def estimate_bias_vom(args):
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
create_signal(args, regions)
hmm_data = HmmData()
learn_dependency_model = hmm_data.get_dependency_model()
slim_dimont_predictor = hmm_data.get_slim_dimont_predictor()
test_fa = hmm_data.get_default_test_fa()
shutil.copy(test_fa, args.output_location)
os.chdir(args.output_location)
print((os.getcwd()))
output_fname_f_obs = os.path.join(args.output_location, "{}_f_obs.fa".format(str(args.k_nb)))
output_fname_f_exp = os.path.join(args.output_location, "{}_f_exp.fa".format(str(args.k_nb)))
output_fname_r_obs = os.path.join(args.output_location, "{}_r_obs.fa".format(str(args.k_nb)))
output_fname_r_exp = os.path.join(args.output_location, "{}_r_exp.fa".format(str(args.k_nb)))
infix = "{}_f_obs".format(str(args.k_nb))
create_model(args, output_fname_f_obs, infix, learn_dependency_model, slim_dimont_predictor)
infix = "{}_f_exp".format(str(args.k_nb))
create_model(args, output_fname_f_exp, infix, learn_dependency_model, slim_dimont_predictor)
infix = "{}_r_obs".format(str(args.k_nb))
create_model(args, output_fname_r_obs, infix, learn_dependency_model, slim_dimont_predictor)
infix = "{}_r_exp".format(str(args.k_nb))
create_model(args, output_fname_r_exp, infix, learn_dependency_model, slim_dimont_predictor)
os.remove(os.path.join(args.output_location, "test.fa"))
compute_bias(args)
def estimate_bias_vom(args):
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
create_signal(args, regions)
hmm_data = HmmData()
learn_dependency_model = hmm_data.get_dependency_model()
slim_dimont_predictor = hmm_data.get_slim_dimont_predictor()
test_fa = hmm_data.get_default_test_fa()
shutil.copy(test_fa, args.output_location)
os.chdir(args.output_location)
print(os.getcwd())
output_fname_f_obs = os.path.join(args.output_location, "{}_f_obs.fa".format(str(args.k_nb)))
output_fname_f_exp = os.path.join(args.output_location, "{}_f_exp.fa".format(str(args.k_nb)))
output_fname_r_obs = os.path.join(args.output_location, "{}_r_obs.fa".format(str(args.k_nb)))
output_fname_r_exp = os.path.join(args.output_location, "{}_r_exp.fa".format(str(args.k_nb)))
infix = "{}_f_obs".format(str(args.k_nb))
create_model(args, output_fname_f_obs, infix, learn_dependency_model, slim_dimont_predictor)
infix = "{}_f_exp".format(str(args.k_nb))
create_model(args, output_fname_f_exp, infix, learn_dependency_model, slim_dimont_predictor)
infix = "{}_r_obs".format(str(args.k_nb))
create_model(args, output_fname_r_obs, infix, learn_dependency_model, slim_dimont_predictor)
infix = "{}_r_exp".format(str(args.k_nb))
create_model(args, output_fname_r_exp, infix, learn_dependency_model, slim_dimont_predictor)
os.remove(os.path.join(args.output_location, "test.fa"))
compute_bias(args)
def diff_analysis_run(args):
# Initializing Error Handler
err = ErrorHandler()
output_location = os.path.join(args.output_location, "Lineplots")
try:
if not os.path.isdir(output_location):
os.makedirs(output_location)
except Exception:
err.throw_error("MM_OUT_FOLDER_CREATION")
# check if they have same length
mpbs_files = args.mpbs_files.strip().split(",")
reads_files = args.reads_files.strip().split(",")
conditions = args.conditions.strip().split(",")
if args.colors is not None:
colors = args.colors.strip().split(",")
else:
colors = [
"#e41a1c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#ffff33",
"#a65628", "#f781bf", "#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3",
"#a6d854", "#ffd92f", "#e5c494", "#b3b3b3", "#8dd3c7", "#ffffb3",
"#bebada", "#fb8072", "#80b1d3", "#fdb462", "#b3de69", "#fccde5",
"#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02",
"#a6761d", "#666666", "#7fc97f", "#beaed4", "#fdc086", "#ffff99",
