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scRNA-seq_analysis
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veghp
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188 changed files with 146184 additions and 0 deletions
64
pipelines/11_plot_dr/dm.py
Executable file
64
pipelines/11_plot_dr/dm.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 Tue Aug 14 15:01:36 2018
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@author: doru
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"""
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import sys
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args = sys.argv
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working_folder = args[1]
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import matplotlib; matplotlib.use('Agg');
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import scanpy.api as sc;
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import pandas as pd
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sc.settings.verbosity = 3
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scObj = sc.read("{CW}/raw_data.mtx".format(CW = working_folder), cache = False).T
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# load gene names
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scObj.var_names = pd.read_csv("{CW}/genenames.csv".format(CW = working_folder)).iloc[:, 1]
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# load cell names
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scObj.obs_names = pd.read_csv("{CW}/cellnames.csv".format(CW = working_folder)).iloc[:, 1]
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# add cell labels
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cell_labels = pd.read_csv("{CW}/cell_labels.csv".format(CW = working_folder), index_col = 0)
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scObj.obs["cell_labels"] = cell_labels
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# filter out genes present in less than 3 cells
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sc.pp.filter_genes(scObj, min_cells=3)
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# log-normalize the data
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scObj.raw = sc.pp.log1p(scObj, copy=True)
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sc.pp.normalize_per_cell(scObj, counts_per_cell_after=1e4)
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# variable genes
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filter_result = sc.pp.filter_genes_dispersion(
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scObj.X, min_mean=0.0125, max_mean=3, min_disp=0.5)
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# subset data on variable genes
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scObj = scObj[:, filter_result.gene_subset]
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# not sure?
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sc.pp.log1p(scObj)
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# scale the data
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sc.pp.scale(scObj, max_value=10)
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# run pca
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sc.tl.pca(scObj)
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# compunte neighborhood graph
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sc.pp.neighbors(scObj, n_neighbors = 15, n_pcs = 20, knn = True, random_state = 10, method = "gauss")
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# compute diffusion map
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sc.tl.diffmap(scObj, n_comps = 20)
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# save diffusion map to disk
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dm = scObj.obsm["X_diffmap"]
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dm = pd.DataFrame(data = dm, index = None, columns = None)
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dm.to_csv("{CW}/dm.csv".format(CW = working_folder), columns = None, header = None)
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90
pipelines/11_plot_dr/make_AGA_app.py
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90
pipelines/11_plot_dr/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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330
pipelines/11_plot_dr/plot_dr.R
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330
pipelines/11_plot_dr/plot_dr.R
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args = commandArgs(trailingOnly=T)
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args = paste(args, collapse = "")
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args = unlist(strsplit(args, ";"))
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arguments.list = "
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seurat.addr.arg = args[1]
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plot.by.arg = args[2]
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type.to.colours.arg = args[3]
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runDiffusionMap.arg = args[4]
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runAGA.arg = args[5]
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overlay.data.arg = args[6]
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overlay.data.ordered.arg = args[7]
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"
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plotW = 8
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plotH = 8
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expected_arguments = unlist(strsplit(arguments.list, "\n"))
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expected_arguments = expected_arguments[!(expected_arguments == "")]
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if(length(args) != length(expected_arguments)){
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error.msg = sprintf('This pipeline requires %s parameters', as.character(length(expected_arguments)))
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expected_arguments = paste(unlist(lapply(strsplit(expected_arguments, ".arg"), "[", 1)), collapse = "\n")
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stop(sprintf('This pipeline requires %s parameters: '))
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}
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eval(parse(text = arguments.list))
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for(n in 1:length(expected_arguments)){
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argument = expected_arguments[n]
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argument = gsub(pattern=" ", replacement="", x=argument)
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argument.name = unlist(strsplit(argument, "="))[1]
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variable.name = gsub(pattern=".arg", replacement="", argument.name)
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argument.content = eval(parse(text = argument.name))
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eval(parse(text = argument.content))
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if (!exists(variable.name)){
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stop(sprintf("Argument %s not passed. Stopping ... ", variable.name))
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}
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}
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# create required folders for output and work material
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output_folder = paste("11_plot_dr", seurat.addr, sep = "_")
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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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output_folder_material = file.path(output_folder, "material")
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dir.create(output_folder_material)
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seurat.addr = file.path("../../data", seurat.addr)
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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(destiny)
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#######################################################################################################
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# load data
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print("loading data ... ")
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seurat.obj = readRDS(seurat.addr)
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print("Data loaded.")
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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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# 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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# run the diffusion map
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if(runDiffusionMap){
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print("Writing .mtx file for diffusion map ...")
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writeMM(raw_data, file.path(output_folder_material, "raw_data.mtx"))
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print("Running the diffusion map ... ")
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command = sprintf("%s ./dm.py %s", python.addr, output_folder_material)
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system(command, wait = T)
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# load dm from disk
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dm = read.csv(file.path(output_folder_material, "dm.csv"), row.names = 1, header = F)
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#dm = DiffusionMap(seurat.obj@dr$pca@cell.embeddings, k = 100, density_norm=F, n_eigs = 20)
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#dm = data.frame(DC1 = dm$DC1, DC2 = dm$DC2, DC2 = dm$DC3)
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}
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print("Computing FDG limits ...")
