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annotate dapars.py @ 11:b2e46d8f9487 draft

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planemo upload for repository https://github.com/mvdbeek/dapars commit deab588a5d5ec7022de63a395fbd04e415ba0a42-dirty
author mvdbeek
date Thu, 29 Oct 2015 18:28:03 -0400
parents a5d8b08af089
children 538c4e2b423e
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bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
mvdbeek
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1 import argparse
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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2 import os
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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3 import csv
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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4 import numpy as np
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5 from scipy import stats
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6 from collections import OrderedDict, namedtuple
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7 import filter_utr
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8 import subprocess
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9 from multiprocessing import Pool
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10 import warnings
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11 import matplotlib.pyplot as plt
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12 import matplotlib.gridspec as gridspec
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13 from tabulate import tabulate
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14
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15 def directory_path(str):
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16 if os.path.exists(str):
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17 return str
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18 else:
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19 os.mkdir(str)
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20 return str
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22 def parse_args():
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23 """
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24 Returns floating point values except for input files.
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25 My initial approach will not filter anything. (FDR. fold_change, PDUI, Num_least ...)
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26 """
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27 parser = argparse.ArgumentParser(prog='DaPars', description='Determines the usage of proximal polyA usage')
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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28 parser.add_argument("-c", "--control_alignments", nargs="+", required=True,
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29 help="Alignment files in BAM format from control condition")
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30 parser.add_argument("-t", "--treatment_alignments", nargs="+", required=True,
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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31 help="Alignment files in BAM format from treatment condition")
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32 parser.add_argument("-u", "--utr_bed_file", required=True, type=file,
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33 help="Bed file describing longest 3UTR positions")
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34 parser.add_argument("-o", "--output_file", required=True, type=argparse.FileType('w'),
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35 help="file containing output")
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36 parser.add_argument("-cpu", required=False, type=int, default=1,
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37 help="Number of CPU cores to use.")
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38 parser.add_argument("-s", "--search_start", required=False, type=int, default=50,
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39 help="Start search for breakpoint n nucleotides downstream of UTR start")
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40 parser.add_argument("-ct", "--coverage_threshold", required=False, type=float, default=20,
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41 help="minimum coverage in each aligment to be considered for determining breakpoints")
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42 parser.add_argument("-b", "--breakpoint_bed", required=False, type=argparse.FileType('w'),
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43 help="Write bedfile with coordinates of breakpoint positions to supplied path.")
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44 parser.add_argument("-v", "--version", action='version', version='%(prog)s 0.2.0')
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45 parser.add_argument("-p", "--plot_path", default=None, required=False, type=directory_path,
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46 help="If plot_path is specified will write a coverage plot for every UTR in that directory.")
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47 parser.add_argument("-html", "--html_file", default=None, required=False, type=argparse.FileType('w'),
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48 help="Write an html file to the specified location. Only to be used within a galaxy wrapper")
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49 return parser.parse_args()
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50
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51
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52 class UtrFinder():
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53 """
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54 This seems to be the main caller.
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55 """
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56
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57 def __init__(self, args):
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58 self.control_alignments = [file for file in args.control_alignments]
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59 self.treatment_alignments = [file for file in args.treatment_alignments]
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60 self.n_cpus = args.cpu
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61 self.search_start = args.search_start
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62 self.coverage_threshold = args.coverage_threshold
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63 self.plot_path = args.plot_path
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64 self.html_file = args.html_file
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65 self.utr = args.utr_bed_file
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66 self.gtf_fields = filter_utr.get_gtf_fields()
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67 self.result_file = args.output_file
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68 self.all_alignments = self.control_alignments + self.treatment_alignments
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69 self.alignment_names = { file: os.path.basename(file) for file in self.all_alignments }
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70 self.num_samples = len(self.all_alignments)
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71 self.utr_dict = self.get_utr_dict(0.2)
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72 self.dump_utr_dict_to_bedfile()
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73 print "Established dictionary of 3\'UTRs"
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74 self.coverage_files = self.run_bedtools_coverage()
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75 self.utr_coverages = self.read_coverage_result()
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76 print "Established dictionary of 3\'UTR coverages"
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77 self.coverage_weights = self.get_coverage_weights()
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78 self.result_tuple = self.get_result_tuple()
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79 self.result_d = self.calculate_apa_ratios()
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80 self.write_results()
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81 if args.breakpoint_bed:
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82 self.bed_output = args.breakpoint_bed
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83 self.write_bed()
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84 if self.plot_path:
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85 self.write_html()
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86
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87 def dump_utr_dict_to_bedfile(self):
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88 w = csv.writer(open("tmp_bedfile.bed", "w"), delimiter="\t")
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89 for gene, utr in self.utr_dict.iteritems():
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90 w.writerow([utr["chr"], utr["new_start"]-1, utr["new_end"], gene, ".", utr["strand"]])
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91
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92 def run_bedtools_coverage(self):
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93 """
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94 Use bedtools coverage to generate pileup data for all alignment files for the regions specified in utr_dict.
