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1 import os;
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2 import re;
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3 import csv;
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4 import string;
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5 import sys;
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6 import math;
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7 import numpy as np;
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8 import matplotlib;
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9 matplotlib.use('Agg');
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10 from pylab import *;
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11
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12 def extractGffFeatures(gffString):
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13 # GFF v3
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14 # 1. seqname - Must be a chromosome or scaffold.
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15 # 2. source - The program that generated this feature.
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16 # 3. feature - The name of this type of feature. Some examples of standard feature types are "CDS", "start_codon", "stop_codon", and "exon".
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17 # 4. start - The starting position of the feature in the sequence. The first base is numbered 1.
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18 # 5. end - The ending position of the feature (inclusive).
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19 # 6. score - A score between 0 and 1000. If there is no score value, enter ".".
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20 # 7. strand - Valid entries include '+', '-', or '.' (for don't know/care).
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21 # 8. frame - If the feature is a coding exon, frame should be a number between 0-2 that represents the reading frame of the first base. If the feature is not a coding exon, the value should be '.'.
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22 # 9. group - All lines with the same group are linked together into a single item.
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23
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24 res = [];
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25
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26 if len(gffString) > 1: # check for empty line (i.e. row in GFF file only contains \n)
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27 try:
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28 gffSeqName, gffSource, gffFeature, gffStart, gffEnd, gffScr, gffStrand, gffFrame, gffGroup = gffString.split('\t');
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29 except:
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30 return []; # exit, since we don't have 9 features
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31
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32 # handle the gffSeqName
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33 try:
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34 chrNo = int(re.findall(r'[0-9]+', gffSeqName)[0]);
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35 except:
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36 return [];
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37
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38 # handle gffStart
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39 try:
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40 probeStart = int(gffStart);
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41 except ValueError:
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42 return []; # exit, invalid gffStart
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43
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44 # handle gffEnd
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45 try:
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46 probeEnd = int(gffEnd);
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47 except ValueError:
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48 return []; # exit, invalid gffEnd
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49
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50 # handle gffScr
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51 try:
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52 probeScr = float(gffScr);
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53 except ValueError:
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54 return []; # exit, invalid gffScr
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55
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56 # if the input string was well-formed, return the extracted features
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57 res = [chrNo, probeStart, probeEnd, probeScr];
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58 return res; # return feature set (if any was found in this record)
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59
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60 # Define Constants
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61 tickPlotAdj = 0.5; # adjust tick marks on x-axis such that they are exactly at the bin boundary (and not in the bin center)
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62
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63 # Initialize Variables
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64 feaNames = [];
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65 feaChrNoList = [];
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66 feaStrandList = [];
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67 feaStartList = [];
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68 feaEndList = [];
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69 feaAttribList = [];
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70 classBoundaryIdxs = [];
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71
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72 gffChrNoList = [];
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73 gffProbeCenterList = [];
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74 gffMatScrList = [];
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75
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76 # get command line arguments
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77 gffFileName = sys.argv[1];
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78 feaFileName = sys.argv[2];
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79 outFileFormat = sys.argv[3];
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80 stepWidth = int(sys.argv[4]);
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81 classBoundaryStr = sys.argv[5];
