def split(parameters):
parser = parsers.split_parser()
args = parser.parse_args(parameters)
if args.colormap is None:
colormap = config.colormap_hic if not args.oe else 'bwr'
else:
colormap = args.colormap
matrix_bottom = fanc.load(os.path.expanduser(args.hic_bottom), mode='r')
matrix_top = fanc.load(os.path.expanduser(args.hic_top), mode='r')
norm = "lin" if not args.log else "log"
sp = kplt.HicComparisonPlot2D(
matrix_top,
matrix_bottom,
colormap=colormap,
norm=norm,
vmin=args.vmin,
adjust_range=args.adjust_range,
vmax=args.vmax,
scale_matrices=args.scaling,
oe=args.oe,
log=args.oe,
colorbar_symmetry=0 if args.oe and args.colorbar_symmetry is None else
args.colorbar_symmetry,
show_colorbar=args.show_colorbar)
return sp, args
def test_bed(self):
this_dir = os.path.dirname(os.path.realpath(__file__))
bed_file = this_dir + '/test_load/test.bed'
with fanc.load(bed_file) as bed:
assert isinstance(bed, gr.Bed)
with pytest.raises(ValueError):
foo_file = this_dir + '/test_load/foo.txt'
fanc.load(foo_file)
def test_sambam(self):
this_dir = os.path.dirname(os.path.realpath(__file__))
sam_file = this_dir + '/test_load/test.sam'
with fanc.load(sam_file, mode='r') as bw:
assert isinstance(bw, pysam.AlignmentFile)
bam_file = this_dir + '/test_load/test.bam'
with fanc.load(bam_file, mode='r') as bw:
assert isinstance(bw, pysam.AlignmentFile)
def mirror(parameters):
parser = parsers.mirror_parser()
args = parser.parse_args(parameters)
if args.colormap_lower is None:
colormap_lower = config.colormap_hic if not args.oe_lower else 'bwr'
else:
colormap_lower = args.colormap_lower
if args.colormap_upper is None:
colormap_upper = config.colormap_hic if not args.oe_upper else 'bwr'
else:
colormap_upper = args.colormap_upper
matrix_upper = fanc.load(os.path.expanduser(args.hic_upper), mode='r')
matrix_lower = fanc.load(os.path.expanduser(args.hic_lower), mode='r')
from fanc.tools.general import str_to_int
norm_upper = "lin" if not args.log_upper else "log"
upper_plot = kplt.HicPlot(matrix_upper,
colormap=colormap_upper,
max_dist=str_to_int(args.max_dist),
norm=norm_upper,
vmin=args.vmin_upper,
vmax=args.vmax_upper,
oe=args.oe_upper,
log=args.oe_upper,
colorbar_symmetry=0 if args.oe_upper
and args.colorbar_symmetry_upper is None else
args.colorbar_symmetry_upper,
show_colorbar=args.show_colorbar,
adjust_range=False)
norm_lower = "lin" if not args.log_lower else "log"
lower_plot = kplt.HicPlot(matrix_lower,
colormap=colormap_lower,
max_dist=str_to_int(args.max_dist),
norm=norm_lower,
vmin=args.vmin_lower,
vmax=args.vmax_lower,
oe=args.oe_lower,
log=args.oe_lower,
colorbar_symmetry=0 if args.oe_lower
and args.colorbar_symmetry_lower is None else
args.colorbar_symmetry_lower,
show_colorbar=args.show_colorbar,
adjust_range=False)
vsp = kplt.VerticalSplitPlot(upper_plot, lower_plot)
return vsp, args
def bar(parameters):
parser = parsers.bar_parser()
args = parser.parse_args(parameters)
regions = [fanc.load(file_name) for file_name in args.regions]
attribute = args.attribute
labels = args.labels
ylim = args.ylim
colors = args.colors
alpha = args.alpha
legend_location = args.legend_location
if labels is not None and len(labels) != len(regions):
parser.error("Number of labels ({}) must be the same as number "
"of datasets ({})".format(len(labels), len(regions)))
p = kplt.BarPlot(regions,
attribute=attribute,
labels=labels,
ylim=ylim,
plot_kwargs={'alpha': alpha},
colors=colors,
legend_location=legend_location)
return p, args
def square(parameters):
parser = parsers.square_parser()
args = parser.parse_args(parameters)
norm = "lin" if not args.log else "log"
if args.colormap is None:
colormap = config.colormap_hic if not args.oe else 'bwr'
else:
colormap = args.colormap
matrix = fanc.load(os.path.expanduser(args.hic), mode='r')
return kplt.HicPlot2D(
matrix,
colormap=colormap,
norm=norm,
vmin=args.vmin,
vmax=args.vmax,
show_colorbar=args.show_colorbar,
adjust_range=args.adjust_range,
oe=args.oe,
log=args.oe,
colorbar_symmetry=0 if args.oe and args.colorbar_symmetry is None else
args.colorbar_symmetry,
flip=args.flip,
matrix_norm=args.norm,
weight_field=args.weight_field), args
def scores(parameters):
parser = parsers.scores_parser()
args = parser.parse_args(parameters)
array = fanc.load(os.path.expanduser(args.scores), mode='r')
norm = "linear" if not args.log else "log"
if args.range is not None:
data_selection = []
for i, y in enumerate(array._parameters):
if args.range[0] <= y <= args.range[1]:
data_selection.append(y)
elif args.parameters is not None:
data_selection = args.parameters
else:
data_selection = array._parameters
colorbar_symmetry = 0 if args.symmetry else None
p = kplt.GenomicVectorArrayPlot(array,
parameters=data_selection,
y_scale=norm,
colormap=args.colormap,
colorbar_symmetry=colorbar_symmetry,
vmin=args.vmin,
vmax=args.vmax,
show_colorbar=args.show_colorbar,
replacement_color=args.replacement_color,
genomic_format=args.genomic_format)
return p, args
def test_conversion(self, tmpdir):
file_name = str(tmpdir) + '/x.hic'
with dummy.sample_hic(file_name=file_name) as hic:
# simulate old-style object
hic.file.remove_node('/meta_information', recursive=True)
