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1 # Consol_fit! It's a script & it'll consolidate your fitness values if you got them from a looping trimming pipeline instead of the standard split-by-transposon pipeline. That's all.
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2
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3 import math
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4 import csv
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5
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11
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13
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14
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15 ##### ARGUMENTS #####
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16
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17 def print_usage():
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18 print "\n" + "You are missing one or more required flags. A complete list of flags accepted by calc_fitness is as follows:" + "\n\n"
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19 print "\033[1m" + "Required" + "\033[0m" + "\n"
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20 print "-i" + "\t\t" + "The calc_fit file to be consolidated" + "\n"
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21 print "-out" + "\t\t" + "Name of a file to enter the .csv output." + "\n"
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22 print "-out2" + "\t\t" + "Name of a file to put the percent blank score in (used in aggregate)." + "\n"
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23 print "-calctxt" + "\t\t" + "The txt file output from calc_fit" + "\n"
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24 print "-normalize" + "\t" + "A file that contains a list of genes that should have a fitness of 1" + "\n"
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25 print "\n"
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26 print "\033[1m" + "Optional" + "\033[0m" + "\n"
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27 print "-cutoff" + "\t\t" + "Discard any positions where the average of counted transcripts at time 0 and time 1 is below this number (default 0)" + "\n"
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28 print "-cutoff2" + "\t\t" + "Discard any positions within the normalization genes where the average of counted transcripts at time 0 and time 1 is below this number (default 0)" + "\n"
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29 print "-wig" + "\t\t" + "Create a wiggle file for viewing in a genome browser. Provide a filename." + "\n"
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30 print "-maxweight" + "\t" + "The maximum weight a transposon gene can have in normalization calculations" + "\n"
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31 print "-multiply" + "\t" + "Multiply all fitness scores by a certain value (e.g., the fitness of a knockout). You should normalize the data." + "\n"
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32 print "\n"
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33
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34 import argparse
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35 parser = argparse.ArgumentParser()
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36 parser.add_argument("-calctxt", action="store", dest="calctxt")
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37 parser.add_argument("-normalize", action="store", dest="normalize")
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38 parser.add_argument("-i", action="store", dest="input")
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39 parser.add_argument("-out", action="store", dest="outfile")
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40 parser.add_argument("-out2", action="store", dest="outfile2")
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41 parser.add_argument("-cutoff", action="store", dest="cutoff")
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42 parser.add_argument("-cutoff2", action="store", dest="cutoff2")
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43 parser.add_argument("-wig", action="store", dest="wig")
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44 parser.add_argument("-maxweight", action="store", dest="max_weight")
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45 parser.add_argument("-multiply", action="store", dest="multiply")
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46 arguments = parser.parse_args()
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47
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48 if (not arguments.input or not arguments.outfile or not arguments.calctxt):
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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.max_weight):
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53 arguments.max_weight = 75
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54
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55 if (not arguments.cutoff):
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56 arguments.cutoff = 0
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57
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58 # Cutoff2 only has an effect if it's larger than cutoff, since the normalization step references a list of insertions already affected by cutoff.
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59
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60 if (not arguments.cutoff2):
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61 arguments.cutoff2 = 10
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62
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63 #Gets total & refname from calc_fit outfile2
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64
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65 with open(arguments.calctxt) as file:
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66 calctxt = file.readlines()
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67 total = float(calctxt[1].split()[1])
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68 refname = calctxt[2].split()[1]
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69
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74
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75
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76
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77
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78
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79 ##### CONSOLIDATING THE CALC_FIT FILE #####
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80
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81 with open(arguments.input) as file:
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82 input = file.readlines()
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83 results = [["position", "strand", "count_1", "count_2", "ratio", "mt_freq_t1", "mt_freq_t2", "pop_freq_t1", "pop_freq_t2", "gene", "D", "W", "nW"]]
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84 i = 1
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85 d = float(input[i].split(",")[10])
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86 while i < len(input):
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87 position = float(input[i].split(",")[0])
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88 strands = input[i].split(",")[1]
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89 c1 = float(input[i].split(",")[2])
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90 c2 = float(input[i].split(",")[3])
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91 gene = input[i].split(",")[9]
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92 while i + 1 < len(input) and float(input[i+1].split(",")[0]) - position <= 4:
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93 if i + 1 < len(input):
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94 i += 1
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95 c1 += float(input[i].split(",")[2])
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96 c2 += float(input[i].split(",")[3])
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97 strands = input[i].split(",")[1]
