https://github.com/galaxyproject/galaxy
Tip revision: a8abf0e4b6196bd4acb38804ae799155bc4af900 authored by Dannon Baker on 23 February 2015, 16:25:45 UTC
Clone .gitignore
Clone .gitignore
Tip revision: a8abf0e
ngs_simulation.py
#!/usr/bin/env python
"""
Runs Ben's simulation.
usage: %prog [options]
-i, --input=i: Input genome (FASTA format)
-g, --genome=g: If built-in, the genome being used
-l, --read_len=l: Read length
-c, --avg_coverage=c: Average coverage
-e, --error_rate=e: Error rate (0-1)
-n, --num_sims=n: Number of simulations to run
-p, --polymorphism=p: Frequency/ies for minor allele (comma-separate list of 0-1)
-d, --detection_thresh=d: Detection thresholds (comma-separate list of 0-1)
-p, --output_png=p: Plot output
-s, --summary_out=s: Whether or not to output a file with summary of all simulations
-m, --output_summary=m: File name for output summary of all simulations
-f, --new_file_path=f: Directory for summary output files
"""
# removed output of all simulation results on request (not working)
# -r, --sim_results=r: Output all tabular simulation results (number of polymorphisms times number of detection thresholds)
# -o, --output=o: Base name for summary output for each run
from rpy import *
import os
import random, sys, tempfile
from galaxy import eggs
import pkg_resources; pkg_resources.require( "bx-python" )
from bx.cookbook import doc_optparse
def stop_err( msg ):
sys.stderr.write( '%s\n' % msg )
sys.exit()
def __main__():
#Parse Command Line
options, args = doc_optparse.parse( __doc__ )
# validate parameters
error = ''
try:
read_len = int( options.read_len )
if read_len <= 0:
raise Exception, ' greater than 0'
except TypeError, e:
error = ': %s' % str( e )
if error:
stop_err( 'Make sure your number of reads is an integer value%s' % error )
error = ''
try:
avg_coverage = int( options.avg_coverage )
if avg_coverage <= 0:
raise Exception, ' greater than 0'
except Exception, e:
error = ': %s' % str( e )
if error:
stop_err( 'Make sure your average coverage is an integer value%s' % error )
error = ''
try:
error_rate = float( options.error_rate )
if error_rate >= 1.0:
error_rate = 10 ** ( -error_rate / 10.0 )
elif error_rate < 0:
raise Exception, ' between 0 and 1'
except Exception, e:
error = ': %s' % str( e )
if error:
stop_err( 'Make sure the error rate is a decimal value%s or the quality score is at least 1' % error )
try:
num_sims = int( options.num_sims )
except TypeError, e:
stop_err( 'Make sure the number of simulations is an integer value: %s' % str( e ) )
if options.polymorphism != 'None':
polymorphisms = [ float( p ) for p in options.polymorphism.split( ',' ) ]
else:
stop_err( 'Select at least one polymorphism value to use' )
if options.detection_thresh != 'None':
detection_threshes = [ float( dt ) for dt in options.detection_thresh.split( ',' ) ]
else:
stop_err( 'Select at least one detection threshold to use' )
# mutation dictionaries
hp_dict = { 'A':'G', 'G':'A', 'C':'T', 'T':'C', 'N':'N' } # heteroplasmy dictionary
mt_dict = { 'A':'C', 'C':'A', 'G':'T', 'T':'G', 'N':'N'} # misread dictionary
# read fasta file to seq string
all_lines = open( options.input, 'rb' ).readlines()
seq = ''
for line in all_lines:
line = line.rstrip()
if line.startswith('>'):
pass
else:
seq += line.upper()
seq_len = len( seq )
