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analyze_neighbors.py
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import csv
import numpy as np
import random as r
linc_hits_file = 'top_hits.csv'
lincs_file = 'combined_lincRNAs_final.csv'
genes_file = 'unique_isoforms_NM_hg19.csv'
sam_hits_file = 'SAM_hits.csv'
baseline_file = '161218_baseline.csv'
sam_output_file = 'sam_output.csv'
rnaseq_output_file = 'rnaseq_output.csv'
power_output_file = 'power_output.csv'
bp_cutoff = 1000000
rnaseq_lincs = ['TCONS_00011252', 'NR_034078', 'TCONS_00010506', 'TCONS_00026344', 'TCONS_00015940', 'NR_125939', 'TCONS_00009861', 'NR_109890',
'NR_033834', 'TCONS_00006579', 'NR_026873']
with open(lincs_file, 'rb') as gf:
data = [row for row in csv.reader(gf.read().splitlines())]
lincs = dict([(row[0], row[1:]) for i,row in enumerate(data)])
with open(genes_file, 'rb') as gf:
data = [row for row in csv.reader(gf.read().splitlines())]
genes = dict([(row[0], row[1:5]) for i,row in enumerate(data)])
genes_map = dict([(row[0], row[5]) for i,row in enumerate(data)])
with open(linc_hits_file, 'rb') as gf:
linc_hits = [row[0] for row in csv.reader(gf.read().splitlines())]
with open(sam_hits_file, 'rb') as gf:
data = [row for row in csv.reader(gf.read().splitlines())]
sam_hits = dict([(row[0], float(row[1])) for i,row in enumerate(data)])
# with open(baseline_file, 'rb') as gf:
# data = [row for row in csv.reader(gf.read().splitlines())]
# baseline = dict([(row[0], row[1:]) for i,row in enumerate(data[1:])])
def get_TSS(loc_list):
strand = loc_list[1]
if strand == "+":
return loc_list[0], int(loc_list[2])
elif strand == "-":
return loc_list[0], int(loc_list[3])
def get_activation(exp, ctrl):
exp = [float(e) for e in exp]
ctrl = [float(c) for c in ctrl]
return np.mean(exp)/(np.mean(ctrl)+0.0000001) > 1
with open(power_output_file, 'wb') as csvfile:
csvwriter = csv.writer(csvfile)
for l in ['TCONS_00006579']:
power_file = l + '_power.csv'
with open(power_file, 'rb') as gf:
data = [row for row in csv.reader(gf.read().splitlines())]
power_genes = dict([(row[0], row[1:]) for i,row in enumerate(data[1:])])
print len(power_genes.keys())
linc_chr, linc_loc = get_TSS(lincs[l])
for g in genes.keys():
gene_chr, gene_loc = get_TSS(genes[g])
if gene_chr == linc_chr:
if abs(linc_loc - gene_loc) < bp_cutoff:
g = genes_map[g]
if g in power_genes.keys():
csvwriter.writerow([g] + power_genes[g])
else:
print g
# # if g in sam_hits.keys():
# # sam_csvwriter.writerow([l, g, genes_map[g], sam_hits[g]])
# if g in diff_genes.keys():
# # rnaseq_csvwriter.writerow([l, g] + diff_genes[g])
# if l == 'TCONS_00026344':
# a = get_activation(diff_genes[g][:7], diff_genes[g][7:])
# else:
# a = get_activation(diff_genes[g][:9], diff_genes[g][9:])
# # if a:
# if g not in sig_genes: sig_genes.append(g)
# else:
# if g == 'C6orf195': g = 'LINC01600'
# if g == 'FLJ44313': continue
# if g == 'LOC101927322': g = 'NM_001289967'
# if g == 'CCDC11': g = 'CFAP53'
# if g == 'KCNA6': continue
# if g == 'GPR133': g = 'ADGRD1'
# num_expressed = [0,0,0,0,0]
# total_exp = 0
# for i, exp in enumerate(baseline[g]):
# total_exp += float(exp)
# if float(exp) >= 1:
# num_expressed[i/3] += 1
# if (num_expressed[3] + num_expressed[4]) == 0 and (num_expressed[0] == 0 or num_expressed[1] == 0 or num_expressed[2] == 0):
# if g not in undetected_genes and g not in lowly_exp_genes:
# if total_exp < 0.001:
# undetected_genes.append(g)
# else:
# lowly_exp_genes.append(g)
# # rnaseq_csvwriter.writerow([l, g] + baseline[g] + ['undetected'])
# if g not in all_genes: all_genes.append(g)
# N = 10000
# prob = float(n_diff)/len(genes_map.values())
# count = 0
# # if l == 'TCONS_00015940': sig_genes = sig_genes + ['MOB3B', 'IFNK', 'EQTN']
# for i in range(N):
