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scRNA-seq_analysis
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veghp
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90
pipelines/25_multiple_AGAs/make_AGA_app.py
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90
pipelines/25_multiple_AGAs/make_AGA_app.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Nov 22 11:03:12 2018
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@author: doru
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"""
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# argument variables
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import sys
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output_folder = sys.argv[1]
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from os.path import join
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# file names
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material_folder = join(output_folder, "AGA_folder")
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save_to = join(output_folder, 'AGAlinkage_map_{cat}.html'.format(cat = sys.argv[2]))
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colors_fname = join(material_folder, 'colours.csv')
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connectivities_fname = join(material_folder, 'connectivities.csv')
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coordinates_fname = join(material_folder, 'coordinates.csv')
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# read data from files in csv formatr
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import pandas as pd
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connectivities = pd.read_csv(connectivities_fname, index_col = 0, header = 0)
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coordinates = pd.read_csv(coordinates_fname, index_col = 0, header = 0)
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try:
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colors = pd.read_csv(colors_fname, index_col = 0, header = 0)
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except FileNotFoundError:
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cell_types = connectivities.columns
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import random
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cell_types = [f for f in connectivities.columns]
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colours = []
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for cell_type in cell_types:
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r = lambda: random.randint(0,255)
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col = '#%02X%02X%02X' % (r(),r(),r())
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colours.append({'CellTypes': cell_type, 'Colours': col})
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colors = pd.DataFrame(colours)
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colors = colors.set_index('CellTypes')
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scaleScale = 1.4
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minX = coordinates.min()[0] * scaleScale
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minY = coordinates.min()[1] * scaleScale
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maxX = coordinates.max()[0] * scaleScale
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maxY = coordinates.max()[1] * scaleScale
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# prepare the coordinates and colors data
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cell_names = list(coordinates.index)
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cell_sizes = coordinates.Size.tolist()
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# reorder cell names by population size - so during drawing smaller cell population are not covered by bigger bubbles
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cell_names = [cell_name for [cell_size, cell_name] in sorted(zip(cell_sizes, cell_names), reverse = True)]
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data_coordinates = []
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for cell_name in cell_names:
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row_data = coordinates.loc[cell_name]
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X, Y, R = row_data.X, row_data.Y, row_data.Size
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X = (X - minX) / (maxX - minX);
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Y = (Y - minY) / (maxY - minY);
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color = colors.loc[cell_name].Colours
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indata = 'data_coordinates["{cell_name}"] = [{X}, {Y}, {R}, "{C}"]'.format(cell_name = cell_name,
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X = X, Y = Y, R = R, C = color)
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data_coordinates.append(indata)
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data_coordinates = '\n'.join(data_coordinates)
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# prepare edge thickness data
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data_edges = []
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# rearrange connectivities by order of cell name
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for cell_name in cell_names:
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indata = connectivities[cell_name][cell_names].tolist()
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indata = ','.join([str(i) for i in indata])
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indata = 'data_edges["{cell_name}"] = [{indata}]'.format(cell_name = cell_name, indata = indata)
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data_edges.append(indata)
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data_edges = '\n'.join(data_edges)
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# make cell_names array
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cell_names = ['"{cell_name}"'.format(cell_name = cell_name) for cell_name in cell_names]
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cell_names = ','.join(cell_names)
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cell_names = 'cell_names = [{cell_names}]'.format(cell_names = cell_names)
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# prepare all the data
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data = '\n'.join([data_coordinates, data_edges, cell_names])
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template_fobj = open('template_for_AGA_app.html', 'r')
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template = template_fobj.read();
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template_fobj.close()
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# insert data in template
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template = template.replace('// insert data here', data)
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# save interactive page
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with open(save_to, 'w') as save_fobj:
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save_fobj.write(template)
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134
pipelines/25_multiple_AGAs/multiple_AGAs.R
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134
pipelines/25_multiple_AGAs/multiple_AGAs.R
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args = commandArgs(trailingOnly=T)
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option.file = args[1]
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# create required folders for output and work material
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output_folder = gsub(pattern="^\\d+_", replacement="", x=basename(getwd()))
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c.time = Sys.time()
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c.time = gsub(pattern=" BST", replacement="", x=c.time)
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c.time = gsub(pattern=":", replacement="", x=c.time)
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c.time = gsub(pattern=" ", replacement="", x=c.time)
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c.time = gsub(pattern="-", replacement="", x=c.time)
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c.time = substr(x=c.time, start=3, stop=nchar(c.time))
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output_folder = paste(output_folder, c.time, sep = "_")
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output_folder = file.path("../../output", output_folder)
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dir.create(output_folder)
