def process_sample(kmer_length, min_support_percentage, n_permutations, p_value_threshold, sample_key=None, fastq_files=None, fasta_file=None, snp_file=None, experiment_name=None,
destination_directory=".", export_gml=False, output_results=None):
if experiment_name == "TP53":
from randomreadsgraph_TP53 import RandomReadsGraph as RRG
else:
from randomreadsgraph import RandomReadsGraph as RRG
# g_reference construction
logger.info("Will build reference graph with k==%d and fasta=%s & snp=%s", kmer_length, fasta_file, snp_file)
g_reference = RG(kmer_length, fasta_file, snp_file)
# Is there cycles in reference graph?
if list(nx.simple_cycles(g_reference.dbg)):
if kmer_length > 70:
logger.info("There are always cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Reference graph] Increasing k to %d to remove cycles", kmer_length)
return process_sample(kmer_length=kmer_length + 1, sample_key=sample_key, fastq_files=fastq_files, fasta_file=fasta_file, snp_file=snp_file,
experiment_name=experiment_name, min_support_percentage=min_support_percentage, n_permutations=n_permutations,
destination_directory=destination_directory, export_gml=export_gml, p_value_threshold=p_value_threshold, output_results=output_results)
# g_patient construction
logger.info("Will build patient graph for %s with k==%d and minimum support = %dpct", fastq_files, kmer_length, min_support_percentage)
fastq_files = fastq_files.split(",")
g_patient = PG(fastq_files, kmer_length)
logger.info("Before cleaning: %d nodes", len(g_patient.dbg))
g_patient.graph_cleaned_init(min_support_percentage)
logger.info("After cleaning: %d nodes", len(g_patient.dbgclean))
# Is there cycles in patient graph?
if list(nx.simple_cycles(g_patient.dbgclean)):
if kmer_length > 70:
logger.info("There are still cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Sample graph] Increasing k to %d to remove cycles", kmer_length)
return process_sample(kmer_length=kmer_length + 1, sample_key=sample_key, fastq_files=",".join(fastq_files), fasta_file=fasta_file, snp_file=snp_file,
experiment_name=experiment_name, min_support_percentage=min_support_percentage, n_permutations=n_permutations,
destination_directory=destination_directory, export_gml=export_gml, p_value_threshold=p_value_threshold, output_results=output_results)
# Some prints for stats
dir_stat = get_or_create_dir("output/statistics")
# graph stat
graph_stat_file = open(dir_stat + "/graph_stat_file" + sample_key + ".tsv", 'w')
graph_stat_file.write(
"%d\t%d\t%s\t%d\t%d\t%d\t%d\t%d\t%d\t%d\n" % (
kmer_length,
g_reference.dbg.size(),
sample_key,
g_patient.coverage['total'],
g_patient.dbg.size(),
g_patient.dbgclean.size(),
g_patient.dbg.in_degree().values().count(0),
g_patient.dbg.out_degree().values().count(0),
g_patient.dbgclean.in_degree().values().count(0),
g_patient.dbgclean.out_degree().values().count(0)
))
# kmer stat
kmer_stat_file = open(dir_stat + "/kmer_stat_file" + sample_key + ".tsv", 'w')
for node_print in g_patient.dbg.nodes():
fragment_print = ",".join(g_patient.dbg.node[node_print]['fastq_id'])
reads_print = len(g_patient.dbg.node[node_print]['read_list_n'])
kmer_stat_file.write(
"%s\t%s\t%s\t%d\n" % (
sample_key,
node_print,
fragment_print,
reads_print,
))
# copy g_patient cleaned and remove reference edges on it (.dbg_refrm creation)
g_patient.graph_rmRefEdges_init(g_patient.dbgclean, g_reference.dbg)