# k = 0
# for j in range(len(all_genes)):
# if r.random() <= prob: k += 1
# if k == len(sig_genes):
# count += 1
# # if l in rnaseq_lincs:
# print l, len(diff_genes.keys()), len(all_genes), len(sig_genes), float(count)/N, len(lowly_exp_genes), len(undetected_genes)
# sig_genes_str = ''
# for g in sig_genes: sig_genes_str += (g + ', ')
# lowly_exp_genes_str = ''
# for g in lowly_exp_genes: lowly_exp_genes_str += (g + ', ')
# undetected_genes_str = ''
# for g in undetected_genes: undetected_genes_str += (g + ', ')
# rnaseq_csvwriter.writerow([l] + [sig_genes_str, lowly_exp_genes_str, undetected_genes_str])
# with open(sam_output_file, 'wb') as sam_csvfile:
# sam_csvwriter = csv.writer(sam_csvfile)
# with open(rnaseq_output_file, 'wb') as rnaseq_csvfile:
# rnaseq_csvwriter = csv.writer(rnaseq_csvfile)
# for l in ['NR_034078']:
# rnaseq_file = l + '_fold_nofilter_2.csv'
# with open(rnaseq_file, 'rb') as gf:
# data = [row for row in csv.reader(gf.read().splitlines())]
# diff_genes = dict([(row[0], row[1:]) for i,row in enumerate(data[1:])])
# baseline_file = l + '_TPM.csv'
# with open(baseline_file, 'rb') as gf:
# data = [row for row in csv.reader(gf.read().splitlines())]
# baseline = dict([(row[0], row[1:]) for i,row in enumerate(data[1:])])
# linc_chr, linc_loc = get_TSS(lincs[l])
# sig_genes = []
# all_genes = []
# undetected_genes = []
# lowly_exp_genes = []
# n_diff = 0
# for d in diff_genes.keys():
# if l == 'TCONS_00026344':
# a = get_activation(diff_genes[d][:7], diff_genes[d][7:])
# else:
# a = get_activation(diff_genes[d][:9], diff_genes[d][9:])
# if a:
# n_diff += 1
# for g in genes.keys():
# gene_chr, gene_loc = get_TSS(genes[g])
# if gene_chr == linc_chr:
# if abs(linc_loc - gene_loc) < bp_cutoff:
# g = genes_map[g]
# if l == 'NR_034078':
# print g + '\t' + diff_genes[g]
# # if g in sam_hits.keys():
# # sam_csvwriter.writerow([l, g, genes_map[g], sam_hits[g]])
# if g in diff_genes.keys():
# # rnaseq_csvwriter.writerow([l, g] + diff_genes[g])
# if l == 'TCONS_00026344':
# a = get_activation(diff_genes[g][:7], diff_genes[g][7:])
# else:
# a = get_activation(diff_genes[g][:9], diff_genes[g][9:])
# # if a:
# if g not in sig_genes: sig_genes.append(g)
# else:
# if g == 'C6orf195': g = 'LINC01600'
# if g == 'FLJ44313': continue
# if g == 'LOC101927322': g = 'NM_001289967'
# if g == 'CCDC11': g = 'CFAP53'
# if g == 'KCNA6': continue
# if g == 'GPR133': g = 'ADGRD1'
# num_expressed = [0,0,0,0,0]
# total_exp = 0
# for i, exp in enumerate(baseline[g]):
# total_exp += float(exp)
# if float(exp) >= 1:
# num_expressed[i/3] += 1
# if (num_expressed[3] + num_expressed[4]) == 0 and (num_expressed[0] == 0 or num_expressed[1] == 0 or num_expressed[2] == 0):
# if g not in undetected_genes and g not in lowly_exp_genes:
# if total_exp < 0.001:
# undetected_genes.append(g)
# else:
# lowly_exp_genes.append(g)
# # rnaseq_csvwriter.writerow([l, g] + baseline[g] + ['undetected'])
# if g not in all_genes: all_genes.append(g)
# N = 10000
# prob = float(n_diff)/len(genes_map.values())
# count = 0
# # if l == 'TCONS_00015940': sig_genes = sig_genes + ['MOB3B', 'IFNK', 'EQTN']
# for i in range(N):
# k = 0
# for j in range(len(all_genes)):
# if r.random() <= prob: k += 1
# if k == len(sig_genes):
# count += 1
# # if l in rnaseq_lincs:
# print l, len(diff_genes.keys()), len(all_genes), len(sig_genes), float(count)/N, len(lowly_exp_genes), len(undetected_genes)
# sig_genes_str = ''
# for g in sig_genes: sig_genes_str += (g + ', ')
# lowly_exp_genes_str = ''
# for g in lowly_exp_genes: lowly_exp_genes_str += (g + ', ')
# undetected_genes_str = ''
# for g in undetected_genes: undetected_genes_str += (g + ', ')
# rnaseq_csvwriter.writerow([l] + [sig_genes_str, lowly_exp_genes_str, undetected_genes_str])