def test_19_matrix_manip(self):
if ONLY and not "19" in ONLY:
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads("lala-map~", resolution=10000)
hic_map(hic_data1, savedata="lala-map.tsv~", savefig="lala.pdf")
hic_map(hic_data1,
by_chrom="intra",
savedata="lala-maps~",
savefig="lalalo~")
hic_map(hic_data1,
by_chrom="inter",
savedata="lala-maps~",
savefig="lalala~")
# slowest part of the all test:
hic_data2 = read_matrix("lala-map.tsv~", resolution=10000)
self.assertEqual(hic_data1, hic_data2)
# vals = plot_distance_vs_interactions(hic_data1)
# self.assertEqual([round(i, 2) if str(i)!="nan" else 0.0 for i in
# reduce(lambda x, y: x + y, vals)],
# [-1.68, -2.08, 0.02, 2.76, -8.99, 0.0, 0.82, -6.8, 0.0])
a, b = insert_sizes("lala-map~")
self.assertEqual([int(a), int(b)], [43, 1033])
hic_data1 = read_matrix(PATH + "/20Kb/chrT/chrT_A.tsv",
resolution=20000)
hic_data2 = read_matrix(PATH + "/20Kb/chrT/chrT_B.tsv",
resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i, 3) for i in corr[0]]
self.assertEqual(corr, [
0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828, 0.757, 0.797,
0.832
])
ecorr = eig_correlate_matrices(hic_data1,
hic_data2,
savefig='lala3.pdf')
ecorr = [round(i, 3) for i in reduce(lambda x, y: x + y, ecorr)]
self.assertEqual(ecorr, [
0.997, 0.322, 0.442, 0.017, 0.243, 0.014, 0.321, 0.999, 0.01,
0.006, 0.0, 0.007, 0.451, 0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013, 0.031, 0.08, 0.974,
0.018, 0.028, 0.004, 0.0, 0.028, 0.034, 0.89
])
system("rm -rf lala*")
if CHKTIME:
self.assertEqual(True, True)
print "19", time() - t0
def test_19_matrix_manip(self):
if ONLY and ONLY != '19':
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads('lala-map~', resolution=10000)
hic_map(hic_data1, savedata='lala-map.tsv~', savefig='lala.pdf~')
hic_map(hic_data1,
by_chrom='intra',
savedata='lala-maps~',
savefig='lalalo~')
hic_map(hic_data1,
by_chrom='inter',
savedata='lala-maps~',
savefig='lalala~')
# slowest part of the all test:
hic_data2 = read_matrix('lala-map.tsv~', resolution=10000)
self.assertEqual(hic_data1, hic_data2)
vals = plot_distance_vs_interactions(hic_data1)
self.assertEqual([
round(i, 2) if str(i) != 'nan' else 0.0
for i in reduce(lambda x, y: x + y, vals)
], [-1.68, -2.08, 0.02, 2.76, -8.99, 0.0, 0.82, -6.8, 0.0])
a, b = insert_sizes('lala-map~')
self.assertEqual([int(a), int(b)], [43, 1033])
hic_data1 = read_matrix('20Kb/chrT/chrT_A.tsv', resolution=20000)
hic_data2 = read_matrix('20Kb/chrT/chrT_B.tsv', resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i, 3) for i in corr[0]]
self.assertEqual(corr, [
0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828, 0.757, 0.797,
0.832
])
ecorr = eig_correlate_matrices(hic_data1, hic_data2)
ecorr = [round(i, 3) for i in reduce(lambda x, y: x + y, ecorr)]
self.assertEqual(ecorr, [
0.997, 0.322, 0.442, 0.017, 0.243, 0.014, 0.321, 0.999, 0.01,
0.006, 0.0, 0.007, 0.451, 0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013, 0.031, 0.08, 0.974,
0.018, 0.028, 0.004, 0.0, 0.028, 0.034, 0.89
])
system('rm -rf lala*')
if CHKTIME:
self.assertEqual(True, True)
print '19', time() - t0
def test_19_matrix_manip(self):
if ONLY and ONLY != '19':
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads('lala-map~', resolution=10000)
hic_map(hic_data1, savedata='lala-map.tsv~', savefig='lala.pdf~')
hic_map(hic_data1, by_chrom='intra', savedata='lala-maps~', savefig='lalalo~')
hic_map(hic_data1, by_chrom='inter', savedata='lala-maps~', savefig='lalala~')
# slowest part of the all test:
hic_data2 = read_matrix('lala-map.tsv~', resolution=10000)
self.assertEqual(hic_data1, hic_data2)
vals = plot_distance_vs_interactions(hic_data1)
self.assertEqual([round(i, 2) if str(i)!='nan' else 0.0 for i in
reduce(lambda x, y: x + y, vals)],
[-1.74, 4.2, 0.52, 1.82, -0.44, 0.0, -0.5, 2.95, 0.0])