"#386cb0", "#f0027f", "#bf5b17", "#666666"
]
assert len(mpbs_files) == len(reads_files) == len(conditions), \
"Number of motif, read and condition names are not same: {}, {}, {}".format(len(mpbs_files), len(reads_files),
len(conditions))
# Check if the index file exists
for reads_file in reads_files:
base_name = "{}.bai".format(reads_file)
if not os.path.exists(base_name):
pysam.index(reads_file)
mpbs = GenomicRegionSet("Motif Predicted Binding Sites of All Conditions")
for i, mpbs_file in enumerate(mpbs_files):
mpbs.read(mpbs_file)
mpbs.sort()
mpbs.remove_duplicates()
mpbs_name_list = list(set(mpbs.get_names()))
signals = np.zeros(shape=(len(conditions), len(mpbs_name_list),
args.window_size),
dtype=np.float32)
motif_len = list()
motif_num = list()
motif_pwm = list()
print((" {} cpus are detected and {} of them will be used...\n".format(
cpu_count(), args.nc)))
genome_data = GenomeData(args.organism)
fasta = Fastafile(genome_data.get_genome())
print("generating signal for each motif and condition...\n")
# differential analysis using bias corrected signal
if args.bc:
hmm_data = HmmData()
table_forward = hmm_data.get_default_bias_table_F_ATAC()
table_reverse = hmm_data.get_default_bias_table_R_ATAC()
bias_table = BiasTable().load_table(table_file_name_F=table_forward,
table_file_name_R=table_reverse)
# do not use multi-processing
if args.nc == 1:
for i, condition in enumerate(conditions):
for j, mpbs_name in enumerate(mpbs_name_list):
mpbs_regions = mpbs.by_names([mpbs_name])
arguments = (mpbs_regions, reads_files[i], args.organism,
args.window_size, args.forward_shift,
args.reverse_shift, bias_table)
try:
signals[i, j, :] = get_bc_signal(arguments)
except Exception:
logging.exception("get bias corrected signal failed")
# get motif length, number and pwm matrix
motif_len.append(mpbs_regions[0].final -
mpbs_regions[0].initial)
motif_num.append(len(mpbs_regions))
motif_pwm.append(
get_pwm(fasta, mpbs_regions, args.window_size))
# use multi-processing
else:
for i, condition in enumerate(conditions):
print((
"generating signal for condition {} \n".format(condition)))
with Pool(processes=args.nc) as pool:
arguments_list = list()
for mpbs_name in mpbs_name_list:
mpbs_regions = mpbs.by_names([mpbs_name])
arguments = (mpbs_regions, reads_files[i],
args.organism, args.window_size,
args.forward_shift, args.reverse_shift,
bias_table)
arguments_list.append(arguments)
# get motif length, number and pwm matrix
motif_len.append(mpbs_regions[0].final -
mpbs_regions[0].initial)
motif_num.append(len(mpbs_regions))
motif_pwm.append(
get_pwm(fasta, mpbs_regions, args.window_size))
res = pool.map(get_bc_signal, arguments_list)
signals[i] = np.array(res)
# differential analysis using raw signal
else:
# do not use multi-processing
if args.nc == 1:
for i, condition in enumerate(conditions):
for j, mpbs_name in enumerate(mpbs_name_list):
mpbs_regions = mpbs.by_names([mpbs_name])
arguments = (mpbs_regions, reads_files[i], args.organism,
args.window_size, args.forward_shift,
args.reverse_shift)
signals[i, j, :] = get_raw_signal(arguments)
# get motif length, number and pwm matrix
motif_len.append(mpbs_regions[0].final -
mpbs_regions[0].initial)
motif_num.append(len(mpbs_regions))
motif_pwm.append(
get_pwm(fasta, mpbs_regions, args.window_size))
# use multi-processing
else:
for i, condition in enumerate(conditions):
print((
"generating signal for condition {} \n".format(condition)))
with Pool(processes=args.nc) as pool:
arguments_list = list()
for mpbs_name in mpbs_name_list:
mpbs_regions = mpbs.by_names([mpbs_name])
arguments = (mpbs_regions, reads_files[i],
args.organism, args.window_size,
args.forward_shift, args.reverse_shift)
arguments_list.append(arguments)
