# Usage: python create_edges.py parameter_for_net.txt > edges.txt.20170227_1618
#
# 01 DEC 2016, hui
import sys, os, operator, itertools
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stat
from datetime import datetime
import rpy2.robjects as r
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector
import warnings
from geneid2name import make_gene_name_AGI_map_dict, get_gene_name
from param4net import make_global_param_dict
####### Utility files #############
GENE_ID_TO_GENE_NAME = '../Data/information/AGI-to-gene-names_v2.txt'
####################################
GLB_PARAM_SYMBOL = '%%'
DATA_SYMBOL = '@'
TOP_N_TF = 50
MIN_NUM_CONDITION = 20
SIGNAL_INPUT_RATIO_TAU = 1.5
REMOVE_HORIZONTAL_STRIP_TAU = 0.05
REMOVE_VERTICAL_STRIP_TAU = 0.05
MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN = 30
####################################
def get_two_components(y, x):
K = 2
epsilon = 1e-4
lam = 0.1
iterations = 25
random_restarts = 2
# Remove NaNs or Infs
warn_msg = ''
sz = len(x)
if sz < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN: # too few points, ignore.
return None, None, 'IGNORE'
# print('DEBUG')
# print(y)
# print(x)
# print(type(y))
# print(type(x))
index = np.isfinite(x) & np.isfinite(y)
if sum(index) < sz:
warn_msg = np.array_str(x) + ',' + np.array_str(y)
if sum(index) < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN:
return None, None, 'IGNORE'
x = x[index]
y = y[index]
# Train the model
model = LinearRegressionsMixture(np.expand_dims(x, axis=1), np.expand_dims(y, axis=1), K=K)
model.train(epsilon=epsilon, lam=lam, iterations=iterations, random_restarts=random_restarts, verbose=False)
idx1 = (model.gamma[:,0] > model.gamma[:,1]) # model.gamma is a vector of posterior probabilities
idx2 = (model.gamma[:,1] > model.gamma[:,0])
return idx1, idx2, warn_msg
def get_three_components(y, x, cond_lst):
K = 3
epsilon = 1e-4
lam = 0.1
iterations = 50
random_restarts = 5
# Remove NaNs or Infs
warn_msg = ''
sz = len(x)
if sz < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN: # too few points, ignore.
return None, None, None, None, None, None, None, None, None, 'IGNORE'
index = np.isfinite(x) & np.isfinite(y)
if sum(index) < sz:
warn_msg = 'HAS_NAN_OR_INIFNITE'
if sum(index) < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN:
return None, None, None, None, None, None, None, None, None, 'IGNORE'
xx = np.array(x[index])
yy = np.array(y[index])
cond_lst2 = np.array(cond_lst)
cond_lst2 = cond_lst2[index]
# Train the model
model = LinearRegressionsMixture(np.expand_dims(xx, axis=1), np.expand_dims(yy, axis=1), K=K)
model.train(epsilon=epsilon, lam=lam, iterations=iterations, random_restarts=random_restarts, verbose=False)
idx1 = np.array(model.gamma[:,0] > model.gamma[:,1]) & np.array(model.gamma[:,0] > model.gamma[:,2]) # model.gamma is a vector of posterior probabilities
idx2 = np.array(model.gamma[:,1] > model.gamma[:,0]) & np.array(model.gamma[:,1] > model.gamma[:,2])
idx3 = np.array(model.gamma[:,2] > model.gamma[:,0]) & np.array(model.gamma[:,2] > model.gamma[:,1])
return xx[idx1], yy[idx1], xx[idx2], yy[idx2], xx[idx3], yy[idx3], list(cond_lst2[idx1]), list(cond_lst2[idx2]), list(cond_lst2[idx3]), warn_msg
def get_three_components_and_evaluate(y, x, cond_lst):
K = 3
epsilon = 1e-4
lam = 0.1
iterations = 50
random_restarts = 5
# Remove NaNs or Infs
warn_msg = ''
sz = len(x)
if sz < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN: # too few points, ignore.
