def computeDifferentialTADs(pMatrixTarget, pMatrixControl, pDomainList,
pCoolOrH5, pPValue, pThreadId, pQueue):
accepted_inter_left = []
accepted_inter_right = []
accepted_intra = []
p_values_list = []
rows = []
for i, row in enumerate(pDomainList):
if pThreadId is None:
log.debug('first thread')
if i == len(pDomainList) - 1:
continue
elif pThreadId == True:
log.debug('middle thread')
if i == 0 or i == len(pDomainList) - 1:
continue
elif pThreadId == False:
log.debug('last thread')
if i == 0:
continue
if i - 1 >= 0:
chromosom = pDomainList[i - 1][0]
start = pDomainList[i - 1][1]
else:
chromosom = pDomainList[i][0]
start = pDomainList[i][1]
if i + 1 < len(pDomainList):
end = pDomainList[i + 1][2]
else:
end = pDomainList[i][2]
# midpos = row[1] + ((row[2] - row[1]) / 2)
if pCoolOrH5:
# # get intra-TAD data
hic_matrix_target = hm.hiCMatrix(
pMatrixFile=pMatrixTarget,
pChrnameList=[
str(row[0]) + ':' + str(row[1]) + '-' + str(row[2])
])
hic_matrix_control = hm.hiCMatrix(
pMatrixFile=pMatrixControl,
pChrnameList=[
str(row[0]) + ':' + str(row[1]) + '-' + str(row[2])
])
matrix_target = hic_matrix_target.matrix.toarray()
matrix_control = hic_matrix_control.matrix.toarray()
hic_matrix_target_inter_tad = hm.hiCMatrix(
pMatrixFile=pMatrixTarget,
pChrnameList=[
str(chromosom) + ':' + str(start) + '-' + str(end)
])
hic_matrix_control_inter_tad = hm.hiCMatrix(
pMatrixFile=pMatrixControl,
pChrnameList=[
str(chromosom) + ':' + str(start) + '-' + str(end)
])
matrix_target_inter_tad = hic_matrix_target_inter_tad.matrix
matrix_control_inter_tad = hic_matrix_control_inter_tad.matrix
else:
# in case of h5 pMatrixTarget is already a HiCMatrix object
hic_matrix_target = pMatrixTarget
hic_matrix_control = pMatrixControl
hic_matrix_target_inter_tad = pMatrixTarget
hic_matrix_control_inter_tad = pMatrixControl
indices_target = hic_matrix_target.getRegionBinRange(
str(row[0]), row[1], row[2])
indices_control = hic_matrix_control.getRegionBinRange(
str(row[0]), row[1], row[2])
matrix_target = hic_matrix_target.matrix[
indices_target[0]:indices_target[1],
indices_target[0]:indices_target[1]].toarray()
matrix_control = hic_matrix_control.matrix[
indices_control[0]:indices_control[1],
indices_control[0]:indices_control[1]].toarray()
matrix_target_inter_tad = pMatrixTarget.matrix
matrix_control_inter_tad = pMatrixControl.matrix
matrix_target = matrix_target.flatten()
matrix_control = matrix_control.flatten()
# tad_midpoint = hic_matrix_target_inter_tad.getRegionBinRange(str(row[0]), midpos, midpos)[0]
# if i - 1 >= 0:
# get index position left tad with tad
left_boundary_index_target = hic_matrix_target_inter_tad.getRegionBinRange(
str(chromosom), row[1], row[1])[0]
left_boundary_index_control = hic_matrix_control_inter_tad.getRegionBinRange(
str(chromosom), row[1], row[1])[0]
if pCoolOrH5:
outer_left_boundary_index_target = 0
outer_left_boundary_index_control = 0
outer_right_boundary_index_control = -1
outer_right_boundary_index_target = -1
else:
outer_left_boundary_index_target = hic_matrix_target_inter_tad.getRegionBinRange(
str(chromosom), start, end)[0]
outer_left_boundary_index_control = hic_matrix_control_inter_tad.getRegionBinRange(
str(chromosom), start, end)[0]
outer_right_boundary_index_control = hic_matrix_control_inter_tad.getRegionBinRange(
str(chromosom), start, end)[1]
outer_right_boundary_index_target = hic_matrix_target_inter_tad.getRegionBinRange(
str(chromosom), start, end)[1]
if i + 1 < len(pDomainList) and not pCoolOrH5:
# get index position left tad with tad
right_boundary_index_target = hic_matrix_target_inter_tad.getRegionBinRange(
str(chromosom), row[2], row[2])[0]
right_boundary_index_control = hic_matrix_control_inter_tad.getRegionBinRange(
str(chromosom), row[2], row[2])[0]
elif i + 1 < len(pDomainList) - 1:
right_boundary_index_target = hic_matrix_target_inter_tad.getRegionBinRange(
str(chromosom), row[2], row[2])[0]
right_boundary_index_control = hic_matrix_control_inter_tad.getRegionBinRange(
str(chromosom), row[2], row[2])[0]
if i - 1 >= 0 and i + 1 < len(pDomainList):
intertad_left_target = matrix_target_inter_tad[
outer_left_boundary_index_target:left_boundary_index_target,
left_boundary_index_target:
right_boundary_index_target].toarray()
intertad_right_target = matrix_target_inter_tad[
left_boundary_index_target:right_boundary_index_target,
right_boundary_index_target:
outer_right_boundary_index_target].toarray()
intertad_left_control = matrix_control_inter_tad[
outer_left_boundary_index_control:left_boundary_index_control,
left_boundary_index_control:
right_boundary_index_control].toarray()
intertad_right_control = matrix_control_inter_tad[
left_boundary_index_control:right_boundary_index_control,
right_boundary_index_control:
outer_right_boundary_index_control].toarray()
elif i - 1 < 0 and i + 1 < len(pDomainList):
intertad_right_target = matrix_target_inter_tad[
left_boundary_index_target:right_boundary_index_target,
right_boundary_index_target:
outer_right_boundary_index_target].toarray()
intertad_right_control = matrix_control_inter_tad[
left_boundary_index_control:right_boundary_index_control,
right_boundary_index_control:
outer_right_boundary_index_control].toarray()
elif i - 1 > 0 and i + 1 >= len(pDomainList):
intertad_left_target = matrix_target_inter_tad[
outer_left_boundary_index_target:left_boundary_index_target,
left_boundary_index_target:
right_boundary_index_target].toarray()
intertad_left_control = matrix_control_inter_tad[
outer_left_boundary_index_control:left_boundary_index_control,
left_boundary_index_control:
right_boundary_index_control].toarray()
significance_level_left = None
significance_level_right = None
statistic_left = None
statistic_right = None
if i - 1 >= 0 and i + 1 < len(pDomainList):
intertad_left_target = intertad_left_target.flatten()
intertad_left_control = intertad_left_control.flatten()
intertad_right_target = intertad_right_target.flatten()
intertad_right_control = intertad_right_control.flatten()
statistic_left, significance_level_left = ranksums(
intertad_left_target, intertad_left_control)
statistic_right, significance_level_right = ranksums(
intertad_right_target, intertad_right_control)
elif i - 1 < 0 and i + 1 < len(pDomainList):
intertad_right_target = intertad_right_target.flatten()
intertad_right_control = intertad_right_control.flatten()
statistic_right, significance_level_right = ranksums(
intertad_right_target, intertad_right_control)
elif i - 1 > 0 and i + 1 >= len(pDomainList):
intertad_left_target = intertad_left_target.flatten()
intertad_left_control = intertad_left_control.flatten()
log.debug('intertad_left_target {}'.format(intertad_left_target))
log.debug('intertad_left_control {}'.format(intertad_left_control))
statistic_left, significance_level_left = ranksums(
intertad_left_target, intertad_left_control)
# log.debug('matrix_target {}'.format(matrix_target))
# log.debug('matrix_control {}'.format(matrix_control))
statistic, significance_level = ranksums(matrix_target, matrix_control)
log.debug('statistic {}, significance_level {}'.format(
statistic, significance_level))
log.debug('right statistic {}, significance_level {}'.format(
statistic_right, significance_level_right))
log.debug('left statistic {}, significance_level {}'.format(
statistic_left, significance_level_left))
p_values = []
if significance_level_left is None or np.isnan(
significance_level_left):
accepted_inter_left.append(0)
p_values.append(np.nan)
elif significance_level_left <= pPValue:
accepted_inter_left.append(1)
p_values.append(significance_level_left)
else:
accepted_inter_left.append(0)
p_values.append(significance_level_left)
if significance_level_right is None or np.isnan(
significance_level_right):
accepted_inter_right.append(0)
p_values.append(np.nan)
elif significance_level_right <= pPValue:
accepted_inter_right.append(1)
p_values.append(significance_level_right)
else:
accepted_inter_right.append(0)
p_values.append(significance_level_right)
if significance_level is None or np.isnan(significance_level):
accepted_intra.append(0)
p_values.append(np.nan)
elif significance_level <= pPValue:
accepted_intra.append(1)
p_values.append(significance_level)
else:
accepted_intra.append(0)
p_values.append(significance_level)
p_values_list.append(p_values)
rows.append(row)
# hic_matrix_target_inter_tad.save('manipulated_target.cool')
# hic_matrix_control_inter_tad.save('manipulated_control.cool')
pQueue.put([
p_values_list, accepted_inter_left, accepted_inter_right,
accepted_intra, rows
])
def open_and_store_matrix(pMatrixName, pMatricesList, pIndex, pXDimension,
pChromosomes, pNorm, pExtraTrack, pHistonMarkType,
pBinarization, pQueue):
compartments_matrix = None
for i, matrix in enumerate(pMatricesList):
ma = hm.hiCMatrix(pMatrixName + '::' + matrix)
# WARNING
# DO NOT APPLY BIN MASKING, WILL LEAD TO DIFFERENT SIZES OF THE CHROMOSOMES
# THIS IS CAUSING A FAIL OF THE COMPUTATION
# ma.maskBins(ma.nan_bins)
k = 1
if pChromosomes:
ma.keepOnlyTheseChr(pChromosomes)
vecs_list = []
chrom_list = []
start_list = []
end_list = []
# PCA is computed per chromosome
length_chromosome = 0
chromosome_count = len(ma.getChrNames())
for chrname in ma.getChrNames():
chr_range = ma.getChrBinRange(chrname)
length_chromosome += chr_range[1] - chr_range[0]
if pExtraTrack and (pExtraTrack.endswith('.bw')
or pExtraTrack.endswith('.bigwig')):
bwTrack = pyBigWig.open(pExtraTrack, 'r')
for chrname in ma.getChrNames():
chr_range = ma.getChrBinRange(chrname)
submatrix = ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
if pNorm:
obs_exp_matrix_ = obs_exp_matrix_norm(submatrix)
else:
obs_exp_matrix_ = obs_exp_matrix_lieberman(
submatrix, length_chromosome, chromosome_count)
obs_exp_matrix_ = convertNansToZeros(
csr_matrix(obs_exp_matrix_)).todense()
obs_exp_matrix_ = convertInfsToZeros(
csr_matrix(obs_exp_matrix_)).todense()
pearson_correlation_matrix = np.corrcoef(obs_exp_matrix_)
pearson_correlation_matrix = convertNansToZeros(
csr_matrix(pearson_correlation_matrix)).todense()
pearson_correlation_matrix = convertInfsToZeros(
csr_matrix(pearson_correlation_matrix)).todense()
corrmatrix = np.cov(pearson_correlation_matrix)
corrmatrix = convertNansToZeros(csr_matrix(corrmatrix)).todense()
corrmatrix = convertInfsToZeros(csr_matrix(corrmatrix)).todense()
evals, eigs = linalg.eig(corrmatrix)
chrom, start, end, _ = zip(
*ma.cut_intervals[chr_range[0]:chr_range[1]])
chrom_list += chrom
start_list += start
end_list += end
if pExtraTrack and (pExtraTrack.endswith('.bw')
or pExtraTrack.endswith('.bigwig')):
assert (len(end) == len(start))
correlateEigenvectorWithHistonMarkTrack(
eigs[:, :k].transpose(), bwTrack, chrname, start, end,
pExtraTrack, pHistonMarkType)
vecs_list += eigs[:, :k].tolist()
if compartments_matrix is None:
compartments_matrix = np.zeros(
[pXDimension, len(np.array(vecs_list).flatten())],
dtype=np.float)
eigenvector = np.real(np.array(vecs_list).flatten())
mask = np.isnan(eigenvector)
if len(mask) > 0:
eigenvector[mask] = 0
mask = np.isinf(eigenvector)
if len(mask) > 0:
eigenvector[mask] = 0
if pBinarization:
mask = eigenvector <= 0
eigenvector[mask] = -1
mask = eigenvector > 0
eigenvector[mask] = 1
compartments_matrix[pIndex + i, :] = eigenvector
pQueue.put(compartments_matrix)
return
def main(args=None):
args = parse_arguments().parse_args(args)
hic_ma = hm.hiCMatrix(pMatrixFile=args.matrix)
indices_values = []
with open(args.regions, 'r') as file:
for line in file.readlines():
_line = line.strip().split('\t')
if len(line) == 0:
continue
if len(_line) == 2:
chrom, start = _line[0], _line[1]
viewpoint = (chrom, start, start)
elif len(_line) >= 3:
chrom, start, end = _line[0], _line[1], _line[2]
viewpoint = (chrom, start, end)
if args.range:
start_range_genomic, end_range_genomic, _ = calculateViewpointRange(
hic_ma, viewpoint, args.range)
# min_length, max_length = hic_ma.getBinPos(hic_ma.getChrBinRange(pViewpoint[0])[1] - 1)[1:]
# if start_range_genomic < min_length:
# log.warning('Ignoring {} {} {} because the reference point minus the range {} is smaller than the chromosome border.'.format(viewpoint[0], viewpoint[1], viewpoint[2], args.range))
# continue
# if end_bin > :
# log.warning('Ignoring {} {} {} because the reference point plus the range {} is greater than the chromosome border.'.format(viewpoint[0], viewpoint[1], viewpoint[2], args.range))
# continue
start_bin, end_bin = getBinIndices(
hic_ma, (chrom, start_range_genomic, end_range_genomic))
else:
start_bin, end_bin = calculateViewpointRangeBins(
hic_ma, viewpoint, args.rangeInBins)
# if start_bin < 0:
# log.warning('Ignoring {} {} {} because the reference point minus the range {} is smaller than the chromosome border.'.format(viewpoint[0], viewpoint[1], viewpoint[2], args.range))
# continue
# if end_bin > :
# log.warning('Ignoring {} {} {} because the reference point plus the range {} is greater than the chromosome border.'.format(viewpoint[0], viewpoint[1], viewpoint[2], args.range))
# continue
indices_values.append([start_bin, end_bin])
if args.range:
dimensions_new_matrix = (args.range[0] // hic_ma.getBinSize()) + (
args.range[1] // hic_ma.getBinSize())
elif args.rangeInBins:
dimensions_new_matrix = args.rangeInBins[0] + args.rangeInBins[1]
# summed_matrix = csr_matrix((dimensions_new_matrix, dimensions_new_matrix), dtype=np.float32)
summed_matrix = lil_matrix((dimensions_new_matrix, dimensions_new_matrix),
dtype=np.float32)
max_length = hic_ma.matrix.shape[1]
for start, end in indices_values:
_start = 0
_end = summed_matrix.shape[1]
if start < 0:
_start = np.absolute(start)
start = 0
if end >= max_length:
_end = end
end = max_length
summed_matrix[_start:_end, _start:_end] += hic_ma.matrix[start:end,
start:end]
summed_matrix /= len(indices_values)
summed_matrix = summed_matrix.tocsr()
save_npz(args.outFileName, summed_matrix)
def main(args=None):
args = parse_arguments().parse_args(args)
if args.verbose:
log.setLevel(logging.INFO)
# args.chromosomes
if check_cooler(args.matrix) and args.chromosomes is not None and len(
args.chromosomes) == 1:
ma = hm.hiCMatrix(args.matrix, pChrnameList=toString(args.chromosomes))
else:
ma = hm.hiCMatrix(args.matrix)
if args.chromosomes:
ma.reorderChromosomes(toString(args.chromosomes))
# mask all zero value bins
if 'correctionMethod' in args:
if args.correctionMethod == 'ICE':
row_sum = np.asarray(ma.matrix.sum(axis=1)).flatten()
log.info("Removing {} zero value bins".format(sum(row_sum == 0)))
ma.maskBins(np.flatnonzero(row_sum == 0))
matrix_shape = ma.matrix.shape
if 'plotName' in args:
row_sum = np.asarray(ma.matrix.sum(axis=1)).flatten()
log.info("Removing {} zero value bins".format(sum(row_sum == 0)))
ma.maskBins(np.flatnonzero(row_sum == 0))
matrix_shape = ma.matrix.shape
ma.matrix = convertNansToZeros(ma.matrix)
ma.matrix = convertInfsToZeros(ma.matrix)
ma.matrix = ma.matrix.astype(np.float64, copy=True)
log.debug('ma.matrix.indices {}'.format(ma.matrix.indices.dtype))
log.debug('ma.matrix.data {}'.format(ma.matrix.data.dtype))
log.debug('ma.matrix.indptr {}'.format(ma.matrix.indptr.dtype))
# log.debug('ma.matrix.indices {}'.format(np.max(ma.matrix.indices)))
# log.debug('ma.matrix.data {}'.format(np.max(ma.matrix.data)))
# log.debug('ma.matrix.indptr {}'.format(np.max(ma.matrix.indptr)))
# ma.matrix.indptr = ma.matrix.indptr.astype(np.int32, copy=False)
# ma.matrix.indices = ma.matrix.indices.astype(np.int32, copy=False)
if 'plotName' in args:
plot_total_contact_dist(ma, args)
log.info("Saving diagnostic plot {}\n".format(args.plotName))
return
log.info("matrix contains {} data points. Sparsity {:.3f}.".format(
len(ma.matrix.data),
float(len(ma.matrix.data)) / (ma.matrix.shape[0]**2)))
if args.skipDiagonal:
ma.diagflat(value=0)
total_filtered_out = set()
if args.correctionMethod == 'ICE':
if not args.filterThreshold:
log.error('min and max filtering thresholds should be set')
sys.exit(1)
outlier_regions = filter_by_zscore(ma,
args.filterThreshold[0],
args.filterThreshold[1],
perchr=args.perchr)
# compute and print some statistics
pct_outlier = 100 * float(len(outlier_regions)) / ma.matrix.shape[0]
ma.printchrtoremove(outlier_regions,
label="Bins that are MAD outliers ({:.2f}%) "
"out of {}".format(pct_outlier,
ma.matrix.shape[0]),
restore_masked_bins=False)
assert matrix_shape == ma.matrix.shape
# mask filtered regions
ma.maskBins(outlier_regions)
total_filtered_out = set(outlier_regions)
if args.sequencedCountCutoff and 0 < args.sequencedCountCutoff < 1:
chrom, _, _, coverage = zip(*ma.cut_intervals)
assert type(coverage[0]) == np.float64
failed_bins = np.flatnonzero(
np.array(coverage) < args.sequencedCountCutoff)
ma.printchrtoremove(failed_bins,
label="Bins with low coverage",
restore_masked_bins=False)
ma.maskBins(failed_bins)
total_filtered_out = set(failed_bins)
"""
ma.matrix, to_remove = fill_gaps(ma, failed_bins)
log.warning("From {} failed bins, {} could "
"not be filled\n".format(len(failed_bins),
len(to_remove)))
ma.maskBins(to_remove)
"""
if args.transCutoff and 0 < args.transCutoff < 100:
cutoff = float(args.transCutoff) / 100
# a usual cutoff is 0.05
ma.truncTrans(high=cutoff)
pre_row_sum = np.asarray(ma.matrix.sum(axis=1)).flatten()
correction_factors = []
corrected_matrix = lil_matrix(ma.matrix.shape)
if args.perchr:
# normalize each chromosome independently
for chrname in list(ma.interval_trees):
chr_range = ma.getChrBinRange(chrname)
chr_submatrix = ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
if args.correctionMethod == 'ICE':
_matrix, _corr_factors = iterative_correction(
chr_submatrix, args)
corrected_matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = _matrix
correction_factors.append(_corr_factors)
else:
# Set the kr matrix along with its correction factors vector
assert (args.correctionMethod == 'KR')
log.debug("Loading a float sparse matrix for KR balancing")
kr = kr_balancing(
chr_submatrix.shape[0], chr_submatrix.shape[1],
chr_submatrix.count_nonzero(),
chr_submatrix.indptr.astype(np.int64, copy=False),
chr_submatrix.indices.astype(np.int64, copy=False),
chr_submatrix.data.astype(np.float64, copy=False))
kr.computeKR()
if args.outFileName.endswith('.h5'):
corrected_matrix[
chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = kr.get_normalised_matrix(
True)
# correction_factors.append(np.true_divide(1,
# kr.get_normalisation_vector(False).todense()))
correction_factors.append(
kr.get_normalisation_vector(False).todense())
correction_factors = np.concatenate(correction_factors)
else:
if args.correctionMethod == 'ICE':
corrected_matrix, correction_factors = iterative_correction(
ma.matrix, args)
ma.setMatrixValues(corrected_matrix)
else:
assert (args.correctionMethod == 'KR')
log.debug("Loading a float sparse matrix for KR balancing")
kr = kr_balancing(ma.matrix.shape[0], ma.matrix.shape[1],
ma.matrix.count_nonzero(),
ma.matrix.indptr.astype(np.int64, copy=False),
ma.matrix.indices.astype(np.int64, copy=False),
ma.matrix.data.astype(np.float64, copy=False))
log.debug('passed pointers')
kr.computeKR()
log.debug('computation done')
# set it to False since the vector is already normalised
# with the previous True
# correction_factors = np.true_divide(1, kr.get_normalisation_vector(False).todense())
correction_factors = kr.get_normalisation_vector(False).todense()
if args.outFileName.endswith('.h5'):
corrected_matrix = kr.get_normalised_matrix(True)
if args.outFileName.endswith('.h5'):
ma.setMatrixValues(corrected_matrix)
# if
ma.setCorrectionFactors(correction_factors)
log.debug("Correction factors {}".format(correction_factors[:10]))
if args.inflationCutoff and args.inflationCutoff > 0 and args.correctionMethod == 'ICE':
after_row_sum = np.asarray(corrected_matrix.sum(axis=1)).flatten()
# identify rows that were expanded more than args.inflationCutoff times
to_remove = np.flatnonzero(
after_row_sum / pre_row_sum >= args.inflationCutoff)
ma.printchrtoremove(to_remove,
label="inflated >={} "
"regions".format(args.inflationCutoff),
restore_masked_bins=False)
total_filtered_out = total_filtered_out.union(to_remove)
ma.maskBins(to_remove)
ma.printchrtoremove(sorted(list(total_filtered_out)),
label="Total regions to be removed",
restore_masked_bins=False)
ma.save(args.outFileName, pApplyCorrection=False)
def test_restoreMaskedBins():
hic = hm.hiCMatrix()
cut_intervals = [('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('b', 30, 40, 1), ('b', 40, 50, 1)]
hic.nan_bins = []
matrix = np.array([[1, 8, 5, 3, 0], [0, 4, 15, 5, 1], [0, 0, 0, 0, 2],
[0, 0, 0, 0, 1], [0, 0, 0, 0, 0]])
hic.matrix = csr_matrix(matrix)
hic.setMatrix(hic.matrix, cut_intervals)
nt.assert_equal(hic.getMatrix(), matrix)
nt.assert_equal(hic.orig_bin_ids, [])
# function should directly return if there are no masked_bins
hic.restoreMaskedBins()
nt.assert_equal(hic.getMatrix(), matrix)
nt.assert_equal(hic.orig_bin_ids, [])
# test general use
# first get some masked bins
masking_ids = [0, 1]
hic.maskBins(masking_ids)
new_matrix = np.array([[0, 0, 2], [0, 0, 1], [0, 0, 0]])
nt.assert_equal(hic.getMatrix(), new_matrix)
nt.assert_equal(sorted(hic.orig_bin_ids), sorted([0, 1, 2, 3, 4]))
# and now restore masked bins
hic.restoreMaskedBins()
result_matrix = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, 0, 0, 2],
[np.nan, np.nan, 0, 0, 1],
[np.nan, np.nan, 0, 0, 0]])
nt.assert_equal(hic.getMatrix(), result_matrix)
nt.assert_equal(hic.orig_bin_ids, [])
row, col = np.triu_indices(5)
cut_intervals = [('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('a', 30, 40, 1), ('b', 40, 50, 1)]
hic = hm.hiCMatrix()
hic.nan_bins = []
matrix = np.array([[0, 10, 5, 3, 0], [0, 0, 15, 5, 1], [0, 0, 0, 7, 3],
[0, 0, 0, 0, 1], [0, 0, 0, 0, 0]],
dtype=np.int32)
# make the matrix symmetric:
hic.matrix = csr_matrix(matrix + matrix.T)
hic.setMatrix(csr_matrix(matrix + matrix.T), cut_intervals)
# Add masked bins masked bins
hic.maskBins([3])
matrix = hic.matrix.todense()
test_matrix = np.array(
[[0, 10, 5, 0], [10, 0, 15, 1], [5, 15, 0, 3], [0, 1, 3, 0]],
dtype=np.int32)
nt.assert_equal(matrix, test_matrix)
cut_int = hic.cut_intervals
test_cut_int = [('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('b', 40, 50, 1)]
nt.assert_equal(cut_int, test_cut_int)
hic.restoreMaskedBins()
dense = hic.matrix.todense()
test_dense = np.array([[0., 10., 5., 0., 0.], [10., 0., 15., 0., 1.],
[5., 15., 0., 0., 3.], [0., 0., 0., 0., 0.],
[0., 1., 3., 0., 0.]])
nt.assert_equal(dense, test_dense)
cut_int = hic.cut_intervals
test_cut_int = [('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('a', 30, 40, 1), ('b', 40, 50, 1)]
nt.assert_equal(cut_int, test_cut_int)
def set_properties_defaults(self):
super(HiCMatrixTrack, self).set_properties_defaults()
region = None
if self.properties['region'] is not None:
if self.properties['region'][2] == 1e15:
region = [str(self.properties['region'][0])]
elif len(self.properties['region']) == 3:
start = int(
self.properties['region'][1]) - self.properties['depth']
if start < 0:
start = 0
end = int(
self.properties['region'][2]) + self.properties['depth']
region = [
str(self.properties['region'][0]) + ':' + str(start) +
'-' + str(end)
]
# try to open with end region + depth to avoid triangle effect in the plot
# if it fails open it with given end region.
try:
self.hic_ma = HiCMatrix.hiCMatrix(self.properties['file'],
pChrnameList=region)
except Exception:
region = [
str(self.properties['region'][0]) + ':' + str(start) + '-' +
str(self.properties['region'][2])
]
self.hic_ma = HiCMatrix.hiCMatrix(self.properties['file'],
pChrnameList=region)
if len(self.hic_ma.matrix.data) == 0:
raise Exception("Matrix {} is empty".format(
self.properties['file']))
if self.properties['show_masked_bins']:
pass
else:
self.hic_ma.maskBins(self.hic_ma.nan_bins)
# check that the matrix can be log transformed
if self.properties['transform'] != 'no':
if self.properties['transform'] == 'log1p':
if self.hic_ma.matrix.data.min() + 1 <= 0:
raise Exception(
"\n*ERROR*\nMatrix contains values below - 1.\n"
"log1p transformation can not be applied to \n"
"values in matrix: {}".format(self.properties['file']))
elif self.properties['transform'] in ['-log', 'log']:
if self.hic_ma.matrix.data.min() < 0:
# For values not filled or equal to zero there will be a
# mask, they will be replaced by the minimum value after 0.
raise Exception(
"\n*ERROR*\nMatrix contains negative values.\n"
"log transformation can not be applied to \n"
"values in matrix: {}".format(self.properties['file']))
new_intervals = hicmatrix.utilities.enlarge_bins(
self.hic_ma.cut_intervals)
self.hic_ma.interval_trees, self.hic_ma.chrBinBoundaries = \
self.hic_ma.intervalListToIntervalTree(new_intervals)
self.hic_ma.cut_intervals = new_intervals
binsize = self.hic_ma.getBinSize()
max_depth_in_bins = int(self.properties['depth'] / binsize)
# work only with the lower matrix
# and remove all pixels that are beyond
# 2 * max_depth_in_bis which are not required
# (this is done by subtracting a second sparse matrix
# that contains only the lower matrix that wants to be removed.
limit = 2 * max_depth_in_bins
self.hic_ma.matrix = scipy.sparse.triu(self.hic_ma.matrix, k=0, format='csr') - \
scipy.sparse.triu(self.hic_ma.matrix, k=limit, format='csr')
self.hic_ma.matrix.eliminate_zeros()
# fill the main diagonal, otherwise it looks
# not so good. The main diagonal is filled
# with an array containing the max value found
# in the matrix
if sum(self.hic_ma.matrix.diagonal()) == 0:
self.log.info(
"Filling main diagonal with max value because it empty and looks bad...\n"
)
max_value = self.hic_ma.matrix.data.max()
main_diagonal = scipy.sparse.dia_matrix(
([max_value] * self.hic_ma.matrix.shape[0], [0]),
shape=self.hic_ma.matrix.shape)
self.hic_ma.matrix = self.hic_ma.matrix + main_diagonal
self.norm = None
self.process_color('colormap',
colormap_possible=True,
colormap_only=True,
default_value_is_colormap=True)
self.cmap = cm.get_cmap(self.properties['colormap'])
self.cmap.set_bad('black')
def main(args=None):
args = parse_arguments().parse_args(args)
log.debug(args)
# parse from hicpro, homer, h5 and hic to cool
if args.inputFormat != 'hic' and args.outputFormat != 'mcool':
if len(args.matrices) != len(args.outFileName):
log.error(
'Number of input matrices does not match number output matrices!'
)
exit(1)
if args.inputFormat == 'hic' and args.outputFormat == 'cool':
log.info('Converting with hic2cool.')
for i, matrix in enumerate(args.matrices):
if args.resolutions is None:
hic2cool_convert(matrix, args.outFileName[i], 0)
else:
for resolution in args.resolutions:
out_name = args.outFileName[i].split('.')
out_name[-2] = out_name[-2] + '_' + str(resolution)
out_name = '.'.join(out_name)
hic2cool_convert(matrix, out_name, resolution)
return
elif args.inputFormat in ['hicpro', 'homer', 'h5', 'cool']:
format_was_h5 = False
if args.inputFormat == 'h5':
format_was_h5 = True
applyCorrection = True
if args.store_applied_correction:
applyCorrection = False
if args.inputFormat == 'hicpro':
if len(args.matrices) != len(args.bedFileHicpro):
log.error(
'Number of matrices and associated bed files need to be the same.'
)
log.error('Matrices: {}; Bed files: {}'.format(
len(args.matrices), len(args.bedFileHicpro)))
sys.exit(1)
for i, matrix in enumerate(args.matrices):
if args.inputFormat == 'hicpro':
matrixFileHandlerInput = MatrixFileHandler(
pFileType=args.inputFormat,
pMatrixFile=matrix,
pBedFileHicPro=args.bedFileHicpro[i])
else:
correction_operator = None
if args.correction_division:
correction_operator = '/'
chromosomes_to_load = None
if args.chromosome:
chromosomes_to_load = [args.chromosome]
applyCorrectionCoolerLoad = True
if args.load_raw_values:
applyCorrectionCoolerLoad = False
matrixFileHandlerInput = MatrixFileHandler(
pFileType=args.inputFormat,
pMatrixFile=matrix,
pCorrectionFactorTable=args.correction_name,
pCorrectionOperator=correction_operator,
pChrnameList=chromosomes_to_load,
pEnforceInteger=args.enforce_integer,
pApplyCorrectionCoolerLoad=applyCorrectionCoolerLoad)
_matrix, cut_intervals, nan_bins, \
distance_counts, correction_factors = matrixFileHandlerInput.load()
log.debug('Setting done')
if args.outputFormat in ['cool', 'h5', 'homer', 'ginteractions']:
if args.outputFormat in ['homer', 'ginteractions']:
# make it a upper triangular matrix in case it is not already
_matrix = triu(_matrix)
# make it a full symmetrical matrix
_matrix = _matrix.maximum(_matrix.T)
matrixFileHandlerOutput = MatrixFileHandler(
pFileType=args.outputFormat,
pEnforceInteger=args.enforce_integer,
pFileWasH5=format_was_h5)
matrixFileHandlerOutput.set_matrix_variables(
_matrix, cut_intervals, nan_bins, correction_factors,
distance_counts)
matrixFileHandlerOutput.save(args.outFileName[i],
pSymmetric=True,
pApplyCorrection=applyCorrection)
elif args.outputFormat in ['mcool']:
log.debug('outformat is mcool')
if args.resolutions and len(args.matrices) > 1:
log.error(
'Please define one matrix and many resolutions which should be created or multiple matrices.'
)
if args.resolutions:
log.info(
'Correction factors are removed. They are not valid for any new created resolution.'