# get motif length, number and pwm matrix
motif_len.append(mpbs_regions[0].final -
mpbs_regions[0].initial)
motif_num.append(len(mpbs_regions))
motif_pwm.append(
get_pwm(fasta, mpbs_regions, args.window_size))
res = pool.map(get_raw_signal, arguments_list)
signals[i] = np.array(res)
print("signal generation is done!\n")
# compute normalization facotr for each condition
factors = compute_factors(signals)
output_factor(args, factors, conditions)
# normalize signals by factor and number of motifs
for i in range(len(conditions)):
for j in range(len(mpbs_name_list)):
signals[i, j, :] = signals[i, j, :] / (factors[i] * motif_num[j])
if args.output_profiles:
output_profiles(mpbs_name_list, signals, conditions,
args.output_location)
print("generating line plot for each motif...\n")
if args.nc == 1:
for i, mpbs_name in enumerate(mpbs_name_list):
output_line_plot(
(mpbs_name, motif_num[i], signals[:, i, :], conditions,
motif_pwm[i], output_location, args.window_size, colors))
else:
with Pool(processes=args.nc) as pool:
arguments_list = list()
for i, mpbs_name in enumerate(mpbs_name_list):
arguments_list.append(
(mpbs_name, motif_num[i], signals[:, i, :], conditions,
motif_pwm[i], output_location, args.window_size, colors))
pool.map(output_line_plot, arguments_list)
ps_tc_results = list()
for i, mpbs_name in enumerate(mpbs_name_list):
ps_tc_results.append(
get_ps_tc_results(signals[:, i, :], motif_len[i],
args.window_size))
# find the significant motifs and generate a scatter plot if two conditions are given
if len(conditions) == 2:
ps_tc_results = scatter_plot(args, ps_tc_results, mpbs_name_list,
conditions)
output_stat_results(ps_tc_results, conditions, mpbs_name_list, motif_num,
args)
def diff_analysis_run(args):
# Initializing Error Handler
err = ErrorHandler()
output_location = os.path.join(args.output_location, "Lineplots")
try:
if not os.path.isdir(output_location):
os.makedirs(output_location)
except Exception:
err.throw_error("MM_OUT_FOLDER_CREATION")
# Check if the index file exists
base_name1 = "{}.bai".format(args.reads_file1)
if not os.path.exists(base_name1):
pysam.index(args.reads_file1)
base_name2 = "{}.bai".format(args.reads_file2)
if not os.path.exists(base_name2):
pysam.index(args.reads_file2)
mpbs1 = GenomicRegionSet("Motif Predicted Binding Sites of Condition1")
mpbs1.read(args.mpbs_file1)
mpbs2 = GenomicRegionSet("Motif Predicted Binding Sites of Condition2")
mpbs2.read(args.mpbs_file2)
mpbs = mpbs1.combine(mpbs2, output=True)
mpbs.sort()
mpbs.remove_duplicates()
mpbs_name_list = list(set(mpbs.get_names()))
signal_dict_by_tf_1 = dict()
signal_dict_by_tf_2 = dict()
motif_len_dict = dict()
motif_num_dict = dict()
pwm_dict_by_tf = dict()
pool = Pool(processes=args.nc)
# differential analysis using bias corrected signal
if args.bc:
hmm_data = HmmData()
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table1 = BiasTable().load_table(table_file_name_F=table_F, table_file_name_R=table_R)
bias_table2 = BiasTable().load_table(table_file_name_F=table_F, table_file_name_R=table_R)
mpbs_list = list()
for mpbs_name in mpbs_name_list:
mpbs_list.append((mpbs_name, args.mpbs_file1, args.mpbs_file2, args.reads_file1, args.reads_file2,
args.organism, args.window_size, args.forward_shift, args.reverse_shift,
bias_table1, bias_table2))
try:
res = pool.map(get_bc_signal, mpbs_list)
except Exception:
logging.exception("get bias corrected signal failed")
# differential analysis using raw signal
else:
mpbs_list = list()
for mpbs_name in mpbs_name_list:
mpbs_list.append((mpbs_name, args.mpbs_file1, args.mpbs_file2, args.reads_file1, args.reads_file2,
args.organism, args.window_size, args.forward_shift, args.reverse_shift))
try:
res = pool.map(get_raw_signal, mpbs_list)
except Exception:
logging.exception("get raw signal failed")
for idx, mpbs_name in enumerate(mpbs_name_list):
signal_dict_by_tf_1[mpbs_name] = res[idx][0]
signal_dict_by_tf_2[mpbs_name] = res[idx][1]
motif_len_dict[mpbs_name] = res[idx][2]
pwm_dict_by_tf[mpbs_name] = res[idx][3]
motif_num_dict[mpbs_name] = res[idx][4]
if args.factor1 is None or args.factor2 is None:
args.factor1, args.factor2 = compute_factors(signal_dict_by_tf_1, signal_dict_by_tf_2)
output_factor(args, args.factor1, args.factor2)
if args.output_profiles:
output_profiles(mpbs_name_list, signal_dict_by_tf_1, output_location, args.condition1)
output_profiles(mpbs_name_list, signal_dict_by_tf_2, output_location, args.condition2)
ps_tc_results_by_tf = dict()
plots_list = list()
for mpbs_name in mpbs_name_list:
plots_list.append((mpbs_name, motif_num_dict[mpbs_name], signal_dict_by_tf_1[mpbs_name],
signal_dict_by_tf_2[mpbs_name], args.factor1, args.factor2, args.condition1,
args.condition2, pwm_dict_by_tf[mpbs_name], output_location, args.window_size,
args.standardize))
pool.map(line_plot, plots_list)
for mpbs_name in mpbs_name_list:
res = get_ps_tc_results(signal_dict_by_tf_1[mpbs_name], signal_dict_by_tf_2[mpbs_name],
args.factor1, args.factor2, motif_num_dict[mpbs_name], motif_len_dict[mpbs_name])
#
# # only use the factors whose protection scores are greater than 0
# if res[0] > 0 and res[1] < 0:
ps_tc_results_by_tf[mpbs_name] = res
#
# stat_results_by_tf = get_stat_results(ps_tc_results_by_tf)
ps_tc_results_by_tf = scatter_plot(args, ps_tc_results_by_tf)
output_stat_results(args, ps_tc_results_by_tf, motif_num_dict)
def get_bc_tracks(args):
# Initializing Error Handler
err = ErrorHandler()
if len(args.input_files) != 2:
err.throw_error("ME_FEW_ARG", add_msg="You must specify reads and regions file.")
regions = GenomicRegionSet("Interested regions")
regions.read(args.input_files[1])
regions.merge()
reads_file = GenomicSignal()
bam = Samfile(args.input_files[0], "rb")
genome_data = GenomeData(args.organism)
fasta = Fastafile(genome_data.get_genome())
hmm_data = HmmData()
if args.bias_table:
bias_table_list = args.bias_table.split(",")
bias_table = BiasTable().load_table(table_file_name_F=bias_table_list[0],
table_file_name_R=bias_table_list[1])
else:
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
if args.strand_specific:
fname_forward = os.path.join(args.output_location, "{}_forward.wig".format(args.output_prefix))
fname_reverse = os.path.join(args.output_location, "{}_reverse.wig".format(args.output_prefix))
f_forward = open(fname_forward, "a")
f_reverse = open(fname_reverse, "a")
for region in regions:
signal_f, signal_r = reads_file.get_bc_signal_by_fragment_length(
ref=region.chrom, start=region.initial, end=region.final, bam=bam, fasta=fasta, bias_table=bias_table,
forward_shift=args.forward_shift, reverse_shift=args.reverse_shift, min_length=None, max_length=None,
strand=True)
if args.norm:
signal_f = reads_file.boyle_norm(signal_f)
perc = scoreatpercentile(signal_f, 98)
std = np.std(signal_f)
signal_f = reads_file.hon_norm_atac(signal_f, perc, std)
signal_r = reads_file.boyle_norm(signal_r)
perc = scoreatpercentile(signal_r, 98)
std = np.std(signal_r)
signal_r = reads_file.hon_norm_atac(signal_r, perc, std)
f_forward.write("fixedStep chrom=" + region.chrom + " start=" + str(region.initial + 1) + " step=1\n" +
"\n".join([str(e) for e in np.nan_to_num(signal_f)]) + "\n")
f_reverse.write("fixedStep chrom=" + region.chrom + " start=" + str(region.initial + 1) + " step=1\n" +
"\n".join([str(-e) for e in np.nan_to_num(signal_r)]) + "\n")
f_forward.close()
f_reverse.close()
if args.bigWig:
genome_data = GenomeData(args.organism)
