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Commit d1e78426 by Mustafa Tekpinar
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Added custom frequency file input option.

parent 05a0fb8c
Showing with 1175 additions and 135 deletions
data/custom-frequency-file.txt 0 → 100644
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examples/example-prescott-script.sh
...@@ -5,3 +5,6 @@ ...@@ -5,3 +5,6 @@
#If you are using GnomAD v4.0.0 data. #If you are using GnomAD v4.0.0 data.
prescott -e ../data/MLH1_normPred_evolCombi.txt -g ../data/gnomAD_v4.0.0_MLH1_HUMAN_ENSG00000076242.csv -s ../data/MLH1.fasta prescott -e ../data/MLH1_normPred_evolCombi.txt -g ../data/gnomAD_v4.0.0_MLH1_HUMAN_ENSG00000076242.csv -s ../data/MLH1.fasta
#If you have a custom frequency file
#prescott -e ../data/MLH1_normPred_evolCombi.txt -g ../data/custom-frequency-file.txt -s ../data/MLH1.fasta
prescott/prescott.py
...@@ -558,6 +558,134 @@ def rankSortData(dataArray): ...@@ -558,6 +558,134 @@ def rankSortData(dataArray):
return (normalizedRankedDataArray) return (normalizedRankedDataArray)
def plotLabeledPositions(myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList, useFrequencies):
clinvarLabeledDF = myBigMergedDF.loc[(myBigMergedDF['labels']==0) | (myBigMergedDF['labels']==1)]
clinvarLabeledDF['labels'] = clinvarLabeledDF['labels'].astype('int64')
if(len(clinvarLabeledDF)>0):
print("\nMutations with ClinVar labels according to the gnomAD file:\n")
print(clinvarLabeledDF)
fprESCOTT, tprESCOTT, AUC_ESCOTT = plotROCandAUCV2(clinvarLabeledDF['labels'], \
clinvarLabeledDF['ESCOTT'])
fprPRESCOTT, tprPRESCOTT, AUC_PRESCOTT = plotROCandAUCV2(clinvarLabeledDF['labels'], \
clinvarLabeledDF['PRESCOTT'])
fig = plt.figure(figsize=(12,6))
# plt.rcParams.update({'font.size': 18})
plt.grid(linestyle='--')
# plt.title(protName + " - "+method+" AUC={:.2f}".format(AUC_ESCOTT))
plt.title("AUC={:.2f} -> AUC={:.2f}".format(AUC_ESCOTT, AUC_PRESCOTT))
plt.ylim([0.0, 1.0])
#plt.xlim([1000, 1863])
plt.scatter(myBigMergedDF.loc[myBigMergedDF['labels'] == 1, 'position'], myBigMergedDF.loc[myBigMergedDF['labels'] == 1, 'ESCOTT'], marker='o', color='red', label='pathogenic')
plt.scatter(myBigMergedDF.loc[myBigMergedDF['labels'] == 0, 'position'], myBigMergedDF.loc[myBigMergedDF['labels'] == 0, 'ESCOTT'], marker='o', color='blue', label='benign')
if (useFrequencies.lower() == 'true'):
#print(selectedPositionsList)
#print(selectedValuesList)
plt.scatter(selectedPositionsList, selectedValuesList, marker='o', color='olive', label='PRESCOTT')
# Add vertical lines connecting old and new values
for i in range(len(selectedPositionsList)):
plt.annotate("", xy=(selectedPositionsList[i], selectedValuesList[i]), xycoords='data', \
xytext=(selectedPositionsList[i], myBigMergedDF.loc[myBigMergedDF['mutant'] == selectedMutantsList[i], 'ESCOTT'].values[0]), textcoords='data',
arrowprops=dict(arrowstyle="->", connectionstyle="arc3"))
plt.xticks(rotation = 90)
plt.ylabel("PR/ESCOTT Score")
plt.xlabel("Position")
plt.legend(loc='upper right')
plt.tight_layout()
plt.savefig("clinvar-vs-position.png")
plt.close()
print("@> AUC= {:.3f} {:.3f}".format( AUC_ESCOTT, AUC_PRESCOTT))
def runPrescottModel(myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList, \
version=2, scalingCoeff=1.0, freqCutoff=-4.0):
# # print(myBigMergedDF)
# scalingCoeff = args.coefficient
# freqCutoff = args.frequencycutoff
for index, row in myBigMergedDF.iterrows():
if(row['log10frequency'] != 999.0):
# print(row['log10frequency'])
# freq = np.log10(row['log10frequency'])
freq = row['log10frequency']
# print(freq)
temp1 = row['PRESCOTT']
label = row['labels']
if(version==1):
if(freq>freqCutoff):
temp2 = temp1 - freq*scalingCoeff/freqCutoff
if(temp2<0.0):
myBigMergedDF.at[index,'PRESCOTT'] = 0.0
selectedValuesList.append(0.0)
else:
myBigMergedDF.at[index,'PRESCOTT'] = temp2
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==2):
if(freq>freqCutoff):
temp2 = temp1 - scalingCoeff*(freqCutoff - freq)/freqCutoff