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source("../../tools/bunddle_utils.R")
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library(Seurat)
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library(RColorBrewer)
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library(plyr)
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library(dplyr)
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library(magrittr)
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#######################################################################################################
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# parse options
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option.file = file(option.file, "r")
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option_lines = readLines(option.file)
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close(option.file)
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#remove comments
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option_lines = option_lines[-grep(pattern="^#", x=option_lines)]
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# split options into blocks
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option_lines =paste(option_lines, collapse = "@@@")
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option_blocks = option_lines %>% strsplit(split="name: ") %>% unlist
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option_blocks = option_blocks[option_blocks != ""]
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# loop through each block of options, load the data sets and make the AGAs
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for(k in seq_along(option_blocks)){
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option_block = option_blocks[k] %>% strsplit(split="@@@") %>% unlist
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print(sprintf("Making AGA for %s", option_block[1]))
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AGA_save_to = file.path(output_folder, option_block[1])
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dir.create(AGA_save_to)
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output_folder_material = file.path(AGA_save_to, "material")
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AGA_folder = file.path(AGA_save_to, "AGA_folder")
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dir.create(output_folder_material)
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dir.create(AGA_folder)
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# load data sets
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data_files = option_block[grep(pattern="^data", x=option_block)] %>% gsub(pattern="^data.: ", replacement="")
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set.idents = option_block[grep(pattern="^set.ident", x=option_block)] %>% gsub(pattern="^set.ident..: ", replacement="")
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label.tags = option_block[grep(pattern="^tag", x=option_block)] %>% gsub(pattern="^tag.: ", replacement="")
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categories = option_block[grep(pattern="^categories", x=option_block)]
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data.list = list()
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for(i in seq_along(data_files)){
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data.file = file.path("../../data", data_files[i])
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print(sprintf("Loading %s", data.file))
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seurat.obj = readRDS(data.file)
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seurat.obj %<>% SetAllIdent(id=set.idents[i])
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cell.labels = categories[i] %>% strsplit(split=": ") %>% unlist %>% (function(x)x[2]) %>% strsplit(split=", ") %>% unlist
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seurat.obj %<>% SubsetData(ident.use=cell.labels)
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seurat.obj@meta.data$AGA_labels = paste(paste(label.tags[i], "::", sep=""), as.vector(seurat.obj@ident), sep="")
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eval(parse(text=sprintf("data.list$data%s = seurat.obj", i)))
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}
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print("Subsetting and merging datasets ...")
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seurat.obj = Reduce(f=MergeSeurat, x=data.list)
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seurat.obj %<>% SetAllIdent(id="AGA_labels")
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write.csv(data.frame(Cells = names(seurat.obj@ident), Labels = seurat.obj@ident), file.path(output_folder_material, "cell_labels.csv"), row.names = F)
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# save raw data to disk
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raw_data = seurat.obj@raw.data
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raw_data = raw_data[rownames(seurat.obj@data), colnames(seurat.obj@data)]
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writeMM(raw_data, file.path(output_folder_material, "raw_data.mtx"))
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# save gene names
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gene_names = rownames(raw_data)
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write.csv(data.frame(Genes = gene_names), file.path(output_folder_material, "genenames.csv"))
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# save cell names
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cell_names = colnames(raw_data)
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write.csv(data.frame(Cells = cell_names), file.path(output_folder_material, "cellnames.csv"))
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# write cell labels to disk
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write.csv(data.frame(Cells = names(seurat.obj@ident), Labels = seurat.obj@ident), file.path(output_folder_material, "cell_labels.csv"), row.names = F)
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# running AGA
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command = file.path(tool_addr, "AGA/AGA_from_Seurat.py")
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command = paste(paste(python.addr, command, sep = " "), AGA_save_to, sep = " ")
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command = paste(command, option_block[1], sep =" ")
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system(command, wait = T)
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# read the AGA output
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coordinates = read.csv(file.path(AGA_folder, "coordinates.csv"), row.names = 1)
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connectivities = read.csv(file.path(AGA_folder, "connectivities.csv"), row.names = 1)
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colnames(connectivities) = rownames(connectivities)
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cell.labels = rownames(coordinates)
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cell.colours = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(cell.labels)))
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######## now make the interactive AGA app
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#########################################
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print("Making the AGA app ... ")
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# save colours
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colours.df = data.frame(CellTypes = cell.labels, Colours = cell.colours)
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write.csv(colours.df, file.path(AGA_folder, "colours.csv"), row.names = F)
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# run python to built the AGA app
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command = sprintf("%s make_AGA_app.py %s %s", python.addr, AGA_save_to, option_block[1])
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system(command, wait = T)
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AGA.file = file.path(AGA_save_to, paste(c("AGAlinkage_map_", option_block[1], ".html"), collapse = ""))
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AGA.final.destination = file.path(output_folder, paste(c("AGAlinkage_map_", option_block[1], ".html"), collapse = ""))
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file.rename(from=AGA.file, to=AGA.final.destination)
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# make FDG
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print("Making force directed graph interactive app ...")