# output file name template
# removed output of all simulation results on request (not working)
# if options.sim_results == "true":
# out_name_template = os.path.join( options.new_file_path, 'primary_output%s_' + options.output + '_visible_tabular' )
# else:
# out_name_template = tempfile.NamedTemporaryFile().name + '_%s'
out_name_template = tempfile.NamedTemporaryFile().name + '_%s'
print 'out_name_template:', out_name_template
# set up output files
outputs = {}
i = 1
for p in polymorphisms:
outputs[ p ] = {}
for d in detection_threshes:
outputs[ p ][ d ] = out_name_template % i
i += 1
# run sims
for polymorphism in polymorphisms:
for detection_thresh in detection_threshes:
output = open( outputs[ polymorphism ][ detection_thresh ], 'wb' )
output.write( 'FP\tFN\tGENOMESIZE=%s\n' % seq_len )
sim_count = 0
while sim_count < num_sims:
# randomly pick heteroplasmic base index
hbase = random.choice( range( 0, seq_len ) )
#hbase = seq_len/2#random.randrange( 0, seq_len )
# create 2D quasispecies list
qspec = map( lambda x: [], [0] * seq_len )
# simulate read indices and assign to quasispecies
i = 0
while i < ( avg_coverage * ( seq_len / read_len ) ): # number of reads (approximates coverage)
start = random.choice( range( 0, seq_len ) )
#start = seq_len/2#random.randrange( 0, seq_len ) # assign read start
if random.random() < 0.5: # positive sense read
end = start + read_len # assign read end
if end > seq_len: # overshooting origin
read = range( start, seq_len ) + range( 0, ( end - seq_len ) )
else: # regular read
read = range( start, end )
else: # negative sense read
end = start - read_len # assign read end
if end < -1: # overshooting origin
read = range( start, -1, -1) + range( ( seq_len - 1 ), ( seq_len + end ), -1 )
else: # regular read
read = range( start, end, -1 )
# assign read to quasispecies list by index
for j in read:
if j == hbase and random.random() < polymorphism: # heteroplasmic base is variant with p = het
ref = hp_dict[ seq[ j ] ]
else: # ref is the verbatim reference nucleotide (all positions)
ref = seq[ j ]
if random.random() < error_rate: # base in read is misread with p = err
qspec[ j ].append( mt_dict[ ref ] )
else: # otherwise we carry ref through to the end
qspec[ j ].append(ref)
# last but not least
i += 1
bases, fpos, fneg = {}, 0, 0 # last two will be outputted to summary file later
for i, nuc in enumerate( seq ):
cov = len( qspec[ i ] )
bases[ 'A' ] = qspec[ i ].count( 'A' )
bases[ 'C' ] = qspec[ i ].count( 'C' )
bases[ 'G' ] = qspec[ i ].count( 'G' )
bases[ 'T' ] = qspec[ i ].count( 'T' )
# calculate max NON-REF deviation
del bases[ nuc ]
maxdev = float( max( bases.values() ) ) / cov
# deal with non-het sites
if i != hbase:
if maxdev >= detection_thresh: # greater than detection threshold = false positive
fpos += 1
# deal with het sites
if i == hbase:
hnuc = hp_dict[ nuc ] # let's recover het variant
if ( float( bases[ hnuc ] ) / cov ) < detection_thresh: # less than detection threshold = false negative
fneg += 1
del bases[ hnuc ] # ignore het variant
maxdev = float( max( bases.values() ) ) / cov # check other non-ref bases at het site
if maxdev >= detection_thresh: # greater than detection threshold = false positive (possible)
fpos += 1
# output error sums and genome size to summary file
output.write( '%d\t%d\n' % ( fpos, fneg ) )
sim_count += 1
# close output up
output.close()