# search for alternative paths in dbg_refrm (.alteration_list creation)
g_patient.alteration_list_init(g_reference.dbg, kmer_length, min_support_percentage)
### Permutation test ###
logger.info("Will create random graphs")
all_possible_kmers = set()
for an_alt in g_patient.alteration_list:
all_possible_kmers.update(an_alt.reference_path)
all_possible_kmers.update(an_alt.alternative_path)
for i, j in time_iterator(range(0, n_permutations), logger, msg_prefix="permuting"):
g_random = RRG(g_patient.coverage, kmer_length, restrict_to=all_possible_kmers)
for i_alteration in range(0, len(g_patient.alteration_list)):
g_random_data = g_random.check_path(g_patient.alteration_list[i_alteration].reference_path, g_patient.alteration_list[i_alteration].alternative_path,
g_patient.alteration_list[i_alteration].min_coverage)
g_patient.alteration_list[i_alteration].random_ratio_list.append(g_random_data[0])
g_patient.alteration_list[i_alteration].random_reference_count_list.append(g_random_data[1])
g_patient.alteration_list[i_alteration].random_alternative_count_list.append(g_random_data[2])
logger.info("Will generate p-values for %d possible alterations", len(g_patient.alteration_list))
for i_alteration in range(0, len(g_patient.alteration_list)):
g_patient.alteration_list[i_alteration].pvalue_init()
g_patient.significant_alteration_list_init(p_value_threshold=p_value_threshold)
# If more than one significant alteration, check if they are not in "spike" (en épis)
if len(g_patient.significant_alteration_list) > 1:
g_patient.multiple_alternative_path_filter()
## Stat
# alteration stat
alt_stat_file = open(dir_stat + "/alt_stat_file" + sample_key + ".tsv", 'w')
for i_alteration in range(0, len(g_patient.alteration_list)):
if g_patient.alteration_list[i_alteration].pvalue_ratio <= 1:
alt_stat_file.write("%d\t%s\t%d\t%s\t%s\t%s\t%s\t%f\t%f\t%s\t%s\n" % (
i_alteration + 1,
sample_key,
g_patient.coverage['total'],
g_patient.alteration_list[i_alteration].reference_sequence,
g_patient.alteration_list[i_alteration].alternative_sequence,
g_patient.alteration_list[i_alteration].reference_read_count,
g_patient.alteration_list[i_alteration].alternative_read_count,
g_patient.alteration_list[i_alteration].ratio_read_count,
g_patient.alteration_list[i_alteration].pvalue_ratio,
str(g_patient.alteration_list[i_alteration].zscore),
"\t".join(map(str, g_patient.alteration_list[i_alteration].random_ratio_list))
))
# For visualisation
graph_name = "G_%s_" % sample_key
merged_graph_name = "G_%s_merged_" % sample_key
cleaned_graph_name = graph_name + "clean%d_" % min_support_percentage
merged_cleaned_graph_name = graph_name + "clean%d_merged_" % min_support_percentage
if export_gml:
logger.info("Will save viz graph for %s with k==%d", sample_key, kmer_length)
get_or_create_dir(destination_directory)
# for the refrence graph
g_reference_merge = VISU.merge_reference_graph(g_reference.dbg.copy())
g_reference_visu = VISU.reference_graph_visualization_formatting(g_reference.dbg.copy())
g_reference_merge_visu = VISU.reference_graph_merged_visualization_formatting(g_reference_merge.copy())
nx.write_gml(g_reference_visu, destination_directory + "/g_reference_visu" + str(kmer_length) + ".gml")
nx.write_gml(g_reference_merge_visu, destination_directory + "/g_reference_merge_visu" + str(kmer_length) + ".gml")
