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281 lines
12 KiB
Python
281 lines
12 KiB
Python
#!/usr/bin/env python
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"""
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Runs Ben's simulation.
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usage: %prog [options]
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-i, --input=i: Input genome (FASTA format)
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-g, --genome=g: If built-in, the genome being used
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-l, --read_len=l: Read length
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-c, --avg_coverage=c: Average coverage
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-e, --error_rate=e: Error rate (0-1)
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-n, --num_sims=n: Number of simulations to run
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-p, --polymorphism=p: Frequency/ies for minor allele (comma-separate list of 0-1)
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-d, --detection_thresh=d: Detection thresholds (comma-separate list of 0-1)
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-p, --output_png=p: Plot output
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-s, --summary_out=s: Whether or not to output a file with summary of all simulations
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-m, --output_summary=m: File name for output summary of all simulations
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-f, --new_file_path=f: Directory for summary output files
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"""
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# removed output of all simulation results on request (not working)
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# -r, --sim_results=r: Output all tabular simulation results (number of polymorphisms times number of detection thresholds)
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# -o, --output=o: Base name for summary output for each run
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from rpy import *
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import os
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import random, sys, tempfile
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from galaxy import eggs
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import pkg_resources; pkg_resources.require( "bx-python" )
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from bx.cookbook import doc_optparse
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def stop_err( msg ):
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sys.stderr.write( '%s\n' % msg )
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sys.exit()
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def __main__():
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#Parse Command Line
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options, args = doc_optparse.parse( __doc__ )
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# validate parameters
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error = ''
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try:
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read_len = int( options.read_len )
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if read_len <= 0:
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raise Exception, ' greater than 0'
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except TypeError, e:
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error = ': %s' % str( e )
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if error:
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stop_err( 'Make sure your number of reads is an integer value%s' % error )
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error = ''
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try:
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avg_coverage = int( options.avg_coverage )
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if avg_coverage <= 0:
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raise Exception, ' greater than 0'
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except Exception, e:
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error = ': %s' % str( e )
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if error:
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stop_err( 'Make sure your average coverage is an integer value%s' % error )
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error = ''
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try:
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error_rate = float( options.error_rate )
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if error_rate >= 1.0:
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error_rate = 10 ** ( -error_rate / 10.0 )
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elif error_rate < 0:
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raise Exception, ' between 0 and 1'
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except Exception, e:
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error = ': %s' % str( e )
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if error:
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stop_err( 'Make sure the error rate is a decimal value%s or the quality score is at least 1' % error )
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try:
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num_sims = int( options.num_sims )
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except TypeError, e:
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stop_err( 'Make sure the number of simulations is an integer value: %s' % str( e ) )
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if options.polymorphism != 'None':
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polymorphisms = [ float( p ) for p in options.polymorphism.split( ',' ) ]
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else:
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stop_err( 'Select at least one polymorphism value to use' )
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if options.detection_thresh != 'None':
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detection_threshes = [ float( dt ) for dt in options.detection_thresh.split( ',' ) ]
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else:
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stop_err( 'Select at least one detection threshold to use' )
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# mutation dictionaries
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hp_dict = { 'A':'G', 'G':'A', 'C':'T', 'T':'C', 'N':'N' } # heteroplasmy dictionary
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mt_dict = { 'A':'C', 'C':'A', 'G':'T', 'T':'G', 'N':'N'} # misread dictionary
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# read fasta file to seq string
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all_lines = open( options.input, 'rb' ).readlines()
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seq = ''
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for line in all_lines:
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line = line.rstrip()
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if line.startswith('>'):
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pass
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else:
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seq += line.upper()
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seq_len = len( seq )
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# output file name template