if(temp2<0.0):
temp2 = 0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==3):
temp2 = temp1 - scalingCoeff*(freqCutoff - freq)/freqCutoff
if(freq>freqCutoff):
if(temp2<0.0):
temp2 = 0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
else:
if(temp2>1.0):
temp2 = 1.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==4):
temp2 = temp1 - scalingCoeff*(freqCutoff - freq)/freqCutoff
if(freq>freqCutoff):
if(temp2<0.0):
temp2 = 0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
else:
print(myBigMergedDF.loc[index, 'Selected Population'])
sys.exit(-1)
if (myBigMergedDF.iloc[index,'Selected Population'].values==None):
if(temp2>1.0):
temp2 = 1.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==5):
if(freq>freqCutoff):
temp2 = temp1*0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
return myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList
def main(): def main():
# Adding the main parser # Adding the main parser
main_parser = argparse.ArgumentParser(description=\ main_parser = argparse.ArgumentParser(description=\
...@@ -634,13 +762,13 @@ def main(): ...@@ -634,13 +762,13 @@ def main():
args = main_parser.parse_args() args = main_parser.parse_args()
print("\n\n@> Running PRESCOTT with the following parameters:\n\n") print("\n\n@> Running PRESCOTT with the following parameters:\n\n")
print("@> ESCOTT file : {}".format(args.escottfile)) print("@> ESCOTT file : {}".format(args.escottfile))
print("@> GNOMAD frequency file : {}".format(args.gnomadfile)) print("@> Frequency file : {}".format(args.gnomadfile))
print("@> Use population max. freq : {}".format(str(args.usepopmax).lower())) print("@> Use population max. freq : {}".format(str(args.usepopmax).lower()))
print("@> Which equation to use (Default=2): {}".format(str(args.equation))) print("@> Which equation to use (Default=2): {}".format(str(args.equation)))
print("@> Scaling coefficient (Default=1.0): {}".format(args.coefficient)) print("@> Scaling coefficient (Default=1.0): {}".format(args.coefficient))
print("@> Frequency cutoff (Default=-4.0) : {}".format(args.frequencycutoff)) print("@> Frequency cutoff (Default=-4.0) : {}".format(args.frequencycutoff))
print("@> Name of the output file : {}".format(args.outputfile)) print("@> Name of the output file : {}".format(args.outputfile))
print("@> GnomAD data version (Default=4) : {}".format(str(args.gnomadversion)))
# End of argument parsing! # End of argument parsing!
protein = os.path.splitext(os.path.basename(args.escottfile))[0] protein = os.path.splitext(os.path.basename(args.escottfile))[0]
...@@ -649,6 +777,9 @@ def main(): ...@@ -649,6 +777,9 @@ def main():
# Check if file exists # Check if file exists
usePopMaxOrNot = args.usepopmax.lower() usePopMaxOrNot = args.usepopmax.lower()
version = args.equation version = args.equation
useFrequencies = args.usefrequencies
if (os.path.exists(args.escottfile)): if (os.path.exists(args.escottfile)):
#Convert the matrix format to singleline format #Convert the matrix format to singleline format
localResidueList = None localResidueList = None
...@@ -703,9 +834,23 @@ def main(): ...@@ -703,9 +834,23 @@ def main():
print("ERROR: ESCOTT input file does not exist!") print("ERROR: ESCOTT input file does not exist!")
sys.exit(-1) sys.exit(-1)
#Create a dataframe to merge ESCOTT data with frequency data
myBigMergedDF = pd.DataFrame() myBigMergedDF = pd.DataFrame()
myBigMergedDF = pd.concat([myBigMergedDF, dfESCOTT], ignore_index=True) myBigMergedDF = pd.concat([myBigMergedDF, dfESCOTT], ignore_index=True)
# Add frequency column and a dummy frequency to each row in myBigMergedDF
myBigMergedDF['log10frequency'] = 999.0
myBigMergedDF['labels'] = np.nan
myBigMergedDF['position'] = ""
# Assign ESCOTT scores to PRESCOTT scores.
# Then, we will modify them according to different conditions.
myBigMergedDF['PRESCOTT'] = myBigMergedDF['ESCOTT']
file_name, file_extension = os.path.splitext(args.gnomadfile)
if(file_extension == ".csv"):
print("@> You frequency data is in gnomAD format!")