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fdg.x = seurat.obj@dr$fdg@cell.embeddings[, 1]
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fdg.y = seurat.obj@dr$fdg@cell.embeddings[, 2]
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fdg.limits = 1.15 * c(quantile(fdg.x, c(.01)), quantile(fdg.x, c(.99)), quantile(fdg.y, c(.01)), quantile(fdg.y, c(.99)))
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print("Making the plots ...")
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for (index in 1:length(plot.by)){
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caty = plot.by[index]
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seurat.obj = SetAllIdent(object=seurat.obj, id=caty)
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if (!is.na(type.to.colours[index])){
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type.to.colour = read.csv(file.path("../../resources", type.to.colours[index]))
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filter.key = type.to.colour$CellTypes %in% as.vector(unique(seurat.obj@ident))
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cell.labels = as.vector(type.to.colour$CellTypes[filter.key])
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cell.colours = as.vector(type.to.colour$Colours[filter.key])
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all_celltypes_missing <- levels(factor(seurat.obj@ident))[!levels(factor(seurat.obj@ident)) %in% levels(factor(type.to.colour$CellTypes))]
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all_colours_missing <- levels(factor(type.to.colour$CellTypes))[!levels(factor(type.to.colour$CellTypes)) %in% levels(factor(seurat.obj@ident))]
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if (length(all_celltypes_missing)>0){
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cat(all_celltypes_missing, "have not been found in your type.to.cols .csv \n", sep="\n")
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}
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if (length(all_colours_missing)>0){
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cat(all_colours_missing, "have not been found in your selected metadata column", sep="\n")
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}
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if (length(all_colours_missing)==0|length(all_colours_missing)==0){
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print("All colours and annotations match")
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}
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}else{
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cell.labels = sort(as.vector(unique(seurat.obj@ident)))
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cell.colours = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(cell.labels)))
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}
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caty = gsub(pattern="\\.", replacement="_", caty)
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# file paths for annotated graphs
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tsne.file.name = file.path(output_folder, paste("tsne", paste(caty, "pdf", sep = "."), sep = "_"))
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umap.file.name = file.path(output_folder, paste("umap", paste(caty, "pdf", sep = "."), sep = "_"))
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fdg.file.name = file.path(output_folder, paste("fdg", paste(caty, "pdf", sep = "."), sep = "_"))
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legend.file.name = file.path(output_folder, paste("legend", paste(caty, "pdf", sep = "."), sep = "_"))
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AGA.file.name = file.path(output_folder, paste("AGA", paste(caty, "pdf", sep = "."), sep = "_"))
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# file paths for unannotated plots
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tsne.file.name_unlabeled = file.path(output_folder, paste("tsne_unlabeled", paste(caty, "pdf", sep = "."), sep = "_"))
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umap.file.name_unlabeled = file.path(output_folder, paste("umap_unlabeled", paste(caty, "pdf", sep = "."), sep = "_"))
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fdg.file.name_unlabeled = file.path(output_folder, paste("fdg_unlabeled", paste(caty, "pdf", sep = "."), sep = "_"))
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# preparing data frame
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if(!is.na(overlay.data)){
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df = data.frame(CellType = as.vector(seurat.obj@ident),
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tSNEx = seurat.obj@dr$tsne@cell.embeddings[, 1],
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tSNEy = seurat.obj@dr$tsne@cell.embeddings[, 2],
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UMAPx = seurat.obj@dr$umap@cell.embeddings[, 1],
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UMAPy = seurat.obj@dr$umap@cell.embeddings[, 2],
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FDGx = seurat.obj@dr$fdg@cell.embeddings[, 1],
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FDGy = seurat.obj@dr$fdg@cell.embeddings[, 2],
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overlay.data = seurat.obj@meta.data[overlay.data])
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colnames(df)[8] <- "overlay.data"
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}
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else
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{
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df = data.frame(CellType = as.vector(seurat.obj@ident),
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tSNEx = seurat.obj@dr$tsne@cell.embeddings[, 1],
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tSNEy = seurat.obj@dr$tsne@cell.embeddings[, 2],
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UMAPx = seurat.obj@dr$umap@cell.embeddings[, 1],
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UMAPy = seurat.obj@dr$umap@cell.embeddings[, 2],
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FDGx = seurat.obj@dr$fdg@cell.embeddings[, 1],
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FDGy = seurat.obj@dr$fdg@cell.embeddings[, 2])
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}
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print("printing header of df made at beginning od r script")
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print(head(df))
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interactive_plot_df = data.frame(X = seurat.obj@dr$tsne@cell.embeddings[, 1],
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Y = seurat.obj@dr$tsne@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$Labels, from = cell.labels, to = cell.colours)
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# make interartive tsne
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interactive_tsne_filename = file.path(output_folder, paste(paste("Interactive_tSNE", caty, sep = "_"), "html", sep = "."))
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make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_tsne_filename)
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# make interactive UMAP
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interactive_plot_df$X = seurat.obj@dr$umap@cell.embeddings[, 1]
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interactive_plot_df$Y = seurat.obj@dr$umap@cell.embeddings[, 2]
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interactive_umap_filename = file.path(output_folder, paste(paste("Interactive_UMAP", caty, sep = "_"), "html", sep = "."))
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make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_umap_filename)
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# make interactive FDG
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interactive_plot_df$X = seurat.obj@dr$fdg@cell.embeddings[, 1]
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interactive_plot_df$Y = seurat.obj@dr$fdg@cell.embeddings[, 2]
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interactive_fdg_filename = file.path(output_folder, paste(paste("Interactive_FDG", caty, sep = "_"), "html", sep = "."))
