", ColorBySampleNameColor = "#FF0000", # ShapeByColumnData = "FACS.selection", SizeByColumnData = "n_genes", # FacetRowBy = "None", FacetColumnBy = "None", ColorBy = "None", # ColorByDefaultColor = "#000000", ColorByFeatureName = "0610005C13Rik", # ColorByFeatureSource = "---", ColorByFeatureDynamicSource = FALSE, # ColorBySampleName = "A10_B000497_B009023_S10.mm10-plus-0-0", # ColorBySampleSource = "---", ColorBySampleDynamicSource = FALSE, # ShapeBy = "None", SizeBy = "None", SelectionAlpha = 0.1, # ZoomData = numeric(0), BrushData = list(), VisualBoxOpen = FALSE, # VisualChoices = "Color", ContourAdd = FALSE, ContourColor = "#0000FF", # PointSize = 1, PointAlpha = 1, Downsample = FALSE, DownsampleResolution = 200, # CustomLabels = FALSE, CustomLabelsText = "A10_B000497_B009023_S10.mm10-plus-0-0", # FontSize = 1, LegendPointSize = 1, LegendPosition = "Bottom", # HoverInfo = TRUE, LabelCenters = FALSE, LabelCentersBy = "FACS.selection", # LabelCentersColor = "#000000", VersionInfo = list(iSEE = structure(list( # c(2L, 5L, 1L)), class = c("package_version", "numeric_version" # ))), PanelId = c(FeatureAssayPlot = 1L), PanelHeight = 500L, # PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---", # ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE, # ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE, # ColumnSelectionRestrict = FALSE, SelectionHistory = list()) # # initial ## ----eval=FALSE--------------------------------------------------------------- # iSEE(sce_all_facs, initial = initial) ## ----echo=FALSE, fig.cap="Screenshot of the iSEE app running on the FACS single cell dataset", out.width='100%'---- knitr::include_graphics("ss_iSEE_facsdataset.jpg") ## ----------------------------------------------------------------------------- sessionInfo()