BRCA2 homozygous cell line specific mutations

Basic idea: Calculate BRCA2 homozygous, and all other mean reference frequencies

  • Cell line specific heterozygous mutations have around 50% reference base count in samples in the cell line, and around 100% in other samples. So the average reference base frequency among the cell line samples is around 50%, and around 100% among other samples. But because of the large number of reads in all samples, the distribution are more localized.
  • To see these mutations I will plot almost all positions on an (average other samples refbase freq, average cell line refbase freq ) plane. Cell line specific mutations are expected to be in the middle right (1,0.5), and are expected to be clearly separated from other positions.
  • These mutations are identified through a very simple and robust method, so they are good candidates for testing the sensitivity of mutation calling methods.

NOTE Code is hiden in this notebook please click below, to see it

In [7]:
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In [ ]:
%%writefile brca2_vs_all.py
#!/usr/bin/python

#First script:
    # Input is a filtered pileup-like format
    # there are lines in Orsi's format, and i dont use them
    # writefile magic writes these files
    # they will be later executed by slurm

#import modules
import subprocess
import sys
import re
import numpy as np
import fnmatch
import os

#input ouput files
input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'
output_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/BRCA2_vs_all/'
#subprocess.call(['mkdir',output_dir])
output_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/BRCA2_vs_all/heatmap/'
#subprocess.call(['mkdir',output_dir])

#which file to run on come in cmdline arg
input_fname=sys.argv[1]

#filenames for samplenames
rm_dup_dir='/nagyvinyok/adat83/sotejedlik/orsi/bam_all_links/'
#collect filenames
fnames=[]
for fname in os.listdir(rm_dup_dir):
    if (fnmatch.fnmatch(fname, '*.bam') and 
        not fnmatch.fnmatch(fname,"*.bai")):
        fnames.append(fname)
fnames=sorted(fnames)

#select the group samples set
group=[]
for i in range(108,118): 
    group.append('DS'+str(i)+'_RMdup_picard_realign.bam')
                 
#create array to index into numpy arrays
group_bool,else_bool,group=[],[],set(group)
for sample in fnames:
    group_bool.append(sample in group)
    else_bool.append(not (sample in group))
group_bool,else_bool=np.array(group_bool),np.array(else_bool)

#matrix for heatmap
resolution=200 #resolution hard coded !!!!!!!!
heat_mat=np.zeros((resolution+1,resolution+1),dtype=np.int32)

#run the pipeline  
f_in=open(input_dir+input_fname)
for line in f_in: 
    #line in Orsis format
    if line[0]=='#':
        continue
              
    #parse line
    linelist=line.strip().upper().split(' ')
    covs=np.array(map(int,linelist[3::2]),dtype=np.int32)
    #cov=0 make freqs meaningless
    if (linelist[3::2].count('0')!=0):
        continue
    bases=linelist[4::2]
    ref_count=[]
    for i in xrange(len(bases)):
        ref_count.append(len(re.findall('[\.\,]',bases[i])))

    if (len(ref_count)!=len(covs)):
        print line
        break

    ref_freq=np.array(ref_count,dtype=np.double)/covs
    #calculate group freqs and save in matrix
    group_freq=np.mean(ref_freq[group_bool])
    else_freq=np.mean(ref_freq[else_bool])
    heat_mat[int(resolution*group_freq),int(resolution*else_freq)]+=1

#close file
f_in.close()
#save it
np.savetxt(output_dir + input_fname.split('.')[0]+'.mat',heat_mat,fmt='%d')
In [ ]:
#Run them in slurm

import os
import subprocess
input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'
for filename in os.listdir(input_dir):
    try:
        print subprocess.check_output([ 'sbatch', #'-C','jimgray85',
                                       '--mem',str(1000),'./brca2_vs_all.py' ,
                                       filename],stderr=subprocess.STDOUT),
    except subprocess.CalledProcessError, e:
        print e.output,

Plot the 'heatmap'