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98 if strands[0] == 'b':
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99 new_strands = 'b/'
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100 elif strands[0] == '+':
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101 if input[i].split(",")[1][0] == 'b':
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102 new_strands = 'b/'
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103 elif input[i].split(",")[1][0] == '+':
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104 new_strands = '+/'
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105 elif input[i].split(",")[1][0] == '-':
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106 new_strands = 'b/'
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107 elif strands[0] == '-':
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108 if input[i].split(",")[1][0] == 'b':
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109 new_strands = 'b/'
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110 elif input[i].split(",")[1][0] == '+':
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111 new_strands = 'b/'
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112 elif input[i].split(",")[1][0] == '-':
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113 new_strands = '-/'
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114 if len(strands) == 3:
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115 if len(input[i].split(",")[1]) < 3:
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116 new_strands += strands[2]
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117 elif strands[0] == 'b':
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118 new_strands += 'b'
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119 elif strands[0] == '+':
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120 if input[i].split(",")[1][2] == 'b':
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121 new_strands += 'b'
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122 elif input[i].split(",")[1][2] == '+':
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123 new_strands += '+'
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124 elif input[i].split(",")[1][2] == '-':
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125 new_strands += 'b'
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126 elif strands[0] == '-':
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127 if input[i].split(",")[1][2] == 'b':
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128 new_strands += 'b'
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129 elif input[i].split(",")[1][2] == '+':
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130 new_strands += 'b'
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131 elif input[i].split(",")[1][2] == '-':
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132 new_strands += '-'
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133 else:
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134 if len(input[i].split(",")[1]) == 3:
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135 new_strands += input[i].split(",")[1][2]
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136 strands = new_strands
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137 i +=1
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138 if c2 != 0:
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139 ratio = c2/c1
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140 else:
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141 ratio = 0
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142 mt_freq_t1 = c1/total
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143 mt_freq_t2 = c2/total
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144 pop_freq_t1 = 1 - mt_freq_t1
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145 pop_freq_t2 = 1 - mt_freq_t2
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146 w = 0
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147 if mt_freq_t2 != 0:
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148 top_w = math.log(mt_freq_t2*(d/mt_freq_t1))
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149 bot_w = math.log(pop_freq_t2*(d/pop_freq_t1))
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150 w = top_w/bot_w
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151 row = [position, strands, c1, c2, ratio, mt_freq_t1, mt_freq_t2, pop_freq_t1, pop_freq_t2, gene, d, w, w]
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152 results.append(row)
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153 with open(arguments.outfile, "wb") as csvfile:
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154 writer = csv.writer(csvfile)
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155 writer.writerows(results)
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166 ##### REDOING NORMALIZATION #####
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167
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168 # The header below is just in a typical WIG file format; if you'd like to look into this more UCSC has notes on formatting WIG files on their site.
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169
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170 if (arguments.wig):
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171 wigstring = "track type=wiggle_0 name=" + arguments.wig + "\n" + "variableStep chrom=" + refname + "\n"
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172
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173 if (arguments.normalize):
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174 with open(arguments.normalize) as file:
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175 transposon_genes = file.read().splitlines()
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176 print "Normalize genes loaded" + "\n"
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177 blank_ws = 0
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178 sum = 0
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179 count = 0
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180 weights = []
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181 scores = []
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182 for list in results:
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183 if list[9] != '' and list[9] in transposon_genes and list[11]:
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184 c1 = list[2]
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185 c2 = list[3]
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186 score = list[11]
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187 avg = (c1 + c2)/2
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188
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189 # Skips over those insertion locations with too few insertions - their fitness values are less accurate because they're based on such small insertion numbers.
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190
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191 if float(c1) >= float(arguments.cutoff2):
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192
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193 # Sets a max weight, to prevent insertion location scores with huge weights from unbalancing the normalization.
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194
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195 if (avg >= float(arguments.max_weight)):
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196 avg = float(arguments.max_weight)
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197
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198 # Tallies how many w values are 0 within the blank_ws value; you might get many transposon genes with a w value of 0 if a bottleneck occurs, which is especially common with in vivo experiments.
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199 # For example, when studying a nasal infection in a mouse model, what bacteria "sticks" and is able to survive and what bacteria is swallowed and killed or otherwise flushed out tends to be a matter of chance not fitness; all mutants with an insertion in a specific transposon gene could be flushed out by chance!