a, b = insert_sizes('lala-map~')
self.assertEqual([int(a),int(b)], [43, 1033])
hic_data1 = read_matrix('20Kb/chrT/chrT_A.tsv', resolution=20000)
hic_data2 = read_matrix('20Kb/chrT/chrT_B.tsv', resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i,3) for i in corr[0]]
self.assertEqual(corr, [0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828,
0.757, 0.797, 0.832])
ecorr = eig_correlate_matrices(hic_data1, hic_data2)
ecorr = [round(i,3) for i in reduce(lambda x, y:x+y, ecorr)]
self.assertEqual(ecorr, [0.997, 0.322, 0.442, 0.017, 0.243, 0.014,
0.321, 0.999, 0.01, 0.006, 0.0, 0.007, 0.451,
0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013,
0.031, 0.08, 0.974, 0.018, 0.028, 0.004, 0.0,
0.028, 0.034, 0.89])
system('rm -rf lala*')
if CHKTIME:
self.assertEqual(True, True)
print '19', time() - t0
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
if opts.bed:
mreads = path.realpath(opts.bed)
else:
mreads = path.join(opts.workdir, load_parameters_fromdb(opts))
print 'loading', mreads
hic_data = load_hic_data_from_reads(mreads, opts.reso)
mkdir(path.join(opts.workdir, '04_normalization'))
print 'Get poor bins...'
try:
hic_data.filter_columns(
perc_zero=opts.perc_zeros,
draw_hist=True,
by_mean=not opts.fast_filter,
savefig=path.join(
opts.workdir, '04_normalization', 'bad_columns_%s_%d_%s.pdf' %
(opts.reso, opts.perc_zeros, param_hash))
if not opts.fast_filter else None)
except ValueError:
hic_data.filter_columns(
perc_zero=100,
draw_hist=True,
by_mean=not opts.fast_filter,
savefig=path.join(
opts.workdir, '04_normalization', 'bad_columns_%s_%d_%s.pdf' %
(opts.reso, opts.perc_zeros, param_hash))
if not opts.fast_filter else None)
# bad columns
bad_columns_file = path.join(
opts.workdir, '04_normalization',
'bad_columns_%s_%d_%s.tsv' % (opts.reso, opts.perc_zeros, param_hash))
out_bad = open(bad_columns_file, 'w')
out_bad.write('\n'.join([str(i) for i in hic_data.bads.keys()]))
out_bad.close()
# Identify biases
print 'Get biases using ICE...'
hic_data.normalize_hic(silent=False,
max_dev=0.1,
iterations=0,
factor=opts.factor)
print 'Getting cis/trans...'
cis_trans_N_D = hic_data.cis_trans_ratio(normalized=True, diagonal=True)
cis_trans_n_D = hic_data.cis_trans_ratio(normalized=False, diagonal=True)
cis_trans_N_d = hic_data.cis_trans_ratio(normalized=True, diagonal=False)
cis_trans_n_d = hic_data.cis_trans_ratio(normalized=False, diagonal=False)
print 'Cis/Trans ratio of normalized matrix including the diagonal', cis_trans_N_D
print 'Cis/Trans ratio of normalized matrix excluding the diagonal', cis_trans_N_d
print 'Cis/Trans ratio of raw matrix including the diagonal', cis_trans_n_D
print 'Cis/Trans ratio of raw matrix excluding the diagonal', cis_trans_n_d
# Plot genomic distance vs interactions
print 'Plot genomic distance vs interactions...'
inter_vs_gcoord = path.join(
opts.workdir, '04_normalization',
'interactions_vs_genomic-coords.pdf_%s_%s.pdf' %
(opts.reso, param_hash))
(_, _, _), (a2, _, _), (_, _, _) = plot_distance_vs_interactions(
hic_data,
max_diff=10000,
resolution=opts.reso,
normalized=True,
savefig=inter_vs_gcoord)
print 'Decay slope 0.7-10 Mb\t%s' % a2
# write biases
bias_file = path.join(opts.workdir, '04_normalization',
'bias_%s_%s.tsv' % (opts.reso, param_hash))
out_bias = open(bias_file, 'w')
out_bias.write(
'\n'.join(['%d\t%f' % (i, hic_data.bias[i])
for i in hic_data.bias]) + '\n')
out_bias.close()
# to feed the save_to_db funciton
intra_dir_nrm_fig = intra_dir_nrm_txt = None
inter_dir_nrm_fig = inter_dir_nrm_txt = None
genom_map_nrm_fig = genom_map_nrm_txt = None
intra_dir_raw_fig = intra_dir_raw_txt = None
inter_dir_raw_fig = inter_dir_raw_txt = None
genom_map_raw_fig = genom_map_raw_txt = None
if "intra" in opts.keep:
print " Saving intra chromosomal raw and normalized matrices..."
