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scRNA-seq_analysis
This commit is contained in:
veghp
commit
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188 changed files with 146184 additions and 0 deletions
354
tools/bunddle_utils.R
Executable file
354
tools/bunddle_utils.R
Executable file
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tool_addr = "../../tools"
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python.addr = "python3.6"
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# a function to compute force-directed graph; return coordinates as a data frame
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# arguments:
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# pca.df: pca data as a data frame
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# snn : a nearest-neighbor graph as a sparse data matrix
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runFDG = function(pca.df, snn, iterations = 600, tool_addr, python.addr){
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current.wd = getwd()
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setwd(file.path(tool_addr, "force_abstract_graph_2D"))
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# generate unique name for pca data file
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pca.data.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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pca.data.fname = paste(pca.data.fname, ".csv", sep = "")
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# generate unique name for snn file
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snn.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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snn.fname = paste(snn.fname, ".smm", sep = "")
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# generate unique name for fdg coordinates
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fdg.coordinates.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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fdg.coordinates.fname = paste(fdg.coordinates.fname, ".csv", sep = "")
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write.csv(pca.df, pca.data.fname)
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writeMM(obj=snn, file=snn.fname)
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command = gsub(pattern="ITER", replacement=as.character(iterations), paste(python.addr, "./make_fdg.py ITER", sep = " "))
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command = paste(command, paste(c(pca.data.fname, snn.fname, fdg.coordinates.fname), collapse = " "), sep = " ")
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system(command, wait = T)
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fdg_coordinates = read.csv(fdg.coordinates.fname, header = FALSE)
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colnames(fdg_coordinates) = c("X", "Y")
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rownames(fdg_coordinates) = rownames(pca.df)
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file.remove(c(pca.data.fname, snn.fname, fdg.coordinates.fname))
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setwd(current.wd)
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return(fdg_coordinates)
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}
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# a function to perform UMAP on a seurat object using PCA coordinates
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RunUMAP = function(pca.df, tool_addr, python.addr){
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current.wd = getwd()
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setwd(file.path(tool_addr, "umap"))
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print("writting pca data to disk...")
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# generate unique name for pca data file
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pca.data.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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pca.data.fname = paste(pca.data.fname, ".csv", sep = "")
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# generate unique name for umap coordinates data file
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umap.coordinates.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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umap.coordinates.fname = paste(umap.coordinates.fname, ".csv", sep = "")
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write.csv(pca.df, pca.data.fname)
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print("perfoming UMAP")
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command = paste(python.addr, "umap_compute.py", sep = " ")
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command = paste(command, pca.data.fname, sep = ' ')
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command = paste(command, umap.coordinates.fname, ' ')
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system(command, wait = T)
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print("Reading results...")
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umap.coordinates = read.csv(umap.coordinates.fname, stringsAsFactors = F)
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file.remove(c(pca.data.fname, umap.coordinates.fname))
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setwd(current.wd)
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umap.coordinates = umap.coordinates[, c("UMAPx", "UMAPy")]
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return(umap.coordinates)
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}
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# a function that load a SVM models and make predictions like cell types or doublet.singlets
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Apply_Classifier_On_Seurat_Object = function(seurat.obj, classifier.fname, tool_addr, python.addr){
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current.wd = getwd()
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setwd(file.path(tool_addr, 'predict_by_classifier'))
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predictor.addr = file.path("../../resources", classifier.fname, sep = "")
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print(predictor.addr)
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if(!dir.exists(predictor.addr)){
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print("classifier does not exists")
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available.classifiers = list.dirs("../../resources", full.names=F)
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available.classifiers = available.classifiers[grepl("classifier_", available.classifiers)]
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print(paste("Available doublets identifiers: ", paste(available.classifiers, collapse = ", "), sep = ""))
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setwd(current.wd)
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return(NULL)
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}
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tryCatch({
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OK = FALSE
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print("reading feature genes ...")