return None, None, None, None, None, None, None, None, None, 'IGNORE'
index = np.isfinite(x) & np.isfinite(y)
if sum(index) < sz:
warn_msg = 'HAS_NAN_OR_INIFNITE'
if sum(index) < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN:
return None, None, None, None, None, None, None, None, None, 'IGNORE'
xx = np.array(x[index])
yy = np.array(y[index])
cond_lst2 = np.array(cond_lst)
cond_lst2 = cond_lst2[index]
# Train the model
model = LinearRegressionsMixture(np.expand_dims(xx, axis=1), np.expand_dims(yy, axis=1), K=K)
model.train(epsilon=epsilon, lam=lam, iterations=iterations, random_restarts=random_restarts, verbose=False)
idx1 = np.array(model.gamma[:,0] > model.gamma[:,1]) & np.array(model.gamma[:,0] > model.gamma[:,2]) # model.gamma is a vector of posterior probabilities
idx2 = np.array(model.gamma[:,1] > model.gamma[:,0]) & np.array(model.gamma[:,1] > model.gamma[:,2])
idx3 = np.array(model.gamma[:,2] > model.gamma[:,0]) & np.array(model.gamma[:,2] > model.gamma[:,1])
rmse_avg, rmse_std = model.cross_validate(k_fold=10, verbose=False, silent=True)
warn_msg = 'rmse_avg=%4.2f,rmse_sd=%4.2f' % (rmse_avg, rmse_std)
return xx[idx1], yy[idx1], xx[idx2], yy[idx2], xx[idx3], yy[idx3], list(cond_lst2[idx1]), list(cond_lst2[idx2]), list(cond_lst2[idx3]), warn_msg
def get_three_components_mixtools(y, x, cond_lst):
# Remove NaNs or Infs
warn_msg = ''
sz = len(x)
if sz < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN: # too few points, ignore.
return None, None, None, None, None, None, None, None, None, 'IGNORE'
index = np.isfinite(x) & np.isfinite(y)
if sum(index) < sz:
warn_msg = 'HAS_NAN_OR_INIFNITE'
if sum(index) < MIN_NUMBER_OF_POINTS_FOR_MIXTURE_OF_GAUSSIAN:
return None, None, None, None, None, None, None, None, None, 'IGNORE'
xx = np.array(x[index])
yy = np.array(y[index])
cond_lst2 = np.array(cond_lst)
cond_lst2 = cond_lst2[index]
# Train the model
mixtools = importr('mixtools')
try:
result = mixtools.regmixEM(FloatVector(yy), FloatVector(xx), epsilon = 1e-04, k=3, maxit=100)
except:
return None, None, None, None, None, None, None, None, None, 'IGNORE'
posterior = result[result.names.index('posterior')]
posterior = np.array(posterior)
l = np.argmax(posterior, axis=1) # class information
idx1 = l == 0
idx2 = l == 1
idx3 = l == 2
warn_msg = 'loglik=%4.2f' % (np.array(result[result.names.index('loglik')])[0])
return xx[idx1], yy[idx1], xx[idx2], yy[idx2], xx[idx3], yy[idx3], list(cond_lst2[idx1]), list(cond_lst2[idx2]), list(cond_lst2[idx3]), warn_msg
def read_matrix_data(fname):
'''
fname - a file, first line is head, first column is row name.
'''
lineno = 0
colid = []
rowid = []
d = {} # {gene1:{cond1:val1, cond2:val2, ...}, gene2: {...}, ...}
d2 = {} # {cond1:{gene1:val1, gene2:val2, ...}, cond2: {...}, ...}
d3 = {} # {gene1: [], gene2: [], ...}
d4 = {} # {cond1:[], cond2:[], ...}
f = open(fname)
lines = f.readlines()
f.close()
head_line = lines[0].strip()
lst = head_line.split()
colid = lst[1:]
for c in colid:
d2[c] = {}
d4[c] = []
for line in lines[1:]:
line = line.strip()
lst = line.split()
g = lst[0]
rowid.append(g)
d[g] = {}
levels = lst[1:]
if len(levels) != len(colid):
print('Incomplete columns at row %s' % (g))
sys.exit()
d3[g] = []
for i in range(len(colid)):
c = colid[i]
d[g][c] = float(levels[i])
d2[c][g] = float(levels[i])
d3[g].append(float(levels[i]))
d4[c].append(float(levels[i]))