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82 upStreamStretch = int(sys.argv[6]); # in bp
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83 maxX = int(sys.argv[7]); # in bp, if -1 maxX <= max(feature length), see below
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84 tickSpacing = int(sys.argv[8]); # in bp
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85 plotTitle = sys.argv[9];
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86 outFileName = sys.argv[10];
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87
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88 # Extract class boundaries
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89 classBoundaries = map(string.atoi, re.findall(r'\b\d+\b',classBoundaryStr));
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90
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91 # Read transcript information
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92 feaFileReader = csv.reader(open(feaFileName, 'rb'), delimiter = '\t');
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93 for row in feaFileReader:
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94 feaName, feaChrNo, feaStrand, feaStart, feaEnd, feaAttrib = row;
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95 feaNames.append(feaName);
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96 feaChrNoList.append(int(feaChrNo));
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97 feaStrandList.append(int(feaStrand));
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98 feaStartList.append(int(feaStart));
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99 feaEndList.append(int(feaEnd));
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100 feaAttribList.append(float(feaAttrib));
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101
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102 # Read enrichment scores
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103 gffFileHandle = open(gffFileName, "rU");
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104 for row in gffFileHandle:
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105 cache = extractGffFeatures(row);
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106 if len(cache) == 4: # if curr row wasn't well-formed, skip row
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107 chrNo, probeStart, probeEnd, probeScr = cache;
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108 gffChrNoList.append(chrNo);
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109 gffProbeCenterList.append(int(float(probeEnd - probeStart)/2.0 + probeStart)); # calc the center pos of each probe
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110 gffMatScrList.append(probeScr);
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111
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112 # convert numerical lists into vectors
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113 feaChrNos = np.array(feaChrNoList);
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114 feaStrands = np.array(feaStrandList);
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115 feaStarts = np.array(feaStartList);
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116 feaEnds = np.array(feaEndList);
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117 feaAttribs = np.array(feaAttribList);
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118
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119 gfChrNos = np.array(gffChrNoList);
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120 gfProbeLocs = np.array(gffProbeCenterList);
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121 gfMatScrs = np.array(gffMatScrList);
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122
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123 # preallocate the results array, bins plus 2 extra cols for feature length and transfreq (or other freature characteristic) plus 1 col for sorting
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124 avgFeatureMatScrs = np.empty((len(feaStarts),\
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125 (int(math.ceil(float(max(feaEnds - feaStarts))/float(stepWidth)) + upStreamStretch/stepWidth + 3))),\
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126 );
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127 avgFeatureMatScrs[:] = np.NAN;
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128
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129 featureCounter = 0;
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130
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131 for chr in range(min(feaChrNos),max(feaChrNos) + 1):
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132 #get all rows from the feature file that contain information about the i'th chromosome
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133 currChrIdxs = (feaChrNos == chr);
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134 currStrands = feaStrands[currChrIdxs];
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135 currStarts = feaStarts[currChrIdxs];
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136 currEnds = feaEnds[currChrIdxs];
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137 currAttribs = feaAttribs[currChrIdxs];
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138
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139 #get all rows from ChIP-* data sets
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140 currGfChrIdxs = (gfChrNos == chr);
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141 currGfProbeLocs = gfProbeLocs[currGfChrIdxs];
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142 currGfScrs = gfMatScrs[currGfChrIdxs];
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143
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144 # iterate over all features of the current chromosome
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145 for j in range(0,len(currStarts)):
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146 feaStrand = currStrands[j];
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147 feaStart = currStarts[j];
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148 feaEnd = currEnds[j];
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149 feaAttrib = currAttribs[j];
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150
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151 #calc the corresponding probe positions for the feature start
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152 if feaStrand == 1: # +strand
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153 refFeaSt = 0;
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154 while feaStart >= currGfProbeLocs[refFeaSt]:
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155 refFeaSt += 1;
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156 else: # -strand
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157 refFeaSt = 0;