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feature.genes = readRDS(file.path(predictor.addr, "feature_genes.RDS"))
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features.present = feature.genes[feature.genes %in% rownames(seurat.obj@data)]
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features.not.present = feature.genes[!(feature.genes %in% rownames(seurat.obj@data))]
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expr.data = as.data.frame(t(as.matrix(seurat.obj@data[features.present, ])))
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if (length(features.not.present) > 0){
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zeros.data = as.data.frame(t(as.matrix(seurat.obj@data[1:length(features.not.present),])))
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zeros.data[] = 0
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colnames(zeros.data) = features.not.present
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expr.data=cbind(expr.data, zeros.data)
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expr.data = expr.data[, feature.genes]
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}
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print("Writting data to disk ... ")
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data.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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data.fname = paste(data.fname, ".csv", sep = '')
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write.csv(expr.data, data.fname)
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print(sprintf("Dims are: %s", dim(expr.data)))
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print("Copying pickle file ... ")
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model.fname = paste(sample(LETTERS, 20, T), collapse = '')
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model.fname = paste(model.fname, '.pickle', sep = "")
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file.copy(from=file.path(predictor.addr, "model.pickle"), to=model.fname)
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pca.fname = paste(sample(LETTERS, 20, T), collapse = '')
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pca.fname = paste(pca.fname, '.pickle', sep = "")
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file.copy(from=file.path(predictor.addr, "pca.pickle"), to=pca.fname)
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print("Running classifier in Python ... ")
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predictions.fname = paste(sample(LETTERS, 20, TRUE), collapse = "")
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predictions.fname = paste(predictions.fname, ".csv", sep = "")
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command = paste(python.addr, "predict.py", sep = " ")
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command = paste(command, paste(c(model.fname, data.fname, predictions.fname, pca.fname), collapse = " "), sep = " ")
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system(command, wait = T)
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predictions = read.csv(predictions.fname)
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OK = TRUE
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},
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warning = function(warning_conditions){print("")},
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error = function(error_condition){
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print("Errors occured. Cleaning up and then returning NULL ...")
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file.remove(c(model.fname, data.fname, predictions.fname, pca.fname))
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setwd(current.wd)
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})
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if(OK){
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if(length(features.not.present) > 0){
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print('Everything went well except the fact that some of the features genes were not present in the data. These are:')
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print(paste(features.not.present, collapse = ", "))
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}else{
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print("Everything went smooth. Cleaning up and returning the predictions ... ")
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}
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file.remove(c(model.fname, data.fname, predictions.fname, pca.fname))
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setwd(current.wd)
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return(predictions$X0)
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}
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return(NULL)
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}
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# a function that takes 3D coordinates (e.g. from diffusion map) and generates an
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# interactive html page
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make_3D_interactive_page = function(data_frame_3D, tool_addr, python.addr, save.to){
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data.frame.fname = paste(sample(LETTERS, 20, T), collapse = '')
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data.frame.fname = paste(data.frame.fname, ".csv", sep = "")
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save.to = file.path(getwd(), save.to)
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command = gsub(pattern="save.to", replacement=save.to, x=paste(python.addr, './html_WebGL_3D_viewer.py save.to', sep = " "))
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command = paste(command, data.frame.fname, sep = " ")
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current.wd = getwd()
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setwd(file.path(tool_addr, "interactive_3D_viewer"))
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write.csv(data_frame_3D, data.frame.fname, row.names = F)
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system(command, wait=T)
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file.remove(data.frame.fname)
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setwd(current.wd)
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}
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# a function that takes 2D coordinates and generates an interactive html page
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make_2D_interactive_page = function(data_frame_2D, tool_addr, python.addr, save.to="./"){
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data.frame.fname = paste(sample(LETTERS, 20, T), collapse = '')
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data.frame.fname = paste(data.frame.fname, ".csv", sep = "")
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save.to = file.path(getwd(), save.to)