# for the patient graph
g_patient_visu = VISU.individu_graph_visualization_formating(g_patient.dbg.copy(), g_reference.dbg.copy())
g_patient_clean_visu = VISU.individu_graph_visualization_formating(g_patient.dbgclean.copy(), g_reference.dbg.copy())
g_patient_merged = VISU.merge_individu_graph(g_patient.dbg.copy(), g_reference.dbg.copy())
g_patient_merged_visu = VISU.individu_graph_merged_visualization_formating(g_patient_merged.copy(), g_reference.dbg.copy())
g_patient_clean_merged = VISU.merge_individu_graph(g_patient.dbgclean.copy(), g_reference.dbg.copy())
g_patient_clean_merged_visu = VISU.individu_graph_merged_visualization_formating(g_patient_clean_merged.copy(), g_reference.dbg.copy())
nx.write_gml(g_patient_visu, destination_directory + "/" + graph_name + str(kmer_length) + ".gml")
nx.write_gml(g_patient_clean_visu, destination_directory + "/" + cleaned_graph_name + str(kmer_length) + ".gml")
nx.write_gml(g_patient_merged_visu, destination_directory + "/" + merged_graph_name + str(kmer_length) + ".gml")
nx.write_gml(g_patient_clean_merged_visu, destination_directory + "/" + merged_cleaned_graph_name + str(kmer_length) + ".gml")
# Annotation
if experiment_name == "TP53":
annotate_and_output_results(g_patient, g_reference, output_results)
# SNP
dir_stat = get_or_create_dir("output/snp")
# graph stat
graph_snp = open(dir_stat + "/snp_" + sample_key + ".tsv", 'w')
for snp_id in g_reference.snp.keys():
if g_reference.snp[snp_id][1] in g_patient.dbgclean:
if g_reference.snp[snp_id][0] in g_patient.dbgclean:
graph_snp.write("%s\t%s\t%d\t%d\n" % (
sample_key, snp_id, len(g_patient.dbg.node[g_reference.snp[snp_id][0]]['read_list_n']), len(g_patient.dbg.node[g_reference.snp[snp_id][1]]['read_list_n'])))
else:
graph_snp.write("%s\t%s\t0\t%d\n" % (sample_key, snp_id, len(g_patient.dbg.node[g_reference.snp[snp_id][1]]['read_list_n'])))
def process_sample(kmer_length,
min_support_percentage,
n_permutations,
p_value_threshold,
max_len,
sample_key=None,
fastq_files=None,
fasta_file=None,
snp_file=None,
experiment_name=None,
output_results=None,
disable_cycle_breaking=False):
import seq_lib as seq_lib_module
seq_lib_module.library_itit(experiment_name)
# g_reference construction
logger.info("Will build reference graph with k==%d and fasta=%s & snp=%s",
kmer_length, fasta_file, snp_file)
g_reference = RG(kmer_length, fasta_file, snp_file)
# Is there cycles in reference graph?
if list(nx.simple_cycles(g_reference.dbg)):
if kmer_length >= 70:
logger.info("There are always cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Reference graph] Increasing k to %d to remove cycles",
kmer_length + 1)
return process_sample(kmer_length=kmer_length + 1,
min_support_percentage=min_support_percentage,
n_permutations=n_permutations,
p_value_threshold=p_value_threshold,
max_len=max_len,
sample_key=sample_key,
fastq_files=fastq_files,
fasta_file=fasta_file,
snp_file=snp_file,
experiment_name=experiment_name,
output_results=output_results,
disable_cycle_breaking=disable_cycle_breaking)
# g_patient construction
logger.info(
"Will build patient graph for %s with k==%d and minimum support = %dpct",
fastq_files, kmer_length, min_support_percentage)
fastq_files = fastq_files.split(",")
g_patient = PG(fastq_files, kmer_length)
logger.info("Before cleaning: %d nodes", len(g_patient.dbg))
g_patient.graph_cleaned_init(min_support_percentage)
logger.info("After cleaning: %d nodes", len(g_patient.dbgclean))