hic = fanc.load(file_name, mode='r')
assert isinstance(hic, fanc.Hic)
hic.close()
hic = fanc.Hic(file_name)
hic.close()
hic = fanc.load(file_name, mode='r')
hic.close()
assert isinstance(hic, fanc.Hic)
def load_score_data(data):
# If it's already an instance of fanc data, just return it
if isinstance(data, RegionBased):
return data
# If it's anything else let's hope fanc.load can deal with it
return fanc.load(data)
def write_insulation(hic_file):
logging.info("working on %s", hic_file)
hic = fanc.load(hic_file, mode='r')
prefix = os.path.basename(hic_file).replace(".hic", "")
res = int(re.findall('([0-9]+)kb', os.path.basename(hic_file))[0])
logging.info("resolution detected as %s", str(res))
window_sizes = [res * 1000 * w for w in [4, 6, 8, 10]]
# calculate insulation index
with InsulationScores.from_hic(hic,
normalise=True,
log=True,
window_sizes=window_sizes,
file_name=os.path.join(
"data", "boundaries",
prefix + "_micro-c.ii")) as ii:
for window_size in window_sizes:
w = window_size / res / 1000
logging.info("Writing insulation index for window size %i",
window_size)
output_file = os.path.join(
"data", "boundaries", prefix + '_micro-c_{}.bw'.format(int(w)))
ii.to_bigwig(output_file, window_size)
def triangular(parameters):
parser = parsers.triangular_parser()
args = parser.parse_args(parameters)
if args.colormap is None:
colormap = config.colormap_hic if not args.oe else 'bwr'
else:
colormap = args.colormap
matrix = fanc.load(os.path.expanduser(args.hic), mode='r')
from fanc.tools.general import str_to_int
norm = "lin" if not args.log else "log"
return kplt.HicPlot(
matrix,
colormap=colormap,
max_dist=str_to_int(args.max_dist)
if args.max_dist is not None else None,
norm=norm,
vmin=args.vmin,
vmax=args.vmax,
show_colorbar=args.show_colorbar,
adjust_range=args.adjust_range,
oe=args.oe,
log=args.oe,
colorbar_symmetry=0 if args.oe and args.colorbar_symmetry is None else
args.colorbar_symmetry,
ylabel=args.ylabel,
weight_field=args.weight_field,
default_value=args.default_value,
matrix_norm=args.norm), args
def line(parameters):
parser = parsers.line_parser()
args = parser.parse_args(parameters)
regions = [fanc.load(file_name) for file_name in args.regions]
attribute = args.attribute
bin_size = args.bin_size
labels = args.labels
colors = args.colors
fill = args.fill
line_style = args.line_style
ylim = args.ylim
alpha = args.alpha
legend_location = args.legend_location
if labels is not None and len(labels) != len(regions):
parser.error("Number of labels ({}) must be the same as number "
"of datasets ({})".format(len(labels), len(regions)))
p = kplt.LinePlot(regions,
bin_size=bin_size,
fill=fill,
attribute=attribute,
labels=labels,
style=line_style,
ylim=ylim,
colors=colors,
legend_location=legend_location,
plot_kwargs={'alpha': alpha})
return p, args
def export_marginals(hic_file, output_file):
hic = fanc.load(hic_file)
marginals = hic.marginals(masked=False, norm=False)
regions = list(hic.regions())
for pos, r in enumerate(regions):
r.set_attribute("score", marginals[pos])
write_bed(output_file, regions)
def test_auto_identification(self, tmpdir):
for class_name in ('Hic', 'AccessOptimisedHic',
'FragmentMappedReadPairs', 'Reads', 'GenomicTrack',
'RaoPeakInfo', 'AccessOptimisedReadPairs'):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(file_name=file_name, mode='w')
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
def test_old_style_index(self, tmpdir):
with dummy.sample_hic() as hic:
for class_name in ('ABDomains', 'ABDomainMatrix',
'ExpectedContacts', 'ObservedExpectedRatio',
'ABDomains', 'PossibleContacts',
'RegionContactAverage', 'InsulationIndex',
'DirectionalityIndex'):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(hic, file_name=file_name, mode='w')
# simulate missing meta-information
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
for class_name in ('FoldChangeMatrix', ):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(hic, hic, file_name=file_name, mode='w')
# simulate missing meta-information
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
for class_name in ('Hic', 'AccessOptimisedHic',
'FragmentMappedReadPairs', 'Reads', 'GenomicTrack'):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(file_name=file_name, mode='w')
# simulate missing meta-information
x.file.remove_node('/meta_information', recursive=True)
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
def calc_and_write(hic_file):
logging.info("working on %s", hic_file)
eig1_out = hic_file.replace(".hic", "_eig1.bed")
eig2_out = hic_file.replace(".hic", "_eig2.bed")
if not os.path.exists(eig1_out):
cor_file = hic_file.replace(".hic", ".cor")
if os.path.exists(cor_file):
logging.info("Correlation matrix %s exists, loading it", cor_file)
ab = fanc.load(cor_file)
else:
logging.info("Calculating correlation matrix and saving to %s",
cor_file)
hic = fanc.load(hic_file)
ab = fanc.ABCompartmentMatrix.from_hic(hic, file_name=cor_file)
eig1, eig2 = calc_2_eigenvectors(ab)
write_bed_eig(ab, eig1, file_name=eig1_out)
write_bed_eig(ab, eig2, file_name=eig2_out)
else:
logging.info("output file exists; skipping!")
def write_stats(input_dir, output_file):
"""Write stats to file."""