if opts.only_txt:
intra_dir_nrm_fig = None
intra_dir_raw_fig = None
else:
intra_dir_nrm_fig = path.join(
opts.workdir, '04_normalization',
'intra_chromosome_nrm_images_%s_%s' % (opts.reso, param_hash))
intra_dir_raw_fig = path.join(
opts.workdir, '04_normalization',
'intra_chromosome_raw_images_%s_%s' % (opts.reso, param_hash))
intra_dir_nrm_txt = path.join(
opts.workdir, '04_normalization',
'intra_chromosome_nrm_matrices_%s_%s' % (opts.reso, param_hash))
intra_dir_raw_txt = path.join(
opts.workdir, '04_normalization',
'intra_chromosome_raw_matrices_%s_%s' % (opts.reso, param_hash))
hic_map(hic_data,
normalized=True,
by_chrom='intra',
cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=intra_dir_nrm_fig,
savedata=intra_dir_nrm_txt)
hic_map(hic_data,
normalized=False,
by_chrom='intra',
cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=intra_dir_raw_fig,
savedata=intra_dir_raw_txt)
if "inter" in opts.keep:
print " Saving inter chromosomal raw and normalized matrices..."
if opts.only_txt:
inter_dir_nrm_fig = None
inter_dir_raw_fig = None
else:
inter_dir_nrm_fig = path.join(
opts.workdir, '04_normalization',
'inter_chromosome_nrm_images_%s_%s' % (opts.reso, param_hash))
inter_dir_raw_fig = path.join(
opts.workdir, '04_normalization',
'inter_chromosome_raw_images_%s_%s' % (opts.reso, param_hash))
inter_dir_nrm_txt = path.join(
opts.workdir, '04_normalization',
'inter_chromosome_nrm_matrices_%s_%s' % (opts.reso, param_hash))
inter_dir_raw_txt = path.join(
opts.workdir, '04_normalization',
'inter_chromosome_raw_matrices_%s_%s' % (opts.reso, param_hash))
hic_map(hic_data,
normalized=True,
by_chrom='inter',
cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=inter_dir_nrm_fig,
savedata=inter_dir_nrm_txt)
hic_map(hic_data,
normalized=False,
by_chrom='inter',
cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=inter_dir_raw_fig,
savedata=inter_dir_raw_txt)
if "genome" in opts.keep:
print " Saving normalized genomic matrix..."
if opts.only_txt:
genom_map_nrm_fig = path.join(
opts.workdir, '04_normalization',
'genomic_maps_nrm_%s_%s.pdf' % (opts.reso, param_hash))
genom_map_raw_fig = path.join(
opts.workdir, '04_normalization',
'genomic_maps_raw_%s_%s.pdf' % (opts.reso, param_hash))
else:
genom_map_nrm_fig = None
genom_map_raw_fig = None
genom_map_nrm_txt = path.join(
opts.workdir, '04_normalization',
'genomic_nrm_%s_%s.tsv' % (opts.reso, param_hash))
genom_map_raw_txt = path.join(
opts.workdir, '04_normalization',
'genomic_raw_%s_%s.tsv' % (opts.reso, param_hash))
hic_map(hic_data,
normalized=True,
cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=genom_map_nrm_fig,
savedata=genom_map_nrm_txt)
hic_map(hic_data,
normalized=False,
cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=genom_map_raw_fig,
savedata=genom_map_raw_txt)
finish_time = time.localtime()
save_to_db(opts, cis_trans_N_D, cis_trans_N_d, cis_trans_n_D,
cis_trans_n_d, a2, bad_columns_file, bias_file, inter_vs_gcoord,
mreads, intra_dir_nrm_fig, intra_dir_nrm_txt, inter_dir_nrm_fig,
inter_dir_nrm_txt, genom_map_nrm_fig, genom_map_nrm_txt,
intra_dir_raw_fig, intra_dir_raw_txt, inter_dir_raw_fig,
inter_dir_raw_txt, genom_map_raw_fig, genom_map_raw_txt,
launch_time, finish_time)
plt.rcParams['savefig.bbox'] = 'tight'
bin_size = {}
bin_size['Mb'] = 1e6
pair_id = filtered_reads.split("/")[-1].replace("_filtered_map.tsv", "")
outfile = '%s/%s_plot_genomic_coverage_filtered_%s.png' % (POSTFILTERING_PLOTS, pair_id, genomic_coverage_resolution)
plt.rcParams['font.size'] = 20
coverages = plot_genomic_distribution(filtered_reads, name='filtered', savefig=outfile, resolution=genomic_coverage_resolution, pair_id=pair_id)