)
hic_matrix = HiCMatrix.hiCMatrix()
hic_matrix.setMatrix(_matrix, cut_intervals)
bin_size = hic_matrix.getBinSize()
for j, resolution in enumerate(args.resolutions):
hic_matrix_res = deepcopy(hic_matrix)
_mergeFactor = int(resolution) // bin_size
log.debug('bin size {}'.format(bin_size))
log.debug('_mergeFactor {}'.format(_mergeFactor))
if int(resolution) != bin_size:
merged_matrix = hicMergeMatrixBins.merge_bins(
hic_matrix_res, _mergeFactor)
else:
merged_matrix = hic_matrix_res
append = False
if j > 0:
append = True
matrixFileHandlerOutput = MatrixFileHandler(
pFileType='cool',
pEnforceInteger=args.enforce_integer,
pAppend=append,
pFileWasH5=format_was_h5)
matrixFileHandlerOutput.set_matrix_variables(
merged_matrix.matrix, merged_matrix.cut_intervals,
merged_matrix.nan_bins,
merged_matrix.correction_factors,
merged_matrix.distance_counts)
matrixFileHandlerOutput.save(
args.outFileName[0] + '::/resolutions/' +
str(resolution),
pSymmetric=True,
pApplyCorrection=applyCorrection)
else:
append = False
if i > 0:
append = True
hic_matrix = HiCMatrix.hiCMatrix()
hic_matrix.setMatrix(_matrix, cut_intervals)
bin_size = hic_matrix.getBinSize()
matrixFileHandlerOutput = MatrixFileHandler(
pFileType='cool',
pAppend=append,
pFileWasH5=format_was_h5)
matrixFileHandlerOutput.set_matrix_variables(
_matrix, cut_intervals, nan_bins, correction_factors,
distance_counts)
matrixFileHandlerOutput.save(
args.outFileName[0] + '::/resolutions/' +
str(bin_size),
pSymmetric=True,
pApplyCorrection=applyCorrection)
def computeInterIntraTADs(pMatrix, pDomainList, pCoolOrH5, pThreadId, pQueue):
try:
inter_left_sum_list = []
inter_right_sum_list = []
inter_left_densit_list = []
inter_right_density_list = []
inter_left_number_of_contacts_list = []
inter_right_number_of_contacts_list = []
inter_left_number_of_contacts_nnz_list = []
inter_right_number_of_contacts_nzz_list = []
intra_sum_list = []
intra_number_of_contacts_list = []
intra_number_of_contacts_nnz_list = []
intra_density_list = []
inter_left_intra_ratio_list = []
inter_right_intra_ratio_list = []
inter_left_inter_right_intra_ratio_list = []
rows = []
chromosome_list = pDomainList
for i, row in enumerate(chromosome_list):
if pThreadId is None:
log.debug('first thread')
if i == len(chromosome_list) - 1:
continue
elif pThreadId == True:
log.debug('middle thread')
if i == 0 or i == len(chromosome_list) - 1:
log.debug('i: {}'.format(i))
log.debug('len(chromosome_list): {}'.format(
len(chromosome_list)))
continue
elif pThreadId == False:
log.debug('last thread')
if i == 0:
continue
if i - 1 >= 0:
chromosom = chromosome_list[i - 1][0]
start = chromosome_list[i - 1][1]
else:
chromosom = chromosome_list[i][0]
start = chromosome_list[i][1]
if i + 1 < len(chromosome_list):
end = chromosome_list[i + 1][2]
else:
end = chromosome_list[i][2]
# midpos = row[1] + ((row[2] - row[1]) / 2)
if pCoolOrH5:
# # get intra-TAD data
hic_matrix = hm.hiCMatrix(pMatrixFile=pMatrix,
pChrnameList=[
str(row[0]) + ':' + str(row[1]) +
'-' + str(row[2])
])
matrix = hic_matrix.matrix
hic_matrix_inter_tad = hm.hiCMatrix(
pMatrixFile=pMatrix,
pChrnameList=[
str(chromosom) + ':' + str(start) + '-' + str(end)
])
matrix_inter_tad = hic_matrix_inter_tad.matrix
else:
# in case of h5 pMatrixTarget is already a HiCMatrix object
hic_matrix = pMatrix
hic_matrix_inter_tad = pMatrix
indices = hic_matrix.getRegionBinRange(str(row[0]), row[1],
row[2])
matrix = hic_matrix.matrix[indices[0]:indices[1],
indices[0]:indices[1]]
matrix_inter_tad = pMatrix.matrix
# matrix = matrix.flatten()
# get index position left tad with tad
left_boundary_index = hic_matrix_inter_tad.getRegionBinRange(
str(chromosom), row[1], row[1])[0]
if pCoolOrH5:
outer_left_boundary_index = 0
outer_right_boundary_index = -1
else:
outer_left_boundary_index = hic_matrix_inter_tad.getRegionBinRange(
str(chromosom), start, end)[0]
outer_right_boundary_index = hic_matrix_inter_tad.getRegionBinRange(
str(chromosom), start, end)[1]
if i + 1 < len(chromosome_list) and not pCoolOrH5:
# get index position right tad with tad
right_boundary_index = hic_matrix_inter_tad.getRegionBinRange(
str(chromosom), row[2], row[2])[0]
elif i + 1 < len(chromosome_list):
right_boundary_index = hic_matrix_inter_tad.getRegionBinRange(
str(chromosom), row[2], row[2])[0]
if i - 1 >= 0 and i + 1 < len(chromosome_list):
intertad_left = matrix_inter_tad[
outer_left_boundary_index:left_boundary_index,
left_boundary_index:right_boundary_index]
intertad_right = matrix_inter_tad[
left_boundary_index:right_boundary_index,
right_boundary_index:outer_right_boundary_index]
elif i - 1 < 0 and i + 1 < len(chromosome_list):
intertad_right = matrix_inter_tad[
left_boundary_index:right_boundary_index,
right_boundary_index:outer_right_boundary_index]
elif i - 1 > 0 and i + 1 >= len(chromosome_list):
intertad_left = matrix_inter_tad[
outer_left_boundary_index:left_boundary_index,
left_boundary_index:right_boundary_index]
inter_left_sum = 0
inter_right_sum = 0
inter_left_density = 0
inter_right_density = 0
inter_left_number_of_contacts = 0
inter_right_number_of_contacts = 0
inter_left_number_of_contacts_nnz = 0
inter_right_number_of_contacts_nzz = 0
intra_sum = matrix.sum()
intra_number_of_contacts = matrix.shape[0] * matrix.shape[1]
intra_number_of_contacts_nnz = matrix.nnz
intra_density = intra_number_of_contacts_nnz / intra_number_of_contacts
# both inter, left and right is available
if i - 1 >= 0 and i + 1 < len(chromosome_list):
# intertad_left = intertad_left.flatten()
# intertad_right = intertad_right.flatten()
inter_left_sum = intertad_left.sum()
inter_right_sum = intertad_right.sum()
inter_left_number_of_contacts = intertad_left.shape[
0] * intertad_left.shape[1]
inter_right_number_of_contacts = intertad_right.shape[
0] * intertad_right.shape[1]
inter_left_number_of_contacts_nnz = intertad_left.nnz
inter_right_number_of_contacts_nzz = intertad_right.nnz
inter_left_density = inter_left_number_of_contacts_nnz / inter_left_number_of_contacts
inter_right_density = inter_right_number_of_contacts_nzz / inter_right_number_of_contacts
# statistic_left, significance_level_left = ranksums(intertad_left, intertad_left_control)
# statistic_right, significance_level_right = ranksums(intertad_right, intertad_right_control)
elif i - 1 < 0 and i + 1 < len(chromosome_list):
# inter right is available
# intertad_right = intertad_right.flatten()
inter_right_sum = intertad_right.sum()
inter_right_number_of_contacts = intertad_right.shape[
0] * intertad_right.shape[1]
inter_right_number_of_contacts_nzz = intertad_right.nnz
inter_right_density = inter_right_number_of_contacts_nzz / inter_right_number_of_contacts
# statistic_right, significance_level_right = ranksums(intertad_right, intertad_right_control)
elif i - 1 > 0 and i + 1 >= len(chromosome_list):
# inter left is available
# intertad_left = intertad_left.flatten()
inter_left_sum = intertad_left.sum()
inter_left_number_of_contacts = intertad_left.shape[
0] * intertad_left.shape[1]
inter_left_number_of_contacts_nnz = intertad_left.nnz
inter_left_density = inter_left_number_of_contacts_nnz / inter_left_number_of_contacts
# statistic_left, significance_level_left = ranksums(intertad_left, intertad_left_control)
inter_left_intra_ratio = inter_left_sum / intra_sum
inter_right_intra_ratio = inter_right_sum / intra_sum
inter_left_inter_right_intra_ratio = (inter_left_sum +
inter_right_sum) / intra_sum
inter_left_sum_list.append(inter_left_sum)
inter_right_sum_list.append(inter_right_sum)
inter_left_densit_list.append(inter_left_density)
inter_right_density_list.append(inter_right_density)
inter_left_number_of_contacts_list.append(
inter_left_number_of_contacts)
inter_right_number_of_contacts_list.append(
inter_right_number_of_contacts)
inter_left_number_of_contacts_nnz_list.append(
inter_left_number_of_contacts_nnz)
inter_right_number_of_contacts_nzz_list.append(
inter_right_number_of_contacts_nzz)
intra_sum_list.append(intra_sum)
intra_number_of_contacts_list.append(intra_number_of_contacts)
intra_number_of_contacts_nnz_list.append(
intra_number_of_contacts_nnz)
intra_density_list.append(intra_density)
inter_left_intra_ratio_list.append(inter_left_intra_ratio)
inter_right_intra_ratio_list.append(inter_right_intra_ratio)
inter_left_inter_right_intra_ratio_list.append(
inter_left_inter_right_intra_ratio)
rows.append(row)
except Exception as exp:
pQueue.put('Fail: ' + str(exp) + traceback.format_exc())
return
pQueue.put([
inter_left_sum_list, inter_right_sum_list, inter_left_densit_list,
inter_right_density_list, inter_left_number_of_contacts_list,
inter_right_number_of_contacts_list,
inter_left_number_of_contacts_nnz_list,
inter_right_number_of_contacts_nzz_list, intra_sum_list,
intra_number_of_contacts_list, intra_number_of_contacts_nnz_list,
intra_density_list, inter_left_intra_ratio_list,
inter_right_intra_ratio_list, inter_left_inter_right_intra_ratio_list,
rows
])
def test_build_matrix_cooler_multiple():
outfile = NamedTemporaryFile(suffix='.cool', delete=False)
outfile.close()
qc_folder = mkdtemp(prefix="testQC_")
args = "-s {} {} --outFileName {} -bs 5000 10000 20000 -b /tmp/test.bam --QCfolder {} --threads 4".format(
sam_R1, sam_R2, outfile.name, qc_folder).split()
hicBuildMatrix.main(args)
test_5000 = hm.hiCMatrix(
ROOT +
"hicBuildMatrix/multi_small_test_matrix.cool::/resolutions/5000")
test_10000 = hm.hiCMatrix(
ROOT +
"hicBuildMatrix/multi_small_test_matrix.cool::/resolutions/10000")
test_20000 = hm.hiCMatrix(
ROOT +
"hicBuildMatrix/multi_small_test_matrix.cool::/resolutions/20000")
new_5000 = hm.hiCMatrix(outfile.name + '::/resolutions/5000')
new_10000 = hm.hiCMatrix(outfile.name + '::/resolutions/10000')
new_20000 = hm.hiCMatrix(outfile.name + '::/resolutions/20000')
nt.assert_equal(test_5000.matrix.data, new_5000.matrix.data)
nt.assert_equal(test_10000.matrix.data, new_10000.matrix.data)
nt.assert_equal(test_20000.matrix.data, new_20000.matrix.data)
# nt.assert_equal(test.cut_intervals, new.cut_intervals)
nt.assert_equal(len(new_5000.cut_intervals), len(test_5000.cut_intervals))
nt.assert_equal(len(new_10000.cut_intervals),
len(test_10000.cut_intervals))
nt.assert_equal(len(new_20000.cut_intervals),
len(test_20000.cut_intervals))
cut_interval_new_ = []
cut_interval_test_ = []
for x in new_5000.cut_intervals:
cut_interval_new_.append(x[:3])
for x in test_5000.cut_intervals:
cut_interval_test_.append(x[:3])
nt.assert_equal(cut_interval_new_, cut_interval_test_)
cut_interval_new_ = []
cut_interval_test_ = []
for x in new_10000.cut_intervals:
cut_interval_new_.append(x[:3])
for x in test_10000.cut_intervals:
cut_interval_test_.append(x[:3])
nt.assert_equal(cut_interval_new_, cut_interval_test_)
cut_interval_new_ = []
cut_interval_test_ = []
for x in new_20000.cut_intervals:
cut_interval_new_.append(x[:3])
for x in test_20000.cut_intervals:
cut_interval_test_.append(x[:3])
nt.assert_equal(cut_interval_new_, cut_interval_test_)
# print(set(os.listdir(ROOT + "QC/")))
assert are_files_equal(ROOT + "QC/QC.log", qc_folder + "/QC.log")
assert set(os.listdir(ROOT + "QC/")) == set(os.listdir(qc_folder))
os.unlink(outfile.name)
shutil.rmtree(qc_folder)
def adjustMatrix(pArgs):
if pArgs.chromosomes is not None and pArgs.regions is not None:
log.error('Please specify either --chromosomes or --regions.')
exit(1)
hic_matrix = None
if pArgs.chromosomes:
if check_cooler(pArgs.matrix) and len(pArgs.chromosomes) == 1 and pArgs.action == 'keep':
chromosomes_list = cooler.Cooler(pArgs.matrix).chromnames
if pArgs.chromosomes[0] in chromosomes_list:
hic_matrix = hm.hiCMatrix(pArgs.matrix, pChrnameList=pArgs.chromosomes)
else:
log.error('Chromosome not available in matrix: {} {}'.format(pArgs.matrix, pArgs.chromosomes[0]))
exit(1)
else:
hic_matrix = hm.hiCMatrix(pArgs.matrix)
chromosomes_list = list(hic_matrix.chrBinBoundaries)
chromosomes_list_to_operate_on = []
for chromosome in pArgs.chromosomes:
if chromosome in chromosomes_list:
chromosomes_list_to_operate_on.append(chromosome)
else:
log.warning('Chromosome not available in matrix: {} {}'.format(pArgs.matrix, chromosome))
if len(chromosomes_list_to_operate_on) == 0:
log.error('No valid chromosome given: {}. Available: {}'.format(pArgs.chromosomes, chromosomes_list))
exit(1)
if pArgs.action == 'keep':
hic_matrix.reorderChromosomes(chromosomes_list_to_operate_on)
elif pArgs.action == 'remove':
# chromosomes = list(hic_matrix.chrBinBoundaries)
for chromosome in chromosomes_list:
if chromosome in chromosomes_list_to_operate_on:
chromosomes_list.remove(chromosome)
hic_matrix.reorderChromosomes(chromosomes_list)
elif pArgs.action == 'mask':
hic_matrix.maskChromosomes(chromosomes_list_to_operate_on)
elif pArgs.regions:
hic_matrix = hm.hiCMatrix(pArgs.matrix)
chromosomes_list = list(hic_matrix.chrBinBoundaries)
genomic_regions = []
with open(pArgs.regions, 'r') as file:
for line in file.readlines():
_line = line.strip().split('\t')
if len(line) < 3:
log.warning("An entry shorter than 3 columns has been found!")
continue
if len(_line) >= 3:
chrom, start, end = _line[0], int(_line[1]), int(_line[2])
if chrom in chromosomes_list:
genomic_regions.append((chrom, start, end))
else:
log.warning('Chromosome not available in matrix, ignoring regions: {} {}'.format(pArgs.matrix, chrom))
if len(genomic_regions) == 0:
log.error('No valid chromosome given. Available: {}'.format(chromosomes_list))
exit(1)
# log.debug('genomic_regions {}'.format(genomic_regions))
matrix_indices_regions = []
for region in genomic_regions:
_regionBinRange = hic_matrix.getRegionBinRange(region[0], region[1], region[2])
if _regionBinRange is not None:
start, end = _regionBinRange
matrix_indices_regions.extend(list(range(start, end)))
# log.debug('matrix_indices_regions {}'.format(matrix_indices_regions))
if pArgs.action == 'keep':
values_submatrix = matrix_indices_regions
instances, features = hic_matrix.matrix.nonzero()
mask = np.isin(instances, values_submatrix)
mask = np.logical_not(mask)
hic_matrix.matrix.data[mask] = 0
hic_matrix.matrix.eliminate_zeros()
elif pArgs.action == 'mask':
hic_matrix.maskBins(matrix_indices_regions)
elif pArgs.action == 'remove':
full_matrix_range = np.array(range(0, max(hic_matrix.matrix.shape[0], hic_matrix.matrix.shape[1])))
matrix_indices_regions = np.array(matrix_indices_regions)
full_matrix_range[matrix_indices_regions] = -1
mask = full_matrix_range != -1
full_matrix_range = full_matrix_range[mask]
hic_matrix.reorderBins(full_matrix_range)
elif pArgs.maskBadRegions:
if check_cooler(pArgs.matrix) and len(pArgs.chromosomes) == 1 and pArgs.action == 'keep':
hic_matrix = hm.hiCMatrix(pArgs.matrix, pChrnameList=pArgs.chromosomes)
else:
hic_matrix = hm.hiCMatrix(pArgs.matrix)
else:
log.info('No data to adjust given. Please specify either --chromosomes or --region parameter.')