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158 while feaEnd >= currGfProbeLocs[refFeaSt]:
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159 refFeaSt += 1;
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160 #now refFeaSt points to the correct probe according to strand orientation
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161
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162 #calc the avg probe scores per bin
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163 if feaStrand == 1:
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164 feaChunkStart = feaStart; # bp level pointer
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165 refChunkStart = refFeaSt; # probe level pointer
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166 refChunkEnd = refFeaSt; # probe level pointer
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167
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168 # determine values for the upstream bins
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169 for k in range(1,upStreamStretch/stepWidth + 1):
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170 feaChunkEnd = feaStart - k * stepWidth; # bp level pointer
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171
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172 # find the correct ref for the current feaChunkEnd; catch potential IndexOutOfBoundEx when upstream region too close to chr boundary
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173 try:
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174 while feaChunkEnd <= currGfProbeLocs[refChunkEnd]:
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175 refChunkEnd -= 1;
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176 except:
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177 pass; # don't handle the exception further
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178
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179 #calc probe mean
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180 avgFeatureMatScrs[featureCounter, upStreamStretch/stepWidth - k] = np.mean(currGfScrs[refChunkEnd:refChunkStart + 1]);
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181
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182 #adjust pointers
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183 refChunkStart = refChunkEnd;
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184
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185 # determine values for bins in feature body
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186 for k in range(1, int(math.ceil(float(feaEnd - feaStart)/float(stepWidth)) + 1)):
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187 feaChunkEnd = feaStart + k*stepWidth; # bp level pointer
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188
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189 try:
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190 while feaChunkEnd >= currGfProbeLocs[refChunkEnd]:
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191 refChunkEnd += 1;
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192 except:
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193 pass; # don't handle the exception further
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194
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195 # calc probe mean
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196 avgFeatureMatScrs[featureCounter, k - 1 + upStreamStretch/stepWidth] = np.mean(currGfScrs[refChunkStart:refChunkEnd + 1]);
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197
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198 # adjust chunk pointers
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199 refChunkStart = refChunkEnd;
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200
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201 # write feature length and attrib in last cols of the array
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202 avgFeatureMatScrs[featureCounter, -1] = feaEnd - feaStart;
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203 avgFeatureMatScrs[featureCounter, -2] = feaAttrib;
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204 featureCounter += 1;
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205 else: # -1 strand
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206 feaChunkStart = feaEnd; # bp level pointer
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207 refChunkStart = refFeaSt; # probe level pointer
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208 refChunkEnd = refFeaSt; # probe level pointer
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209
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210 # determine values for the upStream region bins
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211 for k in range(1,upStreamStretch/stepWidth + 1):
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212 feaChunkEnd = feaEnd + k*stepWidth; # bp level pointer
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213
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214 # find the ref for the current feaChunkEnd, catch potential IndexOutOfBoundEx when upstream region too close to chr boundary
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215 try:
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216 while feaChunkEnd >= currGfProbeLocs[refChunkEnd]:
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217 refChunkEnd += 1;
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218 except:
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219 pass;
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220
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221 # calc probe mean
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222 avgFeatureMatScrs[featureCounter, upStreamStretch/stepWidth - k] = np.mean(currGfScrs[refChunkStart:refChunkEnd+1]);
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223
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224 # adjust chunk pointers
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225 refChunkStart = refChunkEnd;
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226
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227 # determine values for bins in feature body
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228 for k in range(1, int(math.ceil(float(feaEnd - feaStart)/float(stepWidth)) + 1)):
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229 feaChunkEnd = feaEnd - k*stepWidth; # bp level pointer
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230
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231 try:
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232 while feaChunkEnd <= currGfProbeLocs[refChunkEnd]:
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233 refChunkEnd -= 1;
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234 except:
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235 pass;