  • its half heatmap, half scatter because of the high resolution
In [3]:
import os
import numpy as np
from matplotlib.colors import LogNorm
import matplotlib as mpl
import pandas as pd #instead of numpy for much faster csv loading
#import matplotlib as mpl
#mpl.use('Agg')
import matplotlib.pyplot as plt
%matplotlib inline

inputdir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/BRCA2_vs_all/heatmap/'

#load result matrices
m=pd.read_csv(inputdir+os.listdir(inputdir)[0],sep=' ',header=None)
for filename in os.listdir(inputdir):
    try:
        m+=pd.read_csv(inputdir+filename,sep=' ',header=None)
    except:
        pass

#plot
fig,ax=plt.subplots()
fig.set_size_inches(16,16)

# define the colormap
cmap = plt.cm.Purples
# extract all colors from the map
cmaplist = [cmap(i) for i in range(cmap.N)]
# force the first color entry to be white
cmaplist[0] = (1.0,1.0,1.0,1.0)
# create the new map
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)

# define the bins and normalize
bounds = [0,1,5,10,20,100]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

#show the image
cax = ax.imshow(m,interpolation='none',norm=norm,cmap=cmap,alpha=0.8,origin='lower')

cbar=fig.colorbar(cax,shrink=0.8)
cbar.outline.set_edgecolor('lightgrey')

#set grid
ax.grid(True,c='lightgrey',lw=1,linestyle='dotted')
ax.set_frame_on(False)
tics=ax.xaxis.set_ticks(np.linspace(0,200,6))
labs=ax.set_xticklabels(['0%','20%','40%','60%','80%','100%'], rotation='horizontal')
tics=ax.yaxis.set_ticks(np.linspace(0,200,6))
labs=ax.set_yticklabels(['0%','20%','40%','60%','80%','100%'], rotation='horizontal')

ax.set_xlim(-1,201)
ax.set_ylim(-1,201)

#enlarge font
mpl.rcParams['font.size']=14.0

# remove tick marks
ax.xaxis.set_tick_params(size=0)
ax.yaxis.set_tick_params(size=0)

#legend
ax.plot([],[],c='purple',lw=4,label='BRCA2 homo vs others avg freq')
ax.legend(fancybox=True,loc='upper left')

#annotate
ax.set_title('')
ax.set_xlabel('other samples avg freq')
cax=ax.set_ylabel('BRCA2 homo samples avg freq')

Zoom on the interesting region

In [4]:
# define the bins and normalize
bounds = [0,1,5,10,20,40]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

#plot
fig,ax=plt.subplots()
cax = ax.imshow(np.array(m)[:,180:],interpolation='none',extent=[180,200,0,200],
                aspect=0.1,alpha=0.8,origin='lower',cmap=cmap,norm=norm)

cbar=fig.colorbar(cax,shrink=0.8)
cbar.outline.set_edgecolor('lightgrey')

#legend
ax.plot([],[],c='purple',lw=4,label='BRCA2 homo vs others avg freq')
ax.legend(fancybox=True,loc='lower left')

#annotate
ax.set_title('')
ax.set_xlabel('other samples avg freq')
ax.set_ylabel('BRCA2 homo samples avg freq')
fig.set_size_inches(16,16)

#set w grd
ax.grid(True,c='lightgrey',lw=1,linestyle='dotted')
ax.set_frame_on(False)
tics=ax.xaxis.set_ticks(np.linspace(180,200,6))
tics=ax.yaxis.set_ticks(np.linspace(0,200,6))

ax.set_xlim(180,201)
ax.set_ylim(0,205)

labs=ax.set_yticklabels(['0%','20%','40%','60%','80%','100%'], rotation='horizontal')
labs=ax.set_xticklabels(['90%','92%','94%','96%','98%','100%'], rotation='horizontal')

# remove tick marks
ax.xaxis.set_tick_params(size=0)
ax.yaxis.set_tick_params(size=0)

#50% ones
rect=plt.Rectangle((198,-1),2,124, fc='none',ec='r',lw=6, linestyle='dashed')
cax=ax.add_patch(rect)

#lower freq ones
rect=plt.Rectangle((198,124),2,40, fc='none',ec='b',lw=6,linestyle='dashed')
cax=ax.add_patch(rect)

