Mercurial > repos > iuc > seurat
view Seurat.R @ 10:bb9732cfcdbe draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat commit c7b2b60384d54bf3ab5deca109cccba6d30b5c2e"
author | iuc |
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date | Mon, 20 Dec 2021 10:09:33 +0000 |
parents | c4db6ec33fec |
children | fab6ff46e019 |
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#' --- #' title: "Seurat Analysis" #' author: "Performed using Galaxy" #' params: #' counts: "" #' min_cells: "" #' min_genes: "" #' low_thresholds: "" #' high_thresholds: "" #' numPCs: "" #' cells_use: "" #' resolution: "" #' perplexity: "" #' min_pct: "" #' logfc_threshold: "" #' showcode: "" #' warn: "" #' varstate: "" #' vlnfeat: "" #' featplot: "" #' PCplots: "" #' tsne: "" #' heatmaps: "" #' --- # nolint start #+ echo=F, warning = F, message=F options(show.error.messages = F, error = function() { cat(geterrmessage(), file = stderr()); q("no", 1, F) }) showcode <- as.logical(params$showcode) warn <- as.logical(params$warn) varstate <- as.logical(params$varstate) vlnfeat <- as.logical(params$vlnfeat) featplot <- as.logical(params$featplot) pc_plots <- as.logical(params$PCplots) tsne <- as.logical(params$tsne) heatmaps <- as.logical(params$heatmaps) # we need that to not crash Galaxy with an UTF-8 error on German LC settings. loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") #+ echo = F, warning = `warn`, include =`varstate` min_cells <- as.integer(params$min_cells) min_genes <- as.integer(params$min_genes) low_thresholds <- as.integer(params$low_thresholds) high_thresholds <- as.integer(params$high_thresholds) num_pcs <- as.integer(params$numPCs) cells_use <- as.integer(params$cells_use) resolution <- as.double(params$resolution) perplexity <- as.integer(params$perplexity) min_pct <- as.double(params$min_pct) logfc_threshold <- as.double(params$logfc_thresh) print(paste0("Minimum cells: ", min_cells)) print(paste0("Minimum features: ", min_genes)) print(paste0("Umi low threshold: ", low_thresholds)) print(paste0("Umi high threshold: ", high_thresholds)) print(paste0("Number of principal components: ", num_pcs)) print(paste0("Resolution: ", resolution)) print(paste0("Perplexity: ", perplexity)) print(paste0("Minimum percent of cells", min_pct)) print(paste0("Logfold change threshold", logfc_threshold)) #+ echo = FALSE if (showcode == TRUE) print("Read in data, generate inital Seurat object") #+ echo = `showcode`, warning = `warn`, message = F counts <- read.delim(params$counts, row.names = 1) seuset <- Seurat::CreateSeuratObject(counts = counts, min.cells = min_cells, min.features = min_genes) #+ echo = FALSE if (showcode == TRUE && vlnfeat == TRUE) print("Raw data vizualization") #+ echo = `showcode`, warning = `warn`, include=`vlnfeat` Seurat::VlnPlot(object = seuset, features = c("nFeature_RNA", "nCount_RNA")) Seurat::FeatureScatter(object = seuset, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") #+ echo = FALSE if (showcode == TRUE) print("Filter and normalize for UMI counts") #+ echo = `showcode`, warning = `warn` seuset <- subset(seuset, subset = `nCount_RNA` > low_thresholds & `nCount_RNA` < high_thresholds) seuset <- Seurat::NormalizeData(seuset, normalization.method = "LogNormalize", scale.factor = 10000) #+ echo = FALSE if (showcode == TRUE && featplot == TRUE) print("Variable Genes") #+ echo = `showcode`, warning = `warn`, include = `featplot` seuset <- Seurat::FindVariableFeatures(object = seuset, selection.method = "mvp") Seurat::VariableFeaturePlot(seuset, cols = c("black", "red"), selection.method = "disp") seuset <- Seurat::ScaleData(object = seuset, vars.to.regress = "nCount_RNA") #+ echo = FALSE if (showcode == TRUE && pc_plots == TRUE) print("PCA Visualization") #+ echo = `showcode`, warning = `warn`, include = `pc_plots` seuset <- Seurat::RunPCA(seuset, npcs = num_pcs) Seurat::VizDimLoadings(seuset, dims = 1:2) Seurat::DimPlot(seuset, dims = c(1, 2), reduction = "pca") Seurat::DimHeatmap(seuset, dims = 1:num_pcs, nfeatures = 30, reduction = "pca") seuset <- Seurat::JackStraw(seuset, dims = num_pcs, reduction = "pca", num.replicate = 100) seuset <- Seurat::ScoreJackStraw(seuset, dims = 1:num_pcs) Seurat::JackStrawPlot(seuset, dims = 1:num_pcs) Seurat::ElbowPlot(seuset, ndims = num_pcs, reduction = "pca") #+ echo = FALSE if (showcode == TRUE && tsne == TRUE) print("tSNE") #+ echo = `showcode`, warning = `warn`, include = `tsne` seuset <- Seurat::FindNeighbors(object = seuset) seuset <- Seurat::FindClusters(object = seuset) if (perplexity == -1) { seuset <- Seurat::RunTSNE(seuset, dims = 1:num_pcs, resolution = resolution); } else { seuset <- Seurat::RunTSNE(seuset, dims = 1:num_pcs, resolution = resolution, perplexity = perplexity); } Seurat::DimPlot(seuset, reduction = "tsne") #+ echo = FALSE if (showcode == TRUE && heatmaps == TRUE) print("Marker Genes") #+ echo = `showcode`, warning = `warn`, include = `heatmaps` markers <- Seurat::FindAllMarkers(seuset, only.pos = TRUE, min.pct = min_pct, logfc.threshold = logfc_threshold) top10 <- dplyr::group_by(markers, cluster) top10 <- dplyr::top_n(top10, 10, avg_log2FC) Seurat::DoHeatmap(seuset, features = top10$gene) # nolint end