pairs_files = [fn for fn in os.listdir(input_dir) if fn.endswith("pairs")]
for p in pairs_files:
logging.info("Working on %s", p)
pairs = fanc.load(os.path.join(input_dir, p))
statistics, total = stats(pairs, pairs._edges)
with open(output_file, "a+") as out:
out = csv.writer(out)
out.writerow([p, "total", total])
for key, val in statistics.items():
out.writerow([p, key, val])
def test_auto_identification(self, tmpdir):
for class_name in ('Hic', 'LegacyHic', 'ReadPairs', 'AggregateMatrix',
'ComparisonMatrix', 'FoldChangeMatrix',
'DifferenceMatrix', 'ComparisonRegions',
'FoldChangeRegions', 'DifferenceRegions',
'DirectionalityIndex'):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(file_name=file_name, mode='w')
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
def test_hic_based_auto_identification(self, tmpdir):
with dummy.sample_hic() as hic:
for class_name in ('ABDomains', 'ABDomainMatrix',
'ExpectedContacts', 'ObservedExpectedRatio',
'PossibleContacts', 'RegionContactAverage',
'InsulationIndex', 'DirectionalityIndex'):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(hic, file_name=file_name, mode='w')
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
for class_name in ('FoldChangeMatrix', ):
file_name = str(tmpdir) + '/{}.h5'.format(class_name)
cls_ = class_name_dict[class_name]
x = cls_(hic, hic, file_name=file_name, mode='w')
x.close()
x = fanc.load(file_name, mode='r')
assert isinstance(x, cls_)
x.close()
def load_oe_contacts(matrix_file, regions_file=None):
import fanc
try:
# try loading via fanc
reference_loaded = fanc.load(matrix_file)
edges = edges_dict_from_fanc(reference_loaded)
regions = reference_loaded.regions
region_trees = region_interval_trees(regions)
except ValueError:
try:
assert regions_file is not None
regions, ix_converter, _ = load_regions(regions_file)
region_trees = region_interval_trees(regions)
edges = edges_dict_from_sparse(
edges_from_sparse_matrix(matrix_file, ix_converter))
except AssertionError:
raise ValueError
return edges, region_trees, regions
def __init__(self, hic_data, adjust_range=False, buffering_strategy="relative",
buffering_arg=1, weight_field=None, default_value=None, smooth_sigma=None,
matrix_norm=True, oe=False, log=False, **kwargs):
"""
:param hic_data: Path to Hi-C data on disk or
:class:`~fanc.data.genomic.Hic` or :class:`~fanc.data.genomic.RegionMatrix`
:param adjust_range: Draw a slider to adjust vmin/vmax interactively. Default: False
:param buffering_strategy: A valid buffering strategy for :class:`~BufferedMatrix`
:param buffering_arg: Adjust range of buffering for :class:`~BufferedMatrix`
"""
super(BasePlotterHic, self).__init__(**kwargs)
if isinstance(hic_data, string_types):
hic_data = fanc.load(hic_data, mode="r")
self.hic_data = hic_data
self.hic_buffer = prepare_hic_buffer(hic_data, buffering_strategy=buffering_strategy,
buffering_arg=buffering_arg, weight_field=weight_field,
default_value=default_value, smooth_sigma=smooth_sigma,
norm=matrix_norm, oe=oe, log=log)
self.slider = None
self.adjust_range = adjust_range
self.vmax_slider = None
def load_contacts(matrix_file, regions_file=None):
import fanc
from chess.oe import observed_expected
try:
# try loading via fanc
reference_loaded = fanc.load(matrix_file)
edges = oe_edges_dict_from_fanc(reference_loaded)
regions = reference_loaded.regions
region_trees = region_interval_trees(regions)
except ValueError as initial_error:
print(initial_error)
try:
assert regions_file is not None, ("Regions file needs to be"
"specified for sparse input.")
regions, _ix_converter, _ = load_regions(regions_file)
region_trees = region_interval_trees(regions)
_, reference_oe = observed_expected(regions_file, matrix_file)
edges = edges_dict_from_sparse(reference_oe)
except AssertionError as error:
raise ValueError(error)
return edges, region_trees, regions
def write_expected(input_file, output_prefix):
hic = fanc.load(input_file, mode="a")
intra_expected, expected_by_chromosome, inter_expected = hic.expected_values(
)
bin_size = hic.bin_size
distances = list(range(0, len(intra_expected) * bin_size, bin_size))
output_file = output_prefix + "_expected_values_all.txt"
with open(output_file, "w+") as out:
out.write("distance\texpected\n")
for i in range(0, len(intra_expected)):
out.write(str(distances[i]) + "\t" + str(intra_expected[i]) + "\n")
output_file = output_prefix + "_expected_values_per_chrom.txt"
with open(output_file, "w+") as out:
out.write("chrom\tdistance\texpected\n")
for chrom in expected_by_chromosome.keys():
expected = expected_by_chromosome[chrom]
for i in range(0, len(expected)):
out.write(chrom + "\t" + str(distances[i]) + "\t" +
str(expected[i]) + "\n")
def test_call_peaks(self):
dir = os.path.dirname(os.path.realpath(__file__))
hic_10kb = fanc.load(dir +
"/test_peaks/rao2014.chr11_77400000_78600000.hic",
mode='r')
peak_caller = RaoPeakCaller()
peaks = peak_caller.call_peaks(hic_10kb)
assert len(peaks.edges) == 6525
valid_peaks = []
has_43_57 = False
for peak in peaks.edges:
if peak.fdr_ll < 0.1 and peak.fdr_v < 0.1 and peak.fdr_h < 0.1 and peak.fdr_d < 0.1:
valid_peaks.append(peak)
if peak.source == 43 and peak.sink == 57:
has_43_57 = True
assert len(valid_peaks) == 134
assert has_43_57
hic_10kb.close()
peaks.close()
def auto(argv, **kwargs):