# Parameters (heteroplasmy, error threshold, colours)
r( '''
het=c(%s)
err=c(%s)
grade = (0:32)/32
hues = rev(gray(grade))
''' % ( ','.join( [ str( p ) for p in polymorphisms ] ), ','.join( [ str( d ) for d in detection_threshes ] ) ) )
# Suppress warnings
r( 'options(warn=-1)' )
# Create allsum (for FP) and allneg (for FN) objects
r( 'allsum <- data.frame()' )
for polymorphism in polymorphisms:
for detection_thresh in detection_threshes:
output = outputs[ polymorphism ][ detection_thresh ]
cmd = '''
ngsum = read.delim('%s', header=T)
ngsum$fprate <- ngsum$FP/%s
ngsum$hetcol <- %s
ngsum$errcol <- %s
allsum <- rbind(allsum, ngsum)
''' % ( output, seq_len, polymorphism, detection_thresh )
r( cmd )
if os.path.getsize( output ) == 0:
for p in outputs.keys():
for d in outputs[ p ].keys():
sys.stderr.write(outputs[ p ][ d ] + ' '+str( os.path.getsize( outputs[ p ][ d ] ) )+'\n')
if options.summary_out == "true":
r( 'write.table(summary(ngsum), file="%s", quote=FALSE, sep="\t", row.names=FALSE)' % options.output_summary )
# Summary objects (these could be printed)
r( '''
tr_pos <- tapply(allsum$fprate,list(allsum$hetcol,allsum$errcol), mean)
tr_neg <- tapply(allsum$FN,list(allsum$hetcol,allsum$errcol), mean)
cat('\nFalse Positive Rate Summary\n\t', file='%s', append=T, sep='\t')
write.table(format(tr_pos, digits=4), file='%s', append=T, quote=F, sep='\t')
cat('\nFalse Negative Rate Summary\n\t', file='%s', append=T, sep='\t')
write.table(format(tr_neg, digits=4), file='%s', append=T, quote=F, sep='\t')
''' % tuple( [ options.output_summary ] * 4 ) )
# Setup graphs
#pdf(paste(prefix,'_jointgraph.pdf',sep=''), 15, 10)
r( '''
png('%s', width=800, height=500, units='px', res=250)
layout(matrix(data=c(1,2,1,3,1,4), nrow=2, ncol=3), widths=c(4,6,2), heights=c(1,10,10))
''' % options.output_png )
# Main title
genome = ''
if options.genome:
genome = '%s: ' % options.genome
r( '''
par(mar=c(0,0,0,0))
plot(1, type='n', axes=F, xlab='', ylab='')
text(1,1,paste('%sVariation in False Positives and Negatives (', %s, ' simulations, coverage ', %s,')', sep=''), font=2, family='sans', cex=0.7)
''' % ( genome, options.num_sims, options.avg_coverage ) )
# False positive boxplot
r( '''
par(mar=c(5,4,2,2), las=1, cex=0.35)
boxplot(allsum$fprate ~ allsum$errcol, horizontal=T, ylim=rev(range(allsum$fprate)), cex.axis=0.85)
title(main='False Positives', xlab='false positive rate', ylab='')
''' )
# False negative heatmap (note zlim command!)
num_polys = len( polymorphisms )
num_dets = len( detection_threshes )
r( '''
par(mar=c(5,4,2,1), las=1, cex=0.35)
image(1:%s, 1:%s, tr_neg, zlim=c(0,1), col=hues, xlab='', ylab='', axes=F, border=1)
axis(1, at=1:%s, labels=rownames(tr_neg), lwd=1, cex.axis=0.85, axs='i')
axis(2, at=1:%s, labels=colnames(tr_neg), lwd=1, cex.axis=0.85)
title(main='False Negatives', xlab='minor allele frequency', ylab='detection threshold')
''' % ( num_polys, num_dets, num_polys, num_dets ) )
# Scale alongside
r( '''
par(mar=c(2,2,2,3), las=1)
image(1, grade, matrix(grade, ncol=length(grade), nrow=1), col=hues, xlab='', ylab='', xaxt='n', las=1, cex.axis=0.85)
title(main='Key', cex=0.35)
mtext('false negative rate', side=1, cex=0.35)
''' )
# Close graphics
r( '''
layout(1)
dev.off()
''' )
# Tidy up
# r( 'rm(folder,prefix,sim,cov,het,err,grade,hues,i,j,ngsum)' )
if __name__ == "__main__" : __main__()