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Copy pathTennesen_Parse_noPseudoHap.py
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Tennesen_Parse_noPseudoHap.py
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from optparse import OptionParser
import numpy as np
import pandas as pd
import subprocess
from bisect import bisect_left, bisect_right
def parseVCF(VCF):
pos= VCF['POS'].to_numpy()
gen_id= [col for col in VCF if col.startswith('i')]
gen_df=VCF[gen_id]
gen_df.columns = range(gen_df.shape[1])
return(pos, gen_df)
def VCFhaps(gen_df):
s=gen_df.shape
n= s[1]
m= s[0]*2
hap = np.empty((m,n))
for i in gen_df. columns:
hapsdf=gen_df.assign(var=gen_df[i].str.split('|')).explode('var')
hap[:,i]=hapsdf[hapsdf.columns[-1]]
return(hap)
def per_n_SNP(Mut,n):
return Mut.iloc[::n, :]
def to_haplo(gen_df):
s=gen_df.shape
n= s[1]
m= s[0]
hap = np.empty((m,n))
for i in gen_df. columns:
gens = gen_df[i].str.split('|')
for j in range(0,m):
hap[j,i]= (gens[j])[0]
return(hap)
def pseudo_haplo(gen_df):
#print(gens.iloc[j][i])
s=gen_df.shape
n= s[1]
m= s[0]
ps_hap = np.empty((m,n))
for i in gen_df. columns:
gens = gen_df[i].str.split('|')
ber = np.random.binomial(1, 0.5,m)
for j in range(0,m):
ps_hap[j,i]= (gens[j])[ber[j]]
return(ps_hap)
def sparse_pseudo_hap(gen_df,l):
#print(gens.iloc[j][i])
s=gen_df.shape
n= s[1]
m= s[0]
ps_hap = np.empty((m,n))
for i in gen_df.columns:
gens = gen_df[i].str.split('|')
ber = np.random.binomial(1, 0.5,m)
for j in range(0,m):
ps_hap[j,i]= (gens[l*j])[ber[j]]
return(ps_hap)
def parseAT(Mut,pos):
Mut2=np.where(Mut==0, 'A',Mut)
Mut2 =np.where(Mut==1, 'T',Mut2).transpose()
posMut=(np.vstack((pos,Mut2))).transpose()
return posMut
def parseCG(Mut,pos):
Mut2=np.where(Mut==0, 'C',Mut)
Mut2 =np.where(Mut==1, 'G',Mut2).transpose()
posMut=(np.vstack((pos,Mut2))).transpose()
return posMut
def sample_n(VCF,sampl):
VCF_df = VCF.sample(n=sampl)
VCF_df[0] = pd.to_numeric(VCF_df[0])
VCF_df=VCF_df.sort_values(VCF_df.columns[0])
return VCF_df
def sample_n_genes(VCF,sampl):
VCF_df = VCF.sample(n=sampl)
VCF_df=VCF_df.sort_values(by=['POS'])
return VCF_df
def window_sample(VCF):
lower= 120976777 #lower bound for window of approximate size 239976bp
upper = 121216753 #upper bound for window of approximate size 239976bp
l=bisect_left(VCF['POS'].values, lower)
up = bisect_left(VCF['POS'].values, upper)
VCF_window= VCF.iloc[l:(up+1),]
return VCF_window
def per_n_SNP(Mut,n):
return Mut.iloc[::n, :]
def Sperbp(Mut,L): #Mut data frame with snps positions vertically and horizontally observetions
return Mut.shape[0]/L
def Pi(Mut):
k = Mut.shape[0]
N=Mut.shape[1]
pi = np.array(Mut.sum(axis=1)).flatten().astype(np.float)/N
pi=2*np.multiply(pi,1-pi)
return pi
def parseForH12_full(file_df):
pos, muts=parseVCF(file_df)
haps=to_haplo(muts)
posMutsFull = parseAT(haps,pos)
if posMutsFull.shape[0]>=201:
posMuts=posMutsFull[np.random.choice(posMutsFull.shape[0], 201, replace=False)]
posMuts=posMuts[(posMuts[:,0]).astype(int).argsort()]
else:
posMuts=posMutsFull
return posMuts
def parseForH12_psh(file_df):
pos, muts=parseVCF(file_df)
ps_haps=pseudo_haplo(muts)
posMutsFull = parseAT(ps_haps,pos)
if posMutsFull.shape[0]>=201:
posMuts=posMutsFull[np.random.choice(posMutsFull.shape[0], 201, replace=False)]
posMuts=posMuts[(posMuts[:,0]).astype(int).argsort()]
else:
posMuts=posMutsFull
#posMuts=sample_n(pd.DataFrame(posMutsFull),101) #sample 101 SNps after pseudohap
return posMuts
def addMissingData(posMuts,MD,sd): #frac is the fraction of missing data per column
alpha=(MD**2*(1-MD))/sd**2-MD
beta=alpha/MD*(1-MD)
Nan_df=pd.DataFrame(posMuts[:,1:])
for col in Nan_df.columns:
f = np.random.beta(alpha,beta,1)
nanidx =Nan_df[col].sample(frac=f[0]).index
Nan_df[col][nanidx]=np.nan
df = Nan_df.fillna('N')
df.insert(0,'pos',posMuts[:,0])
return df
####################################Main functions#################################################################
def main_haps (MD,sd,inFilePath,outFilePath):
#read VCF files
VCF = pd.read_csv(inFilePath, skiprows=13, sep='\t')
posMuts = parseForH12_full(VCF)
#posMuts_MD=addMissingData(posMuts,MD,sd)
pd.DataFrame(posMuts).to_csv(outFilePath,mode='w', index=False,header=False)
def main_pseudoh (MD,sd,inFilePath,outFilePath):
VCF = pd.read_csv(inFilePath, skiprows=13, sep='\t')
posMuts = parseForH12_psh(VCF)
#posMuts_MD=addMissingData(posMuts,MD,sd)
#pd.DataFrame(posMuts).to_csv('tmp/out_selec_mesolithic.txt',mode = 'w', index=False,header=False)
pd.DataFrame(posMuts).to_csv(outFilePath,mode='w', index=False,header=False)
############################### Main ##############################################################################
if __name__=="__main__":
#Mising data
usage = """%prog <input> <snp data>"""
parser = OptionParser(usage)
parser.add_option("-m", "--MD", type="float", default=0.4, help="mean missing rate per snp")
parser.add_option("-s", "--sd", type="float", default=0.2, help="sd of missing data rate per snp")
parser.add_option("-i", "--inFile", type="string", default="-", help="input file path")
parser.add_option("-o", "--outFile", type="string", default="-", help="output file path")
options, args= parser.parse_args()
MD= options.MD
sd= options.sd
inFilePath= options.inFile
outFilePath= options.outFile
main_haps(MD,sd,inFilePath,outFilePath)
#main_pseudoh(MD,sd,inFilePath,outFilePath)