outfile = '%s/%s_genomic_coverage_filtered_%s.bed' % (COVERAGES, pair_id, genomic_coverage_resolution)
coverages.to_csv(outfile, sep='\t', index=False)
# Filtered reads: interaction matrix
outfile = '%s/%s_plot_interaction_matrix_filtered_%s.png' % (POSTFILTERING_PLOTS, pair_id, genomic_coverage_resolution)
plt.rcParams['font.size'] = 12
hic_map(filtered_reads, resolution=int(bin_size[genomic_coverage_resolution]), savefig=outfile, decay=False, cmap='jet')
# Dangling ends: sequencing coverage along chromosomes
outfile = '%s/%s_plot_genomic_coverage_dangling_ends_%s.png' % (POSTFILTERING_PLOTS, pair_id, genomic_coverage_resolution)
plt.rcParams['font.size'] = 20
coverages = plot_genomic_distribution(dangling_ends, name='dangling_ends', savefig=outfile, resolution=genomic_coverage_resolution, pair_id=pair_id)
outfile = '%s/%s_genomic_coverage_dangling_ends_%s.bed' % (COVERAGES, pair_id, genomic_coverage_resolution)
coverages.to_csv(outfile, sep='\t', index=False)
# self-circle ends: sequencing coverage along chromosomes
outfile = '%s/%s_plot_genomic_coverage_self_circle_%s.png' % (POSTFILTERING_PLOTS, pair_id, genomic_coverage_resolution)
plt.rcParams['font.size'] = 20
coverages = plot_genomic_distribution(self_circle, name='self_circle', savefig=outfile, resolution=genomic_coverage_resolution, pair_id=pair_id)
outfile = '%s/%s_genomic_coverage_self_circle_%s.bed' % (COVERAGES, pair_id, genomic_coverage_resolution)
coverages.to_csv(outfile, sep='\t', index=False)
def make_matrices(left_reads_fastq, right_reads_fastq, reads_fastq, genome_fasta, genome_index, \
output_directory, output_prefix, enzyme, res, chromosomes, threads_number, \
clean_tmp, tmp_dir):
print 'Begin to process reads.'
left_reads = ''
right_reads = ''
if reads_fastq != '': # left and right reads are stored in one file
range_start_left, range_stop_left, \
range_start_right, range_stop_right = calc_left_right_ranges(reads_fastq)
print 'Reads: ', reads_fastq
left_reads = reads_fastq
right_reads = reads_fastq
else: # left and right reads are stored separately
range_start_left, range_stop_left, \
range_start_right, range_stop_right = calc_range(left_reads_fastq)
print 'Left reads: ', left_reads_fastq
print 'Right reads: ', right_reads_fastq
print 'Output prefix: ', output_prefix
left_reads = left_reads_fastq
right_reads = right_reads_fastq
print 'Reference genome FASTA: ', genome_fasta
print 'Reference genome GEM index:', genome_index
print 'Output directory: ', output_directory
print 'Temp directory: ', tmp_dir
print 'Enzyme: ', enzyme
print 'Resolution: ', res, 'bp'
print 'Number of threads: ', threads_number
print 'Start pos for left reads: ', range_start_left
print 'Stop pos for left reads: ', range_stop_left
print 'Start pos for right reads: ', range_start_right
print 'Stop pos for right reads: ', range_stop_right
stdout.flush()
# map left reads to reference genome
out_sam_left_name = splitext(basename(left_reads))[0] + '_left.sam'
out_sam_left_path = join(output_directory, out_sam_left_name)
print 'Iterative mapping of left reads (using ' + str(threads_number) + ' threads)...'
stdout.flush()
sams_left = iterative_mapping(genome_index, left_reads, out_sam_left_path, \
range_start_left, range_stop_left, nthreads=threads_number,
temp_dir=tmp_dir)
print 'Done.'
stdout.flush()
# map right reads to reference genome
out_sam_right_name = splitext(basename(right_reads))[0] + '_right.sam'
out_sam_right_path = join(output_directory, out_sam_right_name)
print 'Iterative mapping of right reads (using ' + str(threads_number) + ' threads)...'