Conclusions:

  • NOTE There is a nice 1,0.5 cluster
  • NOTE altough there is an other cluster not very clearly separated from this, at 1,0,7. This spot might be mostly due to more than diploid regions.
  • NOTE there is another cluster at 96% other freq. This is possibly the mutations shared between db1, and brca2 homo (according the sample tree)

Collect the line specific mutations to files

In [ ]:
%%writefile brca2_vs_all_collect_mut.py
#!/usr/bin/python

#import modules
import subprocess
import sys
import re
import numpy as np
import fnmatch
import os

#input ouput files
input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'
output_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/BRCA2_vs_all'
subprocess.call(['mkdir',output_dir+'/het_snp'])
subprocess.call(['mkdir',output_dir+'/lowfreq_snp'])

#which file to run on come in cmdline arg
input_fname=sys.argv[1]

#filenames for samplenames
rm_dup_dir='/nagyvinyok/adat83/sotejedlik/orsi/bam_all_links/'
#collect filenames
fnames=[]
for fname in os.listdir(rm_dup_dir):
    if (fnmatch.fnmatch(fname, '*.bam') and 
        not fnmatch.fnmatch(fname,"*.bai")): #strange .bai convention!!!
        fnames.append(fname)
fnames=sorted(fnames)

#select the group samples set
group=[]
for i in range(108,118): 
    group.append('DS'+str(i)+'_RMdup_picard_realign.bam')

#create array to index into numpy arrays
group_bool,else_bool,group=[],[],set(group)
for sample in fnames:
    group_bool.append(sample in group)
    else_bool.append(not (sample in group))
group_bool,else_bool=np.array(group_bool),np.array(else_bool)

#output files
f_het=open(output_dir+'/het_snp/'+input_fname,'w')
f_lowfreq=open(output_dir+'/lowfreq_snp/'+input_fname,'w')

#run the pipeline  
f_in=open(input_dir+input_fname)
for line in f_in: 
    
    #line in Orsis format
    if line[0]=='#':
        continue
        
    #parse line
    linelist=line.strip().upper().split(' ')
    covs=np.array(map(int,linelist[3::2]),dtype=np.int32)

    #cov=0 make freqs meaningless
    if (linelist[3::2].count('0')!=0):
        continue

    bases=linelist[4::2]
    ref_count=[]
    for i in xrange(len(bases)):
        ref_count.append(len(re.findall('[\.\,]',bases[i]))) 
    ref_freq=np.array(ref_count,dtype=np.double)/covs

    #calculate group freqs
    group_freq=np.mean(ref_freq[group_bool])
    else_freq=np.mean(ref_freq[else_bool])

    #save the line specific mutations
    if( group_freq <= 0.62  and else_freq >=0.99):
        f_het.write(line)
    if(group_freq >= 0.63 and group_freq <= 0.82  and 
      else_freq >= 0.99):
        f_lowfreq.write(line)

#close files
f_in.close()
f_het.close()
f_lowfreq.close()
In [ ]:
#Run them in slurm

import os
import subprocess
input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'
for filename in os.listdir(input_dir):
    try:
        print subprocess.check_output([ 'sbatch','-C','jimgray83',
                                       '--mem',str(300),'./brca2_vs_all_collect_mut.py' ,
                                       filename],stderr=subprocess.STDOUT),
    except subprocess.CalledProcessError, e:
        print e.output,

How many SNPs are there?

In [10]:
%%bash
dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/BRCA2_vs_all'

rm $dir/het_snp/all.pup
cat $dir/het_snp/* > $dir/het_snp/all.pup 

echo The number of het SNPs:
cat $dir/het_snp/all.pup | wc -l
echo


rm $dir/lowfreq_snp/all.pup
cat $dir/lowfreq_snp/* > $dir/lowfreq_snp/all.pup 

echo The number of low frequency SNPs:
cat $dir/lowfreq_snp/all.pup | wc -l

The number of het SNPs:
673

The number of low frequency SNPs:
193