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95 """
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96 coverage_files = []
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97 cmds = []
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98 for alignment_file in self.all_alignments:
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99 cmd = "sort -k1,1 -k2,2n tmp_bedfile.bed | "
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100 cmd = cmd + "bedtools coverage -d -s -abam {alignment_file} -b stdin |" \
0
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101 " cut -f 4,7,8 > coverage_file_{alignment_name}".format(
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102 alignment_file = alignment_file, alignment_name= self.alignment_names[alignment_file] )
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103 cmds.append(cmd)
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104 pool = Pool(self.n_cpus)
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105 subprocesses = [subprocess.Popen([cmd], shell=True) for cmd in cmds]
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106 [p.wait() for p in subprocesses]
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107 coverage_files = ["gene_position_coverage_{alignment_name}".format(
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108 alignment_name = self.alignment_names[alignment_file]) for alignment_file in self.all_alignments ]
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109 return coverage_files
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110
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111 def read_coverage_result(self):
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112 """
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113 Read coverages back in and store as dictionary of numpy arrays
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114 """
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115 coverage_dict = { gene: { name: np.zeros(utr_d["new_end"]+1-utr_d["new_start"]) for name in self.alignment_names.itervalues() } for gene, utr_d in self.utr_dict.iteritems() }
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116 for alignment_name in self.alignment_names.itervalues():
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117 with open("coverage_file_{alignment_name}".format(alignment_name = alignment_name)) as coverage_file:
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118 for line in coverage_file:
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119 gene, position, coverage= line.strip().split("\t")
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120 coverage_dict[gene][alignment_name][int(position)-1] = coverage
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121 for utr_d in self.utr_dict.itervalues():
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122 if utr_d["strand"] == "-":
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123 for alignment_name in self.alignment_names.values():
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124 coverage_dict[gene][alignment_name] = coverage_dict[gene][alignment_name][::-1]
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125 return coverage_dict
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126
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127 def get_utr_dict(self, shift):
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128 """
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129 The utr end is extended by UTR length * shift, to discover novel distal polyA sites.
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130 Set to 0 to disable.
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131 """
0
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132 utr_dict = OrderedDict()
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133 for line in self.utr:
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134 if not line.startswith("#"):
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135 filter_utr.get_feature_dict( line=line, gtf_fields=self.gtf_fields, utr_dict=utr_dict, feature="UTR" )
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136 gene, utr_d = utr_dict.popitem()
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137 utr_d = utr_d[0]
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138 end_shift = int(round(abs(utr_d["start"] - utr_d["end"]) * shift))
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139 if utr_d["strand"] == "+":
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140 utr_d["new_end"] = utr_d["end"] - end_shift
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141 utr_d["new_start"] = utr_d["start"]
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142 else:
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143 utr_d["new_end"] = utr_d["end"]
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144 utr_d["new_start"] = utr_d["start"] + end_shift
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145 if utr_d["new_start"] + 50 < utr_d["new_end"]:
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146 utr_dict[gene] = utr_d
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147 return utr_dict
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148
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149 def get_coverage_weights(self):
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150 """
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151 Return weights for normalizing coverage.
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152 utr_coverage is still confusing.
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153 """
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154 coverage_per_alignment = []
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155 for utr in self.utr_coverages.itervalues(): # TODO: be smarter about this.
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156 utr_coverage = []
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157 for vector in utr.itervalues():
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158 utr_coverage.append(np.sum(vector))
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159 coverage_per_alignment.append(utr_coverage)
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160 coverages = np.array([ sum(x) for x in zip(*coverage_per_alignment) ])
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diff changeset
161 coverage_weights = coverages / np.mean(coverages) # TODO: proabably median is better suited? Or even no normalization!