return hic_matrix
def main(args=None):
args = parse_arguments().parse_args(args)
mpl.rcParams['pdf.fonttype'] = 42
# read domains file
domains_df = readDomainBoundaries(args.tadDomains)
# log.debug('len(domains_df) {}'.format(len(domains_df)))
domains = domains_df.values.tolist()
old_chromosome = None
tads_per_chromosome = []
for j in range(len(domains)):
if old_chromosome is None:
old_chromosome = domains[j][0]
per_chromosome = []
per_chromosome.append(domains[j])
elif old_chromosome == domains[j][0]:
per_chromosome.append(domains[j])
continue
else:
tads_per_chromosome.append(per_chromosome)
per_chromosome = []
per_chromosome.append(domains[j])
old_chromosome = domains[j][0]
tads_per_chromosome.append(per_chromosome)
# read full h5 or only region if cooler
is_cooler = check_cooler(args.matrix)
if not is_cooler:
hic_matrix = hm.hiCMatrix(args.matrix)
else:
hic_matrix = args.matrix
inter_left_sum_list_chromosomes = []
inter_right_sum_list_chromosomes = []
inter_left_density_list_chromosomes = []
inter_right_density_list_chromosomes = []
inter_left_number_of_contacts_list_chromosomes = []
inter_right_number_of_contacts_list_chromosomes = []
inter_left_number_of_contacts_nnz_list_chromosomes = []
inter_right_number_of_contacts_nzz_list_chromosomes = []
intra_sum_list_chromosomes = []
intra_number_of_contacts_list_chromosomes = []
intra_number_of_contacts_nnz_list_chromosomes = []
intra_density_list_chromosomes = []
inter_left_intra_ratio_list_chromosomes = []
inter_right_intra_ratio_list_chromosomes = []
inter_left_inter_right_intra_ratio_list_chromosomes = []
rows_chromosomes = []
inter_left_sum_list_threads = [[]] * args.threads
inter_right_sum_list_threads = [[]] * args.threads
inter_left_density_list_threads = [[]] * args.threads
inter_right_density_list_threads = [[]] * args.threads
inter_left_number_of_contacts_list_threads = [[]] * args.threads
inter_right_number_of_contacts_list_threads = [[]] * args.threads
inter_left_number_of_contacts_nnz_list_threads = [[]] * args.threads
inter_right_number_of_contacts_nzz_list_threads = [[]] * args.threads
intra_sum_list_threads = [[]] * args.threads
intra_number_of_contacts_list_threads = [[]] * args.threads
intra_number_of_contacts_nnz_list_threads = [[]] * args.threads
intra_density_list_threads = [[]] * args.threads
inter_left_intra_ratio_list_threads = [[]] * args.threads
inter_right_intra_ratio_list_threads = [[]] * args.threads
inter_left_inter_right_intra_ratio_list_threads = [[]] * args.threads
rows_threads = [[]] * args.threads
threads_save = deepcopy(args.threads)
for chromosome in tads_per_chromosome:
# log.debug('tads_per_chromosome {}'.format(chromosome))
domainsPerThread = len(chromosome) // args.threads
if domainsPerThread == 0 and len(chromosome) > 0:
domainsPerThread = 1
args.threads = 1
elif domainsPerThread > 0:
args.threads = threads_save
all_data_collected = False
queue = [None] * args.threads
process = [None] * args.threads
thread_done = [False] * args.threads
# None --> first thread, process first element in list, ignore last one
# True --> middle thread: ignore first and last element in tad processing
# False --> last thread: ignore first element, process last one
thread_id = None
for i in range(args.threads):
if args.threads == 1:
domainListThread = chromosome
elif i == 0:
domainListThread = chromosome[i * domainsPerThread:(
(i + 1) * domainsPerThread) + 1]
thread_id = None
elif i < args.threads - 1:
domainListThread = chromosome[(i * domainsPerThread) -
1:((i + 1) * domainsPerThread) +
1]
thread_id = True
else:
domainListThread = chromosome[(i * domainsPerThread) - 1:]
thread_id = False
if args.threads == 1:
thread_id = ''
# log.debug('len(domainListThread) {}'.format(len(domainListThread)))
# log.debug('len(thread_id) {}'.format(thread_id))
queue[i] = Queue()
process[i] = Process(
target=computeInterIntraTADs,
kwargs=dict(
pMatrix=hic_matrix,
# pMatrixControl=hic_matrix_control,
pDomainList=domainListThread,
pCoolOrH5=is_cooler,
# pPValue=args.pValue,
pThreadId=thread_id,
pQueue=queue[i]))
process[i].start()
fail_flag = False
fail_message = ''
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
queue_data = queue[i].get()
if 'Fail:' in queue_data:
fail_flag = True
fail_message = queue_data
else:
inter_left_sum_list_threads[i], \
inter_right_sum_list_threads[i], \
inter_left_density_list_threads[i], \
inter_right_density_list_threads[i], \
inter_left_number_of_contacts_list_threads[i], \
inter_right_number_of_contacts_list_threads[i], \
inter_left_number_of_contacts_nnz_list_threads[i], \
inter_right_number_of_contacts_nzz_list_threads[i], \
intra_sum_list_threads[i], \
intra_number_of_contacts_list_threads[i], \
intra_number_of_contacts_nnz_list_threads[i], \
intra_density_list_threads[i], \
inter_left_intra_ratio_list_threads[i], \
inter_right_intra_ratio_list_threads[i], \
inter_left_inter_right_intra_ratio_list_threads[i], \
rows_threads[i] = queue_data
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
# elif queue[i] is None and
all_data_collected = True
for thread in thread_done:
if not thread:
all_data_collected = False
time.sleep(1)
if fail_flag:
log.error(fail_message[6:])
exit(1)
inter_left_sum_list_chromosomes.append([
item for sublist in inter_left_sum_list_threads for item in sublist
])
inter_right_sum_list_chromosomes.append([
item for sublist in inter_right_sum_list_threads
for item in sublist
])
inter_left_density_list_chromosomes.append([
item for sublist in inter_left_density_list_threads
for item in sublist
])
inter_right_density_list_chromosomes.append([
item for sublist in inter_right_density_list_threads
for item in sublist
])
inter_left_number_of_contacts_list_chromosomes.append([
item for sublist in inter_left_number_of_contacts_list_threads
for item in sublist
])
inter_right_number_of_contacts_list_chromosomes.append([
item for sublist in inter_right_number_of_contacts_list_threads
for item in sublist
])
inter_left_number_of_contacts_nnz_list_chromosomes.append([
item for sublist in inter_left_number_of_contacts_nnz_list_threads
for item in sublist
])
inter_right_number_of_contacts_nzz_list_chromosomes.append([
item for sublist in inter_right_number_of_contacts_nzz_list_threads
for item in sublist
])
intra_sum_list_chromosomes.append(
[item for sublist in intra_sum_list_threads for item in sublist])
intra_number_of_contacts_list_chromosomes.append([
item for sublist in intra_number_of_contacts_list_threads
for item in sublist
])
intra_number_of_contacts_nnz_list_chromosomes.append([
item for sublist in intra_number_of_contacts_nnz_list_threads
for item in sublist
])
intra_density_list_chromosomes.append([
item for sublist in intra_density_list_threads for item in sublist
])
inter_left_intra_ratio_list_chromosomes.append([
item for sublist in inter_left_intra_ratio_list_threads
for item in sublist
])
inter_right_intra_ratio_list_chromosomes.append([
item for sublist in inter_right_intra_ratio_list_threads
for item in sublist
])
inter_left_inter_right_intra_ratio_list_chromosomes.append([
item for sublist in inter_left_inter_right_intra_ratio_list_threads
for item in sublist
])
rows_chromosomes.append(
[item for sublist in rows_threads for item in sublist])
inter_left_sum_list = [
item for sublist in inter_left_sum_list_chromosomes for item in sublist
]
inter_right_sum_list = [
item for sublist in inter_right_sum_list_chromosomes
for item in sublist
]
inter_left_density_list = [
item for sublist in inter_left_density_list_chromosomes
for item in sublist
]
inter_right_density_list = [
item for sublist in inter_right_density_list_chromosomes
for item in sublist
]
inter_left_number_of_contacts_list = [
item for sublist in inter_left_number_of_contacts_list_chromosomes
for item in sublist
]
inter_right_number_of_contacts_list = [
item for sublist in inter_right_number_of_contacts_list_chromosomes
for item in sublist
]
inter_left_number_of_contacts_nnz_list = [
item for sublist in inter_left_number_of_contacts_nnz_list_chromosomes
for item in sublist
]
inter_right_number_of_contacts_nzz_list = [
item for sublist in inter_right_number_of_contacts_nzz_list_chromosomes
for item in sublist
]
intra_sum_list = [
item for sublist in intra_sum_list_chromosomes for item in sublist
]
intra_number_of_contacts_list = [
item for sublist in intra_number_of_contacts_list_chromosomes
for item in sublist
]
intra_number_of_contacts_nnz_list = [
item for sublist in intra_number_of_contacts_nnz_list_chromosomes
for item in sublist
]
intra_density_list = [
item for sublist in intra_density_list_chromosomes for item in sublist
]
inter_left_intra_ratio_list = [
item for sublist in inter_left_intra_ratio_list_chromosomes
for item in sublist
]
inter_right_intra_ratio_list = [
item for sublist in inter_right_intra_ratio_list_chromosomes
for item in sublist
]
inter_left_inter_right_intra_ratio_list = [
item for sublist in inter_left_inter_right_intra_ratio_list_chromosomes
for item in sublist
]
rows = [item for sublist in rows_chromosomes for item in sublist]
with open(args.outFileName, 'w') as file:
header = '# Created with HiCExplorer\'s hicInterIntraTAD version ' + __version__ + '\n'
header += '# Chromosome\tstart\tend\tname\tscore\tstrand\tinter_left_sum\tinter_right_sum\tinter_left_density\tinter_right_density\tinter_left_number_of_contacts\tinter_right_number_of_contacts\t' \
'inter_left_number_of_contacts_nnz\tinter_right_number_of_contacts_nnz\tintra_sum\tintra_number_of_contacts\tintra_number_of_contacts_nnz\tintra_density\tinter_left_intra_ratio\tinter_right_intra_ratio\tinter_left_inter_right_intra_ratio\n'
file.write(header)
for i, row in enumerate(rows):
row_list = list(map(str, row))
file.write('\t'.join(row_list))
file.write('\t{}'.format(inter_left_sum_list[i]))
file.write('\t{}'.format(inter_right_sum_list[i]))
file.write('\t{}'.format(inter_left_density_list[i]))
file.write('\t{}'.format(inter_right_density_list[i]))
file.write('\t{}'.format(inter_left_number_of_contacts_list[i]))
file.write('\t{}'.format(inter_right_number_of_contacts_list[i]))
file.write('\t{}'.format(
inter_left_number_of_contacts_nnz_list[i]))
file.write('\t{}'.format(
inter_right_number_of_contacts_nzz_list[i]))
file.write('\t{}'.format(intra_sum_list[i]))
file.write('\t{}'.format(intra_number_of_contacts_list[i]))
file.write('\t{}'.format(intra_number_of_contacts_nnz_list[i]))
file.write('\t{}'.format(intra_density_list[i]))
file.write('\t{}'.format(inter_left_intra_ratio_list[i]))
file.write('\t{}'.format(inter_right_intra_ratio_list[i]))
file.write('\t{}'.format(
inter_left_inter_right_intra_ratio_list[i]))
file.write('\n')
plt.scatter(inter_left_intra_ratio_list,
inter_right_intra_ratio_list,
s=20,
alpha=0.7)
plt.xlabel('Inter-left/intra TAD contact ratio', fontsize=args.fontsize)
plt.ylabel('Inter-right/intra TAD contact ratio', fontsize=args.fontsize)
plt.tight_layout()
plt.savefig(args.outFileNameRatioPlot, dpi=args.dpi)
plt.close()
def main(args=None):
args = parse_arguments().parse_args(args)
hic_ma = hm.hiCMatrix(pMatrixFile=args.matrix)
indices_values = []
with open(args.regions, 'r') as file:
for line in file.readlines():
_line = line.strip().split('\t')
if len(line) == 0:
continue
if len(_line) == 2:
chrom, start = _line[0], _line[1]
viewpoint = (chrom, start, start)
elif len(_line) >= 3:
chrom, start, end = _line[0], _line[1], _line[2]
if args.considerStrandDirection and len(_line) < 6:
log.error(
'Strand orientation should be considered but file does not contain the 6th column of the bed file containing this information. Exiting!'
)
exit(1)
viewpoint = (chrom, start, end)
if args.range:
start_range_genomic, end_range_genomic, start_out, end_out = calculateViewpointRange(
hic_ma, viewpoint, args.range,
args.coordinatesToBinMapping)
start_bin, end_bin = getBinIndices(
hic_ma, (chrom, start_range_genomic, end_range_genomic))
else:
start_bin, end_bin, start_out, end_out = calculateViewpointRangeBins(
hic_ma, viewpoint, args.rangeInBins,
args.coordinatesToBinMapping)
if args.considerStrandDirection:
indices_values.append(
[start_bin, end_bin, start_out, end_out, _line[5]])
else:
indices_values.append(
[start_bin, end_bin, start_out, end_out, None])
if args.range:
dimensions_new_matrix = (args.range[0] // hic_ma.getBinSize()) + (
args.range[1] // hic_ma.getBinSize())
elif args.rangeInBins:
dimensions_new_matrix = args.rangeInBins[0] + args.rangeInBins[1]
summed_matrix = lil_matrix((dimensions_new_matrix, dimensions_new_matrix),
dtype=np.float32)
count_matrix = np.zeros(shape=(dimensions_new_matrix,
dimensions_new_matrix))
# max_length = hic_ma.matrix.shape[1]
for start, end, start_out, end_out, orientation in indices_values:
_start = 0
_end = summed_matrix.shape[1]
# if start < 0:
# _start = np.absolute(start)
# start = 0
# if end >= max_length:
# _end = end
# end = max_length
orig_matrix_length = end - start
if start_out:
_start = _end - orig_matrix_length
if end_out:
_end = start + orig_matrix_length
submatrix = hic_ma.matrix[start:end, start:end]
if summed_matrix.shape != submatrix.shape:
log.warning('Shape of a submatrix does not match. It is ignored.')
log.warning('Region: {}'.format(hic_ma.getBinPos(start)))
continue
count_matrix[_start:_end, _start:_end] += 1
if orientation is None or orientation == '+':
summed_matrix[_start:_end, _start:_end] += hic_ma.matrix[start:end,
start:end]
elif orientation == '-':
summed_matrix[_start:_end,
_start:_end] += hic_ma.matrix[start:end, start:end].T
summed_matrix /= count_matrix
summed_matrix = np.array(summed_matrix)
data = summed_matrix[np.nonzero(summed_matrix)]
row = np.nonzero(summed_matrix)[0]
col = np.nonzero(summed_matrix)[1]
summed_matrix = csr_matrix(
(data, (row, col)),
shape=(dimensions_new_matrix, dimensions_new_matrix))
save_npz(args.outFileName, summed_matrix)
def mergeLoops(pDataFrame, pLowestResolution, pTupleX, pTupleY):
hic = hm.hiCMatrix()
target_regions_intervaltree_x = hic.intervalListToIntervalTree(pTupleX)[0]
target_regions_intervaltree_y = hic.intervalListToIntervalTree(pTupleY)[0]
for i, loop in enumerate(pDataFrame.values):
# neighborhood factor to extent the search range. This allows to consider the smaller bin sizes
# like they would be bins of the lowest resolution
neighborhood_factor_x = int(pLowestResolution) - abs(
int(loop[2]) - int(loop[1]))
neighborhood_factor_y = int(pLowestResolution) - abs(
int(loop[5]) - int(loop[4]))
if loop[0] in target_regions_intervaltree_x:
x_interval = target_regions_intervaltree_x[loop[0]].overlap(
loop[1] - neighborhood_factor_x - 1,
loop[2] + neighborhood_factor_x + 1)
if loop[3] in target_regions_intervaltree_y:
y_interval = target_regions_intervaltree_y[loop[0]].overlap(
loop[4] - neighborhood_factor_y - 1,
loop[5] + neighborhood_factor_y + 1)
if len(x_interval) <= 1 or len(y_interval) <= 1:
continue
dict_of_interest_x = {}
list_of_interest = []
for data in x_interval:
dict_of_interest_x[data[2]] = [data[0], data[1]]
for data in y_interval:
if data[2] in dict_of_interest_x:
list_of_interest.append(data)
max_index = 0
max_distance = 0
all_id_list = []
for data in list_of_interest:
if abs(data[0] - data[1]) > max_distance:
max_distance = abs(data[0] - data[1])
max_index = data[2]
all_id_list.append(data[2])
for data in x_interval:
if data[2] == max_index:
continue
if data[2] not in all_id_list:
continue
target_regions_intervaltree_x[loop[0]].remove(data)
for data in y_interval:
if data[2] == max_index:
continue
if data[2] not in all_id_list:
continue
target_regions_intervaltree_y[loop[0]].remove(data)
result_list_index = []
dict_x = {}
dict_y = {}
for chromosome_x, chromosome_y in zip(target_regions_intervaltree_x,
target_regions_intervaltree_y):
target_regions_intervaltree_x[chromosome_x] = sorted(
target_regions_intervaltree_x[chromosome_x])
target_regions_intervaltree_y[chromosome_y] = sorted(
target_regions_intervaltree_y[chromosome_y])
for x in target_regions_intervaltree_x[chromosome_x]:
dict_x[x[2]] = (x[0], x[1])
for y in target_regions_intervaltree_y[chromosome_y]:
dict_y[y[2]] = (y[0], y[1])
for x in dict_x:
if x in dict_y:
result_list_index.append(x)
dict_x = None
dict_x = {}
dict_y = None
dict_y = {}
return result_list_index
def main(args=None):
args = parse_arguments().parse_args(args)
if not args.outFileName.endswith('.h5') or args.outFileName.endswith(
'.cool'):
log.error('Output filetype not known.')