from fanc.tools.general import which
parser = auto_parser()
args = parser.parse_args(argv[2:])
verbosity = kwargs.get("verbosity", 2)
if verbosity > 0:
verbosity_flag = '-' + 'v' * verbosity
fanc_base_command = ['fanc', verbosity_flag]
else:
fanc_base_command = ['fanc']
log_file = kwargs.get("log_file", None)
if log_file is not None:
fanc_base_command += ['-l', log_file]
bin_sizes = args.bin_sizes
split_ligation_junction = args.split_ligation_junction
restriction_enzyme = args.restriction_enzyme
threads = args.threads
genome = args.genome
genome_index = args.genome_index
basename = args.basename
quality_cutoff = args.quality_cutoff
iterative_quality_cutoff = args.iterative_quality_cutoff
tmp = args.tmp
mapper_parallel = args.mapper_parallel
split_fastq = args.split_fastq
memory_map = args.memory_map
iterative = args.iterative
step_size = args.step_size
sam_sort = args.sam_sort
filter_pairs = args.filter_pairs
inward_cutoff = args.inward_cutoff
outward_cutoff = args.outward_cutoff
auto_le_cutoff = args.auto_le_cutoff
process_hic = args.process_hic
ice = args.ice
norm_method = args.norm_method
restore_coverage = args.restore_coverage
run_with = args.run_with
job_prefix = args.job_prefix
grid_startup_commands = os.path.expanduser(args.grid_startup_commands) \
if args.grid_startup_commands is not None else None
grid_cleanup_commands = os.path.expanduser(args.grid_cleanup_commands) \
if args.grid_cleanup_commands is not None else None
force_overwrite = args.force_overwrite
output_folder = os.path.expanduser(args.output_folder)
file_names = [os.path.expanduser(file_name) for file_name in args.input]
file_types = [file_type(file_name) for file_name in file_names]
file_basenames = [file_basename(file_name) for file_name in file_names]
if ice:
warnings.warn("The --ice option is deprecated. Please use '--norm-method ice' instead!")
norm_method = 'ice'
for file_name in file_names:
if not os.path.exists(file_name):
parser.error("File '{}' does not exist!".format(file_name))
runner = None
if run_with == 'parallel':
runner = ParallelTaskRunner(threads)
elif run_with == 'sge':
from fanc.config import config
if which(config.sge_qsub_path) is None:
parser.error("Using SGE not possible: "
"Cannot find 'qsub' at path '{}'. You can change "
"this path using fanc config files and the "
"'sge_qsub_path' parameter".format(config.sge_qsub_path))
from fanc.tools.files import mkdir
sge_log_dir = mkdir(output_folder, 'sge_logs')
runner = SgeTaskRunner(log_dir=sge_log_dir, task_prefix=job_prefix,
startup_commands_file=grid_startup_commands,
cleanup_commands_file=grid_cleanup_commands)
elif run_with == 'slurm':
from fanc.config import config
if which(config.slurm_sbatch_path) is None:
parser.error("Using Slurm not possible: "
"Cannot find 'sbatch' at path '{}'. You can change "
"this path using fanc config files and the "
"'slurm_sbatch_path' parameter".format(config.slurm_sbatch_path))
from fanc.tools.files import mkdir
slurm_log_dir = mkdir(output_folder, 'slurm_logs')
runner = SlurmTaskRunner(log_dir=slurm_log_dir, task_prefix=job_prefix,
startup_commands_file=grid_startup_commands,
cleanup_commands_file=grid_cleanup_commands)
elif run_with == 'test':
runner = ParallelTaskRunner(threads, test=True)
else:
parser.error("Runner '{}' is not valid. See --run-with "
"parameter for options".format(run_with))
for i in range(len(file_types)):
if file_types[i] not in ('fastq', 'sam', 'pairs_txt', 'pairs', 'hic'):
import fanc
try:
ds = fanc.load(file_names[i], mode='r')
if isinstance(ds, fanc.Hic):
file_types[i] = 'hic'
elif isinstance(ds, fanc.ReadPairs):
file_types[i] = 'pairs'
else:
raise ValueError("Could not detect file type using fanc load.")
except ValueError:
parser.error("Not a valid input file type: {}".format(file_types[i]))
if basename is None:
if len(file_basenames) == 1:
basename = file_basenames[0]
else:
basename = []
for pair in zip(*file_basenames):
if pair[0] == pair[1]:
basename.append(pair[0])
else:
break
if len(basename) == 0:
basename = file_basenames[0]
else:
if basename[-1] in ['.', '_']:
basename = "".join(basename[:-1])
else:
basename = "".join(basename)
if not output_folder[-1] == '/':
output_folder += '/'
# 0. Do some sanity checks on required flags
is_bwa = False
is_bowtie2 = False
if 'fastq' in file_types:
if args.genome_index is None:
parser.error("Must provide genome index (-i) when mapping FASTQ files!")
else:
check_path = os.path.expanduser(genome_index)
if check_path.endswith('.'):
check_path = check_path[:-1]
is_bowtie2 = True
for i in range(1, 5):
if not os.path.exists(check_path + '.{}.bt2'.format(i)):
is_bowtie2 = False
for i in range(1, 3):
if not os.path.exists(check_path + '.rev.{}.bt2'.format(i)):
is_bowtie2 = False
is_bwa = True
for ending in ('amb', 'ann', 'bwt', 'pac', 'sa'):
if not os.path.exists(check_path + '.{}'.format(ending)):
is_bwa = False
if not is_bowtie2 and not is_bwa:
parser.error("Cannot detect Bowtie2 or BWA index.")
if is_bowtie2 and not which('bowtie2'):
parser.error("bowtie2 must be in PATH for mapping!")
if is_bwa and not which('bwa'):
parser.error("bwa must be in PATH for mapping!")
if 'fastq' in file_types or 'sam' in file_types:
if genome is None:
parser.error("Must provide genome (-g) to process read pair files!")