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236
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237 # calc probe mean
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238 avgFeatureMatScrs[featureCounter, k-1 + upStreamStretch/stepWidth] = np.mean(currGfScrs[refChunkEnd:refChunkStart+1]);
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239
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240 # adjust chunk pointers
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241 refChunkStart = refChunkEnd;
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242
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243 # write feature length and attrib in last cols of the array
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244 avgFeatureMatScrs[featureCounter, -1] = feaEnd - feaStart;
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245 avgFeatureMatScrs[featureCounter, -2] = feaAttrib;
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246 featureCounter += 1;
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247
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248 # determine idxs of class boundaries and write class value in respective cells
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249 for c in range(len(classBoundaries)-1,-1,-1): # reverse iterate through the boundaries list
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250 idxs = np.nonzero(avgFeatureMatScrs[:,-2] <= classBoundaries[c])[0];
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251 avgFeatureMatScrs[idxs, -3] = c;
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252
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253 # assign names and data format to all cols of the results array and sort by feature attrib
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254 sortByClassCol = 'col' + str(np.size(avgFeatureMatScrs,1)-3);
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255 sortByLengthCol = 'col' + str(np.size(avgFeatureMatScrs,1)-1);
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256
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257 matDtype = {'names':['col%i'%i for i in range(np.size(avgFeatureMatScrs,1))],'formats':(np.size(avgFeatureMatScrs,1))*[np.float]};
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258 avgFeatureMatScrs.dtype = matDtype;
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259 avgFeatureMatScrs.sort(axis = 0, order = [sortByClassCol, sortByLengthCol]);
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260
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261 # generate colormap
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262 BYcmapData = {'red': ((0.0, 0.0, 0.094), (0.5, 0.0, 0.0), (1.0, 0.992, 1.0)), 'green': ((0.0, 0.0,0.658), (0.5, 0.0, 0.0), (1.0, 0.996, 1.0)), 'blue': ((0.0, 1.0, 0.828), (0.5, 0.0, 0.0), (1.0, 0.0, 0.0))};
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263 BYcmap = matplotlib.colors.LinearSegmentedColormap('BYcmap', BYcmapData,9);
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264
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265 lowerHinge = matplotlib.mlab.prctile(gfMatScrs, 25.0);
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266 upperHinge = matplotlib.mlab.prctile(gfMatScrs, 75.0);
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267 cmLim = (upperHinge - lowerHinge) * 1.5 + upperHinge; # upper inner fence
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268
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269 # remove custom array dtype used for sorting and plot avg scrs (without the last 3 cols)
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270 imgplot = imshow(avgFeatureMatScrs.view(np.float)[:,0:-3], aspect='auto', interpolation='nearest', origin='lower', vmin = -cmLim, vmax = cmLim);
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271 axvline(x = int(float(upStreamStretch)/float(stepWidth)) - tickPlotAdj, color = 'w', linewidth = 1);
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272
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273 # if maxX wasn't set by hand, set it to max feature length and set x-coord accordingly
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274 if maxX == -1:
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275 maxX = max(feaEnds - feaStarts);
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276 imgplot.axes.set_xbound(-tickPlotAdj, int(float(maxX)/float(stepWidth)) + int(float(upStreamStretch)/float(stepWidth)) - tickPlotAdj);
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277 else:
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278 imgplot.axes.set_xbound(-tickPlotAdj, int(float(maxX)/float(stepWidth)) - tickPlotAdj);
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279
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280 xTicks = [k * int(float(tickSpacing)/float(stepWidth)) + int(float(upStreamStretch)/float(stepWidth)) - tickPlotAdj for k in range(0, int(math.ceil(float(maxX)/float(tickSpacing)))+1)];
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281 xTicks.insert(-1,-tickPlotAdj);
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282 xTicksLabel = [k * tickSpacing for k in range(0, int(math.ceil(float(maxX)/float(tickSpacing)))+1)];
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283 xTicksLabel.insert(-1, str(-upStreamStretch));
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284 imgplot.axes.set_xticks(xTicks);
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285 imgplot.axes.set_xticklabels(xTicksLabel);
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286
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287 imgplot.axes.set_title(plotTitle);
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288 imgplot.axes.set_xlabel('Distance from feature start (bp)');
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289 imgplot.axes.set_ylabel('Feature class (low to high)');
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290 imgplot.axes.set_yticks([]);
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291
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292 imgplot.set_cmap(BYcmap);
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293 colorbar();
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294 show();
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295
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296 savefig('chromatra_t_tmp_plot', format=outFileFormat);
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297 data = file('chromatra_t_tmp_plot', 'rb').read();
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298 fp = open(outFileName, 'wb');
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299 fp.write(data);
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300 fp.close();
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301 os.remove('chromatra_t_tmp_plot');
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