def test_hicReader():
genome = fastaReader("../input/hg38/test.fa",name="hg38")
faReader = genome.read_data()
now = datetime.datetime.now()
hic = hicReader(fname="../input/4DNFI2TK7L2F.hic", genome=genome, resolution = 100000)
hic = hic.read_data()
print (hic.norms)
result = hic.get_contact(Interval("chr1",0,120000000)) # single float value or NaN
print (result)
result = hic.get_chr_contact("chr1") # returns sparse matrix of the whole chrm as pandas dataframe
print (datetime.datetime.now() - now)
def calc_insulation_around_CTCF(chr, resolution=5000, window_size=20):
logging.basicConfig(
level=logging.DEBUG) # set to INFO for less detailed output
### load data ###
# load genome
faReader = fastaReader("../input/hg38/hg38.fa", useOnlyChromosomes=[chr])
faReader = faReader.read_data()
# load chipSeq1
bwReader1 = bigWigReader("../input/ENCFF473IZV_H1_CTCF.bigWig",
genome=faReader,
inMemory=True)
bwReader1 = bwReader1.readData()
#load contacts
hic = hicReader("../input/4DNFI2TK7L2F.hic",
genome=faReader,
resolution=resolution)
hic = hic.read_data()
### run simple check that contact count correlate with ChipSeq signal ###
### generate some random samples ####
# get size of the chr1
total_length = faReader.get_chr_sizes()[chr]
all_CTCF = bwReader1.get_interval(Interval(chr, 0, total_length))
all_CTCF = np.nan_to_num(all_CTCF)
binsize = 1000
bins = np.arange(0, total_length - 1, binsize)
sums = [np.sum(all_CTCF[a:a + binsize]) for a in bins]
peaks = bins[sums > np.percentile(sums, 90)]
with open("../out/test.bed", "w") as fout:
for i in peaks:
fout.write(chr + "\t" + str(i) + "\t" + str(i + binsize) + "\n")
def calc_corr(chr, resolution=5000, window_size=20):
logging.basicConfig(
level=logging.DEBUG) # set to INFO for less detailed output
### load data ###
# load genome
faReader = fastaReader("../input/hg38/hg38.fa", useOnlyChromosomes=[chr])
faReader = faReader.read_data()
# load chipSeq1
bwReader1 = bigWigReader("../input/ENCFF473IZV_H1_CTCF.bigWig",
genome=faReader,
inMemory=True)
bwReader1 = bwReader1.readData()
#load contacts
hic = hicReader("../input/4DNFI2TK7L2F.hic",
genome=faReader,
resolution=resolution)
hic = hic.read_data()
### run simple check that contact count correlate with ChipSeq signal ###
### generate some random samples ####
# get size of the chr1
total_length = faReader.get_chr_sizes()[chr]
# distance between intercting regions in this particular test, in units of resolution
sample_size = 5000
# select random points on chr1
random_points_starts = np.random.random_integers(
0, total_length - window_size, sample_size)
random_points_starts = np.array(
(random_points_starts // resolution) * resolution, dtype=np.uint64)
random_points_ends = random_points_starts + window_size
# for each of selected points get contact between this point and (point + window_size*resolution)
contacts = []
chipSignals = []
seqSignals = []
now = datetime.datetime.now() # start timer
logging.info("Starting data generation")
for start, end in zip(random_points_starts, random_points_ends):
interval = Interval(chr, start, end)
assert window_size >= 5 * resolution
window = Interval(chr, start + resolution, end)
contact = hic.get_contact(interval)
if contact == None:
contact = 0
if np.isfinite(contact):
# chipSignal = np.concatenate((bwReader1.get_interval(Interval(chr,int(start-resolution),int(start+resolution))),
# bwReader1.get_interval(
# Interval(chr, int(end - resolution), int(end + resolution)))))
chipSignal = bwReader1.get_interval(window)
chipSignal = np.nan_to_num(chipSignal)
chipSignal = np.sum(chipSignal)
if np.isfinite(chipSignal):
chipSignals.append(chipSignal)
seqSignal = np.sum(faReader.get_interval(interval))
seqSignals.append(seqSignal)
contacts.append(contact)
logging.info("Time for data generation: " +
str(datetime.datetime.now() - now))
from scipy.stats import spearmanr, pearsonr
res = []
res.append(spearmanr(np.array(contacts), np.array(chipSignals))[0])
res.append(pearsonr(np.array(contacts), np.array(chipSignals))[0])
res.append(spearmanr(np.array(contacts), np.array(seqSignals))[0])
res.append(pearsonr(np.array(contacts), np.array(seqSignals))[0])
return ("\t".join(list(map(str, res))))
def simple_test():
logging.basicConfig(
level=logging.DEBUG) # set to INFO for less detailed output
### load data ###
# load genome
input_folder = "/home/minja/PycharmProjects/3Dpredictor/nn/input/"
faReader = fastaReader(input_folder + "hg38/hg38.fa",
useOnlyChromosomes=["chr1"])
faReader = faReader.read_data()
# load chipSeq
bwReader1 = bigWigReader(input_folder + "ENCFF473IZV_H1_CTCF.bigWig",
genome=faReader,
inMemory=True)
bwReader1 = bwReader1.readData()
# load chipSeq
bwReader2 = bigWigReader(input_folder + "ENCFF966IHQ.bigWig",
genome=faReader,
inMemory=False)
bwReader2 = bwReader2.readData()
#load contacts