stdout.flush()
sams_right = iterative_mapping(genome_index, right_reads, out_sam_right_path, \
range_start_right, range_stop_right, nthreads=threads_number,
temp_dir=tmp_dir)
print 'Done.'
stdout.flush()
# load reference genome sequence
print 'Load reference genome sequence...'
stdout.flush()
chroms = chromosomes[:]
genome_seq = parse_fasta(genome_fasta, chr_names=chroms)
print 'Done.'
stdout.flush()
# create files with information about every left and right read
# and about their placement with respect to restriction sites
tsv_left_name = splitext(basename(left_reads))[0] + '_left.tsv'
tsv_left = join(output_directory, tsv_left_name)
tsv_right_name = splitext(basename(right_reads))[0] + '_right.tsv'
tsv_right = join(output_directory, tsv_right_name)
print 'Get information about restriction sites and reads placement...'
stdout.flush()
parse_sam(sams_left, sams_right, tsv_left, tsv_right, genome_seq, enzyme, \
verbose=True, ncpus=8)
print 'Done.'
stdout.flush()
# create file with both left and right reads that uniquelly mapped to reference genome
if reads_fastq != '': # left and right reads are stored in one file
common_reads_prefix = splitext(basename(reads_fastq))[0]
else: # left and right reads are stored separately
common_reads_prefix = output_prefix
uniq_reads_name = common_reads_prefix + '_both_map_uniq.tsv'
uniq_reads = join(output_directory, uniq_reads_name)
print 'Merge info about left and right reads in one file...'
stdout.flush()
get_intersection(tsv_left, tsv_right, uniq_reads, verbose=True)
print 'Done.'
stdout.flush()
# find read IDs that are filtered by default TADbit filters
print 'Mask reads...'
stdout.flush()
# debug
print "uniq_reads =", uniq_reads
masked = filter_reads(uniq_reads)
print 'Done.'
stdout.flush()
# apply all filters (exclude reads that were filtered)
print 'Filter masked reads...'
stdout.flush()
filtered_reads_name = common_reads_prefix + '_filtered.tsv'
filtered_reads = join(output_directory, filtered_reads_name)
apply_filter(uniq_reads, filtered_reads, masked)
print 'Done.'
stdout.flush()
# create matrices (one matrix per chromosome)
print 'Create Hi-C maps (one per chromosome)...'
stdout.flush()
hic_map(filtered_reads, resolution=res, by_chrom='intra', savedata=output_directory)
print 'Done.'
stdout.flush()
print 'Add resolution (' + str(resolution) + ') to matrix filenames...'
stdout.flush()
add_resolution(chromosomes, resolution, output_directory)
print 'Done.'
stdout.flush()
print 'Add headers to matrix files...'
stdout.flush()
add_headers(chromosomes, resolution, output_directory)
print 'Done.'
stdout.flush()
if clean_tmp: # Remove all SAM and TSV files from the output directory
print 'Remove SAM and TSV files from the output directory.'
stdout.flush()
map(os.remove, glob.glob(out_sam_left_path + '*'))
map(os.remove, glob.glob(out_sam_right_path + '*'))
map(os.remove, glob.glob(join(output_directory, '*.tsv')))
print 'Done.'
stdout.flush()
# 8, but leave some room for you other applications
max_reads_per_chunk = chunk,
single_end = True)
sams1 = [OUTPATH + fnam for fnam in os.listdir(OUTPATH) if fnam.rsplit('.', 2)[0].endswith('_r1.txt')]
sams2 = [OUTPATH + fnam for fnam in os.listdir(OUTPATH) if fnam.rsplit('.', 2)[0].endswith('_r2.txt')]
print 'created thes SAM files:', sams2
parse_sam(sams1, sams2, frags,
OUTPATH + 'reads1_%s.tsv' % rep, OUTPATH + 'reads2_%s.tsv' % rep,
genome_seq, 'HindIII', verbose=True)
reads1 = OUTPATH + 'reads1_%s.tsv' % rep
reads2 = OUTPATH + 'reads2_%s.tsv' % rep
reads = OUTPATH + 'reads12_%s.tsv' % rep
get_intersection(reads1, reads2, reads, verbose=True)
from pytadbit.mapping.analyze import hic_map
hic_map(reads, genome_seq, resolution=100000, savedata='lala')
from pytadbit.mapping.analyze import plot_genomic_distribution
plot_genomic_distribution(reads, resolution=50000, genome_seq=genome_seq) # because I know it
# 690691 SRR_test_10000000/reads1_SRR_test.tsv
# 690691 SRR_test_100000/reads1_SRR_test.tsv
# 1242927 SRR_test_200000/reads1_SRR_test.tsv
# 1035866 SRR_test_500000/reads1_SRR_test.tsv
def make_matrices(left_reads_fastq, right_reads_fastq, reads_fastq, genome_fasta, genome_index, \
output_directory, output_prefix, enzyme, res, chromosomes, threads_number, \
clean_tmp, tmp_dir):
print 'Begin to process reads.'