print("@> GnomAD data version (Default=4) : {}".format(str(args.gnomadversion)))
if (args.gnomadversion==2 or args.gnomadversion==3): if (args.gnomadversion==2 or args.gnomadversion==3):
gnomadDF = getGnomADOverallFrequency(args.gnomadfile, usePopMax=usePopMaxOrNot) gnomadDF = getGnomADOverallFrequency(args.gnomadfile, usePopMax=usePopMaxOrNot)
elif (args.gnomadversion==4): elif (args.gnomadversion==4):
...@@ -729,19 +874,15 @@ def main(): ...@@ -729,19 +874,15 @@ def main():
if (len(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)]) > 0): if (len(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)]) > 0):
print(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)]) print(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)])
# print(gnomadDF['ClinVar Clinical Significance']) # print(gnomadDF['ClinVar Clinical Significance'])
# Add frequency column and a dummy frequency to each row in myBigMergedDF
myBigMergedDF['frequency'] = 999.0
myBigMergedDF['labels'] = np.nan
myBigMergedDF['position'] = ""
useFrequencies = args.usefrequencies
selectedPositionsList = [] selectedPositionsList = []
selectedValuesList = [] selectedValuesList = []
selectedMutantsList = [] selectedMutantsList = []
# Assign ESCOTT scores to PRESCOTT scores. # # Assign ESCOTT scores to PRESCOTT scores.
# Then, we will modify them according to different conditions. # # Then, we will modify them according to different conditions.
myBigMergedDF['PRESCOTT'] = myBigMergedDF['ESCOTT'] # myBigMergedDF['PRESCOTT'] = myBigMergedDF['ESCOTT']
labelsList = [] labelsList = []
if (useFrequencies.lower() == 'true'): if (useFrequencies.lower() == 'true'):
...@@ -753,147 +894,81 @@ def main(): ...@@ -753,147 +894,81 @@ def main():
temp = (gnomadDF.loc[gnomadDF['mutant'] == row['mutant'], 'Allele Frequency Log'].values) temp = (gnomadDF.loc[gnomadDF['mutant'] == row['mutant'], 'Allele Frequency Log'].values)
#print(temp) #print(temp)
if (len(temp) > 0): if (len(temp) > 0):
myBigMergedDF.at[index,'frequency'] = temp[0] myBigMergedDF.at[index,'log10frequency'] = temp[0]
myBigMergedDF.at[index,'labels'] = gnomadDF.loc[gnomadDF['mutant'] == row['mutant'], 'labels'].values[0] myBigMergedDF.at[index,'labels'] = gnomadDF.loc[gnomadDF['mutant'] == row['mutant'], 'labels'].values[0]
# print(myBigMergedDF) # print(myBigMergedDF)
scalingCoeff = args.coefficient # scalingCoeff = args.coefficient
freqCutoff = args.frequencycutoff # freqCutoff = args.frequencycutoff
for index, row in myBigMergedDF.iterrows():
if(row['frequency'] != 999.0):
# print(row['frequency']) myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList = \
# freq = np.log10(row['frequency']) runPrescottModel(myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList, \
freq = row['frequency'] version=version, scalingCoeff= args.coefficient, freqCutoff=args.frequencycutoff)
# print(freq)
temp1 = row['PRESCOTT']
label = row['labels']
if(version==1):
if(freq>freqCutoff):
temp2 = temp1 - freq*scalingCoeff/freqCutoff
if(temp2<0.0):
myBigMergedDF.at[index,'PRESCOTT'] = 0.0
selectedValuesList.append(0.0)
else:
myBigMergedDF.at[index,'PRESCOTT'] = temp2
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==2):
if(freq>freqCutoff):
temp2 = temp1 - scalingCoeff*(freqCutoff - freq)/freqCutoff
if(temp2<0.0):
temp2 = 0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==3):
temp2 = temp1 - scalingCoeff*(freqCutoff - freq)/freqCutoff
if(freq>freqCutoff):
if(temp2<0.0):
temp2 = 0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
else:
if(temp2>1.0):
temp2 = 1.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==4):
