annotate Seurat.R @ 14:c0fd285eb553 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat commit d9cb4478858f60aacb8577eb16e291961384eb1c
author iuc
date Mon, 21 Nov 2022 14:35:28 +0000
parents c4db6ec33fec
children fab6ff46e019
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1 #' ---
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2 #' title: "Seurat Analysis"
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3 #' author: "Performed using Galaxy"
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4 #' params:
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5 #' counts: ""
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6 #' min_cells: ""
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7 #' min_genes: ""
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8 #' low_thresholds: ""
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9 #' high_thresholds: ""
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10 #' numPCs: ""
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11 #' cells_use: ""
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12 #' resolution: ""
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13 #' perplexity: ""
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14 #' min_pct: ""
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15 #' logfc_threshold: ""
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16 #' showcode: ""
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17 #' warn: ""
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18 #' varstate: ""
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19 #' vlnfeat: ""
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20 #' featplot: ""
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21 #' PCplots: ""
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22 #' tsne: ""
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23 #' heatmaps: ""
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24 #' ---
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25
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26 # nolint start
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27 #+ echo=F, warning = F, message=F
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28 options(show.error.messages = F, error = function() {
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29 cat(geterrmessage(), file = stderr()); q("no", 1, F)
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30 })
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31 showcode <- as.logical(params$showcode)
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32 warn <- as.logical(params$warn)
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33 varstate <- as.logical(params$varstate)
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34 vlnfeat <- as.logical(params$vlnfeat)
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35 featplot <- as.logical(params$featplot)
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36 pc_plots <- as.logical(params$PCplots)
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37 tsne <- as.logical(params$tsne)
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38 heatmaps <- as.logical(params$heatmaps)
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39
2
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40 # we need that to not crash Galaxy with an UTF-8 error on German LC settings.
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41 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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42
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43 #+ echo = F, warning = `warn`, include =`varstate`
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44 min_cells <- as.integer(params$min_cells)
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45 min_genes <- as.integer(params$min_genes)
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46 low_thresholds <- as.integer(params$low_thresholds)
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47 high_thresholds <- as.integer(params$high_thresholds)
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48 num_pcs <- as.integer(params$numPCs)
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49 cells_use <- as.integer(params$cells_use)
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50 resolution <- as.double(params$resolution)
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51 perplexity <- as.integer(params$perplexity)
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52 min_pct <- as.double(params$min_pct)
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53 logfc_threshold <- as.double(params$logfc_thresh)
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54 print(paste0("Minimum cells: ", min_cells))
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55 print(paste0("Minimum features: ", min_genes))
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56 print(paste0("Umi low threshold: ", low_thresholds))
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57 print(paste0("Umi high threshold: ", high_thresholds))
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58 print(paste0("Number of principal components: ", num_pcs))
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59 print(paste0("Resolution: ", resolution))
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60 print(paste0("Perplexity: ", perplexity))
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61 print(paste0("Minimum percent of cells", min_pct))
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62 print(paste0("Logfold change threshold", logfc_threshold))
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63
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64 #+ echo = FALSE
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65 if (showcode == TRUE) print("Read in data, generate inital Seurat object")
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66 #+ echo = `showcode`, warning = `warn`, message = F
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67 counts <- read.delim(params$counts, row.names = 1)
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68 seuset <- Seurat::CreateSeuratObject(counts = counts, min.cells = min_cells, min.features = min_genes)
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70 #+ echo = FALSE
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71 if (showcode == TRUE && vlnfeat == TRUE) print("Raw data vizualization")
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72 #+ echo = `showcode`, warning = `warn`, include=`vlnfeat`
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73 Seurat::VlnPlot(object = seuset, features = c("nFeature_RNA", "nCount_RNA"))
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74 Seurat::FeatureScatter(object = seuset, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
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76 #+ echo = FALSE
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77 if (showcode == TRUE) print("Filter and normalize for UMI counts")
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78 #+ echo = `showcode`, warning = `warn`
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79 seuset <- subset(seuset, subset = `nCount_RNA` > low_thresholds & `nCount_RNA` < high_thresholds)
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80 seuset <- Seurat::NormalizeData(seuset, normalization.method = "LogNormalize", scale.factor = 10000)
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82 #+ echo = FALSE
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83 if (showcode == TRUE && featplot == TRUE) print("Variable Genes")
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84 #+ echo = `showcode`, warning = `warn`, include = `featplot`
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85 seuset <- Seurat::FindVariableFeatures(object = seuset, selection.method = "mvp")
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86 Seurat::VariableFeaturePlot(seuset, cols = c("black", "red"), selection.method = "disp")
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87 seuset <- Seurat::ScaleData(object = seuset, vars.to.regress = "nCount_RNA")
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88
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89 #+ echo = FALSE
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90 if (showcode == TRUE && pc_plots == TRUE) print("PCA Visualization")
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91 #+ echo = `showcode`, warning = `warn`, include = `pc_plots`
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92 seuset <- Seurat::RunPCA(seuset, npcs = num_pcs)
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93 Seurat::VizDimLoadings(seuset, dims = 1:2)
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94 Seurat::DimPlot(seuset, dims = c(1, 2), reduction = "pca")
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95 Seurat::DimHeatmap(seuset, dims = 1:num_pcs, nfeatures = 30, reduction = "pca")
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96 seuset <- Seurat::JackStraw(seuset, dims = num_pcs, reduction = "pca", num.replicate = 100)
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97 seuset <- Seurat::ScoreJackStraw(seuset, dims = 1:num_pcs)
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98 Seurat::JackStrawPlot(seuset, dims = 1:num_pcs)
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99 Seurat::ElbowPlot(seuset, ndims = num_pcs, reduction = "pca")
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100
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101 #+ echo = FALSE
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102 if (showcode == TRUE && tsne == TRUE) print("tSNE")
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103 #+ echo = `showcode`, warning = `warn`, include = `tsne`
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104 seuset <- Seurat::FindNeighbors(object = seuset)
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105 seuset <- Seurat::FindClusters(object = seuset)
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106 if (perplexity == -1) {
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107 seuset <- Seurat::RunTSNE(seuset, dims = 1:num_pcs, resolution = resolution);
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108 } else {
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109 seuset <- Seurat::RunTSNE(seuset, dims = 1:num_pcs, resolution = resolution, perplexity = perplexity);
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110 }
2
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111 Seurat::DimPlot(seuset, reduction = "tsne")
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112
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113 #+ echo = FALSE
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114 if (showcode == TRUE && heatmaps == TRUE) print("Marker Genes")
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115 #+ echo = `showcode`, warning = `warn`, include = `heatmaps`
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116 markers <- Seurat::FindAllMarkers(seuset, only.pos = TRUE, min.pct = min_pct, logfc.threshold = logfc_threshold)
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117 top10 <- dplyr::group_by(markers, cluster)
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118 top10 <- dplyr::top_n(top10, 10, avg_log2FC)
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119 Seurat::DoHeatmap(seuset, features = top10$gene)
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120 # nolint end