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162 return coverage_weights
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163
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164 def get_result_tuple(self):
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165 static_desc = ["chr", "start", "end", "strand", "gene", "t_stat", "p_value", "breakpoint",
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166 "breakpoint_type", "control_mean_percent", "treatment_mean_percent" ]
0
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167 samples_desc = []
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168 for statistic in ["coverage_long", "coverage_short", "percent_long"]:
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169 for i, sample in enumerate(self.control_alignments):
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170 samples_desc.append("control_{i}_{statistic}".format(i=i, statistic = statistic))
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171 for i, sample in enumerate(self.treatment_alignments):
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172 samples_desc.append("treatment_{i}_{statistic}".format(i=i, statistic = statistic))
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173 return namedtuple("result", static_desc + samples_desc)
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174
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175 def calculate_apa_ratios(self):
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176 result_d = OrderedDict()
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177 arg_d = {"result_tuple": self.result_tuple,
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178 "coverage_weights":self.coverage_weights,
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179 "num_samples":self.num_samples,
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180 "num_control":len(self.control_alignments),
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181 "num_treatment":len(self.treatment_alignments),
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182 "result_d":result_d}
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183 pool = Pool(self.n_cpus)
5
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184 tasks = [ (self.utr_coverages[utr], self.plot_path, utr, utr_d, self.coverage_weights, len(self.control_alignments),
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185 len(self.treatment_alignments), self.search_start, self.coverage_threshold) \
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186 for utr, utr_d in self.utr_dict.iteritems() ]
0
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187 processed_tasks = [ pool.apply_async(calculate_all_utr, t) for t in tasks]
5
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188 result_list = [res.get() for res in processed_tasks]
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189 for res_control, res_treatment in result_list:
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190 if not res_control:
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191 continue
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192 for i, result in enumerate(res_control):
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193 if isinstance(result, dict):
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194 t = self.result_tuple(**result)
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195 result_d[result["gene"]+"_bp_control_{i}".format(i=i)] = t
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196 for i, result in enumerate(res_treatment):
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197 if isinstance(result, dict):
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198 t = self.result_tuple(**result)
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199 result_d[result["gene"]+"_bp_treatment_{i}".format(i=i)] = t
0
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200 return result_d
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201
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202 def write_results(self):
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203 w = csv.writer(self.result_file, delimiter='\t')
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204 header = list(self.result_tuple._fields)
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205 header[0] = "#chr"
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206 w.writerow(header) # field header
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207 w.writerows( self.result_d.values())
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208
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209 def write_html(self):
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210 output_lines = [(gene_str_to_link(result.gene), result.breakpoint, result.breakpoint_type, result.p_value ) for result in self.result_d.itervalues()]
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211 if self.html_file:
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212 self.html_file.write(tabulate(output_lines, headers=["gene", "breakpoint", "breakpoint_type", "p_value"], tablefmt="html"))
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213 else:
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214 with open(os.path.join(self.plot_path, "index.html"), "w") as html_file:
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215 html_file.write(tabulate(output_lines, headers=["gene", "breakpoint", "breakpoint_type", "p_value"], tablefmt="html"))
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216
0
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217 def write_bed(self):
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218 w = csv.writer(self.bed_output, delimiter='\t')
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219 bed = [(result.chr, result.breakpoint, int(result.breakpoint)+1, result.gene+"_"+result.breakpoint_type, 0, result.strand) for result in self.result_d.itervalues()]
0
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220 w.writerows(bed)
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221
1
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222
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223 def calculate_all_utr(utr_coverage, plot_path, utr, utr_d, coverage_weights, num_control, num_treatment, search_start, coverage_threshold):
0
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224 if utr_d["strand"] == "+":
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225 is_reverse = False
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226 else:
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227 is_reverse = True
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228 control_breakpoints, control_abundances, treatment_breakpoints, treatment_abundances = \
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229 optimize_breakpoint(plot_path, utr, utr_coverage, utr_d["new_start"], utr_d["new_end"], coverage_weights, search_start, coverage_threshold, num_control)
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230 res_control = breakpoints_to_result(utr, utr_d, control_breakpoints, "control_breakpoint", control_abundances, is_reverse,
1
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231 num_control, num_treatment)
5
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diff changeset
232 res_treatment = breakpoints_to_result(utr, utr_d, treatment_breakpoints, "treatment_breakpoint", treatment_abundances, is_reverse,
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diff changeset
233 num_control, num_treatment)
1
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mvdbeek
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diff changeset
234 return res_control, res_treatment
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diff changeset
235
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diff changeset
236
5
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diff changeset
237 def breakpoints_to_result(utr, utr_d, breakpoints, breakpoint_type,
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diff changeset
238 abundances, is_reverse, num_control, num_treatment):
1
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mvdbeek
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diff changeset
