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EPCNN_PS.py
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EPCNN_PS.py
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import argparse as ap
import pandas as pd
import numpy as np
import sys
from sklearn import preprocessing
from ete3 import NCBITaxa
def read_params(args):
parser = ap.ArgumentParser(description='Specify the probability')
arg = parser.add_argument
arg('-fn', '--fn', type=str, help='datasets')
return vars(parser.parse_args())
def read_files(file_name):
# file_name='Karlsson_T2D'
known = pd.read_csv("data/" + file_name+'_known.csv', index_col=0)
unknown = pd.read_csv("data/" + file_name+'_unknown.csv', index_col=0)
y = pd.read_csv("data/" + file_name+'_y.csv', index_col=0)
le = preprocessing.LabelEncoder()
y=np.array(y).ravel()
y = le.fit_transform(y)
return known, unknown, y
# known=pd.read_csv('Zeller_CRC_known.csv',index_col=0)
# unknown=pd.read_csv('Zeller_CRC_unknown.csv',index_col=0)
par = read_params(sys.argv)
file_name = str(par['fn'])
known, unknown,y=read_files(file_name)
# since we got taxaid from the MicroPro, therefore after using PhyloT,
# some taxid is will be not accurate because some of them are updated,
# so we need to replace some of them.
# for KT2D
known=known.rename(columns={'330':'301','697046':'645','758602':'1073996',
'1315956':'2496551','1834200':'1796646','1870930':'1812935'})
# for QT2D
# known=known.rename(columns={'330':'301','1834200':'1796646'})
# for QLC
# known=known.rename(columns={'330':'301','1834200':'1796646'})
# for ZCRC
# known=known.rename(columns={'330':'301','1834200':'1796646',
# '319938':'288004',
# '1166016':'1905730'})
# here as we descriped in the paper, we PhyloT to generate the tree,
# since PhyloT is not free, so here we offer a free way to genetate by using ETE3
raw_id=known.columns.values.tolist()
ncbi = NCBITaxa()
# Also, we can use the Newick obtained file to get the tree by using PhyloT, just like the
# description in our paper
# import ete3
# tree=ete3.Tree("tree.txt",format=8)
# print(tree)
tree = ncbi.get_topology(raw_id)
print (tree.get_ascii(attributes=["taxid"]))
order = []
num = 1
for node in tree.traverse(strategy='levelorder'):
if node.is_leaf():
order.append(node.name)
postorder = []
num = 1
for node in tree.traverse(strategy='postorder'):
if node.is_leaf():
postorder.append(node.name)
temp = []
for i in order:
if i in known.columns:
temp.append(i)
order = temp
temp1 = []
for i in postorder:
if i in known.columns:
temp1.append(i)
postorder = temp1
known_Xl=known[order]
known_Xp=known[postorder]
known_Xl.to_csv(file_name+'_knownl.csv')
known_Xp.to_csv(file_name+'_knownp.csv')
# for unknown features, we just arrange the taxa with at least genus levels.
import xlrd
data = xlrd.open_workbook("data/unknown_name.xlsx")
# for the first dataset, therefore the sheet number is 0,1,2,3 respectively
table = data.sheets()[0]
binname =table.col_values(0)[2:-1]
binname=["V"+str(int(i)) for i in binname ]
unknown_structure = unknown[binname]
unbinname=[]
unknown_id=unknown.columns.values.tolist()
for i in unknown_id:
if i not in unknown_structure:
unbinname.append(i)
unknown_nostructure=unknown[unbinname]
structure_taxaid=table.col_values(2)[2:-1]
structure_taxaid=[str(int(i)) for i in structure_taxaid]
unknown_structure.columns = structure_taxaid
ncbi = NCBITaxa()
tree = ncbi.get_topology(structure_taxaid)
order = []
num = 1
for node in tree.traverse(strategy='levelorder'):
if node.is_leaf():
order.append(node.name)
postorder = []
num = 1
for node in tree.traverse(strategy='postorder'):
if node.is_leaf():
postorder.append(node.name)
unknown_order = pd.concat([unknown_structure[order], unknown_nostructure], axis=1)
unknown_postorder = pd.concat([unknown_structure[postorder], unknown_nostructure], axis=1)
unknown_order.to_csv(file_name+'_unknownl.csv')
unknown_postorder.to_csv(file_name+'_unknownp.csv')