left_reads = ''
right_reads = ''
if reads_fastq != '': # left and right reads are stored in one file
range_start_left, range_stop_left, \
range_start_right, range_stop_right = calc_left_right_ranges(reads_fastq)
print 'Reads: ', reads_fastq
left_reads = reads_fastq
right_reads = reads_fastq
else: # left and right reads are stored separately
range_start_left, range_stop_left, \
range_start_right, range_stop_right = calc_range(left_reads_fastq)
print 'Left reads: ', left_reads_fastq
print 'Right reads: ', right_reads_fastq
print 'Output prefix: ', output_prefix
left_reads = left_reads_fastq
right_reads = right_reads_fastq
print 'Reference genome FASTA: ', genome_fasta
print 'Reference genome GEM index:', genome_index
print 'Output directory: ', output_directory
print 'Temp directory: ', tmp_dir
print 'Enzyme: ', enzyme
print 'Resolution: ', res, 'bp'
print 'Number of threads: ', threads_number
print 'Start pos for left reads: ', range_start_left
print 'Stop pos for left reads: ', range_stop_left
print 'Start pos for right reads: ', range_start_right
print 'Stop pos for right reads: ', range_stop_right
stdout.flush()
# map left reads to reference genome
out_sam_left_name = splitext(basename(left_reads))[0] + '_left.sam'
out_sam_left_path = join(output_directory, out_sam_left_name)
print 'Iterative mapping of left reads (using ' + str(
threads_number) + ' threads)...'
stdout.flush()
sams_left = iterative_mapping(genome_index, left_reads, out_sam_left_path, \
range_start_left, range_stop_left, nthreads=threads_number,
temp_dir=tmp_dir)
print 'Done.'
stdout.flush()
# map right reads to reference genome
out_sam_right_name = splitext(basename(right_reads))[0] + '_right.sam'
out_sam_right_path = join(output_directory, out_sam_right_name)
print 'Iterative mapping of right reads (using ' + str(
threads_number) + ' threads)...'
stdout.flush()
sams_right = iterative_mapping(genome_index, right_reads, out_sam_right_path, \
range_start_right, range_stop_right, nthreads=threads_number,
temp_dir=tmp_dir)
print 'Done.'
stdout.flush()
# load reference genome sequence
print 'Load reference genome sequence...'
stdout.flush()
chroms = chromosomes[:]
genome_seq = parse_fasta(genome_fasta, chr_names=chroms)
print 'Done.'
stdout.flush()
# create files with information about every left and right read
# and about their placement with respect to restriction sites
tsv_left_name = splitext(basename(left_reads))[0] + '_left.tsv'
tsv_left = join(output_directory, tsv_left_name)
tsv_right_name = splitext(basename(right_reads))[0] + '_right.tsv'
tsv_right = join(output_directory, tsv_right_name)
print 'Get information about restriction sites and reads placement...'
stdout.flush()
parse_sam(sams_left, sams_right, tsv_left, tsv_right, genome_seq, enzyme, \
verbose=True, ncpus=8)
print 'Done.'
stdout.flush()
# create file with both left and right reads that uniquelly mapped to reference genome
if reads_fastq != '': # left and right reads are stored in one file
common_reads_prefix = splitext(basename(reads_fastq))[0]
else: # left and right reads are stored separately
common_reads_prefix = output_prefix
uniq_reads_name = common_reads_prefix + '_both_map_uniq.tsv'
uniq_reads = join(output_directory, uniq_reads_name)
print 'Merge info about left and right reads in one file...'
stdout.flush()
get_intersection(tsv_left, tsv_right, uniq_reads, verbose=True)
print 'Done.'
stdout.flush()
# find read IDs that are filtered by default TADbit filters
print 'Mask reads...'
stdout.flush()
masked = filter_reads(uniq_reads)
print 'Done.'
stdout.flush()
# apply all filters (exclude reads that were filtered)
print 'Filter masked reads...'
stdout.flush()
filtered_reads_name = common_reads_prefix + '_filtered.tsv'
filtered_reads = join(output_directory, filtered_reads_name)
apply_filter(uniq_reads, filtered_reads, masked)
print 'Done.'
stdout.flush()
# create matrices (one matrix per chromosome)
print 'Create Hi-C maps (one per chromosome)...'