chrom_sizes_file = genome_data.get_chromosome_sizes()
bw_filename = os.path.join(args.output_location, "{}_forward.bw".format(args.output_prefix))
os.system(" ".join(["wigToBigWig", fname_forward, chrom_sizes_file, bw_filename, "-verbose=0"]))
os.remove(fname_forward)
bw_filename = os.path.join(args.output_location, "{}_reverse.bw".format(args.output_prefix))
os.system(" ".join(["wigToBigWig", fname_reverse, chrom_sizes_file, bw_filename, "-verbose=0"]))
os.remove(fname_reverse)
else:
output_fname = os.path.join(args.output_location, "{}.wig".format(args.output_prefix))
with open(output_fname, "a") as output_f:
for region in regions:
signal = reads_file.get_bc_signal_by_fragment_length(ref=region.chrom, start=region.initial,
end=region.final,
bam=bam, fasta=fasta, bias_table=bias_table,
forward_shift=args.forward_shift,
reverse_shift=args.reverse_shift,
min_length=None, max_length=None, strand=False)
if args.norm:
signal = reads_file.boyle_norm(signal)
perc = scoreatpercentile(signal, 98)
std = np.std(signal)
signal = reads_file.hon_norm_atac(signal, perc, std)
output_f.write("fixedStep chrom=" + region.chrom + " start=" + str(region.initial + 1) + " step=1\n" +
"\n".join([str(e) for e in np.nan_to_num(signal)]) + "\n")
output_f.close()
if args.bigWig:
genome_data = GenomeData(args.organism)
chrom_sizes_file = genome_data.get_chromosome_sizes()
bw_filename = os.path.join(args.output_location, "{}.bw".format(args.output_prefix))
os.system(" ".join(["wigToBigWig", output_fname, chrom_sizes_file, bw_filename, "-verbose=0"]))
os.remove(output_fname)
def footprint(bam: str,
bed: str,
assembly: str = "hg38",
w: int = 500,
dnase: bool = False,
bias_type="SH"):
# load HMM and bias parameters for ATAC-seq
g = GenomeData(organism=assembly)
hmm_data = HmmData()
if dnase:
hmm_file = hmm_data.get_default_hmm_dnase_bc()
if bias_type == 'SH':
table_F = hmm_data.get_default_bias_table_F_SH()
table_R = hmm_data.get_default_bias_table_R_SH()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
elif bias_type == 'DH':
table_F = hmm_data.get_default_bias_table_F_DH()
table_R = hmm_data.get_default_bias_table_R_DH()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
else:
hmm_file = hmm_data.get_default_hmm_atac_paired()
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
# load reads from BAM
reads_file = GenomicSignal(bam)
reads_file.load_sg_coefs(SG_WINDOW_SIZE)
# open data and sequence
bam = Samfile(bam, "rb")
fasta = Fastafile(g.get_genome())
# load and expand regions
with open(bed, 'r') as f:
regions = [
expandRegion(
*tuple(line.strip().split()[:3]),
line.strip().split()[3]
if len(line.strip().split()) >= 4 else None, w,
line.strip().split()[4]
if len(line.strip().split()) >= 5 else '.') for line in f
]
# load signal
forward = []
reverse = []
failed = 0
get_reads = reads_file.get_signal_atac if not dnase else reads_file.get_signal
for i, x in enumerate(regions):
try:
chromosome, start, end, _, strand = x
atac_norm_f, atac_slope_f, atac_norm_r, atac_slope_r = get_reads(
chromosome, start, end, 0, 0,
FORWARD_SHIFT if not dnase else 0,
REVERSE_SHIFT if not dnase else 0, 1000 if dnase else 150, 98,
98, bias_table, g.get_genome())
atac_norm_f = [float(x) for x in atac_norm_f]
atac_norm_r = [float(x) for x in atac_norm_r]
if strand == '-':
atac_norm_f.reverse()
atac_norm_r.reverse()
forward.append(atac_norm_f if strand != '-' else atac_norm_r)
reverse.append(atac_norm_r if strand != '-' else atac_norm_f)
if i % 500 == 0:
print("INFO: aggregating region %d of %d" % (i, len(regions)),
file=sys.stderr)
except:
if len(forward) <= i: forward.append(None)
if len(reverse) <= i: reverse.append(None)
failed += 1
if failed > 0:
print(
