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__init__.py
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__init__.py
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"""
RNAmotifs2
"""
import rnamotifs2
import os
import pybio
import data
import search
import compute
import draw
import path
import config
import perm
import sequence
import cluster
import results
from Queue import Queue
from threading import Thread
import random
import operator
import pickle
import shutil
random.seed(42)
path.init()
def get_motifs():
return pybio.genomes.make_motifs_nr(4)+pybio.genomes.make_motifs_nr(3)
def start(comps, region, cn, pth):
rnamotifs2.data.read_config(comps)
if rnamotifs2.data.data_type=="apa":
sf = "v17_apa"
if rnamotifs2.data.data_type=="splice":
sf = "v17"
comps_folder = os.path.join(rnamotifs2.path.comps_folder, comps)
region_folder = os.path.join(comps_folder, region)
comps_filename = os.path.join(comps_folder, "%s.tab" % comps)
pickle_folder = os.path.join(region_folder, "pickle")
if os.path.exists(region_folder):
shutil.rmtree(region_folder)
os.makedirs(os.path.join(region_folder))
if not os.path.exists(os.path.join(pickle_folder)):
os.makedirs(os.path.join(pickle_folder))
# make sequences
print "%s.%s: saving sequences to pickle" % (comps, rnamotifs2.data.genome)
rnamotifs2.sequence.save(comps, rnamotifs2.data.genome)
start_cluster(comps, rnamotifs2.data.genome, region, cn, pth, sf)
def start_cluster(comps, genome, region, cn, pth, sf):
comps_folder = os.path.join(rnamotifs2.path.comps_folder, comps)
region_folder = os.path.join(comps_folder, region)
comps_filename = os.path.join(comps_folder, "%s.tab" % comps)
pickle_folder = os.path.join(region_folder, "pickle")
# read data
rnamotifs2.data.read(comps)
motifs = rnamotifs2.results.get_motifs(comps, region)
num_worker_threads = rnamotifs2.data.cores
q = Queue()
def worker():
while True:
task = q.get()
os.system(task)
q.task_done()
tasks = []
for motif in motifs:
pickle_file = os.path.join(pickle_folder, "c%s.%s.pickle" % (cn, "_".join(sorted(motif.split("_")))))
if not os.path.exists(pickle_file):
command = "rnamotifs2.motif %s %s %s %s %s %s %s" % (comps, genome, region, "_".join(motif.split("_")), pth, cn, sf)
print "COMMAND=%s" % command
tasks.append(command)
for i in range(num_worker_threads):
t = Thread(target=worker)
t.daemon = True
t.start()
for task in tasks:
q.put(task)
q.join()
base_motif_fisher = assemble_results(comps, genome, region, cn)
if base_motif_fisher<=rnamotifs2.data.base_motif_thr:
continue_cluster(comps, genome, region, cn, pth, sf)
return base_motif_fisher
def continue_cluster(comps, genome, region, cn, pth, sf):
rnamotifs2.cluster.next_cluster(comps, genome, region, cn, pth=pth, sf=sf)
# try to start new cluster, if base_motif will have fisher < thr, it will stop processing
start_cluster(comps, genome, region, cn+1, pth, sf)
def assemble_results(comps, genome, region, cn):
comps_folder = os.path.join(rnamotifs2.path.comps_folder, comps)
region_folder = os.path.join(comps_folder, region)
comps_filename = os.path.join(comps_folder, "%s.tab" % comps)
pickle_folder = os.path.join(region_folder, "pickle")
motifs = rnamotifs2.get_motifs()
test_results = {}
rtest_results = {}
h = {}
index = 0
for motif in motifs:
pickle_filename = os.path.join(pickle_folder, "c%s.%s.pickle" % (cn, "_".join(sorted(motif.split("_")))))
if not os.path.exists(pickle_filename):
continue
index += 1