stdout.flush()
hic_map(filtered_reads,
resolution=res,
by_chrom='intra',
savedata=output_directory)
print 'Done.'
stdout.flush()
print 'Add resolution (' + str(resolution) + ') to matrix filenames...'
stdout.flush()
add_resolution(chromosomes, resolution, output_directory)
print 'Done.'
stdout.flush()
print 'Add headers to matrix files...'
stdout.flush()
add_headers(chromosomes, resolution, output_directory)
print 'Done.'
stdout.flush()
if clean_tmp: # Remove all SAM and TSV files from the output directory
print 'Remove SAM and TSV files from the output directory.'
stdout.flush()
remove(out_sam_left_path + '*')
remove(out_sam_right_path + '*')
remove(join(output_directory, '*.tsv'))
print 'Done.'
stdout.flush()
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
if opts.bed:
mreads = path.realpath(opts.bed)
else:
mreads = path.join(opts.workdir, load_parameters_fromdb(opts))
print 'loading', mreads
hic_data = load_hic_data_from_reads(mreads, opts.reso)
mkdir(path.join(opts.workdir, '04_normalization'))
print 'Get poor bins...'
try:
hic_data.filter_columns(perc_zero=opts.perc_zeros, min_count=opts.min_count,
draw_hist=True,
by_mean=not opts.fast_filter, savefig=path.join(
opts.workdir, '04_normalization',
'bad_columns_%s_%d_%d_%s.pdf' % (
opts.reso, opts.perc_zeros, opts.min_count,
param_hash)) if
not opts.fast_filter else None)
except ValueError:
raise ValueError('ERROR: probably all columns filtered out...')
# bad columns
bad_columns_file = path.join(opts.workdir, '04_normalization',
'bad_columns_%s_%d_%d_%s.tsv' % (
opts.reso, opts.perc_zeros, opts.min_count, param_hash))
out_bad = open(bad_columns_file, 'w')
out_bad.write('\n'.join([str(i) for i in hic_data.bads.keys()]))
out_bad.close()
# Identify biases
if not opts.filter_only:
print 'Get biases using ICE...'
hic_data.normalize_hic(silent=False, max_dev=0.1, iterations=0,
factor=opts.factor)
print 'Getting cis/trans...'
cis_trans_N_D = cis_trans_N_d = float('nan')
if not opts.filter_only:
cis_trans_N_D = hic_data.cis_trans_ratio(normalized=True , diagonal=True )
cis_trans_N_d = hic_data.cis_trans_ratio(normalized=True , diagonal=False)
cis_trans_n_D = hic_data.cis_trans_ratio(normalized=False, diagonal=True )
cis_trans_n_d = hic_data.cis_trans_ratio(normalized=False, diagonal=False)
if not opts.filter_only:
print 'Cis/Trans ratio of normalized matrix including the diagonal', cis_trans_N_D
print 'Cis/Trans ratio of normalized matrix excluding the diagonal', cis_trans_N_d
print 'Cis/Trans ratio of raw matrix including the diagonal', cis_trans_n_D
print 'Cis/Trans ratio of raw matrix excluding the diagonal', cis_trans_n_d
# Plot genomic distance vs interactions
print 'Plot genomic distance vs interactions...'
inter_vs_gcoord = path.join(opts.workdir, '04_normalization',
'interactions_vs_genomic-coords.pdf_%s_%s.pdf' % (
opts.reso, param_hash))
(_, _, _), (a2, _, _), (_, _, _) = plot_distance_vs_interactions(
hic_data, max_diff=10000, resolution=opts.reso, normalized=not opts.filter_only,
savefig=inter_vs_gcoord)
print 'Decay slope 0.7-10 Mb\t%s' % a2
# write biases
bias_file = path.join(opts.workdir, '04_normalization',
'bias_%s_%s.tsv' % (opts.reso, param_hash))
out_bias = 'NA'
if not opts.filter_only:
out_bias = open(bias_file, 'w')
out_bias.write('\n'.join(['%d\t%f' % (i, hic_data.bias[i])
for i in hic_data.bias])
+ '\n')
out_bias.close()
# pickle the HiC-data object
print 'Saving genomic matrix'
pickle_path = path.join(opts.workdir, '04_normalization',
'hic-data_%s_%s.pickle' % (nice(opts.reso), param_hash))
out = open(pickle_path, 'w')
dump(hic_data, out)
out.close()
# to feed the save_to_db funciton
intra_dir_nrm_fig = intra_dir_nrm_txt = None
inter_dir_nrm_fig = inter_dir_nrm_txt = None
genom_map_nrm_fig = genom_map_nrm_txt = None
intra_dir_raw_fig = intra_dir_raw_txt = None
inter_dir_raw_fig = inter_dir_raw_txt = None
genom_map_raw_fig = genom_map_raw_txt = None
if "intra" in opts.keep:
print " Saving intra chromosomal raw and normalized matrices..."