# Is there cycles in patient graph?
if not disable_cycle_breaking and list(nx.simple_cycles(
g_patient.dbgclean)):
if kmer_length >= 70:
logger.info("There are still cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Sample graph] Increasing k to %d to remove cycles",
kmer_length + 1)
return process_sample(kmer_length=kmer_length + 1,
min_support_percentage=min_support_percentage,
n_permutations=n_permutations,
p_value_threshold=p_value_threshold,
max_len=max_len,
sample_key=sample_key,
fastq_files=",".join(fastq_files),
fasta_file=fasta_file,
snp_file=snp_file,
experiment_name=experiment_name,
output_results=output_results)
# copy g_patient cleaned and remove reference edges on it (.dbg_refrm creation)
g_patient.graph_rmRefEdges_init(g_patient.dbgclean, g_reference.dbg)
# search for alternative paths in dbg_refrm (.alteration_list creation)
g_patient.alteration_list_init(g_reference.dbg, kmer_length,
min_support_percentage, max_len)
### Permutation test ###
logger.info("Will create random graphs")
all_possible_kmers = set()
for an_alt in g_patient.alteration_list:
all_possible_kmers.update(an_alt.reference_path)
all_possible_kmers.update(an_alt.alternative_path)
for _, _ in time_iterator(range(0, n_permutations),
logger,
msg_prefix="permuting"):
g_random = RRG(g_patient.coverage,
kmer_length,
restrict_to=all_possible_kmers,
seq_lib_module=seq_lib_module)
for i in range(0, len(g_patient.alteration_list)):
i_alteration = g_patient.alteration_list[i]
ref_path = i_alteration.reference_path
alt_path = i_alteration.alternative_path
g_random_data = g_random.check_path(ref_path, alt_path,
i_alteration.min_coverage)
i_alteration.random_ratio_list.append(g_random_data[0])
i_alteration.random_reference_count_list.append(g_random_data[1])
i_alteration.random_alternative_count_list.append(g_random_data[2])
logger.info("Will generate p-values for %d possible alterations",
len(g_patient.alteration_list))
for i in range(0, len(g_patient.alteration_list)):
g_patient.alteration_list[i].pvalue_init()
g_patient.significant_alteration_list_init(
p_value_threshold=p_value_threshold)
# Annotation
annotate_and_output_results(g_patient, g_reference, output_results)
# SNP
dir_stat = get_or_create_dir("output/snp")
graph_snp = open(dir_stat + "/snp_" + sample_key + ".tsv", 'w')
for snp_id in g_reference.snp.keys():
if g_reference.snp[snp_id][1] in g_patient.dbgclean:
if g_reference.snp[snp_id][0] in g_patient.dbgclean:
graph_snp.write("%s\t%s\t%d\t%d\n" %
(sample_key, snp_id,
len(g_patient.dbg.node[g_reference.snp[snp_id]
[0]]['read_list_n']),
len(g_patient.dbg.node[g_reference.snp[snp_id]
[1]]['read_list_n'])))
else:
graph_snp.write("%s\t%s\t0\t%d\n" %
(sample_key, snp_id,
len(g_patient.dbg.node[g_reference.snp[snp_id]
[1]]['read_list_n'])))
def process_sample(kmer_length, min_support_percentage, n_permutations, p_value_threshold, max_len, sample_key=None, fastq_files=None,
fasta_file=None, snp_file=None, experiment_name=None, output_results=None, disable_cycle_breaking=False):
import seq_lib as seq_lib_module
seq_lib_module.library_itit(experiment_name)
# g_reference construction
logger.info("Will build reference graph with k==%d and fasta=%s & snp=%s", kmer_length, fasta_file, snp_file)
g_reference = RG(kmer_length, fasta_file, snp_file)
# Is there cycles in reference graph?
if list(nx.simple_cycles(g_reference.dbg)):
if kmer_length >= 70:
logger.info("There are always cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Reference graph] Increasing k to %d to remove cycles", kmer_length+1)
return process_sample(kmer_length=kmer_length + 1, min_support_percentage=min_support_percentage, n_permutations=n_permutations,
p_value_threshold=p_value_threshold, max_len=max_len, sample_key=sample_key, fastq_files=fastq_files,
fasta_file=fasta_file, snp_file=snp_file, experiment_name=experiment_name, output_results=output_results,
disable_cycle_breaking=disable_cycle_breaking)
# g_patient construction
logger.info("Will build patient graph for %s with k==%d and minimum support = %dpct", fastq_files, kmer_length, min_support_percentage)
fastq_files = fastq_files.split(",")
g_patient = PG(fastq_files, kmer_length)
logger.info("Before cleaning: %d nodes", len(g_patient.dbg))
g_patient.graph_cleaned_init(min_support_percentage)
logger.info("After cleaning: %d nodes", len(g_patient.dbgclean))
