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data_preprocess.py
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data_preprocess.py
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import pandas as pd
import numpy as np
import processSeq
import sys
PATH1='.' # director of dataset
def gen_Seq(Range):
print ("Generating Seq...")
table = pd.read_table(PATH1+"prep_data.txt",sep = "\t")
print (len(table))
table.drop_duplicates()
print (len(table))
label_file = open(PATH1+"LabelSeq", "w")
total = len(table)
list = ["chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", \
"chr10", "chr11", "chr12", "chr13", "chr14", "chr15", "chr16", "chr17", \
"chr18", "chr19", "chr20", "chr21", "chr22", "chrX", "chrY","chrM"]
number_positive = 0
dict_pos={}
genome_assemblyPath = PATH1+"Chromosome_38/"
for i in range(total):
if (number_positive % 100 == 0) and (number_positive != 0):
print ("number of seq: %d of %d\r" %(number_positive,total),end = "")
sys.stdout.flush()
chromosome = table["chromosome"][i]
if chromosome in dict_pos.keys():
strs = dict_pos[chromosome]
else:
strs = processSeq.getString(genome_assemblyPath + str(chromosome) + ".fa")
dict_pos[chromosome] = strs
bias = 7
start = int(table["start"][i] - 1 - Range + bias)
end = start + 23 + Range*2
strand = table["strand"][i]
edstrs1 = strs[start : end]
if strand == "-":
edstrs1 = edstrs1[::-1]
edstrs1 = processSeq.get_reverse_str(edstrs1)
if "N" in edstrs1:
table = table.drop(i)
continue
outstr = "%s\n"%(edstrs1)
label_file.write(outstr)
number_positive += 1
table.to_csv(PATH1+"prep_data.txt",sep = "\t",index = False)
def get_target():
table = pd.read_table(PATH1+"prep_data.txt", sep="\t")
print (len(table))
table.drop_duplicates()
print (len(table))
target_file = open(PATH1+"TargetSeq", "w")
for i in range(len(table)):
target = table['target'][i].upper()
target_file.write(target+"\n")
target_file.close()
def prep_data():
chrom_list = ["chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", \
"chr10", "chr11", "chr12", "chr13", "chr14", "chr15", "chr16", "chr17", \
"chr18", "chr19", "chr20", "chr21", "chr22", "chrX", "chrY","chrM"]
tab = pd.read_table(PATH1+"casoffinder_CHANGEseq_joined.tsv",sep = '\t')
tab = tab[tab['chromosome'].isin(chrom_list)]
tab['label'] = 1 - tab['reads'].isna()
tab['end'] = tab['start'] + 23
print (tab['chromosome'].unique())
tab.to_csv(PATH1+"prep_data.txt",sep = "\t",index = False)
def load_file(f_name,length,vec_name):
base_code = {
'A': 0,
'C': 1,
'G': 2,
'T': 3,
}
num_pairs = sum(1 for line in open(f_name))
# number of sample pairs
num_bases = 4
with open(f_name, 'r') as f:
line_num = 0 # number of lines (i.e., samples) read so far
for line in f.read().splitlines():
if (line_num % 100 == 0) and (line_num != 0):
print ("number of input data: %d\r" %(line_num),end= "")
sys.stdout.flush()
if line_num == 0:
# allocate space for output
seg_length = length # number of bases per sample
Xs_seq1 = np.zeros((num_pairs, num_bases, seg_length))
for start in range(len(line)):
if line[start] in base_code:
print (start)
break
base_num = 0
for x in line[start:start+length]:
if x != "N":
Xs_seq1[line_num, base_code[x], base_num] = 1
base_num += 1
line_num += 1
X = Xs_seq1
np.save("../%s" %(vec_name),X)
prep_data()
gen_Seq(100)
load_file(PATH1+"/LabelSeq",223,"vec.npy")
get_target()
load_file(PATH1+"/TargetSeq",23,"t.npy")