lineno += 1
d_return = {}
d_return['xy'] = d # first gene, then condition
d_return['yx'] = d2 # first condition, then gene
d_return['xx'] = d3 # each item is an array of gene expression levels, i.e., each item is a row
d_return['yy'] = d4 # each item is an array of gene expression levels, i.e., each item is a column
d_return['nrow'] = lineno - 1
d_return['ncol'] = len(colid)
d_return['rowid'] = rowid
d_return['colid'] = colid
d4_sorted = {}
for k in d4:
d4_sorted[k] = sorted(d4[k], reverse=True)
d_return['yy_sorted'] = d4_sorted
return d_return
def get_value(s, delimit):
lst = s.split(delimit)
return lst[1].strip()
def read_info_data(fname):
''' Read chip-seq data information '''
if not os.path.exists(fname):
print('%s not exists.' % (fname) )
sys.exit()
d = {'ID_LIST':[]}
f = open(fname)
lines = f.readlines()
f.close()
for line in lines:
line = line.strip()
if line == '' or line.startswith('#') or line.startswith('%'):
continue
if line.startswith(DATA_SYMBOL):
s = line[line.rfind(DATA_SYMBOL[-1])+1:]
s = s.strip()
if s in d:
print('ID %s duplicate' % (s))
sys.exit()
d[s] = {'PROTEIN_ID':'', 'PROTEN_NAME':'', 'DATA_NAME':'', 'DATA_FORMAT':'', 'DESCRIPTION':'', 'LOCATION':'', 'NOTE':''}
d['ID_LIST'].append(s)
if line.startswith('DESCRIPTION:'):
d[s]['DESCRIPTION'] = get_value(line, ':')
elif line.startswith('PROTEN_NAME:'):
d[s]['PROTEN_NAME'] = get_value(line, ':')
elif line.startswith('PROTEIN_ID:'):
d[s]['PROTEIN_ID'] = get_value(line, ':')
elif line.startswith('DATA_NAME:'):
d[s]['DATA_NAME'] = get_value(line, ':')
elif line.startswith('DATA_FORMAT:'):
d[s]['DATA_FORMAT'] = get_value(line, ':')
elif line.startswith('LOCATION:'):
d[s]['LOCATION'] = get_value(line, ':')
elif line.startswith('NOTE:'):
d[s]['NOTE'] = get_value(line, ':')
return d
def get_gene_list(fname):
result = []
f = open(fname)
for line in f:
line = line.strip()
lst = line.split()
result.append(lst[0])
f.close()
return result
def get_related_condition(s, info_dict):
lst = s.split(';')
result = [] # a list of sample IDs
result = info_dict['ID_LIST'] # TBD
return result
def update_global_param_dict(glb_param_dict, info_dict):
if glb_param_dict['RESEARCH_KEYWORDS'] == '':
glb_param_dict['USER_CONDITION_LIST'] = info_dict['ID_LIST']
glb_param_dict['USER_CONDITION_LIST'] = get_related_condition(glb_param_dict['RESEARCH_KEYWORDS'], info_dict)
def get_threshold(lst):
x = np.array(lst)
x = x[x > 0]
return np.median(x)
def get_threshold2(lst, glb_param_dict):
x = np.array(lst)
x = x[x > 0]
max_num = int(glb_param_dict['MAX_NUM_TARGETS'])
percent = float(glb_param_dict['OVERFLOW_TARGETS_PERCENTAGE'])
n = len(x)
if n < max_num:
return x[-1]
else: # include some overflowing targets, but not all
overflow = n - max_num
keep = int(overflow * percent)
index = keep + max_num
return x[index]
def get_tf(g, bind_dict, info_dict, input_dict, glb_param_dict):
tf_dict = {}
d = bind_dict['xy']
input_d = input_dict['xy']
input_cond = input_dict['colid'][0] # use the first column as input (improve)
if g in d.keys():
for c in bind_dict['colid']:
bind_val = d[g][c]
if info_dict[c]['DATA_FORMAT'].upper() == 'BW':
input_val = input_d[g][input_cond]
if g == 'AT1G65480': # FT, target is FT
#print('DEBUG target:%s protein=%s bv=%g, iv=%g, ratio=%g' % (g, info_dict[c]['PROTEIN_ID'], bind_val, input_val, bind_val/input_val))
pass