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make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_fdg_filename)
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n.cols = min(2, length(cell.labels))
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n.rows = ceiling(length(cell.labels) / n.cols)
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# making the plots
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print("making the plots")
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# annotated plots
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print("making annotated plots")
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plot.tsne = dr.plot(point.labels=df$CellType, dr1=df$tSNEx, dr2=df$tSNEy, dr1.name="tSNE-x", dr2.name="tSNE-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels)
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plot.umap = dr.plot(point.labels=df$CellType, dr1=df$UMAPx, dr2=df$UMAPy, dr1.name="UMAP-x", dr2.name="UMAP-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels)
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plot.fdg = dr.plot(point.labels=df$CellType, dr1=df$FDGx, dr2=df$FDGy, dr1.name="FDG-x", dr2.name="FDG-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, limits=fdg.limits, use.labels=cell.labels)
|
||||
plot.legend = plot.indexed.legend(label.vector=cell.labels, color.vector=cell.colours, ncols=n.cols, left.limit=.2, symbol.size=10, text.size=6, padH=.6, padV=.6)
|
||||
|
||||
# unannotated plots
|
||||
print("making un-annotated plots")
|
||||
plot.tsne_unlabeled = dr.plot(point.labels=df$CellType, dr1=df$tSNEx, dr2=df$tSNEy, dr1.name="tSNE-x", dr2.name="tSNE-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels, annotate.plot = F)
|
||||
plot.umap_unlabeled = dr.plot(point.labels=df$CellType, dr1=df$UMAPx, dr2=df$UMAPy, dr1.name="UMAP-x", dr2.name="UMAP-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels, annotate.plot = F)
|
||||
plot.fdg_unlabeled = dr.plot(point.labels=df$CellType, dr1=df$FDGx, dr2=df$FDGy, dr1.name="FDG-x", dr2.name="FDG-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, limits=fdg.limits, use.labels=cell.labels, annotate.plot = F, pt.size=1)
|
||||
|
||||
# print the annotated plots
|
||||
print("printing annotated plots")
|
||||
pdf(tsne.file.name, width = plotW, height = plotH)
|
||||
print(plot.tsne)
|
||||
dev.off()
|
||||
|
||||
pdf(umap.file.name, width = plotW, height = plotH)
|
||||
print(plot.umap)
|
||||
dev.off()
|
||||
|
||||
pdf(fdg.file.name, width = plotW, height = plotH)
|
||||
print(plot.fdg)
|
||||
dev.off()
|
||||
|
||||
pdf(legend.file.name, width = 1.5 + .15 * n.cols * max(unlist(lapply(cell.labels, nchar))), height = .5 + n.rows * .35)
|
||||
print(plot.legend)
|
||||
dev.off()
|
||||
|
||||
# print the unannotated plots
|
||||
print("printing un-annotated plots")
|
||||
pdf(tsne.file.name_unlabeled, width = plotW, height = plotH)
|
||||
print(plot.tsne_unlabeled)
|
||||
dev.off()
|
||||
|
||||
pdf(umap.file.name_unlabeled, width = plotW, height = plotH)
|
||||
print(plot.umap_unlabeled)
|
||||
dev.off()
|
||||
|
||||
pdf(fdg.file.name_unlabeled, width = plotW, height = plotH)
|
||||
print(plot.fdg_unlabeled)
|
||||
dev.off()
|
||||
|
||||
# run diffusion map
|
||||
if(runDiffusionMap){
|
||||
df = as.data.frame(dm[, 1:3])
|
||||
df$Labels = factor(seurat.obj@ident, levels = cell.labels)
|
||||
df$Colours = mapvalues(x = df$Labels, from = cell.labels, to = cell.colours)
|
||||
|
||||
dm.file.name = file.path(output_folder_material, paste(paste("dm_data", caty, sep="_"), "csv", sep = "."))
|
||||
write.csv(df, dm.file.name, row.names = F)
|
||||
|
||||
dm.file.name = file.path(output_folder, paste(paste("DiffusionMap_3D", caty, sep = "_"), "html", sep = "."))
|
||||
make_3D_interactive_page(data_frame_3D=df, tool_addr=tool_addr, python.addr=python.addr, save.to=dm.file.name)
|
||||
}
|
||||
|
||||
if(runAGA){
|
||||
if(runDiffusionMap==F){
|
||||
print("Writing .mtx file for AGA map ...")
|
||||
writeMM(raw_data, file.path(output_folder_material, "raw_data.mtx"))
|
||||
}
|
||||
|
||||
print("running AGA ...")
