cancers = ["brca","gbm","kirc","luad"]
for cancer in cancers:
print "Started: "+str(cancer)
f_path = os.getcwd()+"Output/"+str(cancer)+"/"
f_path = "/Users/rathikannan/Documents/hm_27k_profile/Output/"+str(cancer)+"/"
cut1 = open(f_path+"cut1.csv","r").readlines()
cut1 = [i.strip() for i in cut1]
cut2 = open(f_path+"cut2.csv","r").readlines()
cut2 = [i.strip() for i in cut2]
probe_dict = clust.read_probe_dict(f_path+"probe_dict_dis.txt")
dict1 = {}
dict2 = {}
for key in probe_dict:
if key in cut1:
dict1[key] = probe_dict[key]
else:
dict2[key] = probe_dict[key]
clust.probe_dict_to_txt(dict1,f_path+str(cancer)+"_1.txt")
clust.probe_dict_to_txt(dict2,f_path+str(cancer)+"_2.txt")
cancer = "ov"
import os, time import numpy as np import cluster_methods as clust cancers = ["brca","coad","gbm","kirc","luad","lusc","ov","ucec"] source_path = os.getcwd()+"/Output/" source_path = "/Users/rathikannan/Documents/hm_27k_profile/Output/" for cancer in cancers: # Read probe dict from txt probe_dict = clust.read_probe_dict(source_path+cancer+"/probe_dict_dis.txt") # Convert dictionary to matrix # Rows are samples # Columns are probe IDs matrix_1 = clust.dict_to_matrix_1(dict) # Calculate hamming distance between samples hamming_1 = clust.hamming_full_csv(matrix_1,out_path+cancer+"/probe_dis_hamming_samples.txt")
import os
import cluster_methods as clust
cancer = "brca"
print "Started: "+str(cancer)
f_path = "/Users/rathikannan/Documents/hm_27k_profile/Output/"+str(cancer)+"/"
cut1 = open(f_path+"cut1.csv","r").readlines()
cut1 = [i.strip() for i in cut1]
cut2 = open(f_path+"cut2.csv","r").readlines()
cut2 = [i.strip() for i in cut2]
probe_dict = clust.read_probe_dict(f_path+"probe_dict_dis.txt")
dict1 = {}
dict2 = {}
for key in probe_dict:
if key in cut1:
dict1[key] = probe_dict[key]
else:
dict2[key] = probe_dict[key]
clust.probe_dict_to_txt(dict1,f_path+str(cancer)+"_1.txt")
clust.probe_dict_to_txt(dict2,f_path+str(cancer)+"_2.txt")
print len(dict1)
print len(dict2)
print len(dict1.values()[0])
print len(dict2.values()[0])