log.error('It is: {}'.format(args.outFileName))
log.error('Accepted is .h5 or .cool')
exit(1)
hic_ma = hm.hiCMatrix(pMatrixFile=args.matrix)
log.info("hic_ma.matrix: {}".format(hic_ma.matrix))
if args.chromosomes:
hic_ma.keepOnlyTheseChr(args.chromosomes)
length_chromosome = 0
chromosome_count = len(hic_ma.getChrNames())
for chrname in hic_ma.getChrNames():
chr_range = hic_ma.getChrBinRange(chrname)
length_chromosome += chr_range[1] - chr_range[0]
trasf_matrix = lil_matrix(hic_ma.matrix.shape)
if args.method == 'norm':
trasf_matrix = lil_matrix(hic_ma.matrix.shape)
# trasf_matrix_pearson = lil_matrix(hic_ma.matrix.shape)
# trasf_matrix_corr = lil_matrix(hic_ma.matrix.shape)
for chrname in hic_ma.getChrNames():
chr_range = hic_ma.getChrBinRange(chrname)
submatrix = hic_ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
submatrix.astype(float)
submatrix = _obs_exp_norm(submatrix, length_chromosome,
chromosome_count)
submatrix = __pearson(submatrix)
trasf_matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(submatrix)
# hic_ma.setMatrix(trasf_matrix.tocsr(), cut_intervals=hic_ma.cut_intervals)
# hic_ma.save('obs_norm_pearson.'+ args.outFileName, pSymmetric=False, pApplyCorrection=False)
elif args.method == 'obs_exp':
for chrname in hic_ma.getChrNames():
chr_range = hic_ma.getChrBinRange(chrname)
submatrix = hic_ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
submatrix.astype(float)
trasf_matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
__obs_exp(submatrix, length_chromosome,
chromosome_count))
elif args.method == 'pearson':
for chrname in hic_ma.getChrNames():
chr_range = hic_ma.getChrBinRange(chrname)
submatrix = hic_ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
log.debug("shape: {}".format(submatrix.shape))
submatrix.astype(float)
log.debug("shape: {}".format(submatrix.shape))
trasf_matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
__pearson(submatrix.todense()))
elif args.method == 'covariance':
for chrname in hic_ma.getChrNames():
chr_range = hic_ma.getChrBinRange(chrname)
submatrix = hic_ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
log.debug("shape: {}".format(submatrix.shape))
submatrix.astype(float)
log.debug("shape: {}".format(submatrix.shape))
corrmatrix = np.cov(submatrix.todense())
trasf_matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(corrmatrix)
elif args.method == 'all':
trasf_matrix_obs_exp = lil_matrix(hic_ma.matrix.shape)
trasf_matrix_pearson = lil_matrix(hic_ma.matrix.shape)
trasf_matrix_corr = lil_matrix(hic_ma.matrix.shape)
for chrname in hic_ma.getChrNames():
chr_range = hic_ma.getChrBinRange(chrname)
submatrix = hic_ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
submatrix.astype(float)
submatrix = __obs_exp(submatrix, length_chromosome,
chromosome_count)
trasf_matrix_obs_exp[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
submatrix)
submatrix = __pearson(submatrix)
trasf_matrix_pearson[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
submatrix)
corrmatrix = np.cov(submatrix)
trasf_matrix_corr[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
corrmatrix)
hic_ma.setMatrix(trasf_matrix_obs_exp.tocsr(),
cut_intervals=hic_ma.cut_intervals)
basename_outFileName = basename(args.outFileName)
basename_obs_exp = "obs_exp_" + basename_outFileName
basename_pearson = "pearson_" + basename_outFileName
basename_covariance = "covariance_" + basename_outFileName
path = dirname(args.outFileName)
if path != '':
path += '/'
hic_ma.save(path + basename_obs_exp,
pSymmetric=False,
pApplyCorrection=False)
hic_ma.setMatrix(trasf_matrix_pearson.tocsr(),
cut_intervals=hic_ma.cut_intervals)
hic_ma.save(path + basename_pearson,
pSymmetric=False,
pApplyCorrection=False)
hic_ma.setMatrix(trasf_matrix_corr.tocsr(),
cut_intervals=hic_ma.cut_intervals)
hic_ma.save(path + basename_covariance,
pSymmetric=False,
pApplyCorrection=False)
if not args.method == 'all':
hic_ma.setMatrix(trasf_matrix.tocsr(),
cut_intervals=hic_ma.cut_intervals)
hic_ma.save(args.outFileName, pSymmetric=False, pApplyCorrection=False)
def main(args=None):
args = parse_arguments().parse_args(args)
if args.verbose:
log.setLevel(logging.INFO)
# args.chromosomes
if check_cooler(args.matrix) and args.chromosomes is not None and len(
args.chromosomes) == 1:
ma = hm.hiCMatrix(args.matrix, pChrnameList=toString(args.chromosomes))
else:
ma = hm.hiCMatrix(args.matrix)
if args.chromosomes:
ma.reorderChromosomes(toString(args.chromosomes))
# mask all zero value bins
row_sum = np.asarray(ma.matrix.sum(axis=1)).flatten()
log.info("Removing {} zero value bins".format(sum(row_sum == 0)))
ma.maskBins(np.flatnonzero(row_sum == 0))
matrix_shape = ma.matrix.shape
ma.matrix = convertNansToZeros(ma.matrix)
ma.matrix = convertInfsToZeros(ma.matrix)
if 'plotName' in args:
plot_total_contact_dist(ma, args)
log.info("Saving diagnostic plot {}\n".format(args.plotName))
return
log.info("matrix contains {} data points. Sparsity {:.3f}.".format(
len(ma.matrix.data),
float(len(ma.matrix.data)) / (ma.matrix.shape[0]**2)))
if args.skipDiagonal:
ma.diagflat(value=0)
outlier_regions = filter_by_zscore(ma,
args.filterThreshold[0],
args.filterThreshold[1],
perchr=args.perchr)
# compute and print some statistics
pct_outlier = 100 * float(len(outlier_regions)) / ma.matrix.shape[0]
ma.printchrtoremove(outlier_regions,
label="Bins that are MAD outliers ({:.2f}%) "
"out of".format(pct_outlier, ma.matrix.shape[0]),
restore_masked_bins=False)
assert matrix_shape == ma.matrix.shape
# mask filtered regions
ma.maskBins(outlier_regions)
total_filtered_out = set(outlier_regions)
if args.sequencedCountCutoff and 0 < args.sequencedCountCutoff < 1:
chrom, _, _, coverage = zip(*ma.cut_intervals)
assert type(coverage[0]) == np.float64
failed_bins = np.flatnonzero(
np.array(coverage) < args.sequencedCountCutoff)
ma.printchrtoremove(failed_bins,
label="Bins with low coverage",
restore_masked_bins=False)
ma.maskBins(failed_bins)
total_filtered_out = set(failed_bins)
"""
ma.matrix, to_remove = fill_gaps(ma, failed_bins)
log.warning("From {} failed bins, {} could "
"not be filled\n".format(len(failed_bins),
len(to_remove)))
ma.maskBins(to_remove)
"""
if args.transCutoff and 0 < args.transCutoff < 100:
cutoff = float(args.transCutoff) / 100
# a usual cutoff is 0.05
ma.truncTrans(high=cutoff)
pre_row_sum = np.asarray(ma.matrix.sum(axis=1)).flatten()
correction_factors = []
if args.perchr:
corrected_matrix = lil_matrix(ma.matrix.shape)
# normalize each chromosome independently
for chrname in list(ma.interval_trees):
chr_range = ma.getChrBinRange(chrname)
chr_submatrix = ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
_matrix, _corr_factors = iterative_correction(chr_submatrix, args)
corrected_matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = _matrix
correction_factors.append(_corr_factors)
correction_factors = np.concatenate(correction_factors)
else:
corrected_matrix, correction_factors = iterative_correction(
ma.matrix, args)
ma.setMatrixValues(corrected_matrix)
ma.setCorrectionFactors(correction_factors)
log.info("Correction factors {}".format(correction_factors[:10]))
if args.inflationCutoff and args.inflationCutoff > 0:
after_row_sum = np.asarray(corrected_matrix.sum(axis=1)).flatten()
# identify rows that were expanded more than args.inflationCutoff times
to_remove = np.flatnonzero(
after_row_sum / pre_row_sum >= args.inflationCutoff)
ma.printchrtoremove(to_remove,
label="inflated >={} "
"regions".format(args.inflationCutoff),
restore_masked_bins=False)
total_filtered_out = total_filtered_out.union(to_remove)
ma.maskBins(to_remove)
ma.printchrtoremove(sorted(list(total_filtered_out)),
label="Total regions to be removed",
restore_masked_bins=False)
ma.save(args.outFileName, pApplyCorrection=False)
def main(args=None):
args = parse_arguments().parse_args(args)
if int(args.numberOfEigenvectors) != len(args.outputFileName):
log.error("Number of output file names and number of eigenvectors"
" does not match. Please"
"provide the name of each file.\nFiles: {}\nNumber of "
"eigenvectors: {}".format(args.outputFileName,
args.numberOfEigenvectors))
exit(1)
ma = hm.hiCMatrix(args.matrix)
ma.maskBins(ma.nan_bins)
if args.chromosomes:
ma.keepOnlyTheseChr(args.chromosomes)
vecs_list = []
chrom_list = []
start_list = []
end_list = []
# PCA is computed per chromosome
length_chromosome = 0
chromosome_count = len(ma.getChrNames())
if args.pearsonMatrix:
trasf_matrix_pearson = lil_matrix(ma.matrix.shape)
if args.obsexpMatrix:
trasf_matrix_obsexp = lil_matrix(ma.matrix.shape)
for chrname in ma.getChrNames():
chr_range = ma.getChrBinRange(chrname)
length_chromosome += chr_range[1] - chr_range[0]
if args.extraTrack and (args.extraTrack.endswith('.bw')
or args.extraTrack.endswith('.bigwig')):
bwTrack = pyBigWig.open(args.extraTrack, 'r')
for chrname in ma.getChrNames():
chr_range = ma.getChrBinRange(chrname)
submatrix = ma.matrix[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]]
if args.norm:
obs_exp_matrix_ = obs_exp_matrix_norm(submatrix)
else:
obs_exp_matrix_ = obs_exp_matrix_lieberman(submatrix,
length_chromosome,
chromosome_count)
obs_exp_matrix_ = convertNansToZeros(
csr_matrix(obs_exp_matrix_)).todense()
obs_exp_matrix_ = convertInfsToZeros(
csr_matrix(obs_exp_matrix_)).todense()
if args.obsexpMatrix:
trasf_matrix_obsexp[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
obs_exp_matrix_)
pearson_correlation_matrix = np.corrcoef(obs_exp_matrix_)
pearson_correlation_matrix = convertNansToZeros(
csr_matrix(pearson_correlation_matrix)).todense()
pearson_correlation_matrix = convertInfsToZeros(
csr_matrix(pearson_correlation_matrix)).todense()
if args.pearsonMatrix:
trasf_matrix_pearson[chr_range[0]:chr_range[1],
chr_range[0]:chr_range[1]] = lil_matrix(
pearson_correlation_matrix)
corrmatrix = np.cov(pearson_correlation_matrix)
corrmatrix = convertNansToZeros(csr_matrix(corrmatrix)).todense()
corrmatrix = convertInfsToZeros(csr_matrix(corrmatrix)).todense()
evals, eigs = linalg.eig(corrmatrix)
k = args.numberOfEigenvectors
chrom, start, end, _ = zip(
*ma.cut_intervals[chr_range[0]:chr_range[1]])
chrom_list += chrom
start_list += start
end_list += end
if args.extraTrack and (args.extraTrack.endswith('.bw')
or args.extraTrack.endswith('.bigwig')):
assert (len(end) == len(start))
correlateEigenvectorWithHistonMarkTrack(eigs[:, :k].transpose(),
bwTrack, chrname, start,
end, args.extraTrack,
args.histonMarkType)
vecs_list += eigs[:, :k].tolist()
if args.pearsonMatrix:
file_type = 'cool'
if args.pearsonMatrix.endswith('.h5'):
file_type = 'h5'
matrixFileHandlerOutput = MatrixFileHandler(pFileType=file_type)
matrixFileHandlerOutput.set_matrix_variables(
trasf_matrix_pearson.tocsr(), ma.cut_intervals, ma.nan_bins,
ma.correction_factors, ma.distance_counts)
matrixFileHandlerOutput.save(args.pearsonMatrix,
pSymmetric=True,
pApplyCorrection=False)
if args.obsexpMatrix:
file_type = 'cool'
if args.obsexpMatrix.endswith('.h5'):
file_type = 'h5'
matrixFileHandlerOutput = MatrixFileHandler(pFileType=file_type)
matrixFileHandlerOutput.set_matrix_variables(
trasf_matrix_obsexp.tocsr(), ma.cut_intervals, ma.nan_bins,
ma.correction_factors, ma.distance_counts)
matrixFileHandlerOutput.save(args.obsexpMatrix,
pSymmetric=True,
pApplyCorrection=False)
if args.extraTrack and not args.extraTrack.endswith(
'.bw') and not args.extraTrack.endswith('.bigwig'):
vecs_list = correlateEigenvectorWithGeneTrack(ma, vecs_list,
args.extraTrack)
if args.format == 'bedgraph':
for idx, outfile in enumerate(args.outputFileName):
assert (len(vecs_list) == len(chrom_list))
with open(outfile, 'w') as fh:
for i, value in enumerate(vecs_list):
if len(value) == args.numberOfEigenvectors:
if isinstance(value[idx], np.complex):
value[idx] = value[idx].real
fh.write("{}\t{}\t{}\t{:.12f}\n".format(
toString(chrom_list[i]), start_list[i],
end_list[i], value[idx]))
elif args.format == 'bigwig':
if not pyBigWig.numpy == 1:
log.error("ERROR: Your version of pyBigWig is not supporting "
"numpy: {}".format(pyBigWig.__file__))
exit(1)
old_chrom = chrom_list[0]
header = []
for i, _chrom in enumerate(chrom_list):
if old_chrom != _chrom:
header.append((toString(old_chrom), end_list[i - 1]))
old_chrom = _chrom
header.append((toString(chrom_list[-1]), end_list[-1]))
for idx, outfile in enumerate(args.outputFileName):
log.debug("bigwig: len(vecs_list) {}".format(len(vecs_list)))
log.debug("bigwig: len(chrom_list) {}".format(len(chrom_list)))
assert (len(vecs_list) == len(chrom_list))
_chrom_list = []
_start_list = []
_end_list = []
values = []
bw = pyBigWig.open(outfile, 'w')
# set big wig header
bw.addHeader(header)
# create entry lists
for i, value in enumerate(vecs_list):
# it can happen that some 'value' is having less dimensions than it should
if len(value) == args.numberOfEigenvectors:
if isinstance(value[idx], np.complex):
value[idx] = value[idx].real
values.append(value[idx])
_chrom_list.append(toString(chrom_list[i]))
_start_list.append(start_list[i])
_end_list.append(end_list[i])
# write entries
bw.addEntries(_chrom_list,
_start_list,
ends=_end_list,
values=values)
bw.close()
else:
log.error("Output format not known: {}".format(args.format))
exit(1)
def main(args=None):
args = parse_arguments().parse_args(args)
if args.labels and len(args.matrices) != len(args.labels):
log.error("The number of labels does not match the number of matrices.")
exit(0)
if not args.labels:
args.labels = map(lambda x: os.path.basename(x), args.matrices)
num_files = len(args.matrices)
map(lambda x: os.path.basename(x), args.matrices)
# initialize results matrix
results = np.zeros((num_files, num_files), dtype='float')
rows, cols = np.triu_indices(num_files)
correlation_opts = {'spearman': spearmanr,
'pearson': pearsonr}
hic_mat_list = []
max_value = None
min_value = None
all_mat = None
all_nan = []
for i, matrix in enumerate(args.matrices):
log.info("loading hic matrix {}\n".format(matrix))
if (check_cooler(args.matrices[i])) and args.chromosomes is not None and len(args.chromosomes) == 1:
_mat = hm.hiCMatrix(matrix, pChrnameList=args.chromosomes)
else:
_mat = hm.hiCMatrix(matrix)
if args.chromosomes:
_mat.keepOnlyTheseChr(args.chromosomes)
_mat.filterOutInterChrCounts()
_mat.diagflat(0)
log.info("restore masked bins {}\n".format(matrix))
bin_size = _mat.getBinSize()
all_nan = np.unique(np.concatenate([all_nan, _mat.nan_bins]))
_mat = triu(_mat.matrix, k=0, format='csr')
if args.range:
min_dist, max_dist = args.range.split(":")
min_dist = int(min_dist)
max_dist = int(max_dist)
if max_dist < bin_size:
log.error("Please specify a max range that is larger than bin size ({})".format(bin_size))
exit()
max_depth_in_bins = int(max_dist / bin_size)
max_dist = int(max_dist) // bin_size
min_dist = int(min_dist) // bin_size
# work only with the upper matrix
# and remove all pixels that are beyond
# max_depth_in_bis
# (this is done by subtracting a second sparse matrix
# that contains only the upper matrix that wants to be removed.
_mat = triu(_mat, k=0, format='csr') - triu(_mat, k=max_depth_in_bins, format='csr')
_mat.eliminate_zeros()
_mat_coo = _mat.tocoo()
dist = _mat_coo.col - _mat_coo.row
keep = np.flatnonzero((dist <= max_dist) & (dist >= min_dist))
_mat_coo.data = _mat_coo.data[keep]
_mat_coo.row = _mat_coo.row[keep]
_mat_coo.col = _mat_coo.col[keep]
_mat = _mat_coo.tocsr()
else:
_mat = triu(_mat, k=0, format='csr')
if args.log1p:
_mat.data = np.log1p(_mat.data)
if all_mat is None:
all_mat = _mat
else:
all_mat = all_mat + _mat
if max_value is None or max_value < _mat.data.max():
max_value = _mat.data.max()
if min_value is None or min_value > _mat.data.min():
min_value = _mat.data.min()
hic_mat_list.append(_mat)
# remove nan bins
rows_keep = cols_keep = np.delete(list(range(all_mat.shape[1])), all_nan)
all_mat = all_mat[rows_keep, :][:, cols_keep]
# make large matrix to correlate by
# using sparse matrix tricks
big_mat = None
for mat in hic_mat_list:
mat = mat[rows_keep, :][:, cols_keep]
sample_vector = (mat + all_mat).data - all_mat.data
if big_mat is None:
big_mat = sample_vector
else:
big_mat = np.vstack([big_mat, sample_vector])
# take the transpose such that columns represent each of the samples
big_mat = np.ma.masked_invalid(big_mat).T
grids = gridspec.GridSpec(num_files, num_files)
grids.update(wspace=0, hspace=0)
fig = plt.figure(figsize=(2 * num_files, 2 * num_files))
plt.rcParams['font.size'] = 8.0
min_value = int(big_mat.min())
max_value = int(big_mat.max())
if (min_value % 2 == 0 and max_value % 2 == 0) or \
(min_value % 1 == 0 and max_value % 2 == 1):
# make one value odd and the other even
max_value += 1
if args.log1p:
major_locator = FixedLocator(list(range(min_value, max_value, 2)))
minor_locator = FixedLocator(list(range(min_value, max_value, 1)))
for index in range(len(rows)):
row = rows[index]
col = cols[index]
if row == col:
results[row, col] = 1
# add titles as
# empty plot in the diagonal
ax = fig.add_subplot(grids[row, col])
ax.text(0.6, 0.6, args.labels[row],
verticalalignment='center',
horizontalalignment='center',
fontsize=10, fontweight='bold',
transform=ax.transAxes)
ax.set_axis_off()
continue
log.info("comparing {} and {}\n".format(args.matrices[row],
args.matrices[col]))
# remove cases in which both are zero or one is zero and
# the other is one
_mat = big_mat[:, [row, col]]
_mat = _mat[_mat.sum(axis=1) > 1, :]
vector1 = _mat[:, 0]
vector2 = _mat[:, 1]
results[row, col] = correlation_opts[args.method](vector1, vector2)[0]
# scatter plots
ax = fig.add_subplot(grids[row, col])
if args.log1p:
ax.xaxis.set_major_locator(major_locator)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_major_locator(major_locator)
ax.yaxis.set_minor_locator(minor_locator)
ax.text(0.2, 0.8, "{}={:.2f}".format(args.method,
results[row, col]),
horizontalalignment='left',
transform=ax.transAxes)
ax.get_yaxis().set_tick_params(
which='both',
left='off',
right='off',
direction='out')
ax.get_xaxis().set_tick_params(
which='both',
top='off',
bottom='off',
direction='out')
if col != num_files - 1:
ax.set_yticklabels([])
else:
ax.yaxis.tick_right()
ax.get_yaxis().set_tick_params(
which='both',
left='off',
right='on',
direction='out')
if col - row == 1:
ax.xaxis.tick_bottom()
ax.get_xaxis().set_tick_params(
which='both',
top='off',
bottom='on',
direction='out')
else:
ax.set_xticklabels([])
ax.hist2d(vector1, vector2, bins=150, cmin=0.1)
fig.tight_layout()
log.info("saving {}".format(args.outFileNameScatter))
fig.savefig(args.outFileNameScatter, bbox_inches='tight')
results = results + np.triu(results, 1).T
plot_correlation(results, args.labels,
args.outFileNameHeatmap,
args.zMax,
args.zMin,
args.colorMap,
image_format=args.plotFileFormat)
def __init__(self, *args, **kwargs):
super(EngineHiCTrack, self).__init__(*args, **kwargs)
log.debug('FILE {}'.format(self.properties))
# log.debug('pRegion {}'.format(pRegion))
region = None
if self.properties['region'] is not None:
if self.properties['region'][2] == 1e15:
region = [str(self.properties['region'][0])]
elif len(self.properties['region']) == 3:
start = int(self.properties['region'][1]) - int(
self.properties['depth'])
if start < 0:
start = 0
end = int(self.properties['region'][2]) + int(
self.properties['depth'])
region = [
str(self.properties['region'][0]) + ':' + str(start) +
'-' + str(end)
]
# initialize matrix as HiCMatrix object with no data
self.hic_ma = HiCMatrix.hiCMatrix(pMatrixFile=None,
pChrnameList=region)
# create matrix to fill out data and intervals
if 'matrix shape' not in self.properties:
self.properties['matrix shape'] = 1000
if 'binsize' not in self.properties:
self.properties['binsize'] = 3000
if 'intervals start' not in self.properties:
self.properties['intervals start'] = 0
self.hic_ma.matrix, self.hic_ma.cut_intervals = \
self.definematrix(self.properties['matrix shape'], self.properties['binsize'], self.properties['intervals start'], self.properties['chrom'])
self.hic_ma.interval_trees, self.hic_ma.chrBinBoundaries = \
self.hic_ma.intervalListToIntervalTree(self.hic_ma.cut_intervals)
if len(self.hic_ma.matrix.data) == 0:
self.log.error("Matrix {} is empty".format(
self.properties['file']))
exit(1)
if 'show_masked_bins' in self.properties and self.properties[
'show_masked_bins'] == 'yes':
pass
else:
self.hic_ma.maskBins(self.hic_ma.nan_bins)
# check that the matrix can be log transformed
if 'transform' in self.properties:
if self.properties['transform'] == 'log1p':
if self.hic_ma.matrix.data.min() + 1 < 0:
self.log.error(
"\n*ERROR*\nMatrix contains negative values.\n"
"log1p transformation can not be applied to \n"
"values in matrix: {}".format(self.properties['file']))
exit(1)
elif self.properties['transform'] == '-log':
if self.hic_ma.matrix.data.min() < 0:
self.log.error(
"\n*ERROR*\nMatrix contains negative values.\n"
"log(-1 * <values>) transformation can not be applied to \n"
"values in matrix: {}".format(self.properties['file']))
exit(1)
elif self.properties['transform'] == 'log':
if self.hic_ma.matrix.data.min() < 0:
self.log.error(
"\n*ERROR*\nMatrix contains negative values.\n"
"log transformation can not be applied to \n"
"values in matrix: {}".format(self.properties['file']))
exit(1)
binsize = self.hic_ma.getBinSize()
max_depth_in_bins = int(self.properties['depth'] / binsize)