|
||||
AGA_folder = file.path(output_folder, "AGA_folder")
|
||||
dir.create(AGA_folder)
|
||||
# write cell labels to disk
|
||||
write.csv(data.frame(Cells = names(seurat.obj@ident), Labels = seurat.obj@ident), file.path(output_folder_material, "cell_labels.csv"), row.names = F)
|
||||
|
||||
# running AGA
|
||||
command =file.path(tool_addr, "AGA/AGA_from_Seurat.py")
|
||||
command = paste(paste(python.addr, command, sep = " "), output_folder, sep = " ")
|
||||
command = paste(command, caty, sep =" ")
|
||||
system(command, wait = T)
|
||||
|
||||
# read the AGA output
|
||||
coordinates = read.csv(file.path(AGA_folder, "coordinates.csv"), row.names = 1)
|
||||
connectivities = read.csv(file.path(AGA_folder, "connectivities.csv"), row.names = 1)
|
||||
|
||||
# plot AGA
|
||||
coordinates = coordinates[cell.labels, ]
|
||||
coordinates$Colours = cell.colours
|
||||
label.order = match(cell.labels, rownames(connectivities))
|
||||
connectivities = connectivities[label.order, label.order]
|
||||
|
||||
plot.obj = ggplot(data=coordinates, aes(x = X, y = Y))
|
||||
plot.obj = plot.obj + theme_void() + theme(legend.position="none")
|
||||
xi = c(); xf = c(); yi = c(); yf = c(); vs = c();
|
||||
for(i in 1:dim(connectivities)[1]){
|
||||
for(j in i:dim(connectivities)[2]){
|
||||
v = connectivities[i, j]
|
||||
if(v > 0){
|
||||
xi = c(xi, coordinates$X[i])
|
||||
xf = c(xf, coordinates$X[j])
|
||||
yi = c(yi, coordinates$Y[i])
|
||||
yf = c(yf, coordinates$Y[j])
|
||||
vs = c(vs, v)
|
||||
}
|
||||
}
|
||||
}
|
||||
lineDF = data.frame(Xi = xi, Yi = yi, Xf = xf, Yf = yf, Vs = vs)
|
||||
plot.obj = plot.obj + geom_segment(data = lineDF, aes(x = Xi, y = Yi, xend = Xf, yend = Yf), color = "black", size = 3 * lineDF$Vs)
|
||||
plot.obj = plot.obj + geom_point(size = 2 * log(coordinates$Size), color = coordinates$Colours)
|
||||
plot.obj = plot.obj + annotate("text", x=coordinates$X, y=coordinates$Y, label = 1:dim(coordinates)[1])
|
||||
pdf(AGA.file.name, width = 10, height = 10)
|
||||
print(plot.obj)
|
||||
dev.off()
|
||||
|
||||
######## now make the interactive AGA app
|
||||
#########################################
|
||||
print("Making the AGA app ... ")
|
||||
# save colours
|
||||
colours.df = data.frame(CellTypes = cell.labels, Colours = cell.colours)
|
||||
write.csv(colours.df, file.path(AGA_folder, "colours.csv"), row.names = F)
|
||||
|
||||
# run python to built the AGA app
|
||||
command = sprintf("%s make_AGA_app.py %s %s", python.addr, output_folder, caty)
|
||||
system(command, wait = T)
|
||||
}
|
||||
}
|
||||
|
||||
# cleaning garbage folders
|
||||
unlink(output_folder_material, recursive=T, force=T)
|
||||
if (runAGA){
|
||||
unlink(AGA_folder, recursive=T, force=T)
|
||||
unlink(file.path(output_folder, 'figures'), recursive=T, force=T)
|
||||
}
|
||||
|
||||
print("Ended beautifully ... ")
|
||||
16
pipelines/11_plot_dr/plot_dr.sh
Executable file
16
pipelines/11_plot_dr/plot_dr.sh
Executable file
|
|
@ -0,0 +1,16 @@
|
|||
#!/bin/bash
|
||||
|
||||
#$ -cwd
|
||||
#$ -N plot_dr
|
||||
#$ -V
|
||||
#$ -l h_rt=47:59:59
|
||||
#$ -l h_vmem=200G
|
||||
|
||||
if [ "$#" -ne 1 ]; then
|
||||
echo "Illegal number of parameters"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
Rscript plot_dr.R $1
|
||||
|
||||
echo "End on `date`"
|
||||
289
pipelines/11_plot_dr/plot_dr_numerical.R
Executable file
289
pipelines/11_plot_dr/plot_dr_numerical.R
Executable file
|
|
@ -0,0 +1,289 @@
|
|||
args = commandArgs(trailingOnly=T)
|
||||
args = paste(args, collapse = "")
|
||||
args = unlist(strsplit(args, ";"))
|
||||
|
||||
arguments.list = "
|
||||
seurat.addr.arg = args[1]
|
||||
plot.by.arg = args[2]
|
||||
type.to.colours.arg = args[3]
|
||||
runDiffusionMap.arg = args[4]
|
||||
runAGA.arg = args[5]
|
||||
"
|
||||
|
||||
plotW = 8
|
||||
plotH = 8
|
||||
|
||||
expected_arguments = unlist(strsplit(arguments.list, "\n"))
|
||||
expected_arguments = expected_arguments[!(expected_arguments == "")]
|
||||
if(length(args) != length(expected_arguments)){
|
||||
error.msg = sprintf('This pipeline requires %s parameters', as.character(length(expected_arguments)))
|
||||
expected_arguments = paste(unlist(lapply(strsplit(expected_arguments, ".arg"), "[", 1)), collapse = "\n")
|
||||
stop(sprintf('This pipeline requires %s parameters: '))
|
||||
}
|
||||
|
||||
eval(parse(text = arguments.list))
|
||||
|
||||
for(n in 1:length(expected_arguments)){
|
||||
argument = expected_arguments[n]
|
||||
argument = gsub(pattern=" ", replacement="", x=argument)
|
||||
argument.name = unlist(strsplit(argument, "="))[1]
|
||||
variable.name = gsub(pattern=".arg", replacement="", argument.name)
|
||||
argument.content = eval(parse(text = argument.name))
|
||||
eval(parse(text = argument.content))
|
||||
if (!exists(variable.name)){
|
||||
stop(sprintf("Argument %s not passed. Stopping ... ", variable.name))
|
||||
}
|
||||
}
|
||||
|
||||
# create required folders for output and work material
|
||||
output_folder = paste("11_plot_dr", seurat.addr, sep = "_")
|
||||
c.time = Sys.time()
|
||||
c.time = gsub(pattern=" BST", replacement="", x=c.time)
|
||||
c.time = gsub(pattern=":", replacement="", x=c.time)
|
||||