temp2 = temp1 - scalingCoeff*(freqCutoff - freq)/freqCutoff
if(freq>freqCutoff):
if(temp2<0.0):
temp2 = 0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
else:
print(myBigMergedDF.loc[index, 'Selected Population'])
sys.exit(-1)
if (myBigMergedDF.iloc[index,'Selected Population'].values==None):
if(temp2>1.0):
temp2 = 1.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
if(version==5):
if(freq>freqCutoff):
temp2 = temp1*0.0
myBigMergedDF.at[index,'PRESCOTT'] = temp2
if(label==0 or label==1):
selectedValuesList.append(temp2)
selectedPositionsList.append(row['position'])
selectedMutantsList.append(row['mutant'])
# myBigMergedDF.dropna(subset = ['labels'], inplace=True) # myBigMergedDF.dropna(subset = ['labels'], inplace=True)
clinvarLabeledDF = myBigMergedDF.loc[(myBigMergedDF['labels']==0) | (myBigMergedDF['labels']==1)]
clinvarLabeledDF['labels'] = clinvarLabeledDF['labels'].astype('int64')
if(len(clinvarLabeledDF)>0):
print("\nMutations with ClinVar labels according to the gnomAD file:\n")
print(clinvarLabeledDF)
#print(myBigMergedDF.loc[(myBigMergedDF['labels']=='0') | (myBigMergedDF['labels']=='1'), 'labels'])
# print(clinvarLabeledDF['labels'].values)
# print(clinvarLabeledDF['ESCOTT'].values)
numPathogenic = len(myBigMergedDF.loc[(myBigMergedDF['labels']==1)]) numPathogenic = len(myBigMergedDF.loc[(myBigMergedDF['labels']==1)])
numBenign = len(myBigMergedDF.loc[(myBigMergedDF['labels']==0)]) numBenign = len(myBigMergedDF.loc[(myBigMergedDF['labels']==0)])
if ((numPathogenic >=1) and (numBenign>=1)): if ((numPathogenic >=1) and (numBenign>=1)):
fprESCOTT, tprESCOTT, AUC_ESCOTT = plotROCandAUCV2(clinvarLabeledDF['labels'], \ plotLabeledPositions(myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList, useFrequencies)
clinvarLabeledDF['ESCOTT'])
fprPRESCOTT, tprPRESCOTT, AUC_PRESCOTT = plotROCandAUCV2(clinvarLabeledDF['labels'], \ else:
clinvarLabeledDF['PRESCOTT']) print("@> You're using a custom frequency file!")
# fprPRESCOTT, tprPRESCOTT, AUC_PRESCOTT = plotROCandAUCV2(myBigMergedDF.loc[(myBigMergedDF['labels']==0) | (myBigMergedDF['labels']==1), 'labels'], \ gnomadDF = pd.read_csv(args.gnomadfile, header=None, sep='\s+')
# myBigMergedDF.loc[(myBigMergedDF['labels']==0) | (myBigMergedDF['labels']==1), 'PRESCOTT']) gnomadDF.columns = ['mutant', 'frequency']
print(gnomadDF)
# Assign labels to pathogenic/benign mutations for performance evaluation
# gnomadDF['labels'] = ""
# for index, row in gnomadDF.iterrows():
# if ((row['ClinVar Clinical Significance']=='Benign/Likely benign') or \
# (row['ClinVar Clinical Significance']=='Benign') or \
# (row['ClinVar Clinical Significance']=='Likely benign')):
# gnomadDF.at[index,'labels'] = 0
# if((row['ClinVar Clinical Significance']=='Pathogenic/Likely pathogenic') or \
# (row['ClinVar Clinical Significance']=='Pathogenic') or \
# (row['ClinVar Clinical Significance']=='Likely pathogenic')):
# gnomadDF.at[index,'labels'] = 1
# if (len(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)]) > 0):
# print(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)])
# # print(gnomadDF['ClinVar Clinical Significance'])
fig = plt.figure(figsize=(12,6)) selectedPositionsList = []
# plt.rcParams.update({'font.size': 18}) selectedValuesList = []
plt.grid(linestyle='--') selectedMutantsList = []
# plt.title(protName + " - "+method+" AUC={:.2f}".format(AUC_ESCOTT))
plt.title("AUC={:.2f} -> AUC={:.2f}".format(AUC_ESCOTT, AUC_PRESCOTT)) # # Assign ESCOTT scores to PRESCOTT scores.
plt.ylim([0.0, 1.0]) # # Then, we will modify them according to different conditions.
