#!/usr/bin/env python # -*- coding: utf-8 -*- import sys import argparse import numpy as np import matplotlib matplotlib.use('Agg') matplotlib.rcParams['pdf.fonttype'] = 42 matplotlib.rcParams['svg.fonttype'] = 'none' import matplotlib.pyplot as plt from deeptools.correlation import Correlation from deeptools._version import __version__ old_settings = np.seterr(all='ignore') def parse_arguments(args=None): basic_args = plot_correlation_args() heatmap_parser = heatmap_options() parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=""" Tool for the analysis and visualization of sample correlations based on the output of multiBamSummary or multiBigwigSummary. Pearson or Spearman methods are available to compute correlation coefficients. Results can be saved as multiple scatter plots depicting the pairwise correlations or as a clustered heatmap, where the colors represent the correlation coefficients and the clusters are joined using the Nearest Point Algorithm (also known as "single"). Optionally, the values can be saved as tables, too. detailed help: plotCorrelation -h """, epilog='example usages:\n' 'plotCorrelation -in results_file --whatToPlot heatmap --corMethod pearson -o heatmap.png\n\n' ' \n\n', parents=[basic_args, heatmap_parser]) return parser def plot_correlation_args(): parser = argparse.ArgumentParser(add_help=False) required = parser.add_argument_group('Required arguments') # define the arguments required.add_argument('--corData', '-in', metavar='FILE', help='Compressed matrix of values generated by multiBigwigSummary or multiBamSummary', required=True) required.add_argument('--plotFile', '-o', help='File to save the heatmap to. The file extension determines the format, ' 'so heatmap.pdf will save the heatmap in PDF format. ' 'The available formats are: .png, ' '.eps, .pdf and .svg.', type=argparse.FileType('w'), metavar='FILE', required=True) required.add_argument('--corMethod', '-c', help="Correlation method.", choices=['spearman', 'pearson'], required=True) required.add_argument('--whatToPlot', '-p', help="Choose between a heatmap or pairwise scatter plots", choices=['heatmap', 'scatterplot'], required=True) optional = parser.add_argument_group('Optional arguments') optional.add_argument('--skipZeros', help='By setting this option, genomic regions ' 'that have zero or missing (nan) values in all samples ' 'are excluded.', action='store_true', required=False) optional.add_argument('--labels', '-l', metavar='sample1 sample2', help='User defined labels instead of default labels from ' 'file names. ' 'Multiple labels have to be separated by spaces, e.g. ' '--labels sample1 sample2 sample3', nargs='+') optional.add_argument('--plotTitle', '-T', help='Title of the plot, to be printed on top of ' 'the generated image. Leave blank for no title.', default='') optional.add_argument('--plotFileFormat', metavar='FILETYPE', help='Image format type. If given, this option ' 'overrides the image format based on the plotFile ' 'ending. The available options are: png, ' 'eps, pdf and svg.', choices=['png', 'pdf', 'svg', 'eps']) optional.add_argument( '--removeOutliers', help='If set, bins with very large counts are removed. ' 'Bins with abnormally high reads counts artificially increase ' 'pearson correlation; that\'s why, multiBamSummary tries ' 'to remove outliers using the median absolute deviation (MAD) ' 'method applying a threshold of 200 to only consider extremely ' 'large deviations from the median. The ENCODE blacklist page ' '(https://sites.google.com/site/anshulkundaje/projects/blacklists) ' 'contains useful information about regions with unusually high counts' 'that may be worth removing.', action='store_true') optional.add_argument('--version', action='version', version='%(prog)s {}'.format(__version__)) group = parser.add_argument_group('Output optional options') group.add_argument('--outFileCorMatrix', help='Save matrix with pairwise correlation values to a tab-separated file.', metavar='FILE', type=argparse.FileType('w')) return parser def heatmap_options(): """ Options for generating the correlation heat map """ parser = argparse.ArgumentParser(add_help=False) heatmap = parser.add_argument_group('Heatmap options') heatmap.add_argument('--plotHeight', help='Plot height in cm.', type=float, default=9.5) heatmap.add_argument('--plotWidth', help='Plot width in cm. The minimum value is 1 cm.', type=float, default=11) heatmap.add_argument('--zMin', '-min', default=None, help='Minimum value for the heatmap intensities. ' 'If not specified, the value is set automatically', type=float) heatmap.add_argument('--zMax', '-max', default=None, help='Maximum value for the heatmap intensities.' 'If not specified, the value is set automatically', type=float) heatmap.add_argument( '--colorMap', default='jet', metavar='', help='Color map to use for the heatmap. Available values can be ' 'seen here: ' 'http://matplotlib.org/examples/color/colormaps_reference.html') heatmap.add_argument('--plotNumbers', help='If set, then the correlation number is plotted ' 'on top of the heatmap. This option is only valid when plotting a heatmap.', action='store_true', required=False) return parser def main(args=None): args = parse_arguments().parse_args(args) corr = Correlation(args.corData, args.corMethod, labels=args.labels, remove_outliers=args.removeOutliers, log1p=None, skip_zeros=args.skipZeros) if args.corMethod == 'pearson': # test if there are outliers and write a message recommending the removal if len(corr.get_outlier_indices(np.asarray(corr.matrix).flatten())) > 0: if args.removeOutliers: sys.stderr.write("\nOutliers were detected in the data. They " "will be removed to avoid bias " "in the pearson correlation.\n") else: sys.stderr.write("\nOutliers were detected in the data. Consider " "using the --removeOutliers parameter to avoid a bias " "in the pearson correlation.\n") if args.colorMap: try: plt.get_cmap(args.colorMap) except ValueError as error: sys.stderr.write( "A problem was found. Message: {}\n".format(error)) exit() args.plotFile.close() if args.whatToPlot == 'scatterplot': corr.plot_scatter(args.plotFile.name, plot_title=args.plotTitle, image_format=args.plotFileFormat) else: corr.plot_correlation(args.plotFile.name, vmax=args.zMax, vmin=args.zMin, colormap=args.colorMap, plot_title=args.plotTitle, image_format=args.plotFileFormat, plot_numbers=args.plotNumbers, plotWidth=args.plotWidth, plotHeight=args.plotHeight) if args.outFileCorMatrix: corr.save_corr_matrix(args.outFileCorMatrix)