c.time = gsub(pattern=" ", replacement="", x=c.time)
|
||||
c.time = gsub(pattern="-", replacement="", x=c.time)
|
||||
c.time = substr(x=c.time, start=3, stop=nchar(c.time))
|
||||
output_folder = paste(output_folder, c.time, sep = "_")
|
||||
output_folder = file.path("../../output", output_folder)
|
||||
dir.create(output_folder)
|
||||
|
||||
output_folder_material = file.path(output_folder, "material")
|
||||
dir.create(output_folder_material)
|
||||
|
||||
seurat.addr = file.path("../../data", seurat.addr)
|
||||
|
||||
source("../../tools/bunddle_utils.R")
|
||||
|
||||
library(Seurat)
|
||||
library(RColorBrewer)
|
||||
library(plyr)
|
||||
library(dplyr)
|
||||
library(destiny)
|
||||
|
||||
#######################################################################################################
|
||||
|
||||
# load data
|
||||
print("loading data ... ")
|
||||
seurat.obj = readRDS(seurat.addr)
|
||||
print("Data loaded.")
|
||||
|
||||
# save raw data to disk
|
||||
raw_data = seurat.obj@raw.data
|
||||
raw_data = raw_data[rownames(seurat.obj@data), colnames(seurat.obj@data)]
|
||||
|
||||
# save gene names
|
||||
gene_names = rownames(raw_data)
|
||||
|
||||
# save cell names
|
||||
cell_names = colnames(raw_data)
|
||||
write.csv(data.frame(Cells = cell_names), file.path(output_folder_material, "cellnames.csv"))
|
||||
# write cell labels to disk
|
||||
write.csv(data.frame(Cells = names(seurat.obj@ident), Labels = seurat.obj@ident), file.path(output_folder_material, "cell_labels.csv"), row.names = F)
|
||||
|
||||
# run the diffusion map
|
||||
if(runDiffusionMap){
|
||||
print("Writing .mtx file for diffusion map ...")
|
||||
writeMM(raw_data, file.path(output_folder_material, "raw_data.mtx"))
|
||||
|
||||
print("Running the diffusion map ... ")
|
||||
command = sprintf("%s ./dm.py %s", python.addr, output_folder_material)
|
||||
system(command, wait = T)
|
||||
|
||||
# load dm from disk
|
||||
dm = read.csv(file.path(output_folder_material, "dm.csv"), row.names = 1, header = F)
|
||||
#dm = DiffusionMap(seurat.obj@dr$pca@cell.embeddings, k = 100, density_norm=F, n_eigs = 20)
|
||||
#dm = data.frame(DC1 = dm$DC1, DC2 = dm$DC2, DC2 = dm$DC3)
|
||||
}
|
||||
|
||||
print("Computing FDG limits ...")
|
||||
fdg.x = seurat.obj@dr$fdg@cell.embeddings[, 1]
|
||||
fdg.y = seurat.obj@dr$fdg@cell.embeddings[, 2]
|
||||
fdg.limits = 1.15 * c(quantile(fdg.x, c(.01)), quantile(fdg.x, c(.99)), quantile(fdg.y, c(.01)), quantile(fdg.y, c(.99)))
|
||||
|
||||
print("Making the plots ...")
|
||||
for (index in 1:length(plot.by)){
|
||||
caty = plot.by[index]
|
||||
seurat.obj = SetAllIdent(object=seurat.obj, id=caty)
|
||||
if (!is.na(type.to.colours[index])){
|
||||
type.to.colour = read.csv(file.path("../../resources", type.to.colours[index]))
|
||||
filter.key = type.to.colour$CellTypes %in% as.vector(unique(seurat.obj@ident))
|
||||
cell.labels = as.vector(type.to.colour$CellTypes[filter.key])
|
||||
cell.colours = as.vector(type.to.colour$Colours[filter.key])
|
||||
}else{
|
||||
cell.labels = sort(as.vector(unique(seurat.obj@ident)))
|
||||
cell.colours = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(cell.labels)))
|
||||
}
|
||||
caty = gsub(pattern="\\.", replacement="_", caty)
|
||||
# file paths for annotated graphs
|
||||
tsne.file.name = file.path(output_folder, paste("tsne", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
umap.file.name = file.path(output_folder, paste("umap", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
fdg.file.name = file.path(output_folder, paste("fdg", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
legend.file.name = file.path(output_folder, paste("legend", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
AGA.file.name = file.path(output_folder, paste("AGA", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
|
||||
# file paths for unannotated plots
|
||||
tsne.file.name_unlabeled = file.path(output_folder, paste("tsne_unlabeled", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
umap.file.name_unlabeled = file.path(output_folder, paste("umap_unlabeled", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
fdg.file.name_unlabeled = file.path(output_folder, paste("fdg_unlabeled", paste(caty, "pdf", sep = "."), sep = "_"))
|
||||
|
||||
# preparing data frame
|
||||
df = data.frame(CellType = as.vector(seurat.obj@ident),
|
||||
tSNEx = seurat.obj@dr$tsne@cell.embeddings[, 1],
|
||||
tSNEy = seurat.obj@dr$tsne@cell.embeddings[, 2],
|
||||
UMAPx = seurat.obj@dr$umap@cell.embeddings[, 1],
|
||||
UMAPy = seurat.obj@dr$umap@cell.embeddings[, 2],
|
||||
FDGx = seurat.obj@dr$fdg@cell.embeddings[, 1],
|
||||
FDGy = seurat.obj@dr$fdg@cell.embeddings[, 2])
|
||||
|
||||
interactive_plot_df = data.frame(X = seurat.obj@dr$tsne@cell.embeddings[, 1],
|
||||
Y = seurat.obj@dr$tsne@cell.embeddings[, 2])
|
||||
interactive_plot_df$Labels = factor(seurat.obj@ident, levels = cell.labels)
|
||||
interactive_plot_df$Colours = mapvalues(x = interactive_plot_df$Labels, from = cell.labels, to = cell.colours)
|
||||
|
||||
# make interartive tsne
|
||||
interactive_tsne_filename = file.path(output_folder, paste(paste("Interactive_tSNE", caty, sep = "_"), "html", sep = "."))
