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1 # A translation of aggregate.pl into python! For analysis of Tn-Seq.
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2 # This script requires BioPython just like calc_fitness.py, so you need it installed along with its dependencies if you want to run these scripts on your own.
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3 # How to install BioPython and a list of its dependencies can be found here: http://biopython.org/DIST/docs/install/Installation.html
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4 # K. McCoy
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5
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6
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7
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8
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9
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10
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11
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12
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13
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14 ##### ARGUMENTS #####
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15
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16 def print_usage():
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17 print "Aggregate.py's usage is as follows:" + "\n\n"
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18 print "\033[1m" + "Required" + "\033[0m" + "\n"
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19 print "-o" + "\t\t" + "Output file for aggregated data." + "\n"
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20 print "\n"
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21 print "\033[1m" + "Optional" + "\033[0m" + "\n"
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22 print "-c" + "\t\t" + "Check for missing genes in the data set - provide a reference genome in genbank format. Missing genes will be sent to stdout." + "\n"
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23 print "-m" + "\t\t" + "Place a mark in an extra column for this set of genes. Provide a file with a list of genes seperated by newlines." + "\n"
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24 print "-x" + "\t\t" + "Cutoff: Don't include fitness scores with average counts (c1+c2)/2 < x (default: 0)" + "\n"
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25 print "-b" + "\t\t" + "Blanks: Exclude -b % of blank fitness scores (scores where c2 = 0) (default: 0 = 0%)" + "\n"
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26 print "-f" + "\t\t" + "An in-between file carrying information on the blank count found from calc_fitness or consol_fitness; one of two ways to pass a blank count to this script" + "\n"
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27 print "-w" + "\t\t" + "Use weighted algorithm to calculate averages, variance, sd, se" + "\n"
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28 print "-l" + "\t\t" + "Weight ceiling: maximum value to use as a weight (default: 999,999)" + "\n"
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29 print "\n"
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30 print "All remainder arguements will be treated as fitness files (those files created by calc_fitness.py)" + "\n"
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31 print "\n"
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32
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33 import argparse
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34 parser = argparse.ArgumentParser()
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35 parser.add_argument("-o", action="store", dest="summary")
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36 parser.add_argument("-c", action="store", dest="find_missing")
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37 parser.add_argument("-m", action="store", dest="marked")
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38 parser.add_argument("-x", action="store", dest="cutoff")
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39 parser.add_argument("-b", action="store", dest="blank_pc")
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40 parser.add_argument("-f", action="store", dest="blank_file")
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41 parser.add_argument("-w", action="store", dest="weighted")
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42 parser.add_argument("-l", action="store", dest="weight_ceiling")
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43 parser.add_argument("fitnessfiles", nargs=argparse.REMAINDER)
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44
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45 arguments = parser.parse_args()
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46
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47 if not arguments.summary:
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48 print "\n" + "You are missing a value for the -o flag. "
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49 print_usage()
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50 quit()
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51
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52 if not arguments.fitnessfiles:
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53 print "\n" + "You are missing fitness file(s); these should be entered immediately after all the flags. "
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54 print_usage()
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55 quit()
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56
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57 # 999,999 is a trivial placeholder number
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58
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59 if (not arguments.weight_ceiling):
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60 arguments.weight_ceiling = 999999
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61
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62 # Cutoff exists to discard positions with a low number of counted transcripts, because their fitness may not be as accurate - for the same reasoning that studies with low sample sizes can be innacurate.