# Is there cycles in patient graph?
if not disable_cycle_breaking and list(nx.simple_cycles(g_patient.dbgclean)):
if kmer_length >= 70:
logger.info("There are still cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Sample graph] Increasing k to %d to remove cycles", kmer_length+1)
return process_sample(kmer_length=kmer_length + 1, min_support_percentage=min_support_percentage, n_permutations=n_permutations,
p_value_threshold=p_value_threshold, max_len=max_len, sample_key=sample_key, fastq_files=",".join(fastq_files),
fasta_file=fasta_file, snp_file=snp_file, experiment_name=experiment_name, output_results=output_results)
# copy g_patient cleaned and remove reference edges on it (.dbg_refrm creation)
g_patient.graph_rmRefEdges_init(g_patient.dbgclean, g_reference.dbg)
# search for alternative paths in dbg_refrm (.alteration_list creation)
g_patient.alteration_list_init(g_reference.dbg, kmer_length, min_support_percentage, max_len)
### Permutation test ###
logger.info("Will create random graphs")
all_possible_kmers = set()
for an_alt in g_patient.alteration_list:
all_possible_kmers.update(an_alt.reference_path)
all_possible_kmers.update(an_alt.alternative_path)
for _, _ in time_iterator(range(0, n_permutations), logger, msg_prefix="permuting"):
g_random = RRG(g_patient.coverage, kmer_length, restrict_to=all_possible_kmers, seq_lib_module=seq_lib_module)
for i in range(0, len(g_patient.alteration_list)):
i_alteration = g_patient.alteration_list[i]
ref_path = i_alteration.reference_path
alt_path = i_alteration.alternative_path
g_random_data = g_random.check_path(ref_path,
alt_path,
i_alteration.min_coverage)
i_alteration.random_ratio_list.append(g_random_data[0])
i_alteration.random_reference_count_list.append(g_random_data[1])
i_alteration.random_alternative_count_list.append(g_random_data[2])
logger.info("Will generate p-values for %d possible alterations", len(g_patient.alteration_list))
for i in range(0, len(g_patient.alteration_list)):
g_patient.alteration_list[i].pvalue_init()
g_patient.significant_alteration_list_init(p_value_threshold=p_value_threshold)
# Annotation
annotate_and_output_results(g_patient, g_reference, output_results)
# SNP
dir_stat = get_or_create_dir("output/snp")
graph_snp = open(dir_stat + "/snp_" + sample_key + ".tsv", 'w')
for snp_id in g_reference.snp.keys():
if g_reference.snp[snp_id][1] in g_patient.dbgclean:
if g_reference.snp[snp_id][0] in g_patient.dbgclean:
graph_snp.write("%s\t%s\t%d\t%d\n" % (
sample_key, snp_id, len(g_patient.dbg.node[g_reference.snp[snp_id][0]]['read_list_n']),
len(g_patient.dbg.node[g_reference.snp[snp_id][1]]['read_list_n'])))
else:
graph_snp.write(
"%s\t%s\t0\t%d\n" % (sample_key, snp_id, len(g_patient.dbg.node[g_reference.snp[snp_id][1]]['read_list_n'])))
def process_sample(kmer_length,
min_support_percentage,
n_permutations,
p_value_threshold,
sample_key=None,
fastq_files=None,
fasta_file=None,
snp_file=None,
experiment_name=None,
destination_directory=".",
export_gml=False,
output_results=None):
if experiment_name == "TP53":
from randomreadsgraph_TP53 import RandomReadsGraph as RRG
else:
from randomreadsgraph import RandomReadsGraph as RRG
# g_reference construction
logger.info("Will build reference graph with k==%d and fasta=%s & snp=%s",
kmer_length, fasta_file, snp_file)
g_reference = RG(kmer_length, fasta_file, snp_file)