if input_val > 0 and input_val < 10000 and (bind_val / input_val) > SIGNAL_INPUT_RATIO_TAU: # input_val should also be not too large
g2 = info_dict[c]['PROTEIN_ID']
if g2 != '':
if not g2 in tf_dict:
tf_dict[g2] = [c]
else:
tf_dict[g2].append(c)
elif info_dict[c]['DATA_FORMAT'].upper() == 'NARROWPEAK':
#tau = bind_dict['yy_sorted'][c][TOP_N_TF]
tau = get_threshold2(bind_dict['yy_sorted'][c], glb_param_dict)
#print('DEBUG target=%s %s %g >= %g' % (g, info_dict[c]['PROTEIN_ID'], bind_val, tau))
if bind_val >= tau: # change later
g2 = info_dict[c]['PROTEIN_ID']
if g2 != '':
if not g2 in tf_dict:
tf_dict[g2] = [c]
else:
tf_dict[g2].append(c)
return tf_dict
def get_gene_expression(gene_id, cond_lst, expr_dict, takelog=False):
num_cond = len(cond_lst)
elst = [None]*num_cond
d = expr_dict['xy']
for i in range(num_cond):
c = cond_lst[i]
x = d[gene_id][c]
if takelog == True:
elst[i] = np.log(x+1)
else:
elst[i] = x
return np.array( elst )
def float_equal(x, y):
return np.abs(x-y) < 0.001
def get_gene_expression2(gene_id1, gene_id2, cond_lst, expr_dict, takelog=False):
''' get gene expression for two genes. Conditions in which two genes have zero TPM values are ignored. '''
num_cond = len(cond_lst)
elst1 = [None]*num_cond
elst2 = [None]*num_cond
clst = [None]*num_cond
d = expr_dict['xy']
j = 0
for i in range(num_cond):
c = cond_lst[i]
x = expr_dict['xy'][gene_id1][c]
y = expr_dict['xy'][gene_id2][c]
#print('DEBUG %s %s %g %g c=%s' % (gene_id1, gene_id2, x, y, c))
#print('DEBUG at2g07745 at R0000SRR1802166XX %g' % (expr_dict['xy']['AT2G07754']['R0000SRR1802166XX']))
if not float_equal(x,0.0) or not float_equal(y,0.0): # at least one is not zero
if takelog == True: # increase gene expression uniformly by 1 for taking logarithm
elst1[j] = np.log(x+1)
elst2[j] = np.log(y+1)
else:
elst1[j] = x
elst2[j] = y
clst[j] = c
j += 1
return ( np.array(elst1[0:j]), np.array(elst2[0:j]), clst[0:j] )
def get_gene_expression3(gene_id1, gene_id2, cond_lst, expr_dict, takelog=False):
'''
get gene expression for two genes. Conditions in which two genes have zero TPM values are ignored.
in addition, vertical strip and horizontal strip are removed.
'''
num_cond = len(cond_lst)
elst1 = [None]*num_cond
elst2 = [None]*num_cond
mark_cond = [True]*num_cond # indicate if a condition should be included
clst = []
d = expr_dict['xy']
for i in range(num_cond):
c = cond_lst[i]
x = expr_dict['xy'][gene_id1][c]
y = expr_dict['xy'][gene_id2][c]
if not float_equal(x,0.0) or not float_equal(y,0.0): # at least one is not zero
if takelog == True: # increase gene expression uniformly by 1 for taking logarithm
elst1[i] = np.log(x+1)
elst2[i] = np.log(y+1)
else:
elst1[i] = x
elst2[i] = y
if elst1[i] < REMOVE_VERTICAL_STRIP_TAU or elst2[i] < REMOVE_HORIZONTAL_STRIP_TAU: # don't include this condition if its values are in the strip
mark_cond[i] = False
else:
clst.append(c)
else:
mark_cond[i] = False
a = np.array(elst1)
a = a[mark_cond]
b = np.array(elst2)
b = b[mark_cond]
return (a.astype(np.float64), b.astype(np.float64), clst)
#################### select condition stuff ###############
def get_yhat(slope, intercept, x):
yhat = intercept + slope * x
return yhat
def select_points_theil(x, y, max_diff):
theil_result = stat.mstats.theilslopes(x, y)
slope = theil_result[0]
intercept = theil_result[1]
yhat = get_yhat(slope, intercept, x)
d = y - yhat
d_abs = np.abs(d)
index = d_abs < max_diff
return (x[index], y[index], index)