# work only with the lower matrix
# and remove all pixels that are beyond
# 2 * max_depth_in_bis which are not required
# (this is done by subtracting a second sparse matrix
# that contains only the lower matrix that wants to be removed.
limit = 2 * max_depth_in_bins
self.hic_ma.matrix = scipy.sparse.triu(self.hic_ma.matrix, k=0, format='csr') - \
scipy.sparse.triu(self.hic_ma.matrix, k=limit, format='csr')
self.hic_ma.matrix.eliminate_zeros()
# fill the main diagonal, otherwise it looks
# not so good. The main diagonal is filled
# with an array containing the max value found
# in the matrix
if sum(self.hic_ma.matrix.diagonal()) == 0:
self.log.info(
"Filling main diagonal with max value because it empty and looks bad...\n"
)
max_value = self.hic_ma.matrix.data.max()
main_diagonal = scipy.sparse.dia_matrix(
([max_value] * self.hic_ma.matrix.shape[0], [0]),
shape=self.hic_ma.matrix.shape)
self.hic_ma.matrix = self.hic_ma.matrix + main_diagonal
self.plot_inverted = False
if 'orientation' in self.properties and self.properties[
'orientation'] == 'inverted':
self.plot_inverted = True
self.norm = None
if 'colormap' not in self.properties:
self.properties['colormap'] = DEFAULT_MATRIX_COLORMAP
self.cmap = cm.get_cmap(self.properties['colormap'])
self.cmap.set_bad('white')
#self.cmap.set_over('blue')
self.background = True
def main(args=None):
args = parse_arguments().parse_args(args)
viewpointObj = Viewpoint()
referencePoints, _ = viewpointObj.readReferencePointFile(
args.referencePoints)
# compute for each viewpoint the sparsity and consider these as bad with a sparsity less than given.
referencePointsPerThread = len(referencePoints) // args.threads
queue = [None] * args.threads
process = [None] * args.threads
sparsity = []
fail_flag = False
fail_message = ''
for j, matrix in enumerate(args.matrices):
sparsity_local = [None] * args.threads
hic_ma = hm.hiCMatrix(matrix)
viewpointObj.hicMatrix = hic_ma
all_data_collected = False
thread_done = [False] * args.threads
for i in range(args.threads):
if i < args.threads - 1:
referencePointsThread = referencePoints[
i * referencePointsPerThread:(i + 1) *
referencePointsPerThread]
else:
referencePointsThread = referencePoints[
i * referencePointsPerThread:]
if len(referencePointsThread) == 0:
process[i] = None
queue[i] = None
sparsity_local[i] = []
continue
else:
queue[i] = Queue()
process[i] = Process(
target=compute_sparsity,
kwargs=dict(pReferencePoints=referencePointsThread,
pViewpointObj=viewpointObj,
pArgs=args,
pQueue=queue[i]))
process[i].start()
log.debug('process started {}'.format(i))
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
sparsity_ = queue[i].get()
if 'Fail:' in sparsity_:
fail_flag = True
fail_message = sparsity_[6:]
log.debug('process computed: {}'.format(i))
sparsity_local[i] = sparsity_
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
all_data_collected = True
for thread in thread_done:
if not thread:
all_data_collected = False
time.sleep(1)
del hic_ma
del viewpointObj.hicMatrix
# merge sparsity data per matrix from each thread to one list
if fail_flag:
log.error(fail_message)
exit(1)
sparsity_local = [
item for sublist in sparsity_local for item in sublist
]
sparsity.append(sparsity_local)
# sparsity = np.array(sparsity)
# mask = sparsity == -1.0
# change sparsity to sparsity values per viewpoint per matrix: viewpoint = [matrix1, ..., matrix_n]
sparsity = np.array(sparsity).T
count_accepted = 0
count_rejected = 0
count_failure = 0
with open(args.referencePoints, 'r') as reference_file_input:
with open(args.outFileName + '_raw_filter', 'w') as output_file_raw:
output_file_raw.write(
'# Created with chicQualityControl version {}\n'.format(
__version__))
output_file_raw.write(
'# A sparsity of -1.0 indicates a faulty reference point e.g. no data for this reference point was in the matrix.\n'
)
output_file_raw.write('# Used Matrices ')
for matrix in args.matrices:
output_file_raw.write('{}\t'.format(matrix))
output_file_raw.write('\n# Chromosome\tStart\tEnd')
for matrix in args.matrices:
output_file_raw.write('\tSparsity {}'.format(
os.path.basename(matrix)))
output_file_raw.write('\n')
with open(args.outFileName + '_failed_reference_points',
'w') as output_file_failed:
with open(args.outFileName + '_rejected_filter',
'w') as output_file_rejected:
with open(args.outFileName, 'w') as output_file:
for i, line in enumerate(
reference_file_input.readlines()):
sparsity_str = '\t'.join(
str(x) for x in sparsity[i])
output_file_raw.write(line.strip() + '\t' +
sparsity_str + '\n')
count = 0
count_negative = 0
for j in range(len(sparsity[i])):
if sparsity[i][j] == -1.0:
count_negative += 1
elif sparsity[i][j] > args.sparsity:
count += 1
if count_negative:
output_file_failed.write(line)
count_failure += 1
elif count:
output_file.write(line)
count_accepted += 1
else:
output_file_rejected.write(line)
count_rejected += 1
with open(args.outFileName + '_report', 'w') as output_file_report:
output_file_report.write(
'# Created with chicQualityControl version {}\n'.format(
__version__))
output_file_report.write('# QC report for matrices: ')
for matrix in args.matrices:
output_file_report.write(matrix + ' ')
output_file_report.write('\n')
output_file_report.write(
'#Sparsity threshold for rejection: sparsity <= {} are rejected.\n'
.format(args.sparsity))
output_file_report.write('\nNumber of reference points: {}\n'.format(
str(count_accepted + count_rejected + count_failure)))
output_file_report.write(
'Number of accepted reference points: {}\n'.format(
str(count_accepted)))
output_file_report.write(
'Number of rejected reference points: {}\n'.format(
str(count_rejected)))
output_file_report.write(
'Number of faulty reference points: {}\n'.format(
str(count_failure)))
output_file_report.write(
'\n\nA faulty reference point is caused by the non-presence of the chromosome in one of the given matrices.\n'
)
output_file_report.write(
'It can also be caused by the non-presence of valid Hi-C reads in a region, especially at the chromosome ends.\n'
)
output_file_report.write(
'Please check the results of hicInfo to validate this for your data.\n'
)
# output plot of sparsity distribution per sample
# remove fault reference points from statistics
x = [[]] * len(args.matrices)
y = [[]] * len(args.matrices)
mask = [True] * len(sparsity)
for i in range(len(sparsity)):
delete_instance = False
for j in range(len(args.matrices)):
if sparsity[i][j] == -1.0:
delete_instance = True
if delete_instance:
mask[i] = False
mask = np.array(mask)
sparsity = sparsity[mask]
for i in range(len(args.matrices)):
y[i] = [i] * len(sparsity)
sparsity = sparsity.T
for i in range(len(args.matrices)):
x[i] = sparsity[i].flatten()
for i in range(len(args.matrices)):
plt.plot(x[i],
y[i],
'o',
mfc='none',
markersize=0.3,
label=args.matrices[i].split('/')[-1])
plt.yticks([])
plt.xlabel("Sparsity level")
plt.axvline(x=args.sparsity,
c='r',
label='sparsity threshold',
linewidth=0.3)
plt.xscale('log')
ax = plt.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
plt.legend(loc='center', bbox_to_anchor=(1.4, 0.5))
plt.savefig(args.outFileNameSparsity, dpi=args.dpi)
# plt.xlabel("Length of list (number)")
# plt.ylabel("Time taken (seconds)")
plt.close()
for i in range(len(args.matrices)):
plt.hist(x[i],
bins=100,
alpha=0.5,
label=args.matrices[i].split('/')[-1])
plt.xlabel("Sparsity level")
plt.ylabel("Number of counts")
# plt.legend(loc='upper right')
ax = plt.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
plt.legend(loc='center', bbox_to_anchor=(1.4, 0.5))
plt.savefig(args.outFileNameHistogram, dpi=args.dpi)
def main(args=None):
args = parse_arguments().parse_args(args)
viewpointObj = Viewpoint()
referencePoints, _ = viewpointObj.readReferencePointFile(
args.referencePoints)
# compute for each viewpoint the sparsity and consider these as bad with a sparsity less than given.
referencePointsPerThread = len(referencePoints) // args.threads
queue = [None] * args.threads
process = [None] * args.threads
sparsity = []
for j, matrix in enumerate(args.matrices):
sparsity_local = [None] * args.threads
hic_ma = hm.hiCMatrix(matrix)
viewpointObj.hicMatrix = hic_ma
all_data_collected = False
thread_done = [False] * args.threads
for i in range(args.threads):
if i < args.threads - 1:
referencePointsThread = referencePoints[
i * referencePointsPerThread:(i + 1) *
referencePointsPerThread]
else:
referencePointsThread = referencePoints[
i * referencePointsPerThread:]
if len(referencePointsThread) == 0:
process[i] = None
queue[i] = None
sparsity_local[i] = []
continue
else:
queue[i] = Queue()
process[i] = Process(
target=compute_sparsity,
kwargs=dict(pReferencePoints=referencePointsThread,
pViewpointObj=viewpointObj,
pArgs=args,
pQueue=queue[i]))
process[i].start()
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
sparsity_ = queue[i].get()
sparsity_local[i] = sparsity_
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
all_data_collected = True
for thread in thread_done:
if not thread:
all_data_collected = False
time.sleep(1)
del hic_ma
del viewpointObj.hicMatrix
# merge sparsity data per matrix from each thread to one list
sparsity_local = [
item for sublist in sparsity_local for item in sublist
]
sparsity.append(sparsity_local)
# change sparsity to sparsity values per viewpoint per matrix: viewpoint = [matrix1, ..., matrix_n]
sparsity = np.array(sparsity).T
with open(args.referencePoints, 'r') as reference_file_input:
with open(args.outFileName + '_raw_filter', 'w') as output_file_raw:
output_file_raw.write(
'# Created with chicQualityControl version {}\n'.format(
__version__))
output_file_raw.write('# Chromosome\tStart\tEnd\t')
for matrix in args.matrices:
output_file_raw.write('Sparsity {}\t'.format(matrix))
output_file_raw.write('\n')
with open(args.outFileName + '_rejected_filter',
'w') as output_file_rejected:
with open(args.outFileName, 'w') as output_file:
for i, line in enumerate(reference_file_input.readlines()):
sparsity_str = '\t'.join(str(x) for x in sparsity[i])
output_file_raw.write(line.strip() + '\t' +
sparsity_str + '\n')
count = 0
for j in range(len(sparsity[i])):
if sparsity[i][j] > args.sparsity:
count += 1
if count:
output_file.write(line)
else:
output_file_rejected.write(line)
# output plot of sparsity distribution per sample
# re-arange values again
x = [[]] * len(args.matrices)
y = [[]] * len(args.matrices)
for i in range(len(args.matrices)):
y[i] = [i] * len(sparsity)
sparsity = sparsity.T
for i in range(len(args.matrices)):
x[i] = sparsity[i].flatten()
for i in range(len(args.matrices)):
plt.plot(x[i],
y[i],
'o',
mfc='none',
markersize=0.3,
label=args.matrices[i].split('/')[-1])
plt.yticks([])
plt.xlabel("Sparsity level")
plt.axvline(x=args.sparsity,
c='r',
label='sparsity threshold',
linewidth=0.3)
plt.xscale('log')
ax = plt.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
plt.legend(loc='center', bbox_to_anchor=(1.4, 0.5))
plt.savefig(args.outFileNameSparsity, dpi=args.dpi)
# plt.xlabel("Length of list (number)")
# plt.ylabel("Time taken (seconds)")
plt.close()
for i in range(len(args.matrices)):
plt.hist(x[i],
bins=100,
alpha=0.5,
label=args.matrices[i].split('/')[-1])
plt.xlabel("Sparsity level")
plt.ylabel("Number of counts")
# plt.legend(loc='upper right')
ax = plt.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
plt.legend(loc='center', bbox_to_anchor=(1.4, 0.5))
plt.savefig(args.outFileNameHistogram, dpi=args.dpi)
def main(args=None):
args = parse_arguments().parse_args(args)
viewpointObj = Viewpoint()
referencePoints, gene_list = viewpointObj.readReferencePointFile(
args.referencePoints)
referencePointsPerThread = len(referencePoints) // args.threads
queue = [None] * args.threads
process = [None] * args.threads
file_list = []
background_model = viewpointObj.readBackgroundDataFile(
args.backgroundModelFile, args.range, args.fixateRange)
background_model_mean_values = viewpointObj.readBackgroundDataFile(
args.backgroundModelFile, args.range, args.fixateRange, pMean=True)
# background_sum_of_densities_dict = viewpointObj.computeSumOfDensities(
# background_model, args, pXfoldMaxValue=args.xFoldMaxValueNB)
if not os.path.exists(args.outputFolder):
try:
os.makedirs(args.outputFolder)
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
fail_flag = False
fail_message = ''
for matrix in args.matrices:
hic_ma = hm.hiCMatrix(matrix)
viewpointObj.hicMatrix = hic_ma
file_list_sample = [None] * args.threads
all_data_collected = False
for i in range(args.threads):
if i < args.threads - 1:
referencePointsThread = referencePoints[i * referencePointsPerThread:(i + 1) * referencePointsPerThread]
geneListThread = gene_list[i * referencePointsPerThread:(i + 1) * referencePointsPerThread]
else:
referencePointsThread = referencePoints[i * referencePointsPerThread:]
geneListThread = gene_list[i * referencePointsPerThread:]
if len(referencePointsThread) == 0:
process[i] = None
queue[i] = None
file_list_sample[i] = []
continue
queue[i] = Queue()
process[i] = Process(target=compute_viewpoint, kwargs=dict(
pViewpointObj=viewpointObj,
pArgs=args,
pQueue=queue[i],
pReferencePoints=referencePointsThread,
pGeneList=geneListThread,
pMatrix=matrix,
pBackgroundModel=background_model,
pBackgroundModelRelativeInteractions=background_model_mean_values,
pOutputFolder=args.outputFolder
)
)
process[i].start()
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
file_list_ = queue[i].get()
if 'Fail:' in file_list_:
fail_flag = True
fail_message = file_list_[6:]
file_list_sample[i] = file_list_
process[i].join()
process[i].terminate()
process[i] = None
all_data_collected = True
for i in range(args.threads):
if process[i] is not None:
all_data_collected = False
time.sleep(1)
if fail_flag:
log.error(fail_message)
exit(1)
file_list_sample = [item for sublist in file_list_sample for item in sublist]
file_list.append(file_list_sample)
log.debug('file_list {}'.format(file_list))
if args.writeFileNamesToFile:
with open(args.writeFileNamesToFile, 'w') as file:
log.debug('len(file_list) {}'.format(len(file_list)))
if len(file_list) > 1:
for i, sample in enumerate(file_list):
for sample2 in file_list[i + 1:]:
for viewpoint, viewpoint2 in zip(sample, sample2):
file.write(viewpoint + '\n')
file.write(viewpoint2 + '\n')
else:
for viewpoint in file_list[0]:
file.write(viewpoint + '\n')
if args.allViewpointsList:
with open(args.writeFileNamesToFile + 'all', 'w') as file:
if len(file_list) > 1:
for i, sample in enumerate(file_list[0]):
file.write(sample + '\n')
for j in range(1, len(file_list)):
file.write(file_list[j][i] + '\n')
else:
for viewpoint in file_list[0]:
file.write(viewpoint + '\n')
def main(args=None):
args = parse_arguments().parse_args(args)
if args.chromosomes is not None and args.regions is not None:
log.error('Please specify either --chromosomes or --regions.')
exit(1)
hic_ma = None
if args.chromosomes:
if check_cooler(args.matrix) and len(
args.chromosomes) == 1 and args.action == 'keep':
hic_ma = hm.hiCMatrix(args.matrix, pChrnameList=args.chromosomes)
else:
hic_ma = hm.hiCMatrix(args.matrix)
if args.action == 'keep':
hic_ma.reorderChromosomes(args.chromosomes)
elif args.action == 'remove':
chromosomes = list(hic_ma.chrBinBoundaries)
for chromosome in args.chromosomes:
if chromosome in chromosomes:
chromosomes.remove(chromosome)
hic_ma.reorderChromosomes(chromosomes)
elif args.action == 'mask':
hic_ma.maskChromosomes(args.chromosomes)
elif args.regions:
hic_ma = hm.hiCMatrix(args.matrix)
genomic_regions = []
with open(args.regions, 'r') as file:
for line in file.readlines():
_line = line.strip().split('\t')
if len(line) == 0:
continue
if len(_line) == 3:
chrom, start, end = _line[0], _line[1], int(_line[2]) - 1
genomic_regions.append((chrom, start, end))
# log.debug('genomic_regions {}'.format(genomic_regions))
matrix_indices_regions = []
for region in genomic_regions:
_regionBinRange = hic_ma.getRegionBinRange(region[0], region[1],
region[2])
if _regionBinRange is not None:
start, end = _regionBinRange
matrix_indices_regions.extend(list(range(start, end)))
# log.debug('matrix_indices_regions {}'.format(matrix_indices_regions))
if args.action == 'keep':
hic_ma.reorderBins(matrix_indices_regions)
elif args.action == 'mask':
hic_ma.maskBins(matrix_indices_regions)
elif args.action == 'remove':
full_matrix_range = np.array(
range(0, max(hic_ma.matrix.shape[0], hic_ma.matrix.shape[1])))
matrix_indices_regions = np.array(matrix_indices_regions)
full_matrix_range[matrix_indices_regions] = -1
mask = full_matrix_range != -1
full_matrix_range = full_matrix_range[mask]
hic_ma.reorderBins(full_matrix_range)
elif args.maskBadRegions:
if check_cooler(args.matrix) and len(
args.chromosomes) == 1 and args.action == 'keep':
hic_ma = hm.hiCMatrix(args.matrix, pChrnameList=args.chromosomes)
else:
hic_ma = hm.hiCMatrix(args.matrix)
else:
log.info(
'No data to adjust given. Please specify either --chromosomes or --region parameter.'
