## ----Setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5, out.width = "100%" ) ## ----Install packages, eval = FALSE------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("polyICSFlow") ## ----Load libraries----------------------------------------------------------- library(polyICSFlow) ## ----Load preprocessed data--------------------------------------------------- # define path to data path_data <- system.file("extdata", package = "polyICSFlow") # load pre-processed fcs files as flowSet fs <- flowCore::read.flowSet(path = path_data, pattern = ".fcs", truncate_max_range = FALSE, transformation = FALSE) flowWorkspace::pData(fs) ## ----Create gatingSet--------------------------------------------------------- # create GatingSet from flowSet gs <- flowWorkspace::GatingSet(fs) ## ----Check cell population, warning = FALSE, fig.width=8, fig.height=4, dev='png', eval = requireNamespace("CytoExploreR", quietly = TRUE)---- # # check marker expression on cells # CytoExploreR::cyto_plot_custom(matrix(c(1:2),ncol=2)) # for(channel_plot in c("CD8","CD4")){ # CytoExploreR::cyto_plot(gs[[1]], # parent = "root", # channels = c("CD3",channel_plot), # ylim = c(0,4)) # } # # ## ----Apply gatingTemplate----------------------------------------------------- # apply GatingTemplate to flowSet gt <- openCyto::gatingTemplate(file.path(path_data, "gatingTemp_cytokines.csv")) openCyto::gt_gating(x = gt, y = gs) # see nodes in gatingSet names(gt_get_nodes(gt)) ## ----Plot gating1, warning=FALSE, eval = requireNamespace("CytoExploreR", quietly = TRUE)---- # plot(gs) # CytoExploreR::cyto_plot_gating_tree(gs) ## ----Plot gating2, warning=FALSE,fig.width=12, fig.height=6, dev='png', eval = requireNamespace("CytoExploreR", quietly = TRUE)---- # CytoExploreR::cyto_plot_custom(matrix(c(1:8),ncol=4,byrow=TRUE)) # for(sample_plot in c(1,2)){ # for(channel_plot in c("IFN","TNFa","IL2","CD107a")){ # CytoExploreR::cyto_plot(gs[[sample_plot]], # parent = "root", # channels = c(channel_plot,"CD8"), # alias = "") # } # } ## ----population labels-------------------------------------------------------- markers_clust <- c("CD45RA","CCR7","CD27","CD95","TCF1","Tbet","TIGIT","CD39","CCR4","CD16") channels_clust <- flowCore::pData(flowCore::parameters(fs[[1]])) %>% dplyr::filter(desc %in% markers_clust)%>%dplyr::pull(name) %>% unname fsom <- FlowSOM::FlowSOM(fs, compensate = FALSE, transform = FALSE, scale = FALSE, colsToUse = channels_clust, xdim = 8, ydim = 8, nClus = 10) cell_labels <- fsom$metaclustering[fsom$map$mapping[,1]] ## ----Load cell labels--------------------------------------------------------- cell_labels_external <- readRDS(file.path(path_data, "cell_labels.rds")) ## ----polyICSFlow1------------------------------------------------------------- # Get marker positivity per cell df_markerPos <- getMarkerPositivity(x = gs, gate_names = c("IFN+","TNFa+","IL2+","CD107a+")) head(df_markerPos) ## ----polyICSFlow2a------------------------------------------------------------ # Assign marker combinations to each cell based on marker positivity # Simple mode df_markerComb <- assignMarkerCombinations(data_markerPos = df_markerPos, mode = "simple") head(df_markerComb) ## ----polyICSFlow2b------------------------------------------------------------ # Exhaustive mode df_markerComb <- assignMarkerCombinations(data_markerPos = df_markerPos, mode = "exhaustive") names(df_markerComb) ## ----polyICSFlow3------------------------------------------------------------- # Read metadata df_md <- read.csv(file.path(path_data, "metadata.csv")) # Add metadata to dataframe df <- df_markerComb %>% dplyr::mutate(ID = df_md$ID, Stimulation = df_md$Stimulation, cell_type = cell_labels_external) # Alternatively use the cell labels obtained from FlowSOM clustering in step 2.3 # df <- df_markerComb %>% # dplyr::mutate(ID = df_md$ID, # Stimulation = df_md$Stimulation, # cell_type = cell_labels) ## ----polyICSFlow 3a----------------------------------------------------------- # Calculate by Marker Count result <- calcPolyfunctionality(x = df, md_cols = c("ID"), pop_col = "cell_type", resolution = "MarkerCount", condition_col = "Stimulation", background_val = "Unstim") head(result) ## ----Plot result 3a----------------------------------------------------------- palette_MarkerCounts <- scales::brewer_pal(type = "qual")(4) result %>% dplyr::filter(Stimulation != "Unstim")%>% ggplot2::ggplot(ggplot2::aes(x = cell_type, y = perc_pos_backgroundSubtracted, fill = as.factor(MarkerCount)))+ ggh4x::facet_grid2(Stimulation~ID)+ ggplot2::geom_bar(stat="identity")+ ggplot2::theme_bw()+ ggplot2::ylab("Frequency of HIV-1-Gag specific CD8 T cells")+ ggplot2::xlab("CD8 metacluster")+ ggplot2::scale_fill_manual(values = palette_MarkerCounts, name = "Number of functions") ## ----polyICSFlow3b------------------------------------------------------------ # Calculate by Marker Combination result <- calcPolyfunctionality(x = df, md_cols = c("ID"), pop_col = "cell_type", resolution = "MarkerComb", condition_col = "Stimulation", background_val = "Unstim") head(result) ## ----Plot result 3b----------------------------------------------------------- palette_markerCombs <- c("#DC050C","#FB8072","#1965B0","#7BAFDE", "#882E72","#B17BA6","#FF7F00","#FDB462", "#E7298A","#E78AC3","#33A02C", "#B2DF8A", "#55A1B1","#8DD3C7","#A6761D") result %>% dplyr::filter(Stimulation != "Unstim")%>% ggplot2::ggplot(ggplot2::aes(x = cell_type, y = perc_pos_backgroundSubtracted, fill = MarkerComb))+ ggh4x::facet_grid2(Stimulation~ID)+ ggplot2::geom_bar(stat="identity")+ ggplot2::theme_bw()+ ggplot2::ylab("Frequency of HIV-1-Gag specific CD8 T cells")+ ggplot2::xlab("CD8 metacluster")+ ggplot2::scale_fill_manual(values = palette_markerCombs, name = "Cytokine combinations") ## ----polyICSFlow3c------------------------------------------------------------ # Calculate by Polyfunctional_atleast_IFN+ result <- calcPolyfunctionality(x = df, md_cols = c("ID"), pop_col = "cell_type", resolution = "Polyfunctional_atleast_IFN+", condition_col = "Stimulation", background_val = "Unstim") head(result) ## ----Plot result 3c----------------------------------------------------------- result %>% dplyr::filter(Stimulation != "Unstim")%>% ggplot2::ggplot(ggplot2::aes(x = cell_type, y = perc_pos_backgroundSubtracted, fill = cell_type))+ ggplot2::geom_bar(stat="identity")+ ggplot2::theme_bw()+ ggplot2::ylab("Frequency of polyfunctional IFN+ HIV-1-Gag specific CD8 T cells")+ ggplot2::xlab("CD8 metacluster") ## ----Plotting polyICSFlow1, fig.width=7, fig.height=3.5----------------------- plotMarkerCombHeatmap(marker_names = c("IFN","TNFa","IL2","CD107a"), orientation = "horizontal") ## ----Plotting polyICSFlow2, fig.height = 5, fig.width=3.5, out.width = "50%"---- plotCellCountsExactly(data_markerComb = df_markerComb) ## ----Plotting polyICSFlow3, fig.width=7, fig.height=5------------------------- plotCellCountsAtLeast(data_markerComb = df_markerComb) ## ----Plotting polyICSFlow4, fig.width=10, fig.height=6------------------------ library(patchwork) p1 <- plotCellCountsExactly(data_markerComb = df_markerComb) p2 <- plotCellCountsAtLeast(data_markerComb = df_markerComb) p1+p2+plot_layout(guides = "collect",widths = c(1,4)) ## ----Plotting polyICSFlow5, fig.width=16,fig.height=16------------------------ set.seed(1) # setting a seed is recommended to get reproducible visualizations plotMarkerComb2DScatters(input = fs, assigned_MarkerCombs = df_markerComb$MarkerComb) ## ----Extract numbers---------------------------------------------------------- extractCellCounts(df_markerComb) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()