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63
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64 if (not arguments.cutoff):
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65 arguments.cutoff = 0
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66
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67 # Gets information from the txt output file of calc_fit / consol, if inputted
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68
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69 if arguments.blank_file:
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70 with open(arguments.blank_file) as file:
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71 blank_pc = file.read().splitlines()
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72 arguments.blank_pc = float(blank_pc[0].split()[1])
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73
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74 if (not arguments.blank_pc):
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75 arguments.blank_pc = 0
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76
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77
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78
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79
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80
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81 ##### SUBROUTINES #####
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82
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83 # A subroutine that calculates the average, variance, standard deviation (sd), and standard error (se) of a group of scores; for use when aggregating scores by gene later on
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84
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85 import math
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86 def unweighted_average(scores):
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87 sum = 0
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88 num = 0
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89 i = 0
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90 while i < len(scores):
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91 if not scores[i]:
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92 scores[i] = 0.0
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93 sum += float(scores[i])
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94 num += 1
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95 i += 1
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96 average = sum/num
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97 xminusxbars = 0
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98 while i < len(scores):
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99 xminusxbars += (float(scores[i]) - average)**2
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100 if num <= 1:
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101 variance = 0
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102 else:
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103 variance = xminusxbars/(num-1)
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104 sd = math.sqrt(variance)
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105 se = sd / math.sqrt(num)
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106 return (average, variance, sd, se)
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107
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108 # A subroutine that calculates the weighted average, variance, standard deviation (sd), and standard error (se) of a group of scores; the weights come from the number of reads each insertion location has
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109 # For use when aggregating scores by gene later on, if the weighted argument is called
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110
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111 def weighted_average(scores,weights):
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112 sum = 0
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113 weighted_average = 0
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114 weighted_variance = 0
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115 top = 0
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116 bottom = 0
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117 i = 0
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118 while i < len(weights):
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119 if not scores[i]:
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120 scores[i] = 0.0
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121 top += float(weights[i])*float(scores[i])
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122 bottom += float(weights[i])
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123 i += 1
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124 if bottom == 0:
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125 return 0
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126 weighted_average = top/bottom
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127 top = 0
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128 bottom = 0
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129 i = 0
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130 while i < len(weights):
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131 top += float(weights[i]) * (float(scores[i]) - weighted_average)**2
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132 bottom += float(weights[i])
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133 i += 1
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134 weighted_variance = top/bottom
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135 weighted_stdev = math.sqrt(weighted_variance)
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136 weighted_stder = weighted_stdev/math.sqrt(len(scores))
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137 return (weighted_average, weighted_variance, weighted_stdev, weighted_stder)
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138
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139
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140
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141
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142
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143
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144
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145
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146
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147
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148 ##### AGGREGATION / CALCULATIONS #####
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149
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150 #Reads the genes which should be marked in the final aggregate file into an array
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151
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152 import os.path
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153 if arguments.marked:
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154 with open(arguments.marked) as file:
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155 marked_set = file.read().splitlines()
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156
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157 #Creates a dictionary of dictionaries to contain a summary of all genes and their fitness values
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158 #The fitness values and weights match up, so that the weight of gene_summary[locus]["w"][2] would be gene_summary[locus]["s"][2]
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159
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160 import csv
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161 gene_summary = {}
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162 for eachfile in arguments.fitnessfiles:
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163 with open(eachfile) as csvfile:
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164 lines = csv.reader(csvfile)
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165 for line in lines:
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166 locus = line[9]
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167 w = line[12]
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168 if w == 'nW':
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169 continue
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170 if not w:
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171 w == 0
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172 c1 = float(line[2])
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173 c2 = float(line[3])
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174 avg = (c1+c2)/2
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175 if avg < float(arguments.cutoff):
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176 continue
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177 if avg > float(arguments.weight_ceiling):
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178 avg = arguments.weight_ceiling
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179 if locus not in gene_summary:
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180 gene_summary[locus] = {"w" : [], "s": []}
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181 gene_summary[locus]["w"].append(w)
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182 gene_summary[locus]["s"].append(avg)
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183
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184 #If finding any missing gene loci is requested in the arguments, starts out by loading all the known features from a genbank file
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185