)
if hic_ma is not None:
hic_ma.save(args.outFileName)
def main(args=None):
args = parse_arguments().parse_args(args)
for matrix in args.matrices:
# if
generated_by = None
genome_assembly = None
statistics = None
generated_by_cooler_lib = None
tool_url = None
matrix_generated_by = None
matrix_generated_by_url = None
creation_date = None
chromosomes = None
bin_length = None
size = None
nchroms = None
num_non_zero = None
min_non_zero = None
max_non_zero = None
sum_elements = None
num_nan_bins = None
if check_cooler(matrix) and args.no_metadata:
cooler_file = cooler.Cooler(matrix)
if cooler_file.info is not None:
# log.debug('cooler_file.info {}'.format(cooler_file.info))
if 'bin-size' in cooler_file.info:
bin_length = cooler_file.info['bin-size']
if 'nbins' in cooler_file.info:
size = cooler_file.info['nbins']
if 'nchroms' in cooler_file.info:
nchroms = cooler_file.info['nchroms']
if 'chromosomes' in cooler_file.info:
chromosomes = cooler_file.info['chromosomes']
if 'nnz' in cooler_file.info:
num_non_zero = cooler_file.info['nnz']
if 'min-value' in cooler_file.info:
min_non_zero = cooler_file.info['min-value']
if 'max-value' in cooler_file.info:
max_non_zero = cooler_file.info['max-value']
if 'generated-by' in cooler_file.info:
generated_by = toString(cooler_file.info['generated-by'])
if 'genome-assembly' in cooler_file.info:
genome_assembly = toString(
cooler_file.info['genome-assembly'])
if 'metadata' in cooler_file.info:
if cooler_file.info['metadata'] is not None:
if 'statistics' in cooler_file.info['metadata']:
statistics = cooler_file.info['metadata'][
'statistics']
if 'generated-by-cooler-lib' in cooler_file.info:
generated_by_cooler_lib = toString(
cooler_file.info['generated-by-cooler-lib'])
if 'tool-url' in cooler_file.info:
tool_url = toString(cooler_file.info['tool-url'])
if 'matrix-generated-by' in cooler_file.info:
matrix_generated_by = toString(
cooler_file.info['matrix-generated-by'])
if 'matrix-generated-by-url' in cooler_file.info:
matrix_generated_by_url = toString(
cooler_file.info['matrix-generated-by-url'])
if 'creation-date' in cooler_file.info:
creation_date = cooler_file.info['creation-date']
if 'sum-elements' in cooler_file.info:
sum_elements = cooler_file.info['sum-elements']
else:
hic_ma = hm.hiCMatrix(matrix)
size = hic_ma.matrix.shape[0]
num_non_zero = hic_ma.matrix.nnz
sum_elements = hic_ma.matrix.sum() / 2
bin_length = hic_ma.getBinSize()
num_nan_bins = len(hic_ma.nan_bins)
min_non_zero = hic_ma.matrix.data.min()
max_non_zero = hic_ma.matrix.data.max()
chromosomes = list(hic_ma.chrBinBoundaries)
information = StringIO()
information.write(
"# Matrix information file. Created with HiCExplorer's hicInfo version {}\n"
.format(__version__))
if matrix is not None:
information.write("File:\t{}\n".format(matrix))
if creation_date is not None:
information.write("Date:\t{}\n".format(creation_date))
if genome_assembly is not None:
information.write("Genome assembly:\t{}\n".format(genome_assembly))
if size is not None:
information.write("Size:\t{:,}\n".format(size))
if bin_length is not None:
information.write("Bin_length:\t{}\n".format(bin_length))
if sum_elements is not None:
information.write("Sum of matrix:\t{}\n".format(sum_elements))
if chromosomes is not None:
information.write("Chromosomes:\t{}\n".format(", ".join(
toString(chromosomes))))
if nchroms is not None:
information.write("Number of chromosomes:\t{}\n".format(nchroms))
if num_non_zero is not None:
information.write(
"Non-zero elements:\t{:,}\n".format(num_non_zero))
if min_non_zero is not None:
information.write("Minimum (non zero):\t{}\n".format(min_non_zero))
if max_non_zero is not None:
information.write("Maximum:\t{}\n".format(max_non_zero))
if num_nan_bins is not None:
information.write("NaN bins:\t{}\n".format(num_nan_bins))
if check_cooler(matrix):
information.write(
'The following columns are available: {}\n'.format(
cooler.Cooler(matrix).bins().columns.values))
if generated_by is not None:
information.write("\n\nGenerated by:\t{}\n".format(generated_by))
if generated_by_cooler_lib is not None:
information.write("Cooler library version:\t{}\n".format(
generated_by_cooler_lib))
if tool_url is not None:
information.write("HiCMatrix url:\t{}\n".format(tool_url))
if matrix_generated_by is not None:
information.write("Interaction matrix created with:\t{}\n".format(
matrix_generated_by))
if matrix_generated_by_url is not None:
information.write("URL:\t{}\n".format(matrix_generated_by_url))
if statistics is not None:
information.write("\n\nBuild statistics:\n{}\n".format(statistics))
if args.outFileName:
with open(args.outFileName, 'w') as file:
file.write(information.getvalue())
else:
print(information.getvalue())
information.close()
def main(args=None):
"""
for each distance, compare the
distribution of two samples,
report number of cases were they differ
"""
args = parse_arguments().parse_args(args)
mean_dict = OrderedDict()
matrix_sum = {}
if args.labels is None:
labels = OrderedDict([(x, os.path.basename(x)) for x in args.matrices])
else:
labels = OrderedDict(zip(args.matrices, args.labels))
chroms = set()
for matrix_file in args.matrices:
hic_ma = HiCMatrix.hiCMatrix(matrix_file)
matrix_sum[matrix_file] = hic_ma.matrix.sum()
if args.chromosomeExclude is None:
args.chromosomeExclude = []
chrtokeep = [x for x in list(hic_ma.interval_trees) if x not in args.chromosomeExclude]
hic_ma.keepOnlyTheseChr(chrtokeep)
mean_dict[matrix_file] = compute_distance_mean(hic_ma, maxdepth=args.maxdepth, perchr=args.perchr)
chroms = chroms.union([k for k in list(mean_dict[matrix_file]) if len(mean_dict[matrix_file][k]) > 1])
# compute scale factors such that values are comparable
min_sum = min(matrix_sum.values())
scale_factor = dict([(matrix_file, float(min_sum) / mat_sum) for matrix_file, mat_sum in matrix_sum.items()])
log.info("The scale factors used are: {}".format(scale_factor))
if len(args.matrices) > 1 and args.perchr:
# in this case, for each chromosome a plot is made that combines the data from the
# hic matrices
max_cols = 4
num_rows = int(np.ceil(float(len(chroms)) / max_cols))
num_cols = min(len(chroms), max_cols)
else:
num_cols = num_rows = 1
if args.plotsize is None:
width = 6
height = 4
else:
width, height = args.plotsize
fig = plt.figure(figsize=(width * num_cols, height * num_rows))
axs = np.empty((num_rows, num_cols), dtype='object')
for matrix_file in args.matrices:
idx = 0
for chrom, mean_values in mean_dict[matrix_file].items():
if len(mean_values) <= 1:
log.debug("No values found for: {}, chromosome: {}\n".format(matrix_file, chrom))
continue
x, y = zip(*[(k, v) for k, v in mean_values.items() if v > 0])
if len(x) <= 1:
log.debug("No values found for: {}, chromosome: {}\n".format(matrix_file, chrom))
continue
if args.perchr and len(args.matrices) == 1:
col = 0
row = 0
else:
col = idx % num_cols
row = idx // num_cols
if axs[row, col] is None:
ax = plt.subplot2grid((num_rows, num_cols), (row, col))
ax.set_xlabel('genomic distance')
ax.set_ylabel('corrected Hi-C counts')
try:
ax.set_yscale('log')
ax.set_xscale('log')
except ValueError:
continue
else:
ax = axs[row, col]
y = np.array(y) * scale_factor[matrix_file]
if args.perchr and len(args.matrices) > 1:
label = labels[matrix_file]
ax.set_title(chrom)
elif args.perchr:
label = chrom
else:
label = labels[matrix_file]
ax.plot(x, y, label=label)
axs[row, col] = ax
idx += 1
if args.outFileData is not None:
x_vals = np.stack(x).T
y_vals = np.stack(y).T
table_to_export = pd.DataFrame({'Matrix': labels[matrix_file],
'Chromosome': chrom,
'Distance': x_vals,
'Contacts': y_vals})
table_to_export.to_csv(args.outFileData, sep='\t')
for ax in axs.reshape(-1):
if ax is None:
continue
ax.legend(prop={'size': 'small'})
ax.set_xlim(0, args.maxdepth)
handles, labels = ax.get_legend_handles_labels()
lgd = ax.legend(handles, labels, loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig(args.plotFile.name, bbox_inches='tight', bbox_extra_artists=(lgd,))
plt.close(fig)
def plotMatrix(matrixinputfile,imageoutputfile, regionindex1, regionindex2, comparematrix, title, bigwig):
if not checkExtension(matrixinputfile, '.cool'):
msg = "input matrix must be in cooler format (.cool)"
raise SystemExit(msg)
if comparematrix and not checkExtension(comparematrix, ".cool"):
msg = "if specified, compare matrix must be in cooler format (.cool)"
raise SystemExit(msg)
if not imageoutputfile:
imageoutputfile = matrixinputfile.rstrip('cool') + 'png'
elif imageoutputfile and not checkExtension(imageoutputfile, ".png"):
imageoutputfile = os.path.splitext(imageoutputfile)[0] + ".png"
#get the full matrix first to extract the desired region
ma = hm.hiCMatrix(matrixinputfile)
cuts = ma.cut_intervals
chromosome = cuts[0][0]
maxIndex = len(cuts) - 1
#check indices and get the region if ok
if regionindex1 > maxIndex:
msg = "invalid start region. Allowed is 0 to {0:d} (0 to {1:d})".format(maxIndex, cuts[maxIndex][1])
raise SystemExit(msg)
if regionindex2 < regionindex1:
msg = "region index 2 must be smaller than region index 1"
raise SystemExit(msg)
if regionindex2 > maxIndex:
regionindex2 = maxIndex
print("region index 2 clamped to max. value {0:d}".format(maxIndex))
region = str(chromosome) +":"+str(cuts[regionindex1][1])+"-"+ str(cuts[regionindex2][1])
#now get the data for the input matrix, restricted to the desired region
upperHiCMatrix = hm.hiCMatrix(matrixinputfile ,pChrnameList=[region])
upperMatrix = triu(upperHiCMatrix.matrix, k=1, format="csr")
#if set, get data from the same region also for the compare matrix
#there's no compatibility check so far
lowerHiCMatrix = None
lowerMatrix = None
if comparematrix:
lowerHiCMatrix = hm.hiCMatrix(comparematrix)
if chromosome not in [row[0] for row in lowerHiCMatrix.cut_intervals]:
msg = "compare matrix must contain the same chromosome as the input matrix"
raise SystemExit(msg)
lowerHiCMatrix = hm.hiCMatrix(comparematrix , pChrnameList=[region])
lowerMatrix = tril(lowerHiCMatrix.matrix, k=0, format="csr")
if lowerMatrix.get_shape() != upperMatrix.get_shape():
msg = "shapes of input matrix and compare matrix do not match. Check resolutions"
raise SystemExit(msg)
#arguments for plotting
plotArgs = Namespace(bigwig=bigwig,
chromosomeOrder=None,
clearMaskedBins=False,
colorMap='RdYlBu_r',
disable_tight_layout=False,
dpi=300,
flipBigwigSign=False,
log=False, log1p=True,
perChromosome=False,
region=region,
region2=None,
scaleFactorBigwig=1.0,
scoreName=None,
title=title,
vMax=None, vMaxBigwig=None,
vMin=1.0, vMinBigwig=None,
matrix = matrixinputfile)
#following code is largely duplicated from hicPlotMatrix
#not exactly beautiful, but works for now
chrom, region_start, region_end, idx1, start_pos1, chrom2, region_start2, region_end2, idx2, start_pos2 = hicPlot.getRegion(plotArgs, upperHiCMatrix)
mixedMatrix = None
if comparematrix:
mixedMatrix = np.asarray((lowerMatrix + upperMatrix).todense().astype(float))
else:
mixedMatrix = np.asarray(upperHiCMatrix.matrix.todense().astype(float))
#colormap for plotting
cmap = cm.get_cmap(plotArgs.colorMap) # pylint: disable=no-member
cmap.set_bad('black')
bigwig_info = None
if plotArgs.bigwig: # pylint: disable=no-member
bigwig_info = {'args': plotArgs, 'axis': None, 'axis_colorbar': None, 'nan_bins': upperHiCMatrix.nan_bins}
norm = None
if plotArgs.log or plotArgs.log1p: # pylint: disable=no-member
mask = mixedMatrix == 0
try:
mixedMatrix[mask] = np.nanmin(mixedMatrix[mask == False])
except ValueError:
log.info('Matrix contains only 0. Set all values to {}'.format(np.finfo(float).tiny))
mixedMatrix[mask] = np.finfo(float).tiny
if np.isnan(mixedMatrix).any() or np.isinf(mixedMatrix).any():
log.debug("any nan {}".format(np.isnan(mixedMatrix).any()))
log.debug("any inf {}".format(np.isinf(mixedMatrix).any()))
mask_nan = np.isnan(mixedMatrix)
mask_inf = np.isinf(mixedMatrix)
mixedMatrix[mask_nan] = np.nanmin(mixedMatrix[mask_nan == False])
mixedMatrix[mask_inf] = np.nanmin(mixedMatrix[mask_inf == False])
log.debug("any nan after remove of nan: {}".format(np.isnan(mixedMatrix).any()))
log.debug("any inf after remove of inf: {}".format(np.isinf(mixedMatrix).any()))
if plotArgs.log1p: # pylint: disable=no-member
mixedMatrix += 1
norm = LogNorm()
elif plotArgs.log: # pylint: disable=no-member
norm = LogNorm()
if plotArgs.bigwig: # pylint: disable=no-member
# increase figure height to accommodate bigwig track
fig_height = 8.5
else:
fig_height = 7
height = 4.8 / fig_height
fig_width = 8
width = 5.0 / fig_width
left_margin = (1.0 - width) * 0.5
fig = plt.figure(figsize=(fig_width, fig_height), dpi=plotArgs.dpi) # pylint: disable=no-member
if plotArgs.bigwig: # pylint: disable=no-member
gs = gridspec.GridSpec(2, 2, height_ratios=[0.90, 0.1], width_ratios=[0.97, 0.03])
gs.update(hspace=0.05, wspace=0.05)
ax1 = plt.subplot(gs[0, 0])
ax2 = plt.subplot(gs[1, 0])
ax3 = plt.subplot(gs[0, 1])
bigwig_info['axis'] = ax2
bigwig_info['axis_colorbar'] = ax3
else:
ax1 = None
bottom = 1.3 / fig_height
position = [left_margin, bottom, width, height]
hicPlot.plotHeatmap(mixedMatrix, ma.get_chromosome_sizes(), fig, position,
plotArgs, cmap, xlabel=chrom, ylabel=chrom2,
start_pos=start_pos1, start_pos2=start_pos2, pNorm=norm, pAxis=ax1, pBigwig=bigwig_info)
plt.savefig(imageoutputfile, dpi=plotArgs.dpi) # pylint: disable=no-member
plt.close(fig)
def main(args=None):
args = parse_arguments().parse_args(args)
short_v_long_range = []
sum_smaller = []
sum_greater = []
for matrix in args.matrices:
is_cooler = check_cooler(matrix)
if not is_cooler:
hic_matrix = hm.hiCMatrix(matrix)
else:
hic_matrix = matrix
if args.chromosomes is None:
# get all chromosomes from cooler file
if not is_cooler:
chromosomes_list = list(hic_matrix.chrBinBoundaries)
else:
chromosomes_list = cooler.Cooler(matrix).chromnames
else:
chromosomes_list = args.chromosomes
short_v_long_range_matrix_threads = [None] * args.threads
sum_smaller_threads = [None] * args.threads
sum_greater_threads = [None] * args.threads
chromosomesListPerThread = len(chromosomes_list) // args.threads
all_data_collected = False
queue = [None] * args.threads
process = [None] * args.threads
thread_done = [False] * args.threads
for i in range(args.threads):
if i < args.threads - 1:
chromosomeListThread = chromosomes_list[
i * chromosomesListPerThread:(i + 1) *
chromosomesListPerThread]
else:
chromosomeListThread = chromosomes_list[
i * chromosomesListPerThread:]
queue[i] = Queue()
process[i] = Process(target=compute_relation_short_long_range,
kwargs=dict(pHiCMatrix=hic_matrix,
pChromosomes=chromosomeListThread,
pDistance=args.distance,
pIsCooler=is_cooler,
pQueue=queue[i]))
process[i].start()
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
short_v_long_range_matrix_threads[i], sum_smaller_threads[
i], sum_greater_threads[i] = queue[i].get()
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
all_data_collected = True
for thread in thread_done:
if not thread:
all_data_collected = False
time.sleep(1)
short_v_long_range_matrix = [
item for sublist in short_v_long_range_matrix_threads
for item in sublist
]
sum_smaller_matrix = [
item for sublist in sum_smaller_threads for item in sublist
]
sum_greater_matrix = [
item for sublist in sum_greater_threads for item in sublist
]
short_v_long_range.append(short_v_long_range_matrix)
sum_smaller.append(sum_smaller_matrix)
sum_greater.append(sum_greater_matrix)
log.debug(short_v_long_range)
plt.ylabel('Sum short range / long range')
plt.tick_params(axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
box_plot = plt.boxplot(short_v_long_range, patch_artist=True)
legend_handels_color = []
for i, patch in enumerate(box_plot['boxes']):
patch.set_facecolor(args.colorList[i % len(args.colorList)])
legend_handels_color.append(
mpatches.Patch(color=args.colorList[i % len(args.colorList)],
label=args.matrices[i].split('/')[-1]))
plt.legend(handles=legend_handels_color)
plt.savefig(args.plotFileName, dpi=args.dpi)
if len(args.matrices) > 1:
p_values = []
for i, sample in enumerate(short_v_long_range):
for sample2 in short_v_long_range[i + 1:]:
statistic, significance_level = ranksums(sample, sample2)
p_values.append(significance_level)
log.debug('p_values {}'.format(p_values))
with open(args.outFileName, 'w') as file:
header = '# Created with HiCExplorer\'s hicPlotSVL ' + __version__ + '\n'
header += "# Short range vs long range contacts per chromosome, p-values of each distribution against each other distribution with Wilcoxon rank-sum\n"
header += '# Short range contacts: <= ' + str(args.distance) + '\n'
file.write(header)
counter = 0
for i, matrix_0 in enumerate(args.matrices):
for j, matrix_1 in enumerate(args.matrices[i + 1:]):
file.write(matrix_0 + '\t' + matrix_1 + '\t' +
str(p_values[counter]) + '\n')
counter += 1
with open(args.outFileNameData, 'w') as file:
header = '# Created with HiCExplorer\'s hicPlotSVL ' + __version__ + '\n'
header += "# Short range vs long range contacts per chromosome: raw data\n"
header += '# Short range contacts: <= ' + str(args.distance) + '\n'
matrices_names = '\t\t\t'.join(args.matrices)
header += '#\t{}\n'.format(matrices_names)
header += '# Chromosome\t'
header += '\t'.join([
'Ratio', 'Sum <= {}'.format(args.distance), 'Sum > {}'.format(
args.distance)
] * len(args.matrices))
header += '\n'
file.write(header)
counter = 0
for i, chromosome in enumerate(chromosomes_list):
file.write('{}\t'.format(chromosome))
for j, matrix in enumerate(args.matrices):
if i < len(short_v_long_range[j]):
file.write('{}\t{}\t{}\t'.format(short_v_long_range[j][i],
sum_smaller[j][i],
sum_greater[j][i]))
else:
file.write('\t')
file.write('\n')
def main(args=None):
args = parse_arguments().parse_args(args)
viewpointObj = Viewpoint()
referencePoints, _ = viewpointObj.readReferencePointFile(
args.referencePoints)
relative_positions = set()
bin_size = 0
# - compute for each condition (matrix):
# - all viewpoints and smooth them: sliding window approach
# - after smoothing, sum all viewpoints up to one
# - compute the percentage of each position with respect to the total interaction count
# for models of all conditions:
# - compute nbinom parameters
referencePointsPerThread = len(referencePoints) // args.threads
queue = [None] * args.threads
process = [None] * args.threads
background_model_data = None
fail_flag = False
fail_message = ''
for matrix in args.matrices:
hic_ma = hm.hiCMatrix(matrix)
viewpointObj.hicMatrix = hic_ma
bin_size = hic_ma.getBinSize()
all_data_collected = False
thread_done = [False] * args.threads
for i in range(args.threads):
if i < args.threads - 1:
referencePointsThread = referencePoints[i * referencePointsPerThread:(i + 1) * referencePointsPerThread]
else:
referencePointsThread = referencePoints[i * referencePointsPerThread:]
queue[i] = Queue()
process[i] = Process(target=compute_background, kwargs=dict(
pReferencePoints=referencePointsThread,
pViewpointObj=viewpointObj,
pArgs=args,
pQueue=queue[i]
)
)
process[i].start()
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
background_data_thread = queue[i].get()
if 'Fail:' in background_data_thread:
fail_flag = True
fail_message = background_data_thread[6:]
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
continue
background_model_data_thread, relative_positions_thread = background_data_thread
if background_model_data is None:
background_model_data = background_model_data_thread
else:
for relativePosition in background_model_data_thread:
if relativePosition in background_model_data:
background_model_data[relativePosition].extend(
background_model_data_thread[relativePosition])
else:
background_model_data[relativePosition] = background_model_data_thread[relativePosition]
relative_positions = relative_positions.union(
relative_positions_thread)
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
all_data_collected = True
for thread in thread_done:
if not thread:
all_data_collected = False
time.sleep(1)
del hic_ma
del viewpointObj.hicMatrix
if fail_flag:
log.error('An error occurred caused by one or many faulty reference points.')