"WARNING: failed to generate bias-corrected signal profiles for %d regions"
% failed,
file=sys.stderr)
return [
regionDict(regions[i], forward[i], reverse[i])
for i in range(len(regions))
if forward[i] is not None and reverse[i] is not None
]
def diff_analysis_run(args):
# Initializing Error Handler
err = ErrorHandler()
output_location = os.path.join(args.output_location, "Lineplots")
try:
if not os.path.isdir(output_location):
os.makedirs(output_location)
except Exception:
err.throw_error("MM_OUT_FOLDER_CREATION")
mpbs1 = GenomicRegionSet("Motif Predicted Binding Sites of Condition1")
mpbs1.read(args.mpbs_file1)
mpbs2 = GenomicRegionSet("Motif Predicted Binding Sites of Condition2")
mpbs2.read(args.mpbs_file2)
mpbs = mpbs1.combine(mpbs2, output=True)
mpbs.sort()
mpbs_name_list = list(set(mpbs.get_names()))
signal_dict_by_tf_1 = dict()
signal_dict_by_tf_2 = dict()
motif_len_dict = dict()
motif_num_dict = dict()
pwm_dict_by_tf = dict()
pool = Pool(processes=args.nc)
# differential analysis using bias corrected signal
if args.bc:
hmm_data = HmmData()
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table1 = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
bias_table2 = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
mpbs_list = list()
for mpbs_name in mpbs_name_list:
mpbs_list.append(
(mpbs_name, args.mpbs_file1, args.mpbs_file2, args.reads_file1,
args.reads_file2, args.organism, args.window_size,
args.forward_shift, args.reverse_shift, bias_table1,
bias_table2))
try:
res = pool.map(get_bc_signal, mpbs_list)
except Exception:
logging.exception("get bias corrected signal failed")
# differential analysis using raw signal
else:
mpbs_list = list()
for mpbs_name in mpbs_name_list:
mpbs_list.append(
(mpbs_name, args.mpbs_file1, args.mpbs_file2, args.reads_file1,
args.reads_file2, args.organism, args.window_size,
args.forward_shift, args.reverse_shift))
try:
res = pool.map(get_raw_signal, mpbs_list)
except Exception:
logging.exception("get raw signal failed")
for idx, mpbs_name in enumerate(mpbs_name_list):
signal_dict_by_tf_1[mpbs_name] = res[idx][0]
signal_dict_by_tf_2[mpbs_name] = res[idx][1]
motif_len_dict[mpbs_name] = res[idx][2]
pwm_dict_by_tf[mpbs_name] = res[idx][3]
motif_num_dict[mpbs_name] = res[idx][4]
if args.factor1 is None or args.factor2 is None:
args.factor1, args.factor2 = compute_factors(signal_dict_by_tf_1,
signal_dict_by_tf_2)
output_factor(args, args.factor1, args.factor2)
if args.output_profiles:
output_profiles(mpbs_name_list, signal_dict_by_tf_1, output_location,
args.condition1)
output_profiles(mpbs_name_list, signal_dict_by_tf_2, output_location,
args.condition2)
ps_tc_results_by_tf = dict()
plots_list = list()
for mpbs_name in mpbs_name_list:
plots_list.append(
(mpbs_name, motif_num_dict[mpbs_name],
signal_dict_by_tf_1[mpbs_name], signal_dict_by_tf_2[mpbs_name],
args.factor1, args.factor2, args.condition1, args.condition2,
pwm_dict_by_tf[mpbs_name], output_location, args.window_size,
args.standardize))
pool.map(line_plot, plots_list)
for mpbs_name in mpbs_name_list:
res = get_ps_tc_results(signal_dict_by_tf_1[mpbs_name],
signal_dict_by_tf_2[mpbs_name], args.factor1,
args.factor2, motif_num_dict[mpbs_name],
motif_len_dict[mpbs_name])
#
# # only use the factors whose protection scores are greater than 0
# if res[0] > 0 and res[1] < 0:
ps_tc_results_by_tf[mpbs_name] = res
#
stat_results_by_tf = get_stat_results(ps_tc_results_by_tf)
scatter_plot(args, stat_results_by_tf)
output_stat_results(args, stat_results_by_tf, motif_num_dict)
def get_bc_tracks(args):
# Initializing Error Handler
err = ErrorHandler()
if len(args.input_files) != 2:
err.throw_error("ME_FEW_ARG",
add_msg="You must specify reads and regions file.")