def common_elements(list1, list2):
return sorted(list(set(list1).intersection(list2)))
def two_points(p1, p2):
'''Return slope and intercept '''
x1, y1 = p1
x2, y2 = p2
m = (y2 - y1) / (x2 - x1)
b = y1 - m * x1
return (m, b)
def select_points_diagonal(x, y, max_diff, direction):
''' positive direction '''
n = len(x)
if n < 3:
return (x, y)
if direction.lower() == 'pos':
index_x = np.argsort(x)
elif direction.lower() == 'neg':
index_x = np.argsort(-1*x)
else:
print('%s must be pos or neg' % (direction))
sys.exit()
index_y = np.argsort(y)
# get lower (or upper) end point
idx = None
for i in range(2,n+1): # get common index
s1 = index_x[0:i]
s2 = index_y[0:i]
s = common_elements(s1, s2)
if s != []:
idx = s[0]
break
p1 = (x[idx], y[idx])
# get upper (or lower) end point
index_x = list(reversed(index_x)) # reverse list
index_y = list(reversed(index_y))
idx = None
for i in range(2,n+1): # get common index
s1 = index_x[0:i]
s2 = index_y[0:i]
s = common_elements(s1, s2)
if s != []:
idx = s[0]
break
p2 = (x[idx], y[idx])
slope, intercept = two_points(p1, p2)
yhat = get_yhat(slope, intercept, x)
d = y - yhat
d_abs = np.abs(d)
try:
index = d_abs < max_diff
except RuntimeWarning:
print(d_abs)
print(max_diff)
sys.exit()
return (x[index], y[index], index)
def correlation_is_significant(r, p, glb_param_dict):
return (not np.isnan(r)) and np.abs(r) > float(glb_param_dict['TWO_WAY_CORRELATION_CUTOFF']) and p < float(glb_param_dict['TWO_WAY_CORRELATION_PVALUE_CUTOFF'])
def subset_list(lst, bool_index):
if len(lst) != len(bool_index):
print('subset_list: list size not equal (%d %s)', len(lst), len(bool_index))
sys.exit()
n = len(lst)
result = []
for i in range(n):
if bool_index[i] == True:
result.append(lst[i])
return result
def print_dict(d, glb_param_dict):
agi2name_dict = glb_param_dict['name_conversion_dict']
curr_date = datetime.now().strftime('%Y%m%d') # add date to the end of each line, for future reference or filtering
if d['type'] == 'two-way':
gene_id = d['target']
head = '%s %s\t' % (gene_id, get_gene_name(gene_id, agi2name_dict))
gene_id = d['TF']
head += '%s %s\t' % (gene_id, get_gene_name(gene_id, agi2name_dict))
d2 = d['significant']
if 'all' in d2:
s = '%4.2f\t%s\t%s\t%s\t%s' % (d2['all']['score'], 'all', '.', ' '.join(d2['all']['chip_id']), '.')
s += '\t' + curr_date
print(head + s)
sys.stdout.flush() # flush to stdout, so we can see the results immedialtely.
if 'user' in d2:
s = '%4.2f\t%s\t%s\t%s\t%s' % (d2['user']['score'], 'user', ' '.join(d2['user']['signal_set']), ' '.join(d2['user']['chip_id']), '.')
print(head + s)
if 'pos' in d2:
s = '%4.2f\t%s\t%s\t%s\t%s' % (d2['pos']['score'], 'pos', ' '.join(d2['pos']['signal_set']), ' '.join(d2['pos']['chip_id']), '.')
print(head + s)
if 'neg' in d2:
s = '%4.2f\t%s\t%s\t%s\t%s' % (d2['neg']['score'], 'neg', ' '.join(d2['neg']['signal_set']), ' '.join(d2['neg']['chip_id']), '.')
print(head + s)
if 'mix' in d2:
n = len(d2['mix']['score'])
for i in range(n):
s = '%4.2f\t%s\t%s\t%s\t%s' % (d2['mix']['score'][i], 'mix', ' '.join(d2['mix']['signal_set'][i]), ' '.join(d2['mix']['chip_id']), d2['mix']['message'][i])
s += '\t' + curr_date
print(head + s)
sys.stdout.flush() # flush to stdout, so we can see the results immedialtely.
def two_way(target, tf_dict, expr_dict, expr_info_dict, glb_param_dict):
'''
Check if target has relationship with each of TFs.