if restriction_enzyme is None:
from fanc.regions import genome_regions
try:
genome_regions(genome)
except ValueError:
parser.error("Must provide restriction enzyme (-r) to process read pair files!")
else:
res = restriction_enzyme.split(",")
from Bio import Restriction
for r in res:
try:
getattr(Restriction, r)
except AttributeError:
parser.error("Restriction enzyme string '{}' is not recognized".format(restriction_enzyme))
logger.info("Output folder: {}".format(output_folder))
logger.info("Input files: {}".format(", ".join(file_names)))
logger.info("Input file types: {}".format(", ".join(file_types)))
logger.info("Final basename: %s (you can change this with the -n option!)" % basename)
# 1. create default folders in root directory
if run_with != 'test':
logger.info("Creating output folders...")
from ..tools.general import mkdir
mkdir(output_folder, 'fastq')
mkdir(output_folder, 'sam')
mkdir(output_folder, 'pairs/')
mkdir(output_folder, 'hic/binned')
mkdir(output_folder, 'plots/stats')
# 2. If input files are (gzipped) FASTQ, map them iteratively first
fastq_files = []
for i in range(len(file_names)):
if file_types[i] != 'fastq':
continue
fastq_files.append(i)
sam_created = [False] * len(file_names)
mapping_tasks = []
if len(fastq_files) > 0:
if genome_index.endswith('.'):
genome_index = genome_index[:-1]
bam_files = []
for i, ix in enumerate(fastq_files):
bam_file = output_folder + 'sam/' + file_basenames[ix] + '.bam'
if not force_overwrite and os.path.exists(bam_file):
parser.error("File exists ({}), use -f to force overwriting it.".format(bam_file))
bam_files.append(bam_file)
mapping_command = fanc_base_command + ['map', '-m', '25',
'-s', str(step_size),
'-t', str(threads)]
if iterative_quality_cutoff is not None:
mapping_command += ['-q', str(iterative_quality_cutoff)]
if tmp:
mapping_command.append('-tmp')
if not mapper_parallel:
mapping_command.append('--fanc-parallel')
if split_fastq:
mapping_command.append('--split-fastq')
if memory_map:
mapping_command.append('--memory-map')
if not iterative:
mapping_command.append('--no-iterative')
if split_ligation_junction:
mapping_command.append('--restriction-enzyme')
mapping_command.append(restriction_enzyme)
mapping_command += [file_names[ix], genome_index, bam_file]
mapping_task = CommandTask(mapping_command)
runner.add_task(mapping_task, threads=threads)
mapping_tasks.append(mapping_task)
for ix, i in enumerate(fastq_files):
file_names[i] = bam_files[ix]
file_types[i] = 'sam'
sam_created[i] = True
if sam_sort:
sort_threads = min(4, threads)
sam_sort_tasks = []
# sort SAM files
sam_files = []
in_place = []
for i in range(len(file_names)):
if file_types[i] != 'sam':
continue
sam_files.append(i)
in_place.append(sam_created[i])
if len(sam_files) > 0:
sorted_sam_files = []
for i, ix in enumerate(sam_files):
sort_command = fanc_base_command + ['sort_sam', '-t', str(sort_threads),
file_names[ix]]
if in_place[i]:
sorted_sam_files.append(file_names[ix])
else:
sam_path, sam_extension = os.path.splitext(file_names[ix])
sam_basename = os.path.basename(sam_path)
sorted_sam_file = os.path.join(output_folder, 'sam', sam_basename + '_sort' + sam_extension)
if not force_overwrite and os.path.exists(sorted_sam_file):
parser.error("File exists ({}), use -f to force overwriting it.".format(sorted_sam_file))
sorted_sam_files.append(sorted_sam_file)
sort_command.append(sorted_sam_file)
if tmp:
sort_command.append('-tmp')
sam_sort_task = CommandTask(sort_command)
runner.add_task(sam_sort_task, wait_for=mapping_tasks, threads=1)
sam_sort_tasks.append(sam_sort_task)
for ix, i in enumerate(sam_files):
file_names[i] = sorted_sam_files[ix]
else:
sam_sort_tasks = mapping_tasks
total_pairs = 0
pairs_txt_tasks = []
# sort SAM files
pairs_txt_files = []
for i in range(len(file_names)):
if file_types[i] != 'pairs_txt':
continue
pairs_txt_files.append(i)
if len(pairs_txt_files) > 0:
load_threads = max(int(threads / len(pairs_txt_files)), 1)
pairs_files = []
for ix in pairs_txt_files:
pairs_txt_file = file_names[ix]
pairs_file = os.path.join(output_folder, 'pairs', '{}_{}.pairs'.format(basename, total_pairs))
total_pairs += 1
if not force_overwrite and os.path.exists(pairs_file):
parser.error("File exists ({}), use -f to force overwriting it.".format(pairs_file))
pairs_files.append(pairs_file)
pairs_command = fanc_base_command + ['pairs', '-f',
# loading
'-g', genome,
'-t', str(load_threads)]
if restriction_enzyme is not None:
pairs_command += ['-r', restriction_enzyme]
if is_bwa:
pairs_command.append('--bwa')
if tmp:
pairs_command.append('-tmp')
if sam_sort:
pairs_command.append('-S')
pairs_command += [pairs_txt_file, pairs_file]
pairs_task = CommandTask(pairs_command)
runner.add_task(pairs_task, wait_for=[], threads=load_threads)
pairs_txt_tasks.append(pairs_task)