if opts.only_txt:
intra_dir_nrm_fig = None
intra_dir_raw_fig = None
else:
intra_dir_nrm_fig = path.join(opts.workdir, '04_normalization',
'intra_chromosome_nrm_images_%s_%s' % (opts.reso, param_hash))
intra_dir_raw_fig = path.join(opts.workdir, '04_normalization',
'intra_chromosome_raw_images_%s_%s' % (opts.reso, param_hash))
intra_dir_nrm_txt = path.join(opts.workdir, '04_normalization',
'intra_chromosome_nrm_matrices_%s_%s' % (opts.reso, param_hash))
intra_dir_raw_txt = path.join(opts.workdir, '04_normalization',
'intra_chromosome_raw_matrices_%s_%s' % (opts.reso, param_hash))
if not opts.filter_only:
hic_map(hic_data, normalized=True, by_chrom='intra', cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=intra_dir_nrm_fig, savedata=intra_dir_nrm_txt)
hic_map(hic_data, normalized=False, by_chrom='intra', cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=intra_dir_raw_fig, savedata=intra_dir_raw_txt)
if "inter" in opts.keep:
print " Saving inter chromosomal raw and normalized matrices..."
if opts.only_txt:
inter_dir_nrm_fig = None
inter_dir_raw_fig = None
else:
if not opts.filter_only:
inter_dir_nrm_fig = path.join(opts.workdir, '04_normalization',
'inter_chromosome_nrm_images_%s_%s' % (opts.reso, param_hash))
inter_dir_raw_fig = path.join(opts.workdir, '04_normalization',
'inter_chromosome_raw_images_%s_%s' % (opts.reso, param_hash))
if not opts.filter_only:
inter_dir_nrm_txt = path.join(opts.workdir, '04_normalization',
'inter_chromosome_nrm_matrices_%s_%s' % (opts.reso, param_hash))
inter_dir_raw_txt = path.join(opts.workdir, '04_normalization',
'inter_chromosome_raw_matrices_%s_%s' % (opts.reso, param_hash))
if not opts.filter_only:
hic_map(hic_data, normalized=True, by_chrom='inter', cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=inter_dir_nrm_fig, savedata=inter_dir_nrm_txt)
hic_map(hic_data, normalized=False, by_chrom='inter', cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=inter_dir_raw_fig, savedata=inter_dir_raw_txt)
if "genome" in opts.keep:
print " Saving normalized genomic matrix..."
if opts.only_txt:
genom_map_nrm_fig = None
genom_map_raw_fig = None
else:
if not opts.filter_only:
genom_map_nrm_fig = path.join(opts.workdir, '04_normalization',
'genomic_maps_nrm_%s_%s.pdf' % (opts.reso, param_hash))
genom_map_raw_fig = path.join(opts.workdir, '04_normalization',
'genomic_maps_raw_%s_%s.pdf' % (opts.reso, param_hash))
if not opts.filter_only:
genom_map_nrm_txt = path.join(opts.workdir, '04_normalization',
'genomic_nrm_%s_%s.tsv' % (opts.reso, param_hash))
genom_map_raw_txt = path.join(opts.workdir, '04_normalization',
'genomic_raw_%s_%s.tsv' % (opts.reso, param_hash))
if not opts.filter_only:
hic_map(hic_data, normalized=True, cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=genom_map_nrm_fig, savedata=genom_map_nrm_txt)
hic_map(hic_data, normalized=False, cmap='jet',
name=path.split(opts.workdir)[-1],
savefig=genom_map_raw_fig, savedata=genom_map_raw_txt)
finish_time = time.localtime()
save_to_db (opts, cis_trans_N_D, cis_trans_N_d, cis_trans_n_D, cis_trans_n_d,
a2, bad_columns_file, bias_file, inter_vs_gcoord, mreads,
len(hic_data.bads.keys()), len(hic_data),
intra_dir_nrm_fig, intra_dir_nrm_txt,
inter_dir_nrm_fig, inter_dir_nrm_txt,
genom_map_nrm_fig, genom_map_nrm_txt,
intra_dir_raw_fig, intra_dir_raw_txt,
inter_dir_raw_fig, inter_dir_raw_txt,
genom_map_raw_fig, genom_map_raw_txt,
pickle_path, launch_time, finish_time)