tf_dict: a dictionary of TFs, {tf_name:ChIP_ID_LIST)
Return a list of dictionaries. Each dictionary has the following format:
'type' :'two-way'
'target':''
'TF' :''
'significant': {
'all': {'signal_set':[], score=2, chip_id:''}
'pos_direction':{'signal_set':[], score:.0, chip_id:''}
'neg_direction':{'signal_set':[], score:.1, chip_id:''}
'user_defined': {'signal_set':[], score:.3, chip_id:''}
'mix': {'signal_set':[], score:[.7,-.5], chip_id:''}
}
'''
result_dict_lst = [] # a list of dictionaries, one for each TF, each dict contains info for a Target-TF pair
target_gene_id = target
all_cond_lst = expr_dict['colid'] # Use all RNA-seq samples. TBD, can be glb_param_dict['USER_CONDITION_LIST']
logrithmize = glb_param_dict['LOGRITHMIZE'].upper() == 'YES' # take logarithmic of TPM values
#target_elst = get_gene_expression(target_gene_id, all_cond_lst, expr_dict, takelog=logrithmize) # a list of gene expression levels
if not target_gene_id in expr_dict['rowid']: # target gene not in expression table, cannot do anything
return result_dict_lst
for tf_gene_id in sorted(tf_dict.keys()): # y is a TF gene id
chip_id = tf_dict[tf_gene_id] # a list of chip experiment IDs, e.g., C00000000000
if not tf_gene_id in expr_dict['rowid']: # tf gene not in expression table, cannot do anything
continue
# get gene expression profiles for target and TF. If in a RNA-seq sample, both target and TF is 0, then this sample is ignored.
target_elst, tf_elst, clist = get_gene_expression3(target_gene_id, tf_gene_id, all_cond_lst, expr_dict, takelog=logrithmize)
r, p = stat.pearsonr(target_elst, tf_elst)
d = {}
d['target'] = target_gene_id
d['TF'] = tf_gene_id
d['type'] = 'two-way'
d['significant'] = {}
all_good = False
if correlation_is_significant(r, p, glb_param_dict):
d['significant']['all'] = {}
d['significant']['all']['signal_set'] = clist # a list of sample IDs, returned by get_gene_expression3
d['significant']['all']['score'] = r
d['significant']['all']['chip_id'] = chip_id
all_good = True
user_cond_lst = glb_param_dict['USER_CONDITION_LIST']
if glb_param_dict['RESEARCH_KEYWORDS'] != '' and user_cond_lst != []:
target_elst_user, tf_elst_user, clist_user = get_gene_expression3(target_gene_id, tf_gene_id, user_cond_lst, expr_dict, takelog=logrithmize)
r, p = stat.pearsonr(target_elst_user, tf_elst_user)
if correlation_is_significant(r, p, glb_param_dict):
d['significant']['user'] = {}
d['significant']['user']['signal_set'] = user_cond_lst
d['significant']['user']['score'] = r
d['significant']['user']['chip_id'] = chip_id
# obsolete
max_diff = glb_param_dict['SELECT_POINTS_DIAGONAL_MAX_DIFF']
if glb_param_dict['LOOK_FOR_POS_CORRELATION'] == 'YES':
aa, bb, index_pos = select_points_diagonal(target_elst, tf_elst, max_diff, 'pos')
r_pos, p_pos = stat.pearsonr(aa, bb)
if correlation_is_significant(r_pos, p_pos, glb_param_dict) and sum(index_pos) >= MIN_NUM_CONDITION:
d['significant']['pos'] = {}
d['significant']['pos']['signal_set'] = subset_list(all_cond_lst, index_pos)
d['significant']['pos']['score'] = r_pos
d['significant']['pos']['chip_id'] = chip_id
# obsolete
if glb_param_dict['LOOK_FOR_NEG_CORRELATION'] == 'YES':
aa, bb, index_neg = select_points_diagonal(target_elst, tf_elst, max_diff, 'neg')
r_neg, p_neg = stat.pearsonr(aa, bb)
if correlation_is_significant(r_neg, p_neg, glb_param_dict) and sum(index_neg) >= MIN_NUM_CONDITION:
d['significant']['neg'] = {}
d['significant']['neg']['signal_set'] = subset_list(all_cond_lst, index_neg)
