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cov_plot2.py
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cov_plot2.py
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### Boas Pucker ###
### bpucker@cebitec.uni-bielefeld.de ###
### v0.15 ###
### reference: https://doi.org/10.3390/genes10090671 ####
__usage__ = """
python cov_plot2.py
--in <FULL_PATH_TO_COVERAGE_FILE>
--out <FULL_PATH_TO_OUTPUT_FILE>
--ref <FULL_PATH_TO_REFERENCE_COVERAGE_FILE>
optional:
--name <NAME>
"""
import sys, os
import matplotlib.pyplot as plt
import numpy as np
from operator import itemgetter
# --- end of imports --- #
def load_cov( cov_file ):
"""! @brief load all information from coverage file """
cov = {}
with open( cov_file, "r" ) as f:
line = f.readline()
header = line.split('\t')[0]
tmp = []
while line:
parts = line.strip().split('\t')
if parts[0] != header:
cov.update( { header: tmp } )
header = parts[0]
tmp = []
tmp.append( float( parts[-1] ) )
line = f.readline()
cov.update( { header: tmp } )
return cov
def generate_plot( cov, ref_cov, out_file, resolution, saturation, name ):
"""! @brief generate figure """
fig, ax = plt.subplots( figsize=( 10, 7 ) )
ymax = 10
collected_values = {}
# --- generate list for plotting --- #
all_data = []
for idx, key in enumerate( sorted( cov.keys() ) ):
y = ymax-idx-1
x = []
blocks = [ cov[ key ] [ i : i + resolution ] for i in xrange( 0, len( cov[ key ] ), resolution ) ]
ref_blocks = [ ref_cov[ key ] [ i : i + resolution ] for i in xrange( 0, len( ref_cov[ key ] ), resolution ) ]
for k, block in enumerate( blocks ):
b = np.mean( block )
r = np.mean( ref_blocks[ k ] )
if r > 0:
if b > 0:
if b != r:
ratio = np.log2( b / r )
else:
ratio = 0
else:
ratio = -1 * saturation
else:
if b > 0:
ratio = np.log2( b )
else:
ratio = 0
if ratio > 0:
x.append( min( [ ratio, saturation ] ) )
else:
x.append( max( [ ratio, -1 * saturation ] ) )
all_data.append( { 'chr': key, 'pos': (k+1)*resolution, 'value': ratio } )
collected_values.update( { key: x } )
# --- plot values --- #
for idx, key in enumerate( sorted( cov.keys() )[:5] ):
y = ymax - ( idx*2.3 )
x = []
for each in collected_values[ key ]:
x.append( y + min( [ 1, ( each / saturation ) ] ) )
ax.plot( np.arange( 0, len( x ), 1 ), x, marker="o", markersize=1, linewidth=0, color="lime" )
ax.text( 1, y, key )
ax.plot( [ 0, len( x ) ], [ y, y ], color="black" , linewidth=0.1)
ax.plot( [ 0, len( x ) ], [ y-0.66, y-0.66 ], color="grey" , linewidth=0.1)
ax.plot( [ 0, len( x ) ], [ y-0.33, y-0.33 ], color="grey" , linewidth=0.1)
ax.plot( [ 0, len( x ) ], [ y, y ], color="grey" , linewidth=0.1)
ax.plot( [ 0, len( x ) ], [ y+0.33, y+0.33 ], color="grey" , linewidth=0.1)
ax.plot( [ 0, len( x ) ], [ y+0.66, y+0.66 ], color="grey" , linewidth=0.1)
ax.plot( [ 0, 0 ], [ y-1, y+1 ], color="black", linewidth=1, markersize=1 )
ax.text( 0, y+1, "3", ha="right", fontsize=5 )
ax.text( 0, y+0.66, "2", ha="right", fontsize=5 )
ax.text( 0, y+0.33, "1", ha="right", fontsize=5 )
ax.text( 0, y, "0", ha="right", fontsize=5 )
ax.text( 0, y-0.33, "-1", ha="right", fontsize=5 )
ax.text( 0, y-0.66, "-2", ha="right", fontsize=5 )
ax.text( 0, y-1, "-3", ha="right", fontsize=5 )
ax.set_xlabel( "position on chromosome [ Mbp ]" )
ax.set_ylabel( "log2( relative coverage )" )
ax.set_xlim( 0, 30500 )
ax.set_title( name )
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_yaxis().set_ticks([])
ax.yaxis.labelpad = 10
ax.xaxis.set_ticks( np.arange( 0, 31000, 1000 ) )
labels = map( str, np.arange( 0, 31, 1 ) )
ax.set_xticklabels( labels )
plt.subplots_adjust( left=0.03, right=0.999, top=0.95, bottom=0.1 )
fig.savefig( out_file, dpi=300 )
plt.close( "all" )
return all_data
def generate_data_output( data_output_file, all_data ):
"""! @brief generate data outputfile for manual inspection """
all_values = []
for each in all_data:
all_values.append( each['value'] )
mean = np.mean( all_values )
sd = np.std( all_values )
all_data_with_zscore = []
for each in all_data:
each.update( { 'z': ( each['value'] - mean ) / sd, 'absz': abs( ( each['value'] - mean ) / sd ) } )
all_data_with_zscore.append( each )
sorted_data = sorted( all_data_with_zscore, key=itemgetter('absz') )
with open( data_output_file, "w" ) as out:
out.write( "Chr\tPos\tLog2(CovRatio)\tZscore\n" )
for each in sorted_data:
out.write( "\t".join( map( str, [ each['chr'], each['pos'], each['value'], each['z'] ] ) ) + '\n' )
def main( arguments ):
"""! @brief runs everything """
cov_file = arguments[ arguments.index( '--in' ) + 1 ]
output_folder = arguments[ arguments.index( '--out' ) + 1 ]
ref_cov_file = arguments[ arguments.index( '--ref' ) + 1 ]
if not os.path.exists( output_folder ):
os.makedirs( output_folder )
resolution = 1000
saturation = 3.0
if '--name' in arguments:
name = arguments[ arguments.index( '--name' ) + 1 ]
else:
name = ""
cov = load_cov( cov_file )
ref_cov = load_cov( ref_cov_file )
# --- generate per chromosome position coveage plot --- #
out_file = output_folder + name + ".pdf"
all_data = generate_plot( cov, ref_cov, out_file, resolution, saturation, name )
# --- generate data output file --- #
data_output_file = output_folder + name + ".txt"
generate_data_output( data_output_file, all_data )
if '--in' in sys.argv and '--out' in sys.argv and '--ref' in sys.argv:
main( sys.argv )
else:
sys.exit( __usage__ )