pairs_files.append(pairs_file)
for ix, i in enumerate(pairs_txt_files):
file_names[i] = pairs_files[ix]
file_types[i] = 'pairs'
# load pairs directly from SAM
sam_file_pairs = []
i = 0
while i < len(file_names):
if file_types[i] == 'sam':
if not file_types[i + 1] == 'sam':
parser.error("Cannot create SAM pairs, because {} "
"is missing a partner file".format(file_names[i]))
sam_file_pairs.append((i, i + 1))
i += 1
i += 1
if len(sam_file_pairs) > 0:
sam_to_pairs_tasks = pairs_txt_tasks
load_threads = max(int(threads/len(sam_file_pairs)), 1)
pairs_files = []
for i, j in sam_file_pairs:
if len(sam_file_pairs) > 1 or total_pairs > 0:
pairs_file = os.path.join(output_folder, 'pairs', '{}_{}.pairs'.format(basename, total_pairs))
total_pairs += 1
else:
pairs_file = output_folder + 'pairs/' + basename + '.pairs'
if not force_overwrite and os.path.exists(pairs_file):
parser.error("File exists ({}), use -f to force overwriting it.".format(pairs_file))
pairs_command = fanc_base_command + ['pairs', '-f',
# loading
'-g', genome,
'-t', str(load_threads),
# filtering
'-us']
if restriction_enzyme is not None:
pairs_command += ['-r', restriction_enzyme]
if quality_cutoff is not None:
pairs_command += ['-q', str(quality_cutoff)]
if is_bwa:
pairs_command.append('--bwa')
if tmp:
pairs_command.append('-tmp')
if sam_sort:
pairs_command.append('-S')
pairs_command += [file_names[i], file_names[j], pairs_file]
pairs_task = CommandTask(pairs_command)
runner.add_task(pairs_task, wait_for=sam_sort_tasks, threads=load_threads)
sam_to_pairs_tasks.append(pairs_task)
pairs_files.append(pairs_file)
for ix, sam_pair in enumerate(reversed(sam_file_pairs)):
file_names[sam_pair[0]] = pairs_files[ix]
del file_names[sam_pair[1]]
file_types[sam_pair[0]] = 'pairs'
del file_types[sam_pair[1]]
else:
sam_to_pairs_tasks = pairs_txt_tasks + sam_sort_tasks
# 7. Pairs stats and filtering
pairs_files = []
for i in range(len(file_names)):
if file_types[i] != 'pairs':
continue
pairs_files.append(i)
if len(pairs_files) > 0 and filter_pairs:
pairs_tasks = []
for ix in pairs_files:
pair_basename = os.path.basename(os.path.splitext(file_names[ix])[0])
pairs_stats_file = output_folder + 'plots/stats/' + pair_basename + '.pairs.stats.pdf'
ligation_error_file = output_folder + 'plots/stats/' + pair_basename + '.pairs.ligation_error.pdf'
re_dist_file = output_folder + 'plots/stats/' + pair_basename + '.pairs.re_dist.pdf'
pairs_command = fanc_base_command + ['pairs',
# filtering
'-d', '10000',
'-l', '-p', '2']
if tmp:
pairs_command.append('-tmp')
if inward_cutoff is not None:
pairs_command += ['-i', str(inward_cutoff)]
if outward_cutoff is None:
pairs_command += ['-o', '0']
if outward_cutoff is not None:
pairs_command += ['-o', str(outward_cutoff)]
if inward_cutoff is None:
pairs_command += ['-i', '0']
if inward_cutoff is None and outward_cutoff is None and auto_le_cutoff:
pairs_command += ['--filter-ligation-auto']
pairs_command += ['--statistics-plot', pairs_stats_file, file_names[ix]]
pairs_task = CommandTask(pairs_command)
runner.add_task(pairs_task, wait_for=sam_to_pairs_tasks, threads=1)
pairs_tasks.append(pairs_task)
ligation_error_command = fanc_base_command + ['pairs', '--ligation-error-plot',
ligation_error_file, file_names[ix]]
ligation_error_task = CommandTask(ligation_error_command)
runner.add_task(ligation_error_task, wait_for=pairs_task, threads=1)
re_dist_command = fanc_base_command + ['pairs', '--re-dist-plot',
re_dist_file, file_names[ix]]
re_dist_task = CommandTask(re_dist_command)
runner.add_task(re_dist_task, wait_for=pairs_task, threads=1)
else:
pairs_tasks = sam_to_pairs_tasks
# 8. Pairs to Hic
pairs_files = []
for i in range(len(file_names)):
if file_types[i] != 'pairs':
continue
pairs_files.append(i)
if len(pairs_files) > 0 and process_hic:
hic_tasks = []
hic_files = []
for ix in pairs_files:
hic_basename = os.path.basename(os.path.splitext(file_names[ix])[0])
if hic_basename.endswith('_filtered'):
hic_basename = hic_basename[:-9]
hic_file = output_folder + 'hic/' + hic_basename + '.hic'
if not force_overwrite and os.path.exists(hic_file):
parser.error("File exists ({}), use -f to force overwriting it.".format(hic_file))
hic_command = fanc_base_command + ['hic', '-f']
if tmp:
hic_command.append('-tmp')
hic_command += [file_names[ix], hic_file]
hic_task = CommandTask(hic_command)
runner.add_task(hic_task, wait_for=pairs_tasks, threads=1)
hic_tasks.append(hic_task)
hic_files.append(hic_file)
for ix, i in enumerate(pairs_files):
file_names[i] = hic_files[ix]
file_types[i] = 'hic'
else:
hic_tasks = pairs_tasks
# 9. Merge Hic
hic_files = []
for i in range(len(file_names)):
if file_types[i] != 'hic':
continue
hic_files.append(i)
if len(hic_files) > 1:
merge_hic_tasks = []
output_hic = output_folder + 'hic/' + basename + '.hic'