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command = gsub(pattern="save.to", replacement=save.to, x=paste(python.addr, './html_WebGL_2D_viewer.py save.to', sep = " "))
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command = paste(command, data.frame.fname, sep = " ")
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current.wd = getwd()
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setwd(file.path(tool_addr, "interactive_2D_viewer"))
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write.csv(data_frame_2D, data.frame.fname, row.names = F)
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system(command, wait=T)
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file.remove(data.frame.fname)
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setwd(current.wd)
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}
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# a function that create interactic html pages to explore gene expression in data parsed by different categories
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create_gene_expression_viewer_apps = function(seurat.obj, dim.type = 'umap', save.to, tool_addr, python.addr, categories.colours=NA){
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categories = c("cell.labels", "fetal.ids", "sort.ids", "lanes", "stages", "gender", "doublets")
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if(is.na(categories.colours)){
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categories.colours = rep(NA, length(categories))
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}
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categories.data = as.data.frame(seurat.obj@meta.data[names(seurat.obj@ident), categories])
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for(j in 1:length(categories)){
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category = categories[j]
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category.colour.scheme = categories.colours[j]
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if (!is.na(category.colour.scheme)){
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category.colour.scheme = read.csv(category.colour.scheme)
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category.colour.scheme = mapvalues(x=categories.data[, category], from=as.vector(unique(category.colour.scheme$CellTypes)), to=as.vector(unique(category.colour.scheme$Colours)))
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}else{
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category.colour.scheme = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(as.vector(unique(categories.data[, category])))))
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category.colour.scheme = mapvalues(x=categories.data[, category], from=as.vector(unique(categories.data[, category])), to=category.colour.scheme)
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}
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category = paste(category, "colours", sep = "_")
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categories.data[, category] = category.colour.scheme
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}
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eval(parse(text = sprintf("dim.data = seurat.obj@dr$%s@cell.embeddings[names(seurat.obj@ident), 1:2]", dim.type)))
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n.categories = length(categories)
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genes.by.file = round( 20 * 26149846 / ncol(seurat.obj@data))
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approx.no.of.files = round(nrow(seurat.obj@data) / genes.by.file)
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gene.names = sort(rownames(seurat.obj@data))
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gene.names = sort(seurat.obj@var.genes)
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gene.splits = split(gene.names, sort(1:length(gene.names) %% approx.no.of.files))
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curdir = getwd()
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unlink(x=save.to, recursive=T)
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dir.create(save.to)
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setwd(file.path(tool_addr, 'gene_expression_viewer_apps'))
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folder_name = paste(sample(LETTERS, 20, T), collapse = "")
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dir.create(folder_name)
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for (l in 1:length(gene.splits)){
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gene.split = unlist(gene.splits[[l]])
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expression.data = as.data.frame(as.matrix(t(seurat.obj@data[gene.split, names(seurat.obj@ident)])))
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expression.data = cbind(dim.data, categories.data, expression.data)
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first_gene = gene.split[1]
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last_gene = gene.split[length(gene.split)]
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print(sprintf("Creating app for: %s", paste(c(first_gene, "to", last_gene), collapse = "_")))
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expression.data.fname = paste(c(first_gene, "to", last_gene), collapse = "_")
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expression.data.fname = paste(expression.data.fname, ".csv", sep = "")
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expression.data.fname = file.path(folder_name, expression.data.fname)
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save_to = paste(c(first_gene, "to", last_gene), collapse = "_")
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save_to = paste(save_to, ".html", sep = "")
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save_to = file.path(file.path(curdir, save.to), save_to)
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command = sprintf("%s gene_expression_viewer_apps.py %s %s %s", python.addr, save_to, expression.data.fname, n.categories)
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write.csv(expression.data, expression.data.fname, row.names = F)
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system(command, wait = T)
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}
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unlink(x=folder_name, recursive=T, force=T)
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setwd(curdir)
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}
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# a plotting function for indexed legend
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plot.indexed.legend = function(label.vector, color.vector, ncols = 2, left.limit = 3.4, symbol.size = 8, text.size = 10, padH = 1, padV = 1, padRight = 0){
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if (length(label.vector) != length(color.vector)){
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stop("number of labels is different from number colors\nAdvice: learn to count!")