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seurat.obj = FindVariableGenes(object = seurat.obj, mean.function = ExpMean,
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dispersion.function = LogVMR, x.low.cutoff = .0125,
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x.high.cutoff = 3, y.cutoff = .625, do.plot=F)
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seurat.obj = ScaleData(object=seurat.obj)
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seurat.obj = RunPCA(object = seurat.obj, pc.genes = seurat.obj@var.genes, do.print = FALSE)
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seurat.obj = BuildSNN(object=seurat.obj, reduction.type="pca", dims.use=1:20, plot.SNN=F, force.recalc=T)
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fdg_coordinates = runFDG(pca.df=seurat.obj@dr$pca@cell.embeddings, snn=seurat.obj@snn, iterations=2000, tool_addr=tool_addr, python.addr=python.addr)
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seurat.obj = SetDimReduction(object=seurat.obj, reduction.type="fdg", slot="cell.embeddings", new.data=as.matrix(fdg_coordinates))
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seurat.obj = SetDimReduction(object=seurat.obj, reduction.type="fdg", slot = "key", new.data = "fdg")
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interactive_plot_df = data.frame(X = seurat.obj@dr$fdg@cell.embeddings[, 1],
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Y = seurat.obj@dr$fdg@cell.embeddings[, 2])
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interactive_plot_df$Labels = factor(seurat.obj@ident, levels = cell.labels)
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interactive_plot_df$Colours = mapvalues(x = interactive_plot_df$Labels, from = cell.labels, to = cell.colours)
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interactive_fdg_filename = file.path(output_folder, paste(paste("Interactive_FDG", option_block[1], sep = "_"), "html", sep = "."))
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make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_fdg_filename)
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unlink(AGA_save_to, recursive=T, force=T)
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}
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print("Ended beautifully ... ")
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11
pipelines/25_multiple_AGAs/multiple_AGAs.sh
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11
pipelines/25_multiple_AGAs/multiple_AGAs.sh
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#!/bin/bash
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#$ -cwd
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#$ -N multiple_AGAs
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#$ -V
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#$ -l h_rt=23:59:59
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#$ -l h_vmem=100G
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Rscript multiple_AGAs.R $1
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echo "End on `date`"
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29
pipelines/25_multiple_AGAs/options/options_file_example.txt
Executable file
29
pipelines/25_multiple_AGAs/options/options_file_example.txt
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################################################################################
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# making AGA on HSC and lymphoid in bone marrow and liver
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# pDCs are also included
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name: lymphoids_bm_lv
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data1: bonemarrow_reference_annotation_10X.RDS
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data2: liver_F21F22F23_reference_annotation_10X.RDS
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set.ident.1: cell.labels
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set.ident.2: cell.labels
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tag1: BM
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tag2: LV
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categories1: HSPC, pro-B, pre-B, T cell, naïve B cell, pDC, pre pDC
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categories2: pro-B cell, B cell, pre-B cell, NK, Pre pro B cell, HSC/MPP, pDC precursor, ILC progenitor
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################################################################################
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# making AGA on HSC and a few myeloids in bone marrow, liver
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# pDCs are also included
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name: myeloids_bm_liver
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data1: bonemarrow_reference_annotation_10X.RDS
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data2: liver_F21F22F23_reference_annotation_10X.RDS
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data3: spleen_reference_annotation_10X.RDS
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set.ident.1: cell.labels
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set.ident.2: cell.labels
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set.ident.3: cell.labels
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tag1: BM
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tag2: LV
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tag2: SP
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categories1: myeloid precursor, HSPC, tissue macrophage, pDC, DC2
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categories2: Mono-Mac, DC2, Monocyte, HSC/MPP
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categories3: cDC2, HSC, Macrophage, Neut_Myeloid
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################################################################################
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215