regions = GenomicRegionSet("Interested regions")
regions.read(args.input_files[1])
regions.merge()
reads_file = GenomicSignal()
bam = Samfile(args.input_files[0], "rb")
genome_data = GenomeData(args.organism)
fasta = Fastafile(genome_data.get_genome())
hmm_data = HmmData()
if args.bias_table:
bias_table_list = args.bias_table.split(",")
bias_table = BiasTable().load_table(
table_file_name_F=bias_table_list[0],
table_file_name_R=bias_table_list[1])
else:
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
if args.strand_specific:
fname_forward = os.path.join(
args.output_location, "{}_forward.wig".format(args.output_prefix))
fname_reverse = os.path.join(
args.output_location, "{}_reverse.wig".format(args.output_prefix))
f_forward = open(fname_forward, "a")
f_reverse = open(fname_reverse, "a")
for region in regions:
signal_f, signal_r = reads_file.get_bc_signal_by_fragment_length(
ref=region.chrom,
start=region.initial,
end=region.final,
bam=bam,
fasta=fasta,
bias_table=bias_table,
forward_shift=args.forward_shift,
reverse_shift=args.reverse_shift,
min_length=None,
max_length=None,
strand=True)
if args.norm:
signal_f = reads_file.boyle_norm(signal_f)
perc = scoreatpercentile(signal_f, 98)
std = np.std(signal_f)
signal_f = reads_file.hon_norm_atac(signal_f, perc, std)
signal_r = reads_file.boyle_norm(signal_r)
perc = scoreatpercentile(signal_r, 98)
std = np.std(signal_r)
signal_r = reads_file.hon_norm_atac(signal_r, perc, std)
f_forward.write(
"fixedStep chrom=" + region.chrom + " start=" +
str(region.initial + 1) + " step=1\n" +
"\n".join([str(e) for e in np.nan_to_num(signal_f)]) + "\n")
f_reverse.write(
"fixedStep chrom=" + region.chrom + " start=" +
str(region.initial + 1) + " step=1\n" +
"\n".join([str(-e) for e in np.nan_to_num(signal_r)]) + "\n")
f_forward.close()
f_reverse.close()
if args.bigWig:
genome_data = GenomeData(args.organism)
chrom_sizes_file = genome_data.get_chromosome_sizes()
bw_filename = os.path.join(
args.output_location,
"{}_forward.bw".format(args.output_prefix))
os.system(" ".join([
"wigToBigWig", fname_forward, chrom_sizes_file, bw_filename,
"-verbose=0"
]))
os.remove(fname_forward)
bw_filename = os.path.join(
args.output_location,
"{}_reverse.bw".format(args.output_prefix))
os.system(" ".join([
"wigToBigWig", fname_reverse, chrom_sizes_file, bw_filename,
"-verbose=0"
]))
os.remove(fname_reverse)
else:
output_fname = os.path.join(args.output_location,
"{}.wig".format(args.output_prefix))
with open(output_fname, "a") as output_f:
for region in regions:
signal = reads_file.get_bc_signal_by_fragment_length(
ref=region.chrom,
start=region.initial,
end=region.final,
bam=bam,
fasta=fasta,
bias_table=bias_table,
forward_shift=args.forward_shift,
reverse_shift=args.reverse_shift,
min_length=None,
max_length=None,
strand=False)
if args.norm:
signal = reads_file.boyle_norm(signal)
perc = scoreatpercentile(signal, 98)
std = np.std(signal)
signal = reads_file.hon_norm_atac(signal, perc, std)
output_f.write(
"fixedStep chrom=" + region.chrom + " start=" +
str(region.initial + 1) + " step=1\n" +
"\n".join([str(e) for e in np.nan_to_num(signal)]) + "\n")
output_f.close()
if args.bigWig:
genome_data = GenomeData(args.organism)
chrom_sizes_file = genome_data.get_chromosome_sizes()
bw_filename = os.path.join(args.output_location,
"{}.bw".format(args.output_prefix))
os.system(" ".join([
"wigToBigWig", output_fname, chrom_sizes_file, bw_filename,
"-verbose=0"
]))
os.remove(output_fname)