|
||||
make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_tsne_filename)
|
||||
|
||||
# make interactive UMAP
|
||||
interactive_plot_df$X = seurat.obj@dr$umap@cell.embeddings[, 1]
|
||||
interactive_plot_df$Y = seurat.obj@dr$umap@cell.embeddings[, 2]
|
||||
interactive_umap_filename = file.path(output_folder, paste(paste("Interactive_UMAP", caty, sep = "_"), "html", sep = "."))
|
||||
make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_umap_filename)
|
||||
|
||||
# make interactive FDG
|
||||
interactive_plot_df$X = seurat.obj@dr$fdg@cell.embeddings[, 1]
|
||||
interactive_plot_df$Y = seurat.obj@dr$fdg@cell.embeddings[, 2]
|
||||
interactive_fdg_filename = file.path(output_folder, paste(paste("Interactive_FDG", caty, sep = "_"), "html", sep = "."))
|
||||
make_2D_interactive_page(data_frame_2D=interactive_plot_df, tool_addr=tool_addr, python.addr=python.addr, save.to=interactive_fdg_filename)
|
||||
|
||||
n.cols = min(2, length(cell.labels))
|
||||
n.rows = ceiling(length(cell.labels) / n.cols)
|
||||
|
||||
# making the plots
|
||||
print("making the plots")
|
||||
|
||||
# annotated plots
|
||||
plot.tsne = dr.plot.numerical(point.labels=df$CellType, dr1=df$tSNEx, dr2=df$tSNEy, dr1.name="tSNE-x", dr2.name="tSNE-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels)
|
||||
plot.umap = dr.plot.numerical(point.labels=df$CellType, dr1=df$UMAPx, dr2=df$UMAPy, dr1.name="UMAP-x", dr2.name="UMAP-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels)
|
||||
plot.fdg = dr.plot.numerical(point.labels=df$CellType, dr1=df$FDGx, dr2=df$FDGy, dr1.name="FDG-x", dr2.name="FDG-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, limits=fdg.limits, use.labels=cell.labels)
|
||||
#plot.legend = plot.indexed.legend(label.vector=cell.labels, color.vector=cell.colours, ncols=n.cols, left.limit=.2, symbol.size=10, text.size=6, padH=.6, padV=.6)
|
||||
|
||||
# unannotated plots
|
||||
plot.tsne_unlabeled = dr.plot.numerical(point.labels=df$CellType, dr1=df$tSNEx, dr2=df$tSNEy, dr1.name="tSNE-x", dr2.name="tSNE-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels, annotate.plot = F)
|
||||
plot.umap_unlabeled = dr.plot.numerical(point.labels=df$CellType, dr1=df$UMAPx, dr2=df$UMAPy, dr1.name="UMAP-x", dr2.name="UMAP-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, use.labels=cell.labels, annotate.plot = F)
|
||||
plot.fdg_unlabeled = dr.plot.numerical(point.labels=df$CellType, dr1=df$FDGx, dr2=df$FDGy, dr1.name="FDG-x", dr2.name="FDG-y", no.legend=T, plt.lb.sz=7, txt.lb.size=4, use.cols=cell.colours, limits=fdg.limits, use.labels=cell.labels, annotate.plot = F)
|
||||
|
||||
# print the annotated plots
|
||||
pdf(tsne.file.name, width = plotW, height = plotH)
|
||||
print(plot.tsne)
|
||||
dev.off()
|
||||
|
||||
pdf(umap.file.name, width = plotW, height = plotH)
|
||||
print(plot.umap)
|
||||
dev.off()
|
||||
|
||||
pdf(fdg.file.name, width = plotW, height = plotH)
|
||||
print(plot.fdg)
|
||||
dev.off()
|
||||
|
||||
#pdf(legend.file.name, width = 1.5 + .15 * n.cols * max(unlist(lapply(cell.labels, nchar))), height = .5 + n.rows * .35)
|
||||
#print(plot.legend)
|
||||
#dev.off()
|
||||
|
||||
# print the unannotated plots
|
||||
pdf(tsne.file.name_unlabeled, width = plotW, height = plotH)
|
||||
print(plot.tsne_unlabeled)
|
||||
dev.off()
|
||||
|
||||
pdf(umap.file.name_unlabeled, width = plotW, height = plotH)
|
||||
print(plot.umap_unlabeled)
|
||||
dev.off()
|
||||
|
||||
pdf(fdg.file.name_unlabeled, width = plotW, height = plotH)
|
||||
print(plot.fdg_unlabeled)
|
||||
dev.off()
|
||||
|
||||
# run diffusion map
|
||||
if(runDiffusionMap){
|
||||
df = as.data.frame(dm[, 1:3])
|
||||
df$Labels = factor(seurat.obj@ident, levels = cell.labels)
|
||||
df$Colours = mapvalues(x = df$Labels, from = cell.labels, to = cell.colours)
|
||||
|
||||
dm.file.name = file.path(output_folder_material, paste(paste("dm_data", caty, sep="_"), "csv", sep = "."))