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# removed output of all simulation results on request (not working)
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# if options.sim_results == "true":
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# out_name_template = os.path.join( options.new_file_path, 'primary_output%s_' + options.output + '_visible_tabular' )
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# else:
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# out_name_template = tempfile.NamedTemporaryFile().name + '_%s'
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out_name_template = tempfile.NamedTemporaryFile().name + '_%s'
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print 'out_name_template:', out_name_template
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# set up output files
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outputs = {}
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i = 1
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for p in polymorphisms:
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outputs[ p ] = {}
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for d in detection_threshes:
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outputs[ p ][ d ] = out_name_template % i
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i += 1
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# run sims
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for polymorphism in polymorphisms:
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for detection_thresh in detection_threshes:
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output = open( outputs[ polymorphism ][ detection_thresh ], 'wb' )
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output.write( 'FP\tFN\tGENOMESIZE=%s\n' % seq_len )
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sim_count = 0
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while sim_count < num_sims:
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# randomly pick heteroplasmic base index
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hbase = random.choice( range( 0, seq_len ) )
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#hbase = seq_len/2#random.randrange( 0, seq_len )
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# create 2D quasispecies list
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qspec = map( lambda x: [], [0] * seq_len )
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# simulate read indices and assign to quasispecies
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i = 0
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while i < ( avg_coverage * ( seq_len / read_len ) ): # number of reads (approximates coverage)
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start = random.choice( range( 0, seq_len ) )
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#start = seq_len/2#random.randrange( 0, seq_len ) # assign read start
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if random.random() < 0.5: # positive sense read
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end = start + read_len # assign read end
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if end > seq_len: # overshooting origin
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read = range( start, seq_len ) + range( 0, ( end - seq_len ) )
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else: # regular read
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read = range( start, end )
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else: # negative sense read
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end = start - read_len # assign read end
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if end < -1: # overshooting origin
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read = range( start, -1, -1) + range( ( seq_len - 1 ), ( seq_len + end ), -1 )
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else: # regular read
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read = range( start, end, -1 )
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# assign read to quasispecies list by index
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for j in read:
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if j == hbase and random.random() < polymorphism: # heteroplasmic base is variant with p = het
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ref = hp_dict[ seq[ j ] ]
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else: # ref is the verbatim reference nucleotide (all positions)
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ref = seq[ j ]
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if random.random() < error_rate: # base in read is misread with p = err
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qspec[ j ].append( mt_dict[ ref ] )
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else: # otherwise we carry ref through to the end
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qspec[ j ].append(ref)
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# last but not least
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i += 1
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bases, fpos, fneg = {}, 0, 0 # last two will be outputted to summary file later
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for i, nuc in enumerate( seq ):
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cov = len( qspec[ i ] )
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bases[ 'A' ] = qspec[ i ].count( 'A' )
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bases[ 'C' ] = qspec[ i ].count( 'C' )
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bases[ 'G' ] = qspec[ i ].count( 'G' )
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bases[ 'T' ] = qspec[ i ].count( 'T' )
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# calculate max NON-REF deviation
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del bases[ nuc ]
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maxdev = float( max( bases.values() ) ) / cov
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# deal with non-het sites
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if i != hbase:
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if maxdev >= detection_thresh: # greater than detection threshold = false positive
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fpos += 1
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# deal with het sites
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if i == hbase:
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hnuc = hp_dict[ nuc ] # let's recover het variant
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if ( float( bases[ hnuc ] ) / cov ) < detection_thresh: # less than detection threshold = false negative
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fneg += 1
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del bases[ hnuc ] # ignore het variant
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maxdev = float( max( bases.values() ) ) / cov # check other non-ref bases at het site
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if maxdev >= detection_thresh: # greater than detection threshold = false positive (possible)
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fpos += 1
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# output error sums and genome size to summary file
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output.write( '%d\t%d\n' % ( fpos, fneg ) )
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sim_count += 1
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# close output up
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output.close()