239 """
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diff changeset
240 Takes in a result dictionary res and fills the necessary fields
0
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parents:
diff changeset
241 """
5
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diff changeset
242 if not breakpoints:
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diff changeset
243 return False
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diff changeset
244 result = []
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245 for breakpoint, abundance in zip(breakpoints, abundances):
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diff changeset
246 res = {}
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diff changeset
247 long_coverage_vector = abundance[0]
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248 short_coverage_vector = abundance[1]
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249 percentage_long = long_coverage_vector/(long_coverage_vector+short_coverage_vector)
1
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diff changeset
250 for i in range(num_control):
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diff changeset
251 res["control_{i}_coverage_long".format(i=i)] = float(long_coverage_vector[i])
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252 res["control_{i}_coverage_short".format(i=i)] = float(short_coverage_vector[i])
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253 res["control_{i}_percent_long".format(i=i)] = percentage_long[i]
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254 for k in range(num_treatment):
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diff changeset
255 i = k + num_control
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diff changeset
256 res["treatment_{i}_coverage_long".format(i=k)] = float(long_coverage_vector[i])
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257 res["treatment_{i}_coverage_short".format(i=k)] = float(short_coverage_vector[i])
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258 res["treatment_{i}_percent_long".format(i=k)] = percentage_long[i]
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diff changeset
259 res["t_stat"], res["p_value"] = stat_test(percentage_long[:num_control], percentage_long[num_control:])
1
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diff changeset
260 control_mean_percent = np.mean(np.array(percentage_long[:num_control]))
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diff changeset
261 treatment_mean_percent = np.mean(np.array(percentage_long[num_control:]))
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diff changeset
262 res["chr"] = utr_d["chr"]
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diff changeset
263 res["start"] = utr_d["start"]
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diff changeset
264 res["end"] = utr_d["end"]
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diff changeset
265 res["strand"] = utr_d["strand"]
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266 if is_reverse:
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267 breakpoint = utr_d["new_end"] - breakpoint
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268 else:
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diff changeset
269 breakpoint = utr_d["new_start"] + breakpoint
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270 res["breakpoint"] = breakpoint
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271 res["breakpoint_type"] = breakpoint_type
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272 res["control_mean_percent"] = control_mean_percent
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273 res["treatment_mean_percent"] = treatment_mean_percent
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274 res["gene"] = utr
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275 result.append(res)
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276 return result
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277
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278
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279 def optimize_breakpoint(plot_path, utr, utr_coverage, UTR_start, UTR_end, coverage_weigths, search_start, coverage_threshold, num_control):
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280 """
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281 We are searching for a point within the UTR that minimizes the mean squared error, if the coverage vector was divided
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282 at that point. utr_coverage is a list with items corresponding to numpy arrays of coverage for a sample.
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283 """
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284 num_samples = len(utr_coverage)
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285 normalized_utr_coverage = np.array(utr_coverage.values())/np.expand_dims(coverage_weigths, axis=1)
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286 start_coverage = [np.mean(coverage[0:99]) for coverage in utr_coverage.values()] # filters threshold on mean coverage over first 100 nt
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287 is_above_threshold = sum(np.array(start_coverage) >= coverage_threshold) >= num_samples # This filters on the raw threshold. Why?
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288 is_above_length = UTR_end - UTR_start >= 150
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289 if (is_above_threshold) and (is_above_length):
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290 search_end = UTR_end - UTR_start
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291 breakpoints = range(search_start, search_end + 1)
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292 mse_list = [ estimate_mse(normalized_utr_coverage, bp, num_samples, num_control) for bp in breakpoints ]
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293 mse_list = [mse_list[0] for i in xrange(search_start)] + mse_list
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294 if plot_path:
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295 plot_coverage_breakpoint(plot_path, utr, mse_list, normalized_utr_coverage, num_control)
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296 if len(mse_list) > 0:
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297 return mse_to_breakpoint(mse_list, normalized_utr_coverage, num_samples)
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298 return False, False, False, False
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299
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300
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301 def plot_coverage_breakpoint(plot_path, utr, mse_list, normalized_utr_coverage, num_control):
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302 """
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303
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304 """
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305 fig = plt.figure(figsize=(8, 8))
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306 gs = gridspec.GridSpec(2, 1)
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307 ax1 = plt.subplot(gs[0, :])
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308 ax2 = plt.subplot(gs[1, :])
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309 ax1.set_title("mean-squared error plot")
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310 ax1.set_ylabel("mean-squared error")
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311 ax1.set_xlabel("nt after UTR start")
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312 ax2.set_title("coverage plot")
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313 ax2.set_xlabel("nt after UTR start")
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314 ax2.set_ylabel("normalized nucleotide coverage")
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315 mse_control = [ condition[0] for condition in mse_list]
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316 mse_treatment = [ condition[1] for condition in mse_list]
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317 minima_control = get_minima(np.array(mse_control))