log.error('Please run chicQualityControl to remove these from your reference point file: {}'.format(args.referencePoints))
log.error(fail_message)
exit(1)
# for models of all conditions:
# - fit negative binomial for each relative distance
relative_positions = sorted(relative_positions)
nbinom_parameters = {}
max_value = {}
mean_value = {}
sum_all_values = 0
data_of_distribution = None
for relative_position in relative_positions:
if args.truncateZeros:
data_of_distribution = np.array(background_model_data[relative_position])
mask = data_of_distribution > 0.0
data_of_distribution = data_of_distribution[mask]
else:
data_of_distribution = np.array(background_model_data[relative_position])
nbinom_parameters[relative_position] = fit_nbinom.fit(data_of_distribution)
if len(data_of_distribution) > 0:
max_value[relative_position] = np.max(data_of_distribution)
average_value = np.average(data_of_distribution)
mean_value[relative_position] = average_value
sum_all_values += average_value
else:
max_value[relative_position] = 0.0
average_value = 0.0
mean_value[relative_position] = 0.0
sum_all_values += 0.0
for relative_position in relative_positions:
mean_value[relative_position] /= sum_all_values
# write result to file
with open(args.outFileName, 'w') as file:
file.write(
'Relative position\tsize nbinom\tprob nbinom\tmax value\tmean value\n')
for relative_position in relative_positions:
relative_position_in_genomic_scale = relative_position * bin_size
file.write("{}\t{:.12f}\t{:.12f}\t{:.12f}\t{:.12f}\n".format(relative_position_in_genomic_scale, nbinom_parameters[relative_position]['size'],
nbinom_parameters[relative_position]['prob'], max_value[relative_position], mean_value[relative_position]))
def main(args=None):
args = parse_arguments().parse_args(args)
hic_matrix_list = []
sum_list = []
for matrix in args.matrices:
hic_ma = hm.hiCMatrix(matrix)
if args.normalize == 'smallest':
sum_list.append(hic_ma.matrix.sum())
hic_matrix_list.append(hic_ma)
if args.normalize == 'norm_range':
for i, hic_matrix in enumerate(hic_matrix_list):
hic_matrix.matrix.data = hic_matrix.matrix.data.astype(np.float32)
mask = np.isnan(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
mask = np.isinf(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
min_value = np.min(hic_matrix.matrix.data)
max_value = np.max(hic_matrix.matrix.data)
min_max_difference = np.float64(max_value - min_value)
hic_matrix.matrix.data -= min_value
hic_matrix.matrix.data /= min_max_difference
mask = np.isnan(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
mask = np.isinf(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
hic_matrix.matrix.eliminate_zeros()
hic_matrix.save(args.outFileName[i], pApplyCorrection=False)
elif args.normalize == 'smallest':
argmin = np.argmin(sum_list)
for i, hic_matrix in enumerate(hic_matrix_list):
hic_matrix.matrix.data = hic_matrix.matrix.data.astype(np.float32)
if i != argmin:
mask = np.isnan(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
mask = np.isinf(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
adjust_factor = sum_list[i] / sum_list[argmin]
hic_matrix.matrix.data /= adjust_factor
mask = np.isnan(hic_matrix.matrix.data)
mask = np.isnan(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
mask = np.isinf(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
hic_matrix.matrix.eliminate_zeros()
hic_matrix.save(args.outFileName[i], pApplyCorrection=False)
elif args.normalize == 'multiplicative':
for i, hic_matrix in enumerate(hic_matrix_list):
hic_matrix.matrix.data = hic_matrix.matrix.data.astype(np.float32)
mask = np.isnan(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
mask = np.isinf(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
# adjust_factor = sum_list[i] / sum_list[argmin]
hic_matrix.matrix.data *= args.multiplicativeValue
mask = np.isnan(hic_matrix.matrix.data)
mask = np.isnan(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
mask = np.isinf(hic_matrix.matrix.data)
hic_matrix.matrix.data[mask] = 0
hic_matrix.matrix.eliminate_zeros()
hic_matrix.save(args.outFileName[i], pApplyCorrection=False)
def test_maskBins():
hic = hm.hiCMatrix()
cut_intervals = [('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('b', 30, 40, 1), ('b', 40, 50, 1)]
hic.nan_bins = []
matrix = np.array([[1, 8, 5, 3, 0], [0, 4, 15, 5, 1], [0, 0, 0, 0, 2],
[0, 0, 0, 0, 1], [0, 0, 0, 0, 0]])
hic.matrix = csr_matrix(matrix)
hic.setMatrix(hic.matrix, cut_intervals)
nt.assert_equal(hic.getMatrix(), matrix)
nt.assert_equal(hic.orig_bin_ids, [])
new_matrix = np.array([[0, 0, 2], [0, 0, 1], [0, 0, 0]])
masking_ids = [0, 1]
hic.maskBins(masking_ids)
nt.assert_equal(hic.getMatrix(), new_matrix)
nt.assert_equal(
sorted(hic.orig_cut_intervals),
sorted([('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('b', 30, 40, 1), ('b', 40, 50, 1)]))
nt.assert_equal(
sorted(hic.cut_intervals),
sorted([('a', 20, 30, 1), ('b', 30, 40, 1), ('b', 40, 50, 1)]))
nt.assert_equal(hic.chrBinBoundaries,
OrderedDict([('a', (0, 1)), ('b', (1, 3))]))
nt.assert_equal(sorted(hic.orig_bin_ids), sorted([0, 1, 2, 3, 4]))
# direct return if masking_ids is None or has len() == 0, thus no changes to matrix
masking_ids = None
hic.maskBins(masking_ids)
nt.assert_equal(hic.getMatrix(), new_matrix)
nt.assert_equal(
sorted(hic.orig_cut_intervals),
sorted([('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('b', 30, 40, 1), ('b', 40, 50, 1)]))
nt.assert_equal(
sorted(hic.cut_intervals),
sorted([('a', 20, 30, 1), ('b', 30, 40, 1), ('b', 40, 50, 1)]))
nt.assert_equal(hic.chrBinBoundaries,
OrderedDict([('a', (0, 1)), ('b', (1, 3))]))
masking_ids = []
hic.maskBins(masking_ids)
nt.assert_equal(hic.getMatrix(), new_matrix)
nt.assert_equal(
sorted(hic.orig_cut_intervals),
sorted([('a', 0, 10, 1), ('a', 10, 20, 1), ('a', 20, 30, 1),
('b', 30, 40, 1), ('b', 40, 50, 1)]))
nt.assert_equal(
sorted(hic.cut_intervals),
sorted([('a', 20, 30, 1), ('b', 30, 40, 1), ('b', 40, 50, 1)]))
nt.assert_equal(hic.chrBinBoundaries,
OrderedDict([('a', (0, 1)), ('b', (1, 3))]))
nt.assert_equal(sorted(hic.orig_bin_ids), sorted([0, 1, 2, 3, 4]))
def main(args=None):
args = parse_arguments().parse_args(args)
# read domains file
domains_df = readDomainBoundaries(args.tadDomains)
# read full h5 or only region if cooler
is_cooler_target = check_cooler(args.targetMatrix)
is_cooler_control = check_cooler(args.controlMatrix)
if is_cooler_target != is_cooler_control:
log.error('Matrices are not given in the same format!')
exit(1)
if not is_cooler_control:
hic_matrix_target = hm.hiCMatrix(args.targetMatrix)
hic_matrix_control = hm.hiCMatrix(args.controlMatrix)
else:
hic_matrix_target = args.targetMatrix
hic_matrix_control = args.controlMatrix
# accepted_H0 = []
# rejected_H0 = []
# log.debug('domains_df {}'.format(domains_df))
domains = domains_df.values.tolist()
p_values_threads = [None] * args.threads
accepted_left_inter_threads = [None] * args.threads
accepted_right_inter_threads = [None] * args.threads
accepted_intra_threads = [None] * args.threads
rows_threads = [None] * args.threads
domainsPerThread = len(domains) // args.threads
all_data_collected = False
queue = [None] * args.threads
process = [None] * args.threads
thread_done = [False] * args.threads
# None --> first thread, process first element in list, ignore last one
# True --> middle thread: ignore first and last element in tad processing
# False --> last thread: ignore first element, process last one
thread_id = None
for i in range(args.threads):
if i == 0:
domainListThread = domains[i * domainsPerThread:(
(i + 1) * domainsPerThread) + 1]
thread_id = None
elif i < args.threads - 1:
domainListThread = domains[(i * domainsPerThread) -
1:((i + 1) * domainsPerThread) + 1]
thread_id = True
else:
domainListThread = domains[(i * domainsPerThread) - 1:]
thread_id = False
if args.threads == 1:
thread_id = ''
queue[i] = Queue()
process[i] = Process(target=computeDifferentialTADs,
kwargs=dict(pMatrixTarget=hic_matrix_target,
pMatrixControl=hic_matrix_control,
pDomainList=domainListThread,
pCoolOrH5=is_cooler_control,
pPValue=args.pValue,
pThreadId=thread_id,
pQueue=queue[i]))
process[i].start()
while not all_data_collected:
for i in range(args.threads):
if queue[i] is not None and not queue[i].empty():
p_values_threads[i], accepted_left_inter_threads[i], \
accepted_right_inter_threads[i], \
accepted_intra_threads[i], rows_threads[i] = queue[i].get()
queue[i] = None
process[i].join()
process[i].terminate()
process[i] = None
thread_done[i] = True
all_data_collected = True
for thread in thread_done:
if not thread:
all_data_collected = False
time.sleep(1)
# outfile_names = [item for sublist in outfile_names for item in sublist]
# target_list_name = [
# item for sublist in target_list_name for item in sublist]
p_values_list = [item for sublist in p_values_threads for item in sublist]
accepted_inter_left = [
item for sublist in accepted_left_inter_threads for item in sublist
]
accepted_inter_right = [
item for sublist in accepted_right_inter_threads for item in sublist
]
accepted_intra = [
item for sublist in accepted_intra_threads for item in sublist
]
rows = [item for sublist in rows_threads for item in sublist]
p_values_list = np.array(p_values_list)
accepted_inter_left = np.array(accepted_inter_left)
accepted_inter_right = np.array(accepted_inter_right)
accepted_intra = np.array(accepted_intra)
rows = np.array(rows)
if args.mode == 'intra-TAD':
mask = accepted_intra
elif args.mode == 'left-inter-TAD':
if args.modeReject == 'all':
mask = np.logical_and(accepted_inter_left, accepted_intra)
else:
mask = np.logical_or(accepted_inter_left, accepted_intra)
elif args.mode == 'right-inter-TAD':
if args.modeReject == 'all':
mask = np.logical_and(accepted_intra, accepted_inter_right)
else:
mask = np.logical_or(accepted_intra, accepted_inter_right)
else:
if args.modeReject == 'all':
mask = np.logical_and(accepted_inter_left, accepted_inter_right)
mask = np.logical_and(mask, accepted_intra)
else:
mask = np.logical_or(accepted_inter_left, accepted_inter_right)
mask = np.logical_or(mask, accepted_intra)
accepted_H0 = p_values_list[~mask]
rejected_H0 = p_values_list[mask]
accepted_rows = rows[~mask]
rejected_rows = rows[mask]
with open(args.outFileNamePrefix + '_accepted.diff_tad', 'w') as file:
header = '# Created with HiCExplorer\'s hicDifferentialTAD version ' + __version__ + '\n'
header += '# H0 \'regions are equal\' H0 is accepted for all p-value greater the user given p-value threshold; i.e. regions in this file are not considered as differential.\n'
header += '# Accepted regions with Wilcoxon rank-sum test to p-value: {} with used mode: {} and modeReject: {} \n'.format(
args.pValue, args.mode, args.modeReject)
header += '# Chromosome\tstart\tend\tname\tscore\tstrand\tp-value left-inter-TAD\tp-value right-inter-TAD\tp-value intra-TAD\n'
file.write(header)
for i, row in enumerate(accepted_rows):
row_list = list(map(str, row))
file.write('\t'.join(row_list))
file.write('\t')
pvalue_list = list(map(str, accepted_H0[i]))
file.write('\t'.join(pvalue_list))
file.write('\n')
with open(args.outFileNamePrefix + '_rejected.diff_tad', 'w') as file:
header = '# Created with HiCExplorer\'s hicDifferentialTAD version ' + __version__ + '\n'
header += '# H0 \'regions are equal\' H0 is rejected for all p-value smaller or equal the user given p-value threshold; i.e. regions in this file are considered as differential.\n'
header += '# Rejected regions with Wilcoxon rank-sum test to p-value: {} with used mode: {} and modeReject: {} \n'.format(
args.pValue, args.mode, args.modeReject)
header += '# Chromosome\tstart\tend\tname\tscore\tstrand\tp-value left-inter-TAD\tp-value right-inter-TAD\tp-value intra-TAD\n'
file.write(header)
for i, row in enumerate(rejected_rows):
row_list = list(map(str, row))
file.write('\t'.join(row_list))
file.write('\t')
pvalue_list = list(map(str, rejected_H0[i]))
file.write('\t'.join(pvalue_list))
file.write('\n')