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200
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201 if score == 0:
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202 blank_ws += 1
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203 sum += score
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204 count += 1
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205 weights.append(avg)
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206 scores.append(score)
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207
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208 print str(list[9]) + " " + str(score) + " " + str(c1)
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209
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210 # Counts and removes all "blank" fitness values of normalization genes - those that = 0 - because they most likely don't really have a fitness value of 0, and you just happened to not get any reads from that location at t2.
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211
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212 blank_count = 0
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213 original_count = len(scores)
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214 i = 0
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215 while i < original_count:
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216 w_value = scores[i]
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217 if w_value == 0:
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218 blank_count += 1
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219 weights.pop[i]
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220 scores.pop[i]
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221 i-=1
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222 i += 1
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223
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224 # If no normalization genes can pass the cutoff, normalization cannot occur, so this ends the script advises the user to try again and lower cutoff and/or cutoff2.
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225
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226 if len(scores) == 0:
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227 print 'ERROR: The normalization genes do not have enough reads to pass cutoff and/or cutoff2; please lower one or both of those arguments.' + "\n"
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228 quit()
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229
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230 pc_blank_normals = float(blank_count) / float(original_count)
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231 print "# blank out of " + str(original_count) + ": " + str(pc_blank_normals) + "\n"
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232 with open(arguments.outfile2, "w") as f:
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233 f.write("blanks: " + str(pc_blank_normals) + "\n" + "total: " + str(total) + "\n" + "refname: " + refname)
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234
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235 average = sum / count
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236 i = 0
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237 weighted_sum = 0
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238 weight_sum = 0
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239 while i < len(weights):
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240 weighted_sum += weights[i]*scores[i]
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241 weight_sum += weights[i]
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242 i += 1
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243 weighted_average = weighted_sum/weight_sum
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244
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245 print "Normalization step:" + "\n"
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246 print "Regular average: " + str(average) + "\n"
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247 print "Weighted Average: " + str(weighted_average) + "\n"
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248 print "Total Insertions: " + str(count) + "\n"
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249
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250 old_ws = 0
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251 new_ws = 0
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252 wcount = 0
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253 for list in results:
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254 if list[11] == 'W':
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255 continue
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256 new_w = float(list[11])/weighted_average
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257
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258 # Sometimes you want to multiply all the fitness values by a constant; this does that.
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259 # For example you might multiply all the values by a constant for a genetic interaction screen - where Tn-Seq is performed as usual except there's one background knockout all the mutants share.
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260
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261 if arguments.multiply:
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262 new_w *= float(arguments.multiply)
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263
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264 if float(list[11]) > 0:
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265 old_ws += float(list[11])
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266 new_ws += new_w
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267 wcount += 1
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268
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269 list[12] = new_w
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270
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271 if (arguments.wig):
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272 wigstring += str(list[0]) + " " + str(new_w) + "\n"
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273
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274 old_w_mean = old_ws / wcount
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275 new_w_mean = new_ws / wcount
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276 print "Old W Average: " + str(old_w_mean) + "\n"
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277 print "New W Average: " + str(new_w_mean) + "\n"
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278
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279 with open(arguments.outfile, "wb") as csvfile:
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280 writer = csv.writer(csvfile)
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281 writer.writerows(results)
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282
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283 if (arguments.wig):
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284 if (arguments.normalize):
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285 with open(arguments.wig, "wb") as wigfile:
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286 wigfile.write(wigstring)
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287 else:
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288 for list in results:
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289 wigstring += str(list[0]) + " " + str(list[11]) + "\n"
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290 with open(arguments.wig, "wb") as wigfile:
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291 wigfile.write(wigstring)
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292
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293
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294 # ___ ___ ___ ___ ___ ___ ___ ___
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295 # /\__\ /\ \ /\__\ /\__\ /\ \ /\ \ /\ \ /\__\
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296 # /:/ _/_ /::\ \ |::L__L /::L_L_ /::\ \ /::\ \ /::\ \ |::L__L
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297 # /::-"\__\ /::\:\__\ |:::\__\ /:/L:\__\ /:/\:\__\ /:/\:\__\ /:/\:\__\ |:::\__\
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298 # \;:;-",-" \/\::/ / /:;;/__/ \/_/:/ / \:\ \/__/ \:\ \/__/ \:\/:/ / /:;;/__/
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299 # |:| | /:/ / \/__/ /:/ / \:\__\ \:\__\ \::/ / \/__/
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300 # \|__| \/__/ \/__/ \/__/ \/__/ \/__/ |