resolution = 5000
hic = hicReader(input_folder + "4DNFI2TK7L2F.hic",
genome=faReader,
binsize=resolution,
indexedData=True)
hic = hic.read_data()
### run simple check that contact count correlate with ChipSeq signal ###
### generate some random samples ####
# get size of the chr1
total_length = faReader.get_chr_sizes()["chr1"]
window_size = 20 * resolution # distance between intercting regions in this particular test, in units of resolution
sample_size = 100000
# select random points on chr1
random_points_starts = np.random.random_integers(
0, total_length - window_size, sample_size)
random_points_starts = np.array(
(random_points_starts // resolution) * resolution, dtype=np.uint64)
random_points_ends = random_points_starts + window_size
# for each of selected points get contact between this point and (point + window_size*resolution)
contacts = []
chipSignals = []
seqSignals = []
now = datetime.datetime.now() # start timer
logging.info("Starting data generation")
for start, end in zip(random_points_starts, random_points_ends):
interval = Interval("chr1", start, end)
contact = hic.get_contact(interval)
if contact == None:
continue
else:
chipSignal = np.nansum(bwReader1.get_interval(interval))
if np.isfinite(chipSignal):
chipSignals.append(chipSignal)
seqSignal = np.sum(faReader.get_interval(interval))
seqSignals.append(seqSignal)
contacts.append(contact)
logging.info("Time for data generation1: " +
str(datetime.datetime.now() - now))
# now = datetime.datetime.now()
# chipSignals = []
# seqSignals = []
# contacts = []
# for start,end in zip(random_points_starts,random_points_ends):
# interval = Interval("chr1",start,end)
# contact = hic.get_contact(interval)
# if contact == None:
# continue
# else:
# chipSignal = np.nansum(bwReader2.get_interval(interval))
# if np.isfinite(chipSignal):
# chipSignals.append(chipSignal)
# seqSignal = np.sum(faReader.get_interval(interval))
# seqSignals.append(seqSignal)
# contacts.append(contact)
#
# logging.info("Time for data generation2: " + str(datetime.datetime.now() - now))
from scipy.stats import spearmanr
import matplotlib.pyplot as plt
print(contacts)
print(chipSignals)
print(spearmanr(np.array(contacts), np.array(chipSignals)))
print(np.all(np.isfinite(contacts)))
print(np.all(np.isfinite(chipSignals)))
plt.scatter(contacts, chipSignals)
plt.show()
rearrangement = False
# deletion = Interval("chr" + chr_num, start, end)
write_all_chrms_in_file = False #set True if you want write training file consisting several chromosomes
fill_empty_contacts = False #set True if you want use all contacts in region, without empty contacts
logging.getLogger(__name__).debug("Using input folder " + input_folder)
# Read contacts data
genome = fastaReader(args['path_to_genome'],
useOnlyChromosomes=[chromosome]) #str(chr_num)])
genome = genome.read_data()
print(genome.data)
now = datetime.datetime.now()
params.contacts_reader = hicReader(fname=input_folder + "/" + cell_type +
"/" + hic_name,
genome=genome,
binsize=params.binsize)
params.contacts_reader = params.contacts_reader.read_data(
fill_empty_contacts=fill_empty_contacts, noDump=False)
if params.use_only_contacts_with_CTCF == "cont_with_CTCF":
params.proportion = 1
params.contacts_reader.use_contacts_with_CTCF(CTCFfile=input_folder+"/" + cell_type+"/CTCF/"+CTCF_file_name,
maxdist=params.maxdist,
proportion=params.proportion,
keep_only_orient=params.keep_only_orient,
CTCForientfile=input_folder + "/" + cell_type + \
"/CTCF/"+CTCF_file_name+"-orient.bed")
params.use_only_contacts_with_CTCF += str(
params.contacts_reader.conts_with_ctcf)
#make deletion
write_all_chrms_in_file = False #set True if you want write training file consisting several chromosomes
fill_empty_contacts = True #set True if you want use all contacts in region, without empty contacts
logging.getLogger(__name__).debug("Using input folder " + input_folder)
# Read contacts data
genome = fastaReader(input_folder + "sequence/hg38/hg38.fa",
name="hg38",
useOnlyChromosomes=["chr3"])
genome = genome.read_data()
# print(genome)
# print(genome.data.keys())
now = datetime.datetime.now()
params.contacts_reader = hicReader(fname=input_folder +
"H1/4DNFI2TK7L2F.hic",
genome=genome,
binsize=1000)
# params.contacts_reader = hicReader(fname=input_folder + "H1/control.chr4.50KBhic", genome=genome, binsize=1000)
params.contacts_reader = params.contacts_reader.read_data()
if params.use_only_contacts_with_CTCF == "cont_with_CTCF":
params.proportion = 1
params.contacts_reader.use_contacts_with_CTCF(
CTCFfile=input_folder +
"H1/CTCF/CTCF_H1_conservative_peaks.bed.gz",
maxdist=params.maxdist,
proportion=params.proportion,
keep_only_orient=params.keep_only_orient,
CTCForientfile=input_folder +
"H1/CTCF/CTCF_H1_conservative_peaks_orient.bed")
params.use_only_contacts_with_CTCF += str(