pipelines/25_multiple_AGAs/template_for_AGA_app.html
Executable file
215
pipelines/25_multiple_AGAs/template_for_AGA_app.html
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>Interactive linkage plot</title>
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<meta name="description" content="An interactive plot for the linkage map">
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<meta name="author" content="Dorin-Mirel Popescu">
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</head>
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<body>
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<ul>
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<li>Bubble size reflects population size; Edge thickness reflects connectivity scores;</li>
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<li>Use the sliders to set plotting parameters;</li>
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<li>Click the canvas area to select a cell population and reposition it by dragging;</li>
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<li>Plot can be saved by right click on canvas area and choose 'Save as'; For higher image resolution increase canvas area, font size and scales before saving;</li>
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</ul>
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<table>
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<tr>
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<td>Canvas width</td><td>Canvas height</td><td>Size scale</td><td>Edge scale</td><td>Edge threshold</td><td>Font size</td>
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<tr>
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<td><input type = 'range' min = '100' max = '3000' value = '500' onchange = 'setWidth(this.value)' /></td>
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<td><input type = 'range' min = '100' max = '3000' value = '500' onchange = 'setHeight(this.value)' /></td>
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<td><input type = 'range' min = '0' max = '300' value = '10' onchange = 'setSizeScale(this.value)' /></td>
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<td><input type = 'range' min = '.1' max = '15' step = '.1' value = '5' onchange = 'setEdgeScale(this.value)' /></td>
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<td><input type = 'range' min = '0' max = '1' step = '.001' value = '0' onchange = 'setEdgeThreshold(this.value)'/></td>
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<td><input type = 'range' min = '5' max = '80' value = '10' step = '1' onchange = 'setFontSize(this.value)' /></td>
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</tr>
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</tr>
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</table>
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<canvas id = 'canvas' width = '500' height = '500'></canvas>
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<script type = 'text/javascript'>
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// global parameters
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var canvas = document.getElementById('canvas'),
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canvasW = 500,
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canvasH = 500,
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sizeScale = .1,
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edgeScale = 5,
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edgeT = 0,
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fontSize = 10,
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context = canvas.getContext('2d'),
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mouseX = 0,
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mouseY = 0,
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currentX = 0,
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currentY = 0,
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selectedX = 0,
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selectedY = 0,
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selectedPopulation = null;
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// data placeholders
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var data_coordinates = [], // for each cell name include x coordinate, y coordinate, and radius values
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data_edges = [], // for each cell name include and array of edge values
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data_composition = []; // for each cell name include 8 numbers (first 4 for male gender, last 4 for female gender)
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// insert data here
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// function to set the width of canvas. called from slider
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function setWidth(value){
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canvasW = parseFloat(value)
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canvas.width = canvasW
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context = canvas.getContext('2d')
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draw()
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||||
}
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||||
// function to set height of canvas. called from slider
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function setHeight(value){
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canvasH = parseFloat(value)
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canvas.height = canvasH
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context = canvas.getContext('2d')
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draw()
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}
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||||
// function to set bubble size scale. called from slider
|
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function setSizeScale(value){