if not force_overwrite and os.path.exists(output_hic):
parser.error("File exists ({}), use -f to force overwriting it.".format(output_hic))
merge_hic_command = fanc_base_command + ['hic', '-f']
if tmp:
merge_hic_command.append('-tmp')
hics = [file_names[i] for i in hic_files]
merge_hic_command += hics + [output_hic]
merge_hic_task = CommandTask(merge_hic_command)
runner.add_task(merge_hic_task, wait_for=hic_tasks, threads=1)
merge_hic_tasks.append(merge_hic_task)
file_names[hic_files[0]] = output_hic
hic_files.pop(0)
for ix, i in enumerate(reversed(hic_files)):
del file_names[i]
del file_types[i]
else:
merge_hic_tasks = hic_tasks
from fanc.tools.general import human_format, str_to_int
hic_files = []
for i in range(len(file_names)):
if file_types[i] != 'hic':
continue
hic_files.append(i)
if len(hic_files) > 0:
for ix in hic_files:
hic_file = file_names[ix]
binned_hic_file_base = output_folder + 'hic/binned/' + basename + '_'
bin_threads = min(4, threads)
for bin_size in bin_sizes:
bin_size = str_to_int(str(bin_size))
bin_size_str = human_format(bin_size, 0).lower() + 'b'
binned_hic_file = binned_hic_file_base + bin_size_str + '.hic'
if not force_overwrite and os.path.exists(binned_hic_file):
parser.error("File exists ({}), use -f to force overwriting it.".format(binned_hic_file))
hic_basename = os.path.basename(os.path.splitext(binned_hic_file)[0])
hic_stats_file = output_folder + 'plots/stats/' + \
hic_basename + '.stats.pdf'
hic_command = fanc_base_command + ['hic', '-f', '-b', str(bin_size),
'-r', '0.1', '-t', str(bin_threads),
'--statistics-plot', hic_stats_file,
'-n', '--norm-method', norm_method]
if tmp:
hic_command.append('-tmp')
if restore_coverage:
hic_command.append('-c')
hic_command += [hic_file, binned_hic_file]
hic_task = CommandTask(hic_command)
runner.add_task(hic_task, wait_for=merge_hic_tasks, threads=bin_threads)
runner.run()
return 0
# chr18: 899140-(899308[-1])-899476 -- chr18: 1509911-(1510021[1])-1510076
# end snippet pairs filter masked
# start snippet pairs filter exclude
for pair in pairs.pairs(excluded_filters=['self-ligation']):
print(pair)
# end snippet pairs filter exclude
# start snippet hic convert
hic_folder = mkdir(os.path.join(output_folder, 'hic'))
hic_file = os.path.join(hic_folder, 'example.hic')
hic = pairs.to_hic(file_name=hic_file)
# end snippet hic convert
hic.close()
hic = fanc.load(hic_file)
# start snippet hic bin
binned_hic = hic.bin(1000000,
file_name=os.path.join(hic_folder, 'binned_1mb.hic'),
threads=4)
# end snippet hic bin
# start snippet hic filter
from fanc.hic import LowCoverageFilter
lc_filter = LowCoverageFilter(binned_hic, rel_cutoff=0.2)
binned_hic.filter(lc_filter)
binned_hic.run_queued_filters()
# end snippet hic filter
# start snippet hic balance
# mode="r")
# Tollrm910_nc14_hic_plot = fancplot.HicPlot(Tollrm910_nc14_hic, vmin=1e-03,
# vmax=1e-01, norm="log",
# draw_minor_ticks=False, title="Tollrm910")
# Toll10B_nc14_hic = fanc.load(os.path.join("data", "hic", "merged",
# "Toll10B-nc14", "hic",
# "Toll10B-nc14_1kb.hic"), mode="r")
# Toll10B_nc14_hic_plot = fancplot.HicPlot(Toll10B_nc14_hic, vmin=1e-03,
# vmax=1e-01, norm="log",
# draw_minor_ticks=False,
# title="Toll10B")
gd7_microc = fanc.load(os.path.join("data", "micro-c", "merged",
"gd7", "hic",
"gd7_" + res + ".hic"), mode="r")
gd7_microc_plot = fancplot.HicPlot(gd7_microc, vmin=1e-03,
vmax=1e-01, norm="log",
draw_minor_ticks=False, title="gd7")
control_microc = fanc.load(os.path.join("data", "micro-c", "merged",
"control", "hic",
"control_" + res + ".hic"),
mode="r")
control_microc_plot = fancplot.HicPlot(control_microc, vmin=1e-03,
vmax=1e-01, norm="log",
draw_minor_ticks=False, title="control")
genes = "external_data/flybase/dmel-all-r6.30.gtf.gz"
# start snippet ab setup
import fanc
import fanc.plotting as fancplot
import matplotlib.pyplot as plt
hic_1mb = fanc.load("output/hic/binned/fanc_example_1mb.hic")
# end snippet ab setup
# start snippet alternative cooler
hic_1mb = fanc.load("architecture/other-hic/fanc_example.mcool@1mb")
# end snippet alternative cooler
# start snippet alternative juicer
hic_1mb = fanc.load("architecture/other-hic/fanc_example.juicer.hic@1mb")
# end snippet alternative juicer
# start snippet ab matrix
ab = fanc.ABCompartmentMatrix.from_hic(hic_1mb)
# end snippet ab matrix
# start snippet ab subset
ab_chr18 = ab.matrix(('chr18', 'chr18'))
# end snippet ab subset
# start snippet ab fancplot-correlation
fig, ax = plt.subplots()
mp = fancplot.SquareMatrixPlot(ab, ax=ax,
norm='lin', colormap='RdBu_r',
vmin=-1, vmax=1,
draw_minor_ticks=False)
mp.plot('chr18')
def plot_regions(regions):
h = fanc.load(os.path.join("data", "hic", "merged", "3-4h", "hic",
"3-4h_2kb.hic"),
mode="r")
h_plot = fancplot.HicPlot(h,