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}
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if (length(ncol) > length(label.vector)){
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stop("You cannot have more columns than labels\nSolution: Learn to count")
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}
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indices.vector = 1:length(label.vector)
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label.no = length(label.vector)
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nrows = ceiling(label.no / ncols)
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legend.frame = data.frame(X = rep(0, label.no), Y = rep(0, label.no), CS = color.vector, Txt = label.vector)
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legend.frame$X = rep(1:ncols, each=nrows)[1:nrow(legend.frame)]
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legend.frame$Y = rep(nrows:1, times = ncols)[1:nrow(legend.frame)]
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Xrange = range(legend.frame$X)
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Yrange = range(legend.frame$Y)
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plot.obj = ggplot(data = legend.frame, aes(x = X, y = Y))
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plot.obj = plot.obj + geom_point(size = symbol.size, colour = color.vector)
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plot.obj = plot.obj + scale_x_continuous(limits = c(Xrange[1] - padRight, Xrange[2] + padH))
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plot.obj = plot.obj + scale_y_continuous(limits = c(Yrange[1] - padV, Yrange[2] + padV))
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plot.obj = plot.obj + theme_void()
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plot.obj = plot.obj + annotate("text", x=legend.frame$X, y = legend.frame$Y, label = indices.vector, size = text.size)
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plot.obj = plot.obj + annotate("text", x=legend.frame$X+.1, y = legend.frame$Y, label=legend.frame$Txt, hjust = 0, size = text.size)
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return(plot.obj)
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}
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# plotting function for dimensionaly-reduced data to label population by a round indexed label
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dr.plot = function(point.labels, dr1, dr2, dr1.name, dr2.name, no.legend = F, plt.lb.sz = 5, txt.lb.size = 3, pt.size = .2, random_state = 2, use.cols = NULL, use.labels = NULL, limits = NULL, annotate.plot = T, index.map = NA){
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if(!is.na(overlay.data)){
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df.dr = data.frame("Cell Labels" = point.labels, DR1 = dr1, DR2 = dr2, overlay.data=factor(df$overlay.data))
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}
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else{
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df.dr = data.frame("Cell Labels" = point.labels, DR1 = dr1, DR2 = dr2)
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}
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if(is.null(use.labels)){
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p.labels = sort(unique(as.vector(point.labels)))
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}
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else{
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p.labels = use.labels
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}
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df.dr$Cell.Labels = factor(df.dr$Cell.Labels, levels=p.labels)
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p.labels.medians = aggregate(df.dr[, 2:3], list(df.dr$Cell.Labels), median)
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df.dr$Cell.Labels = mapvalues(x = df.dr$Cell.Labels, from = p.labels, to = paste(1:length(p.labels), p.labels, sep = " "))
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if(is.null(use.cols)){
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set.seed(random_state)
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plt.colours = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(p.labels)))
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}else{
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plt.colours = use.cols
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}
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if(is.na(index.map)){
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index.map = 1:length(p.labels)
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}
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if(!is.na(overlay.data)){
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plot.obj = ggplot(data = df.dr, aes(x = DR1, y = DR2, color = Cell.Labels, shape = factor(overlay.data, levels=overlay.data.ordered)))
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print("levels=overlay.data.ordered")
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print(overlay.data.ordered)
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}
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else{
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plot.obj = ggplot(data = df.dr, aes(x = DR1, y = DR2, color = Cell.Labels))
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}
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# this line should give different sizes for different values in overlay.data metadata column
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if(!is.na(overlay.data)){