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# Parameters (heteroplasmy, error threshold, colours)
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r( '''
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het=c(%s)
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err=c(%s)
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grade = (0:32)/32
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hues = rev(gray(grade))
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''' % ( ','.join( [ str( p ) for p in polymorphisms ] ), ','.join( [ str( d ) for d in detection_threshes ] ) ) )
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# Suppress warnings
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r( 'options(warn=-1)' )
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# Create allsum (for FP) and allneg (for FN) objects
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r( 'allsum <- data.frame()' )
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for polymorphism in polymorphisms:
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for detection_thresh in detection_threshes:
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output = outputs[ polymorphism ][ detection_thresh ]
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cmd = '''
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ngsum = read.delim('%s', header=T)
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ngsum$fprate <- ngsum$FP/%s
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ngsum$hetcol <- %s
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ngsum$errcol <- %s
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allsum <- rbind(allsum, ngsum)
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''' % ( output, seq_len, polymorphism, detection_thresh )
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r( cmd )
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if os.path.getsize( output ) == 0:
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for p in outputs.keys():
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for d in outputs[ p ].keys():
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sys.stderr.write(outputs[ p ][ d ] + ' '+str( os.path.getsize( outputs[ p ][ d ] ) )+'\n')
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if options.summary_out == "true":
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r( 'write.table(summary(ngsum), file="%s", quote=FALSE, sep="\t", row.names=FALSE)' % options.output_summary )
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# Summary objects (these could be printed)
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r( '''
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tr_pos <- tapply(allsum$fprate,list(allsum$hetcol,allsum$errcol), mean)
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tr_neg <- tapply(allsum$FN,list(allsum$hetcol,allsum$errcol), mean)
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cat('\nFalse Positive Rate Summary\n\t', file='%s', append=T, sep='\t')
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write.table(format(tr_pos, digits=4), file='%s', append=T, quote=F, sep='\t')
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cat('\nFalse Negative Rate Summary\n\t', file='%s', append=T, sep='\t')
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write.table(format(tr_neg, digits=4), file='%s', append=T, quote=F, sep='\t')
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''' % tuple( [ options.output_summary ] * 4 ) )
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# Setup graphs
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#pdf(paste(prefix,'_jointgraph.pdf',sep=''), 15, 10)
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r( '''
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png('%s', width=800, height=500, units='px', res=250)
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layout(matrix(data=c(1,2,1,3,1,4), nrow=2, ncol=3), widths=c(4,6,2), heights=c(1,10,10))
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''' % options.output_png )
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# Main title
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genome = ''
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if options.genome:
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genome = '%s: ' % options.genome
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r( '''
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par(mar=c(0,0,0,0))
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plot(1, type='n', axes=F, xlab='', ylab='')
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text(1,1,paste('%sVariation in False Positives and Negatives (', %s, ' simulations, coverage ', %s,')', sep=''), font=2, family='sans', cex=0.7)
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''' % ( genome, options.num_sims, options.avg_coverage ) )
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# False positive boxplot
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r( '''
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par(mar=c(5,4,2,2), las=1, cex=0.35)
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boxplot(allsum$fprate ~ allsum$errcol, horizontal=T, ylim=rev(range(allsum$fprate)), cex.axis=0.85)
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title(main='False Positives', xlab='false positive rate', ylab='')
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''' )
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# False negative heatmap (note zlim command!)
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num_polys = len( polymorphisms )
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num_dets = len( detection_threshes )
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r( '''
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par(mar=c(5,4,2,1), las=1, cex=0.35)
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image(1:%s, 1:%s, tr_neg, zlim=c(0,1), col=hues, xlab='', ylab='', axes=F, border=1)
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axis(1, at=1:%s, labels=rownames(tr_neg), lwd=1, cex.axis=0.85, axs='i')
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axis(2, at=1:%s, labels=colnames(tr_neg), lwd=1, cex.axis=0.85)
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title(main='False Negatives', xlab='minor allele frequency', ylab='detection threshold')
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''' % ( num_polys, num_dets, num_polys, num_dets ) )
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# Scale alongside
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r( '''
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par(mar=c(2,2,2,3), las=1)
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image(1, grade, matrix(grade, ncol=length(grade), nrow=1), col=hues, xlab='', ylab='', xaxt='n', las=1, cex.axis=0.85)
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title(main='Key', cex=0.35)
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mtext('false negative rate', side=1, cex=0.35)
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''' )
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# Close graphics
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r( '''
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layout(1)
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dev.off()
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''' )
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# Tidy up
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# r( 'rm(folder,prefix,sim,cov,het,err,grade,hues,i,j,ngsum)' )
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if __name__ == "__main__" : __main__()
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