|
||||
write.csv(df, dm.file.name, row.names = F)
|
||||
|
||||
dm.file.name = file.path(output_folder, paste(paste("DiffusionMap_3D", caty, sep = "_"), "html", sep = "."))
|
||||
make_3D_interactive_page(data_frame_3D=df, tool_addr=tool_addr, python.addr=python.addr, save.to=dm.file.name)
|
||||
}
|
||||
|
||||
if(runAGA){
|
||||
if(runDiffusionMap==F){
|
||||
print("Writing .mtx file for AGA map ...")
|
||||
writeMM(raw_data, file.path(output_folder_material, "raw_data.mtx"))
|
||||
}
|
||||
print("running AGA ...")
|
||||
AGA_folder = file.path(output_folder, "AGA_folder")
|
||||
dir.create(AGA_folder)
|
||||
# write cell labels to disk
|
||||
write.csv(data.frame(Cells = names(seurat.obj@ident), Labels = seurat.obj@ident), file.path(output_folder_material, "cell_labels.csv"), row.names = F)
|
||||
|
||||
# running AGA
|
||||
command =file.path(tool_addr, "AGA/AGA_from_Seurat.py")
|
||||
command = paste(paste(python.addr, command, sep = " "), output_folder, sep = " ")
|
||||
command = paste(command, caty, sep =" ")
|
||||
system(command, wait = T)
|
||||
|
||||
# read the AGA output
|
||||
coordinates = read.csv(file.path(AGA_folder, "coordinates.csv"), row.names = 1)
|
||||
connectivities = read.csv(file.path(AGA_folder, "connectivities.csv"), row.names = 1)
|
||||
|
||||
# plot AGA
|
||||
coordinates = coordinates[cell.labels, ]
|
||||
coordinates$Colours = cell.colours
|
||||
label.order = match(cell.labels, rownames(connectivities))
|
||||
connectivities = connectivities[label.order, label.order]
|
||||
|
||||
plot.obj = ggplot(data=coordinates, aes(x = X, y = Y))
|
||||
plot.obj = plot.obj + theme_void() + theme(legend.position="none")
|
||||
xi = c(); xf = c(); yi = c(); yf = c(); vs = c();
|
||||
for(i in 1:dim(connectivities)[1]){
|
||||
for(j in i:dim(connectivities)[2]){
|
||||
v = connectivities[i, j]
|
||||
if(v > 0){
|
||||
xi = c(xi, coordinates$X[i])
|
||||
xf = c(xf, coordinates$X[j])
|
||||
yi = c(yi, coordinates$Y[i])
|
||||
yf = c(yf, coordinates$Y[j])
|
||||
vs = c(vs, v)
|
||||
}
|
||||
}
|
||||
}
|
||||
lineDF = data.frame(Xi = xi, Yi = yi, Xf = xf, Yf = yf, Vs = vs)
|
||||
plot.obj = plot.obj + geom_segment(data = lineDF, aes(x = Xi, y = Yi, xend = Xf, yend = Yf), color = "black", size = 3 * lineDF$Vs)
|
||||
plot.obj = plot.obj + geom_point(size = 2 * log(coordinates$Size), color = coordinates$Colours)
|
||||
plot.obj = plot.obj + annotate("text", x=coordinates$X, y=coordinates$Y, label = 1:dim(coordinates)[1])
|
||||
pdf(AGA.file.name, width = 10, height = 10)
|
||||
print(plot.obj)
|
||||
dev.off()
|
||||
|
||||
######## now make the interactive AGA app
|
||||
#########################################
|
||||
print("Making the AGA app ... ")
|
||||
# save colours
|
||||
colours.df = data.frame(CellTypes = cell.labels, Colours = cell.colours)
|
||||
write.csv(colours.df, file.path(AGA_folder, "colours.csv"), row.names = F)
|
||||
|
||||
# run python to built the AGA app
|
||||
command = sprintf("%s make_AGA_app.py %s %s", python.addr, output_folder, caty)
|
||||
system(command, wait = T)
|
||||
}
|
||||
}
|
||||
|
||||
# cleaning garbage folders
|
||||
unlink(output_folder_material, recursive=T, force=T)
|
||||
if (runAGA){
|
||||
unlink(AGA_folder, recursive=T, force=T)
|
||||
unlink(file.path(output_folder, 'figures'), recursive=T, force=T)
|
||||
}
|
||||
|
||||
print("Ended beautifully ... ")
|
||||
16
pipelines/11_plot_dr/plot_dr_numerical.sh
Executable file
16
pipelines/11_plot_dr/plot_dr_numerical.sh
Executable file
|
|
@ -0,0 +1,16 @@
|
|||
#!/bin/bash
|
||||
|
||||
#$ -cwd
|
||||
#$ -N plot_dr
|
||||
#$ -V
|
||||
#$ -l h_rt=47:59:59
|
||||
#$ -l h_vmem=200G
|
||||
|
||||
if [ "$#" -ne 1 ]; then
|
||||
echo "Illegal number of parameters"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
Rscript plot_dr_numerical.R $1
|
||||
|
||||
echo "End on `date`"
|
||||
215
pipelines/11_plot_dr/template_for_AGA_app.html