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plot.obj = plot.obj + geom_point(size=pt.size)
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plot.obj = plot.obj + geom_point(data = subset(df.dr, overlay.data == overlay.data.ordered[2]))
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print("overlay.data.ordered[2]")
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print(overlay.data.ordered[2])
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}
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else{
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plot.obj = plot.obj + geom_point(size=pt.size)
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}
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plot.obj = plot.obj + scale_color_manual(values=plt.colours)
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if(annotate.plot){
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if(!is.na(overlay.data)){
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plot.obj = plot.obj + geom_point(data=p.labels.medians,aes(x = DR1, y = DR2), colour = "gray", size = plt.lb.sz, fill = plt.colours, alpha = .5, pch = 21, shape=factor(df.dr$overlay.data, levels=overlay.data.ordered))
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}
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else{
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plot.obj = plot.obj + geom_point(data=p.labels.medians,aes(x = DR1, y = DR2), colour = "gray", size = plt.lb.sz, fill = plt.colours, alpha = .5, pch = 21)
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}
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plot.obj = plot.obj + annotate("text", x=p.labels.medians$DR1, y = p.labels.medians$DR2, label = index.map, size = txt.lb.size)
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}
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if (no.legend){
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plot.obj = plot.obj + theme(legend.position="none")
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}else{
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plot.obj = plot.obj + guides(color = guide_legend(override.aes = list(size=5)))
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}
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plot.obj = plot.obj + xlab(dr1.name) + ylab(dr2.name)
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if(!is.null(limits)){
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X0 = limits[1]; X1 = limits[2]; Y0 = limits[3]; Y1 = limits[4];
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plot.obj = plot.obj + scale_x_continuous(limits = c(X0, X1))
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plot.obj = plot.obj + scale_y_continuous(limits = c(Y0, Y1))
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}
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return(plot.obj)
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}
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# plotting function for dimensionaly-reduced data to label population by a round indexed label
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dr.plot.numerical = function(point.labels, dr1, dr2, dr1.name, dr2.name, no.legend = F, plt.lb.sz = 5, txt.lb.size = 3, pt.size = .2, random_state = 2, use.cols = NULL, use.labels = NULL, limits = NULL, annotate.plot = T, index.map = NA){
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df.dr = data.frame("Cell Labels" = point.labels, DR1 = dr1, DR2 = dr2)
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if(is.null(use.labels)){
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p.labels = sort(unique(as.vector(point.labels)))
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}
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else{
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p.labels = use.labels
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}
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df.dr$Cell.Labels = factor(df.dr$Cell.Labels, levels=p.labels)
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p.labels.medians = aggregate(df.dr[, 2:3], list(df.dr$Cell.Labels), median)
|
||||
#df.dr$Cell.Labels = mapvalues(x = df.dr$Cell.Labels, from = p.labels, to = paste(1:length(p.labels), p.labels, sep = " "))
|
||||
if(is.null(use.cols)){
|
||||
set.seed(random_state)
|
||||
plt.colours = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(p.labels)))
|
||||
}else{
|
||||
plt.colours = use.cols
|
||||
}
|
||||
if(is.na(index.map)){
|
||||
#index.map = 1:length(p.labels)
|
||||
index.map = p.labels
|
||||
}
|
||||
plot.obj = ggplot(data = df.dr, aes(x = DR1, y = DR2, color = Cell.Labels))
|
||||
plot.obj = plot.obj + geom_point(size = pt.size)
|
||||
plot.obj = plot.obj + scale_color_manual(values=plt.colours)
|
||||
if(annotate.plot){
|
||||
plot.obj = plot.obj + geom_point(data=p.labels.medians,aes(x = DR1, y = DR2), colour = "gray", size = plt.lb.sz, fill = plt.colours, alpha = .5, pch = 21)
|
||||
plot.obj = plot.obj + annotate("text", x=p.labels.medians$DR1, y = p.labels.medians$DR2, label = index.map, size = txt.lb.size)
|
||||
}
|
||||
if (no.legend){
|
||||
plot.obj = plot.obj + theme(legend.position="none")
|
||||
}else{
|
||||
plot.obj = plot.obj + guides(color = guide_legend(override.aes = list(size=5)))
|
||||
}
|
||||
plot.obj = plot.obj + xlab(dr1.name) + ylab(dr2.name)
|
||||
if(!is.null(limits)){
|
||||
X0 = limits[1]; X1 = limits[2]; Y0 = limits[3]; Y1 = limits[4];
|
||||
plot.obj = plot.obj + scale_x_continuous(limits = c(X0, X1))
|
||||
plot.obj = plot.obj + scale_y_continuous(limits = c(Y0, Y1))
|
||||
}
|
||||
return(plot.obj)
|
||||
}
|
||||
Loading…
Add table
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Reference in a new issue