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186 from Bio import SeqIO
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187 if (arguments.find_missing):
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188 output = [["locus","mean","var","sd","se","gene","Total","Blank","Not Blank","Blank Removed","M\n"]]
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189 handle = open(arguments.find_missing, "rU")
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190 for record in SeqIO.parse(handle, "genbank"):
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191 refname = record.id
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192 features = record.features
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193 handle.close()
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194
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195 #Goes through the features to find which are genes
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196
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197 for feature in features:
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198 gene = ""
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199 if feature.type == "gene":
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200 locus = "".join(feature.qualifiers["locus_tag"])
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201 if "gene" in feature.qualifiers:
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202 gene = "".join(feature.qualifiers["gene"])
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203 else:
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204 continue
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205
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206 #Goes through the fitness scores of insertions within each gene, and removes whatever % of blank fitness scores were requested along with their corresponding weights
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207
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208 sum = 0
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209 num = 0
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210 avgsum = 0
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211 blank_ws = 0
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212 i = 0
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213 if locus in gene_summary.keys():
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214 for w in gene_summary[locus]["w"]:
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215 if float(w) == 0:
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216 blank_ws += 1
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217 else:
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218 sum += float(w)
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219 num += 1
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220 count = num + blank_ws
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221 removed = 0
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222 to_remove = int(float(arguments.blank_pc)*count)
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223 if blank_ws > 0:
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224 i = 0
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225 while i < len(gene_summary[locus]["w"]):
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226 w = gene_summary[locus]["w"][i]
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227 if removed == to_remove:
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228 break
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229 if float(w) == 0:
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230 del gene_summary[locus]["w"][i]
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231 del gene_summary[locus]["s"][i]
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232 removed += 1
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233 i -= 1
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234 i += 1
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235
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236 #If all the fitness values within a gene are empty, sets mean/var to 0.10 and Xs out sd/se; marks the gene if that's requested
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237
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238 if num == 0:
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239 if (arguments.marked and locus in marked_set):
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240 output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "M", "\n"])
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241 else:
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242 output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "\n"])
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243
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244 #Otherwise calls average() or weighted_average() to find the aggregate w / count / standard deviation / standard error of the insertions within each gene; marks the gene if that's requested
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245
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246 else:
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247 if not arguments.weighted:
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248 (average, variance, stdev, stderr) = unweighted_average(gene_summary[locus]["w"])
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249 else:
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250 (average, variance, stdev, stderr) = weighted_average(gene_summary[locus]["w"],gene_summary[locus]["s"])
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251 if (arguments.marked and locus in marked_set):
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252 output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "M", "\n"])
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253 else:
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254 output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "\n"])
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255
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256 #If a gene doesn't have any insertions, sets mean/var to 0.10 and Xs out sd/se, plus leaves count through removed blank because there were no reads.
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257
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258 else:
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259 if (arguments.marked and locus in marked_set):
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260 output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "M", "\n"])
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261 else:
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262 output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "\n"])
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263
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264 #Writes the aggregated fitness file
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265
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266 with open(arguments.summary, "wb") as csvfile:
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267 writer = csv.writer(csvfile)
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268 writer.writerows(output)
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269
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270 #If finding missing genes is not requested, just finds the aggregate w / count / standard deviation / standard error of the insertions within each gene, and writes them to a file, plus marks the genes requested
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271 #This is never called through Galaxy since finding missing genes is just better than not finding them.
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272
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273 else:
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274 output = [["Locus","W","Count","SD","SE","M\n"]]
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275 for gene in gene_summary.keys():
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276 sum = 0
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277 num = 0
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278 average = 0
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279 if "w" not in gene_summary[gene]:
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280 continue
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281 for i in gene_summary[gene]["w"]:
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282 sum += i
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283 num += 1
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284 average = sum/num
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285 xminusxbars = 0
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286 for i in w:
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287 xminusxbars += (i-average)**2
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288 if num > 1:
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289 sd = math.sqrt(xminusxbars/(num-1))
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290 se = sd / math.sqrt(num)
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291 if (arguments.marked and locus in marked_set):
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292 output.append([gene, average, num, sd, se, "M", "\n"])
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293 else:
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294 output.append([gene, average, num, sd, se, "\n"])
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295 with open(arguments.summary, "wb") as csvfile:
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296 writer = csv.writer(csvfile)
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297 writer.writerows(output) |