d['significant']['neg']['score'] = r_neg
d['significant']['neg']['chip_id'] = chip_id
K = int(glb_param_dict['NUMBER_OF_COMPONENTS'])
if glb_param_dict['MIXTURE_OF_REGRESSION'] == 'YES' and not all_good: # look hard only when using all RNA-seq data does not produce good results
if K == 2: # for now consider two components
#print('DEBUG len1=%d, len=%d' % (len(target_elst), len(tf_elst)))
#print('DEBUG %s, %s, %s' % (target_gene_id, tf_gene_id, ' '.join(clist)))
index1, index2, msg = get_two_components(target_elst, tf_elst) # get two Gaussian Mixture Model components
if msg != 'IGNORE':
aa = target_elst[index1]
bb = tf_elst[index1]
r_mix1, p_mix1 = stat.pearsonr(aa, bb)
aa = target_elst[index2]
bb = tf_elst[index2]
r_mix2, p_mix2 = stat.pearsonr(aa, bb)
#print('DEBUG %s %s r_mix1:%g r_mix2:%g' % (target_gene_id, tf_gene_id, r_mix1, r_mix2))
flag1 = correlation_is_significant(r_mix1, p_mix1, glb_param_dict)
flag2 = correlation_is_significant(r_mix2, p_mix2, glb_param_dict)
if flag1 or flag2:
d['significant']['mix'] = {}
d['significant']['mix']['signal_set'] = []
d['significant']['mix']['score'] = []
d['significant']['mix']['chip_id'] = chip_id
if flag1:
d['significant']['mix']['signal_set'].append(subset_list(clist, index1))
d['significant']['mix']['score'].append(r_mix1)
if flag2:
d['significant']['mix']['signal_set'].append(subset_list(clist, index2))
d['significant']['mix']['score'].append(r_mix2)
if K == 3: # three components
aa1, bb1, aa2, bb2, aa3, bb3, cond1, cond2, cond3, msg = get_three_components_mixtools(target_elst, tf_elst, clist) # get two Gaussian Mixture Model components
if msg != 'IGNORE':
r_mix1, p_mix1 = stat.pearsonr(aa1, bb1)
r_mix2, p_mix2 = stat.pearsonr(aa2, bb2)
r_mix3, p_mix3 = stat.pearsonr(aa3, bb3)
#print('DEBUG %s, %s' % (target_gene_id, tf_gene_id))
#print('DEBUG rmix1=%g, pmix1=%g' % (r_mix1, p_mix1))
#print('DEBUG rmix2=%g, pmix2=%g' % (r_mix2, p_mix2))
#print('DEBUG rmix3=%g, pmix3=%g' % (r_mix3, p_mix3))
#print('DEBUG %d %d %d' %(len(aa1), len(aa2), len(aa3)))
min_num_points = int(glb_param_dict['CORRELATION_BASED_ON_AT_LEAST_N_POINTS'])
flag1 = correlation_is_significant(r_mix1, p_mix1, glb_param_dict) and len(aa1) > min_num_points
flag2 = correlation_is_significant(r_mix2, p_mix2, glb_param_dict) and len(aa2) > min_num_points
flag3 = correlation_is_significant(r_mix3, p_mix3, glb_param_dict) and len(aa3) > min_num_points
if flag1 or flag2 or flag3:
d['significant']['mix'] = {}
d['significant']['mix']['signal_set'] = []
d['significant']['mix']['score'] = []
d['significant']['mix']['chip_id'] = chip_id
d['significant']['mix']['message'] = []
if flag1:
d['significant']['mix']['signal_set'].append(cond1)
d['significant']['mix']['score'].append(r_mix1)
d['significant']['mix']['message'].append(msg)
if flag2:
d['significant']['mix']['signal_set'].append(cond2)
d['significant']['mix']['score'].append(r_mix2)
d['significant']['mix']['message'].append(msg)
if flag3:
d['significant']['mix']['signal_set'].append(cond3)
d['significant']['mix']['score'].append(r_mix3)
d['significant']['mix']['message'].append(msg)
if len(d['significant']) > 0: # significant edges exist
print_dict(d, glb_param_dict)
#result_dict_lst.append(d)
return result_dict_lst
def three_way(target, tf_lst, expr_dict, expr_info_dict, glb_param_dict):
''' TBD '''
return []
def establish_edges(expr_dict, expr_info_dict, bind_dict, bind_info_dict, input_dict, glb_param_dict):