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sizeScale = parseFloat(value) / 100
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draw()
|
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}
|
||||
|
||||
// function to set edge scale. called from slider
|
||||
function setEdgeScale(value){
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edgeScale = parseFloat(value)
|
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draw()
|
||||
}
|
||||
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||||
// function to set edge theshold. Any edge smaller than this threshold will not be drawn. called from slider
|
||||
function setEdgeThreshold(value){
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edgeT = parseFloat(value)
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draw()
|
||||
}
|
||||
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||||
// function to set font size of cell name labels in the plot. called from slider
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||||
function setFontSize(value){
|
||||
fontSize = parseInt(value)
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draw()
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||||
}
|
||||
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||||
// function to draw the canvas
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||||
function draw(){
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||||
// clear canvas by drawing a rectangle
|
||||
context.fillStyle = '#efefef'
|
||||
context.fillRect(0, 0, canvas.width, canvas.height)
|
||||
// loop through all the cell name and draw their coresponding bubble reflect population size and write the label above the bubble
|
||||
for (key in data_coordinates){
|
||||
// get bubble parameters
|
||||
var bubble_data = data_coordinates[key],
|
||||
bubbleX = canvasW * bubble_data[0],
|
||||
bubbleY = canvasH * (1 - bubble_data[1]),
|
||||
bubbleA = sizeScale * bubble_data[2],
|
||||
bubbleR = Math.sqrt(bubbleA),
|
||||
bubbleC = bubble_data[3];
|
||||
// draw edges
|
||||
var edges = data_edges[key]
|
||||
context.strokeStyle = '#888888'
|
||||
edges.forEach(function(edgeVal, i){
|
||||
if (edgeVal > edgeT){
|
||||
var connectingCellName = cell_names[i],
|
||||
connectingBubble = data_coordinates[connectingCellName],
|
||||
endX = canvasW * connectingBubble[0],
|
||||
endY = canvasH * (1 - connectingBubble[1])
|
||||
edgeVal *= edgeScale
|
||||
context.lineWidth = edgeVal;
|
||||
context.beginPath()
|
||||
context.moveTo(bubbleX, bubbleY)
|
||||
context.lineTo(endX, endY)
|
||||
context.stroke()
|
||||
}
|
||||
})
|
||||
}
|
||||
// loop through all values in connectivities and draw corresponding edges if great the edge threshold
|
||||
for (key in data_coordinates){
|
||||
// get bubble parameters
|
||||
var bubble_data = data_coordinates[key],
|
||||
bubbleX = canvasW * bubble_data[0],
|
||||
bubbleY = canvasH * (1 - bubble_data[1]),
|
||||
bubbleA = sizeScale * bubble_data[2],
|
||||
bubbleR = Math.sqrt(bubbleA),
|
||||
bubbleC = bubble_data[3];
|
||||
// draw bubble
|
||||
context.fillStyle = bubbleC
|
||||
context.beginPath()
|
||||
context.arc(bubbleX, bubbleY, bubbleR, 0, 2 * Math.PI, false)
|
||||
context.fill()
|
||||
// write cell name
|
||||
context.fillStyle = 'black';
|
||||
context.font = parseInt(fontSize) + 'px arial'
|
||||
context.textAlign = 'center'
|
||||
context.textBaseline = 'Alphabetical'
|
||||
context.fillText(key, bubbleX, bubbleY - bubbleR - 2)
|
||||
}
|
||||
}
|
||||
|
||||
// function that takes an event as input and return x, y values of mouse cursor
|
||||
function getEventCoordinates(event){
|
||||
var canvasRect = canvas.getBoundingClientRect(),
|
||||
X = event.clientX - canvasRect.x,
|
||||
Y = event.clientY - canvasRect.y;
|
||||
return [X, Y]
|
||||
}
|
||||
|
||||
// function that stops dragging of selected cell name
|
||||
function stopDraging(event){
|
||||
// first draw the data point at dropping location
|
||||
dragDataPoint(event)
|
||||
// remove dragDataPoint from canvas event listeners
|
||||
canvas.removeEventListener('mousemove', dragDataPoint)
|
||||
// remove stopDraging from canvas event listeners
|
||||
canvas.removeEventListener('mouseup', stopDraging)
|
||||
}
|
||||
|
||||
// function that drags a selected bubble to follow the movement of the cursor
|
||||
function dragDataPoint(event){
|
||||
var XY = getEventCoordinates(event)
|
||||
currentX = XY[0];
|
||||
currentY = XY[1];
|
||||
var dx = (mouseX - currentX) / canvasW,
|
||||
dy = (mouseY - currentY) / canvasH;
|
||||
// reset coordinates of selected data point
|
||||
data_coordinates[selectedPopulation][0] = selectedX - dx;
|
||||
data_coordinates[selectedPopulation][1] = selectedY + dy;
|
||||
// then draw
|
||||
draw()
|
||||
}
|
||||
|
||||
// draw the canvas and add the event listeners only when the entire document is loaded
|
||||
window.onload = function(){
|
||||
draw()
|
||||
canvas.addEventListener('mousedown', function(event){
|
||||
var XY = getEventCoordinates(event),
|
||||
hit = false;
|
||||
mouseX = XY[0];
|
||||
mouseY = XY[1];
|
||||
// loop through all the data poins and check for hit
|
||||
for (key in data_coordinates){
|
||||
var bubble_data = data_coordinates[key],
|
||||
bubbleX = canvasW * bubble_data[0],
|
||||
bubbleY = canvasH * (1 - bubble_data[1]),
|
||||
bubbleA = sizeScale * bubble_data[2],
|
||||
bubbleR = Math.sqrt(bubbleA),
|
||||
dx = mouseX - bubbleX,
|
||||
dy = mouseY - bubbleY,
|
||||
distance = Math.sqrt(Math.pow(dx, 2) + Math.pow(dy, 2))
|
||||
if (distance < bubbleR){
|
||||
hit = true;
|
||||
selectedPopulation = key;
|
||||
selectedX = data_coordinates[selectedPopulation][0]
|
||||
selectedY = data_coordinates[selectedPopulation][1]
|
||||
}
|
||||
}
|
||||
if (hit){
|
||||
canvas.addEventListener('mousemove', dragDataPoint)
|
||||
canvas.addEventListener('mouseup', stopDraging)
|
||||
}else{selectedPopulation = null}
|
||||
})
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Loading…
Add table
Add a link
Reference in a new issue