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# ------------------ | |
# from bgen to bed | |
# ------------------ | |
plink2 --bgen myfile.bgen \ | |
# filter by chr or by snps names | |
--chr 1 --extract {params.snps} \ | |
--make-bed --out myoutput | |
# 1. plink will create temporary/intermediate files such as *-temporary.{pgen,psam,pvar} | |
# 2. sample ids in .fam will be `ID1_ID1` |
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import re | |
import math | |
import sys | |
## Part 1 | |
FileIn = 'D1_INPUT.txt' | |
CurFreq =0 | |
with open(FileIn) as FreqIn: | |
for line in FreqIn: | |
FreqAdju = int(line.strip()) | |
CurFreq += FreqAdju |
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import pandas as pd | |
import numpy as np | |
import keras | |
from sklearn.model_selection import train_test_split | |
from sklearn.preprocessing import OneHotEncoder | |
from sklearn.preprocessing import StandardScaler | |
import matplotlib.pyplot as plt | |
%matplotlib inline | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import confusion_matrix |
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import re | |
import subprocess | |
from subprocess import PIPE | |
import sys | |
import time | |
import numpy as np | |
import numpy | |
impute_file = sys.argv[1] |
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from __future__ import print_function | |
import re | |
import subprocess | |
import sys | |
file_in = sys.argv[1] | |
#file_out = sys.argv[2] | |
### define functions | |
def num_ret(str=str): |
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import sys | |
import time | |
import subprocess | |
from subprocess import PIPE | |
import re | |
from itertools import chain | |
import pandas as pd | |
import numpy as np | |
import copy | |
## import filenames as a list |
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import re | |
import subprocess | |
from subprocess import PIPE | |
import pandas as pd | |
import numpy as np | |
import scipy.stats as stats | |
import glob | |
import scipy | |
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import re | |
import subprocess | |
from subprocess import PIPE | |
import pandas as pd | |
import numpy as np | |
import scipy.stats as stats | |
import glob | |
import scipy | |
############################ read in the DBID and TCR identifiers from ling's csv file |
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import pandas as pd | |
import numpy as np | |
total_TCR = pd.read_excel('total done list 7-18-17.xlsx', sheetname='total file') | |
TCR_5merBC = pd.read_excel('early deep SEQ with 5 barcode.xlsx', sheetname='total file') | |
TCR_5mer_sub=TCR_5merBC.loc[:, ('DbID','cell type', 'Dx', '# to SEQ1', 'alpha', '# to SEQ2', 'beta')] | |
TCR_5mer_sub.groupby(['cell type', 'Dx']).size() |
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import sys | |
clone_sum=0 | |
validclones=0 | |
outfile = open('HS115_clustered_low_rem.rcl', 'w') | |
with open('HS115_CLUSTERED.rcl') as rclfile: | |
for rcl in rclfile: | |
if rcl: | |
parsedrcl= rcl.strip('\r\n').split(',') | |
if int(parsedrcl[7]) > 2 : |
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