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318 minima_treatment = get_minima(np.array(mse_treatment))
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319 control = normalized_utr_coverage[:num_control]
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320 treatment = normalized_utr_coverage[num_control:]
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321 ax1.plot(mse_control, "b-")
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322 ax1.plot(mse_treatment, "r-")
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323 [ax2.plot(cov, "b-") for cov in control]
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324 [ax2.plot(cov, "r-") for cov in treatment]
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325 [ax2.axvline(val, color="b", alpha=0.25) for val in minima_control]
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326 ax2.axvline(mse_control.index(min(mse_control)), color="b", alpha=1)
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327 [ax2.axvline(val, color="r", alpha=0.25) for val in minima_treatment]
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328 ax2.axvline(mse_treatment.index(min(mse_treatment)), color="r", alpha=1)
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329 fig.add_subplot(ax1)
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330 fig.add_subplot(ax2)
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331 gs.tight_layout(fig)
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332 fig.savefig(os.path.join(plot_path, "{utr}.svg".format(utr=utr)))
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333
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334
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335 def mse_to_breakpoint(mse_list, normalized_utr_coverage, num_samples):
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336 """
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337 Take in mse_list with control and treatment mse and return breakpoint and utr abundance for all local minima
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338 in mse_list
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339 """
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340 mse_control = np.array([mse[0] for mse in mse_list])
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341 mse_treatment = np.array([mse[1] for mse in mse_list])
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342 control_breakpoints = list(get_minima(mse_control))
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343 treatment_breakpoints = list(get_minima(mse_treatment))
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344 control_abundances = [estimate_abundance(normalized_utr_coverage, bp, num_samples) for bp in control_breakpoints]
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345 treatment_abundances = [estimate_abundance(normalized_utr_coverage, bp, num_samples) for bp in treatment_breakpoints]
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346 return control_breakpoints, control_abundances, treatment_breakpoints, treatment_abundances
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347
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348 def get_minima(a):
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349 """
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350 get minima for numpy array a
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351 """
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352 return np.where(np.r_[True, a[1:] < a[:-1]] & np.r_[a[:-1] < a[1:], True])[0]+1
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353
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354 def estimate_mse(cov, bp, num_samples, num_control):
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355 """
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356 get abundance of long utr vs short utr with breakpoint specifying the position of long and short utr.
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357 """
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358 with warnings.catch_warnings():
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359 warnings.simplefilter("ignore", category=RuntimeWarning)
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360 long_utr_vector = cov[:num_samples, bp:]
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361 short_utr_vector = cov[:num_samples, 0:bp]
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362 mean_long_utr = np.mean(long_utr_vector, 1)
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363 mean_short_utr = np.mean(short_utr_vector, 1)
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364 square_mean_centered_short_utr_vector = (short_utr_vector[:num_samples] - mean_short_utr[:, np.newaxis] )**2
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365 square_mean_centered_long_utr_vector = (long_utr_vector[:num_samples] - mean_long_utr[:, np.newaxis])**2
1
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366 mse_control = np.mean(np.append(square_mean_centered_long_utr_vector[:num_control], square_mean_centered_short_utr_vector[:num_control]))
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mvdbeek
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367 mse_treatment = np.mean(np.append(square_mean_centered_long_utr_vector[num_control:], square_mean_centered_short_utr_vector[num_control:]))
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mvdbeek
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368 return mse_control, mse_treatment
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369
0
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370
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mvdbeek
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371 def estimate_abundance(cov, bp, num_samples):
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372 with warnings.catch_warnings():
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373 warnings.simplefilter("ignore", category=RuntimeWarning)
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mvdbeek
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374 long_utr_vector = cov[:num_samples, bp:]
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mvdbeek
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375 short_utr_vector = cov[:num_samples, 0:bp]
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
mvdbeek
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376 mean_long_utr = np.mean(long_utr_vector, 1)
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mvdbeek
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377 mean_short_utr = np.mean(short_utr_vector, 1)
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mvdbeek
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378 return mean_long_utr, mean_short_utr
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379
5
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mvdbeek
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380 def stat_test(a,b):
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381 return stats.ttest_ind(a,b)
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mvdbeek
parents: 4
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382
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mvdbeek
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383 def gene_str_to_link(str):
a5d8b08af089 planemo upload for repository https://github.com/mvdbeek/dapars commit deab588a5d5ec7022de63a395fbd04e415ba0a42
mvdbeek
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384 return "<a href=\"{str}.svg\" type=\"image/svg+xml\" target=\"_blank\">{str}</a>".format(str=str)
0
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385
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mvdbeek
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386 if __name__ == '__main__':
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mvdbeek
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387 args = parse_args()
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mvdbeek
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388 find_utr = UtrFinder(args)
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mvdbeek
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389