print "%s.%s: loading %s (%s)" % (comps, genome, "_".join(motif.split("_")), index)
_, test_results[motif], h[motif], _, _, _, _ = pickle.load(open(pickle_filename))
# FDR
"""
print "%s.%s: correct p-values" % (comps, genome)
for rt in ["r1", "r2", "r3"]:
for event_class in ["s", "e"]:
# fdr correct p-value
temp = []
for motif in motifs_all:
motif = "_".join(motif)
temp.append(test_results[motif]["%s.%s" % (rt, event_class)][0])
temp = rnamotifs2.fdr(temp)
for index, motif in enumerate(motifs_all):
motif = "_".join(motif)
test_results[motif]["%s.%s" % (rt, event_class)][0] = temp[index]
# fdr correct bootstrapped p-values
temp = []
for p in range(0, rnamotifs2.config.perms):
temp.append([])
for p in range(0, rnamotifs2.config.perms):
for motif in motifs_all:
motif = "_".join(motif)
temp[p].append(test_results[motif]["%s.%s" % (rt, event_class)][1][p])
for p in range(0, rnamotifs2.config.perms):
temp[p] = rnamotifs2.fdr(temp[p])
for index, motif in enumerate(motifs_all):
motif = "_".join(motif)
test_results[motif]["%s.%s" % (rt, event_class)][1][p] = temp[p][index]
"""
data = []
for motif in motifs:
# some motifs were not considered
if test_results.get(motif, None)==None:
continue
row = [motif, h[motif]]
fisher, p_emp, ig = test_results[motif]
row.append(fisher)
row.append(ig)
row.append(ig)
p_emp = [1 if x<=fisher else 0 for x in p_emp]
p_emp = sum(p_emp)
p_emp = (1+p_emp)/float(1+rnamotifs2.config.perms)
row.append(p_emp)
data.append(row)
data = sorted(data, key = operator.itemgetter(2))
f = open(os.path.join(region_folder, "results%s.tab" % cn), "wt")
header = ["motif", "h", "fisher", "ig", "raw_ig", "p_emp"]
f.write("\t".join(header)+"\n")
for row in data:
f.write("\t".join(str(x) for x in row)+"\n")
f.close()
# correct for fdr?
rnamotifs2.data.read_config(comps)
if rnamotifs2.data.use_FDR:
pybio.utils.FDR_tab(os.path.join(region_folder, "results%s.tab" % cn), "fisher")
# read the most significant motif fisher value back
data = []
f = open(os.path.join(region_folder, "results%s.tab" % cn))
header = f.readline()
r = f.readline().replace("\n", "").replace("\r", "").split("\t")
try: # the results file could be empty
base_motif_fisher = float(r[2]) # fisher column
except:
base_motif_fisher = 1 # there are no results
else:
try:
base_motif_fisher, _, _ = test_results[data[0][0]] # get fisher value of top (base) motif
except:
base_motif_fisher = 1 # there are no results
return base_motif_fisher
def fdr(pvalues, correction_type = "Benjamini-Hochberg"):
"""
consistent with R - print correct_pvalues_for_multiple_testing([0.0, 0.01, 0.029, 0.03, 0.031, 0.05, 0.069, 0.07, 0.071, 0.09, 0.1])
"""
from numpy import array, empty
pvalues = array(pvalues)
n = float(pvalues.shape[0])
new_pvalues = empty(n)
if correction_type == "Bonferroni":
new_pvalues = n * pvalues
elif correction_type == "Bonferroni-Holm":
values = [ (pvalue, i) for i, pvalue in enumerate(pvalues) ]
values.sort()
for rank, vals in enumerate(values):
pvalue, i = vals
new_pvalues[i] = (n-rank) * pvalue
elif correction_type == "Benjamini-Hochberg":
values = [ (pvalue, i) for i, pvalue in enumerate(pvalues) ]
values.sort()
values.reverse()
new_values = []
for i, vals in enumerate(values):
rank = n - i
pvalue, index = vals
new_values.append((n/rank) * pvalue)
for i in xrange(0, int(n)-1):
if new_values[i] < new_values[i+1]:
new_values[i+1] = new_values[i]
for i, vals in enumerate(values):
pvalue, index = vals
new_pvalues[index] = new_values[i]
return new_pvalues