vmin=1e-03,
vmax=1e-01,
norm="log",
draw_minor_ticks=False)
genes = "external_data/flybase/dmel-all-r6.30.gtf.gz"
genes_plot = fancplot.GenePlot(genes,
squash=True,
group_by="gene_symbol",
aspect=0.15,
label_field="gene_symbol",
show_labels=False,
draw_minor_ticks=False)
rnaseq_dict = {
name:
os.path.join("external_data", "koenecke_2016_2017", "rnaseq_aligned",
name + "_sorted_filtered_merged_canonical_chrs_rnaseq.bw")
for name in ["gd7", "tlrm910", "tl10b"]
}
h3k27ac_dict = {
name: os.path.join(
"external_data", "koenecke_2016_2017", "chipseq_aligned",
"H3K27ac_" + name + "_sorted_filtered_merged_canonical_chrs.bw")
for name in ["gd7", "tl10b"]
}
h3k27ac_dict["Tollrm910"] = os.path.join(
"external_data", "extra_chip-seq", "chipseq_aligned",
"H3K27ac_Tollrm910_sorted_filtered_merged_canonical_chrs.bw")
rnaseq_ylim = fancplot.helpers.LimitGroup()
h3k27ac_ylim = fancplot.helpers.LimitGroup()
polii_ylim = fancplot.helpers.LimitGroup()
# polii_chip_early = os.path.join("external_data", "blythe_2015", "aligned",
# "PolII-pSer5_NC14-early_sorted_filtered_merged_canonical_chrs.bw")
# polii_chip_mid = os.path.join("external_data", "blythe_2015", "aligned",
# "PolII-pSer5_NC14-middle_sorted_filtered_merged_canonical_chrs.bw")
polii_chip_late = os.path.join(
"external_data", "blythe_2015", "aligned",
"PolII-pSer5_NC14-late_sorted_filtered_merged_canonical_chrs.bw")
# polii_early_plot = fancplot.LinePlot(polii_chip_early, fill=True, plot_kwargs={'color': "black"},
# draw_minor_ticks=False, aspect=0.05,
# ylim=polii_ylim, n_yticks=2)
# polii_mid_plot = fancplot.LinePlot(polii_chip_mid, fill=True, plot_kwargs={'color': "black"},
# draw_minor_ticks=False, aspect=0.05,
# ylim=polii_ylim, n_yticks=2)
polii_late_plot = fancplot.LinePlot(polii_chip_late,
fill=True,
plot_kwargs={'color': "black"},
draw_minor_ticks=False,
aspect=0.05,
ylim=polii_ylim,
n_yticks=2)
rnaseq_plot_gd7 = fancplot.LinePlot(rnaseq_dict['gd7'],
fill=True,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
n_yticks=2)
h3k27ac_plot_gd7 = fancplot.LinePlot(h3k27ac_dict['gd7'],
fill=True,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
rnaseq_plot_Tollrm910 = fancplot.LinePlot(rnaseq_dict['tlrm910'],
fill=True,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
n_yticks=2)
h3k27ac_plot_Tollrm910 = fancplot.LinePlot(
h3k27ac_dict['Tollrm910'],
fill=True,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
rnaseq_plot_toll10b = fancplot.LinePlot(rnaseq_dict['tl10b'],
fill=True,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
n_yticks=2)
h3k27ac_plot_toll10b = fancplot.LinePlot(h3k27ac_dict['tl10b'],
fill=True,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
gd7_enh = "data/supplementary_tables/gd7_candidate_enhancers.bed"
gd7_enh_plot = fancplot.GenomicFeaturePlot(gd7_enh,
aspect=0.02,
color="#648fff",
draw_minor_ticks=False)
Tollrm910_enh = "data/supplementary_tables/Tollrm910_candidate_enhancers.bed"
Tollrm910_enh_plot = fancplot.GenomicFeaturePlot(Tollrm910_enh,
aspect=0.02,
color="#dc267f",
draw_minor_ticks=False)
toll10b_enh = "data/supplementary_tables/Toll10B_candidate_enhancers.bed"
toll10b_enh_plot = fancplot.GenomicFeaturePlot(toll10b_enh,
aspect=0.02,
color="#ffb000",
draw_minor_ticks=False)
plots = [
h_plot,
# ins_plot,
# boundaries_plot,
genes_plot,
# hk_plot,
# polii_early_plot, polii_mid_plot,
polii_late_plot,
rnaseq_plot_gd7,
rnaseq_plot_Tollrm910,
rnaseq_plot_toll10b,
h3k27ac_plot_gd7,
h3k27ac_plot_Tollrm910,
h3k27ac_plot_toll10b,
gd7_enh_plot,
Tollrm910_enh_plot,
toll10b_enh_plot
]
with PdfPages(output_file) as pdf:
with fancplot.GenomicFigure(plots, ticks_last=True) as gfig:
for name, region, rnaseq_ylim in regions:
logging.info(region)
fig, axes = gfig.plot(region)
axes[3].set_ylim([0, rnaseq_ylim])
axes[4].set_ylim([0, rnaseq_ylim])
axes[5].set_ylim([0, rnaseq_ylim])
pdf.savefig()
# start snippet oe setup
import fanc
import matplotlib.pyplot as plt
import fanc.plotting as fancplot
hic_500kb = fanc.load("output/hic/binned/fanc_example_500kb.hic")
# end snippet oe setup
hic_500kb.close()
# start snippet oe append
hic_500kb = fanc.load("output/hic/binned/fanc_example_500kb.hic", mode='a')
# end snippet oe append
# start snippet alternative cooler
hic_500kb = fanc.load("architecture/other-hic/fanc_example.mcool@500kb")
# end snippet alternative cooler
# start snippet alternative juicer
hic_500kb = fanc.load("architecture/other-hic/fanc_example.juicer.hic@500kb")
# end snippet alternative juicer
# start snippet oe basic
intra_expected, intra_expected_chromosome, inter_expected = hic_500kb.expected_values(
)
# end snippet oe basic
# start snippet oe ddplot
# obtain bin distances
bin_size = hic_500kb.bin_size
distance = list(
range(0, bin_size * len(intra_expected_chromosome['chr19']), bin_size))
