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- # import modules
- import numpy as np
- import pandas as pd
- from sklearn import svm
- from sklearn import preprocessing
- import sys, argparse, os
- from vcf2bed import position_to_bed,padding_region
-
-
-
- parser = argparse.ArgumentParser(description="this script is to preform one calss svm on each chromosome")
-
- parser.add_argument('-train', '--trainDataset', type=str, help='training dataset generated from extracting vcf information part, with mutaitons supported by callsets', required=True)
- parser.add_argument('-test', '--testDataset', type=str, help='testing dataset generated from extracting vcf information part, with mutaitons not called by all callsets', required=True)
- parser.add_argument('-name', '--sampleName', type=str, help='sample name for output file name', required=True)
-
- args = parser.parse_args()
-
- # Rename input:
- train_input = args.trainDataset
- test_input = args.testDataset
- sample_name = args.sampleName
-
- # default columns, which will be included in the included in the calssifier
- chromosome = ['chr1','chr2','chr3','chr4','chr5','chr6','chr7','chr8','chr9','chr10','chr11','chr12','chr13','chr14','chr15' ,'chr16','chr17','chr18','chr19','chr20','chr21','chr22','chrX','chrY']
- feature_heter_cols = ['AltDP','BaseQRankSum','DB','DP','FS','GQ','MQ','MQRankSum','QD','ReadPosRankSum','RefDP','SOR','af']
- feature_homo_cols = ['AltDP','DB','DP','FS','GQ','MQ','QD','RefDP','SOR','af']
-
-
- # import datasets sepearate the records with or without BaseQRankSum annotation, etc.
- def load_dat(dat_file_name):
- dat = pd.read_table(dat_file_name)
- dat['DB'] = dat['DB'].fillna(0)
- dat = dat[dat['DP'] != 0]
- dat['af'] = dat['AltDP']/(dat['AltDP'] + dat['RefDP'])
- homo_rows = dat[dat['BaseQRankSum'].isnull()]
- heter_rows = dat[dat['BaseQRankSum'].notnull()]
- return homo_rows,heter_rows
-
-
- train_homo,train_heter = load_dat(train_input)
- test_homo,test_heter = load_dat(test_input)
- clf = svm.OneClassSVM(nu=0.05,kernel='rbf', gamma='auto_deprecated',cache_size=500)
-
- def prepare_dat(train_dat,test_dat,feature_cols,chromo):
- chr_train = train_dat[train_dat['chromo'] == chromo]
- chr_test = test_dat[test_dat['chromo'] == chromo]
-
- train_dat = chr_train.loc[:,feature_cols]
- test_dat = chr_test.loc[:,feature_cols]
- train_dat_scaled = preprocessing.scale(train_dat)
- test_dat_scaled = preprocessing.scale(test_dat)
- return chr_test,train_dat_scaled,test_dat_scaled
-
- def oneclass(X_train,X_test,chr_test):
- clf.fit(X_train)
- y_pred_test = clf.predict(X_test)
- test_true_dat = chr_test[y_pred_test == 1]
- test_false_dat = chr_test[y_pred_test == -1]
- return test_true_dat,test_false_dat
-
- predicted_true = pd.DataFrame(columns=train_homo.columns)
- predicted_false = pd.DataFrame(columns=train_homo.columns)
-
- for chromo in chromosome:
- # homo datasets
- chr_test_homo,X_train_homo,X_test_homo = prepare_dat(train_homo,test_homo,feature_homo_cols,chromo)
- test_true_homo,test_false_homo = oneclass(X_train_homo,X_test_homo,chr_test_homo)
- predicted_true = predicted_true.append(test_true_homo)
- predicted_false = predicted_false.append(test_false_homo)
- # heter datasets
- chr_test_heter,X_train_heter,X_test_heter = prepare_dat(train_heter,test_heter,feature_heter_cols,chromo)
- test_true_heter,test_false_heter = oneclass(X_train_heter,X_test_heter,chr_test_heter)
- predicted_true = predicted_true.append(test_true_heter)
- predicted_false = predicted_false.append(test_false_heter)
-
- predicted_true_filename = sample_name + '_predicted_true.txt'
- predicted_false_filename = sample_name + '_predicted_false.txt'
-
- predicted_true.to_csv(predicted_true_filename,sep='\t',index=False)
- predicted_false.to_csv(predicted_false_filename,sep='\t',index=False)
-
- # output the bed file and padding bed region 50bp
-
- predicted_true_bed_filename = sample_name + '_predicted_true.bed'
- predicted_false_bed_filename = sample_name + '_predicted_false.bed'
- padding_filename = sample_name + '_padding.bed'
-
- predicted_true_bed = open(predicted_true_bed_filename,'w')
- predicted_false_bed = open(predicted_false_bed_filename,'w')
- padding = open(padding_filename,'w')
-
- #
- for index,row in predicted_false.iterrows():
- chromo,pos1,pos2 = position_to_bed(row['chromo'],row['pos'],row['ref'],row['alt'])
- outline_pos = chromo + '\t' + str(pos1) + '\t' + str(pos2) + '\n'
- predicted_false_bed.write(outline_pos)
- chromo,pad_pos1,pad_pos2,pad_pos3,pad_pos4 = padding_region(chromo,pos1,pos2,50)
- outline_pad_1 = chromo + '\t' + str(pad_pos1) + '\t' + str(pad_pos2) + '\n'
- outline_pad_2 = chromo + '\t' + str(pad_pos3) + '\t' + str(pad_pos4) + '\n'
- padding.write(outline_pad_1)
- padding.write(outline_pad_2)
-
- for index,row in predicted_true.iterrows():
- chromo,pos1,pos2 = position_to_bed(row['chromo'],row['pos'],row['ref'],row['alt'])
- outline_pos = chromo + '\t' + str(pos1) + '\t' + str(pos2) + '\n'
- predicted_true_bed.write(outline_pos)
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