Executable file
215
pipelines/11_plot_dr/template_for_AGA_app.html
Executable file
|
|
@ -0,0 +1,215 @@
|
|||
<!doctype html>
|
||||
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title>Interactive linkage plot</title>
|
||||
<meta name="description" content="An interactive plot for the linkage map">
|
||||
<meta name="author" content="Dorin-Mirel Popescu">
|
||||
</head>
|
||||
<body>
|
||||
<ul>
|
||||
<li>Bubble size reflects population size; Edge thickness reflects connectivity scores;</li>
|
||||
<li>Use the sliders to set plotting parameters;</li>
|
||||
<li>Click the canvas area to select a cell population and reposition it by dragging;</li>
|
||||
<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>
|
||||
</ul>
|
||||
<table>
|
||||
<tr>
|
||||
<td>Canvas width</td><td>Canvas height</td><td>Size scale</td><td>Edge scale</td><td>Edge threshold</td><td>Font size</td>
|
||||
<tr>
|
||||
<td><input type = 'range' min = '100' max = '3000' value = '500' onchange = 'setWidth(this.value)' /></td>
|
||||
<td><input type = 'range' min = '100' max = '3000' value = '500' onchange = 'setHeight(this.value)' /></td>
|
||||
<td><input type = 'range' min = '0' max = '300' value = '10' onchange = 'setSizeScale(this.value)' /></td>
|
||||
<td><input type = 'range' min = '.1' max = '15' step = '.1' value = '5' onchange = 'setEdgeScale(this.value)' /></td>
|
||||
<td><input type = 'range' min = '0' max = '1' step = '.001' value = '0' onchange = 'setEdgeThreshold(this.value)'/></td>
|
||||
<td><input type = 'range' min = '5' max = '80' value = '10' step = '1' onchange = 'setFontSize(this.value)' /></td>
|
||||
</tr>
|
||||
</tr>
|
||||
</table>
|
||||
<canvas id = 'canvas' width = '500' height = '500'></canvas>
|
||||
<script type = 'text/javascript'>
|
||||
// global parameters
|
||||
var canvas = document.getElementById('canvas'),
|
||||
canvasW = 500,
|
||||
canvasH = 500,
|
||||
sizeScale = .1,
|
||||
edgeScale = 5,
|
||||
edgeT = 0,
|
||||
fontSize = 10,
|
||||
context = canvas.getContext('2d'),
|
||||
mouseX = 0,
|
||||
mouseY = 0,
|
||||
currentX = 0,
|
||||
currentY = 0,
|
||||
selectedX = 0,
|
||||
selectedY = 0,
|
||||
selectedPopulation = null;
|
||||
|
||||
// data placeholders
|
||||
var data_coordinates = [], // for each cell name include x coordinate, y coordinate, and radius values
|
||||
data_edges = [], // for each cell name include and array of edge values
|
||||
data_composition = []; // for each cell name include 8 numbers (first 4 for male gender, last 4 for female gender)
|
||||
// insert data here
|
||||
|
||||
// function to set the width of canvas. called from slider
|
||||
function setWidth(value){
|
||||
canvasW = parseFloat(value)
|
||||
canvas.width = canvasW
|
||||
context = canvas.getContext('2d')
|
||||
draw()
|
||||
}
|
||||
|
||||
// function to set height of canvas. called from slider
|
||||
function setHeight(value){
|
||||
canvasH = parseFloat(value)
|
||||
canvas.height = canvasH
|
||||
context = canvas.getContext('2d')
|
||||
draw()
|
||||
}
|
||||
|
||||
// function to set bubble size scale. called from slider
|
||||
function setSizeScale(value){
|
||||
sizeScale = parseFloat(value) / 100
|
||||
draw()
|
||||
}
|
||||
|
||||
// function to set edge scale. called from slider
|
||||
function setEdgeScale(value){
|
||||
edgeScale = parseFloat(value)
|
||||
draw()
|
||||
}
|
||||
|
||||
// function to set edge theshold. Any edge smaller than this threshold will not be drawn. called from slider
|
||||
function setEdgeThreshold(value){
|
||||
edgeT = parseFloat(value)
|
||||
draw()
|
||||
}
|
||||
|
||||
// function to set font size of cell name labels in the plot. called from slider
|
||||
function setFontSize(value){
|
||||
fontSize = parseInt(value)
|
||||
draw()
|
||||
}
|
||||
|
||||
// function to draw the canvas
|
||||
function draw(){
|
||||
// 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