high_gene_lst = glb_param_dict['HIGH_PRIORITY_GENE'].split()
gene_lst = get_gene_list(glb_param_dict['GENE_LIST'])
final_gene_lst = list(set(high_gene_lst)) # unique genes
for x in gene_lst:
if not x in high_gene_lst:
final_gene_lst.append(x)
update_global_param_dict(glb_param_dict, expr_info_dict)
result_d = {'two_way_edges':{}, 'three_way_edges':{}}
for g in final_gene_lst:
tf_dict = get_tf(g, bind_dict, bind_info_dict, input_dict, glb_param_dict)
if len(tf_dict) > 0:
key = g
if glb_param_dict['TWO_WAY'] == 'YES':
two_dict = two_way(g, tf_dict, expr_dict, expr_info_dict, glb_param_dict)
result_d['two_way_edges'][key] = two_dict
if glb_param_dict['THREE_WAY'] == 'YES':
three_dict = three_way(g, tf_dict, expr_dict, expr_info_dict, glb_param_dict)
result_d['three_way_edges'][key] = three_dict
return result_d
def dumpclean(obj):
'''
show dictionary content, recursively
'''
if type(obj) == dict:
for k, v in obj.items():
if hasattr(v, '__iter__'):
print(k)
dumpclean(v)
else:
print('%s : %s' % (k, v))
elif type(obj) == list:
for v in obj:
if hasattr(v, '__iter__'):
dumpclean(v)
else:
print(v)
else:
print(obj)
# obsolete
def print_dict_list(dict_lst, agi2name_dict):
for d in dict_lst:
#dumpclean(d)
if d['type'] == 'two-way':
gene_id = d['target']
head = '%s %s\t' % (gene_id, get_gene_name(gene_id, agi2name_dict))
gene_id = d['TF']
head += '%s %s\t' % (gene_id, get_gene_name(gene_id, agi2name_dict))
d2 = d['significant']
if 'all' in d2:
s = '%4.2f\t%s\t%s\t%s' % (d2['all']['score'], 'all', '.', ' '.join(d2['all']['chip_id']))
print(head + s)
if 'user' in d2:
s = '%4.2f\t%s\t%s\t%s' % (d2['user']['score'], 'user', ' '.join(d2['user']['signal_set']), ' '.join(d2['user']['chip_id']))
print(head + s)
if 'pos' in d2:
s = '%4.2f\t%s\t%s\t%s' % (d2['pos']['score'], 'pos', ' '.join(d2['pos']['signal_set']), ' '.join(d2['pos']['chip_id']))
print(head + s)
if 'neg' in d2:
s = '%4.2f\t%s\t%s\t%s' % (d2['neg']['score'], 'neg', ' '.join(d2['neg']['signal_set']), ' '.join(d2['neg']['chip_id']))
print(head + s)
if 'mix' in d2:
n = len(d2['mix']['score'])
for i in range(n):
s = '%4.2f\t%s\t%s\t%s' % (d2['mix']['score'][i], 'mix', ' '.join(d2['mix']['signal_set'][i]), ' '.join(d2['mix']['chip_id']))
print(head + s)
def print_result(d, agi2name_dict):
for k in d:
print(k) # two-way or three-way
d2 = d[k]
for k2 in d2: # k2 is a gene
dlst = d2[k2]
print_dict_list(dlst, agi2name_dict)
########## main ##################################################
r.r['options'](warn=-1) # supress warning message from rpy2
warnings.filterwarnings("ignore")
param_file = sys.argv[1] # a single prameter file
glb_param_dict = make_global_param_dict(param_file)
agi2name_dict = make_gene_name_AGI_map_dict(GENE_ID_TO_GENE_NAME)
glb_param_dict['name_conversion_dict'] = agi2name_dict
#print('Read expression data')
expr_dict = read_matrix_data(glb_param_dict['EXPRESSION_MATRIX'])
#print('DEBUG at2g07754 at R0000SRR1802166XX %g' % (expr_dict['xy']['AT2G07754']['R0000SRR1802166XX']))
expr_info_dict = read_info_data(glb_param_dict['EXPRESSION_INFO'])
#print('Read binding data')
bind_dict = read_matrix_data(glb_param_dict['BINDING_MATRIX'])
bind_info_dict = read_info_data(glb_param_dict['BINDING_INFO'])
input_dict = read_matrix_data(glb_param_dict['INPUT_MATRIX']) # newly added, for comparing with bw files
#print('Establish edges')
edge_d = establish_edges(expr_dict, expr_info_dict, bind_dict, bind_info_dict, input_dict, glb_param_dict)
#print_result(edge_d, agi2name_dict)