#plt.xlim([1000, 1863]) # myBigMergedDF['PRESCOTT'] = myBigMergedDF['ESCOTT']
plt.scatter(myBigMergedDF.loc[myBigMergedDF['labels'] == 1, 'position'], myBigMergedDF.loc[myBigMergedDF['labels'] == 1, 'ESCOTT'], marker='o', color='red', label='pathogenic')
plt.scatter(myBigMergedDF.loc[myBigMergedDF['labels'] == 0, 'position'], myBigMergedDF.loc[myBigMergedDF['labels'] == 0, 'ESCOTT'], marker='o', color='blue', label='benign') labelsList = []
if (useFrequencies.lower() == 'true'): if (useFrequencies.lower() == 'true'):
#print(selectedPositionsList)
#print(selectedValuesList)
plt.scatter(selectedPositionsList, selectedValuesList, marker='o', color='olive', label='PRESCOTT')
# Add vertical lines connecting old and new values # print(myBigMergedDF)
for i in range(len(selectedPositionsList)): for index, row in myBigMergedDF.iterrows():
plt.annotate("", xy=(selectedPositionsList[i], selectedValuesList[i]), xycoords='data', \ myBigMergedDF.at[index,'position'] = row['mutant'][1:-1]
xytext=(selectedPositionsList[i], myBigMergedDF.loc[myBigMergedDF['mutant'] == selectedMutantsList[i], 'ESCOTT'].values[0]), textcoords='data', # print(row['mutant'], row['ESCOTT'])
arrowprops=dict(arrowstyle="->", connectionstyle="arc3")) # print(row['mutant'][1:-1])
temp = (gnomadDF.loc[gnomadDF['mutant'] == row['mutant'], 'frequency'].values)
#print(temp)
if (len(temp) > 0):
myBigMergedDF.at[index,'log10frequency'] = np.log10(temp[0])
# myBigMergedDF.at[index,'labels'] = gnomadDF.loc[gnomadDF['mutant'] == row['mutant'], 'labels'].values[0]
plt.xticks(rotation = 90) # print(myBigMergedDF)
plt.ylabel("PR/ESCOTT Score") # scalingCoeff = args.coefficient
plt.xlabel("Position") # freqCutoff = args.frequencycutoff
plt.legend(loc='upper right')
plt.tight_layout() myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList = \
plt.savefig("clinvar-vs-position.png") runPrescottModel(myBigMergedDF, selectedPositionsList, selectedValuesList, selectedMutantsList, \
plt.close() version=version, scalingCoeff= args.coefficient, freqCutoff=args.frequencycutoff)
print("@> AUC= {:.3f} {:.3f}".format( AUC_ESCOTT, AUC_PRESCOTT))
# Renaming the column just to make clear that the frequency column in the csv # myBigMergedDF.dropna(subset = ['labels'], inplace=True)
# is actually log10 frequencies. Normally, one can deduce it from the values as well
# but it is always better to be clear. # sys.exit(-1)
myBigMergedDF = myBigMergedDF.rename(columns={'frequency': 'log10frequency'})
#Write the results to csv files.
myBigMergedDF['mutant'] = myBigMergedDF['mutant'].str.upper() myBigMergedDF['mutant'] = myBigMergedDF['mutant'].str.upper()
# myBigMergedDF = myBigMergedDF['mutant'].apply(lambda x: x.upper()) # myBigMergedDF = myBigMergedDF['mutant'].apply(lambda x: x.upper())
myBigMergedDF.to_csv(outfile+'-details.csv', index=None) myBigMergedDF.to_csv(outfile+'-details.csv', index=None)
...@@ -925,7 +1000,6 @@ def main(): ...@@ -925,7 +1000,6 @@ def main():
# print(myBigMergedDF.loc[myBigMergedDF['mutant']==variant, 'PRESCOTT'].values) # print(myBigMergedDF.loc[myBigMergedDF['mutant']==variant, 'PRESCOTT'].values)
my_file.write("{:.2f},".format(float(myBigMergedDF.loc[myBigMergedDF['mutant']==variant, 'PRESCOTT'].values[0]))) my_file.write("{:.2f},".format(float(myBigMergedDF.loc[myBigMergedDF['mutant']==variant, 'PRESCOTT'].values[0])))
if(os.path.exists(protein+'_singleline.txt')): if(os.path.exists(protein+'_singleline.txt')):
os.remove(protein+'_singleline.txt') os.remove(protein+'_singleline.txt')
if(os.path.exists(protein+'_singleline_1-ranksort.txt')): if(os.path.exists(protein+'_singleline_1-ranksort.txt')):
......
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