# Is there cycles in reference graph?
if list(nx.simple_cycles(g_reference.dbg)):
if kmer_length > 70:
logger.info("There are always cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Reference graph] Increasing k to %d to remove cycles",
kmer_length)
return process_sample(kmer_length=kmer_length + 1,
sample_key=sample_key,
fastq_files=fastq_files,
fasta_file=fasta_file,
snp_file=snp_file,
experiment_name=experiment_name,
min_support_percentage=min_support_percentage,
n_permutations=n_permutations,
destination_directory=destination_directory,
export_gml=export_gml,
p_value_threshold=p_value_threshold,
output_results=output_results)
# g_patient construction
logger.info(
"Will build patient graph for %s with k==%d and minimum support = %dpct",
fastq_files, kmer_length, min_support_percentage)
fastq_files = fastq_files.split(",")
g_patient = PG(fastq_files, kmer_length)
logger.info("Before cleaning: %d nodes", len(g_patient.dbg))
g_patient.graph_cleaned_init(min_support_percentage)
logger.info("After cleaning: %d nodes", len(g_patient.dbgclean))
# Is there cycles in patient graph?
if list(nx.simple_cycles(g_patient.dbgclean)):
if kmer_length > 70:
logger.info("There are still cycle(s) with k==70...exiting")
sys.exit(0)
# Check non depassement valeur limite de k
logger.info("[Sample graph] Increasing k to %d to remove cycles",
kmer_length)
return process_sample(kmer_length=kmer_length + 1,
sample_key=sample_key,
fastq_files=",".join(fastq_files),
fasta_file=fasta_file,
snp_file=snp_file,
experiment_name=experiment_name,
min_support_percentage=min_support_percentage,
n_permutations=n_permutations,
destination_directory=destination_directory,
export_gml=export_gml,
p_value_threshold=p_value_threshold,
output_results=output_results)
# Some prints for stats
dir_stat = get_or_create_dir("output/statistics")
# graph stat
graph_stat_file = open(dir_stat + "/graph_stat_file" + sample_key + ".tsv",
'w')
graph_stat_file.write("%d\t%d\t%s\t%d\t%d\t%d\t%d\t%d\t%d\t%d\n" %
(kmer_length, g_reference.dbg.size(),
sample_key, g_patient.coverage['total'],
g_patient.dbg.size(), g_patient.dbgclean.size(),
g_patient.dbg.in_degree().values().count(0),
g_patient.dbg.out_degree().values().count(0),
g_patient.dbgclean.in_degree().values().count(0),
g_patient.dbgclean.out_degree().values().count(0)))
# kmer stat
kmer_stat_file = open(dir_stat + "/kmer_stat_file" + sample_key + ".tsv",
'w')
for node_print in g_patient.dbg.nodes():
fragment_print = ",".join(g_patient.dbg.node[node_print]['fastq_id'])
reads_print = len(g_patient.dbg.node[node_print]['read_list_n'])
kmer_stat_file.write("%s\t%s\t%s\t%d\n" % (
sample_key,
node_print,
fragment_print,
reads_print,
))
# copy g_patient cleaned and remove reference edges on it (.dbg_refrm creation)
g_patient.graph_rmRefEdges_init(g_patient.dbgclean, g_reference.dbg)
# search for alternative paths in dbg_refrm (.alteration_list creation)
g_patient.alteration_list_init(g_reference.dbg, kmer_length,
min_support_percentage)
### Permutation test ###
logger.info("Will create random graphs")
all_possible_kmers = set()
for an_alt in g_patient.alteration_list:
all_possible_kmers.update(an_alt.reference_path)
all_possible_kmers.update(an_alt.alternative_path)
for i, j in time_iterator(range(0, n_permutations),
logger,
msg_prefix="permuting"):
g_random = RRG(g_patient.coverage,
kmer_length,
restrict_to=all_possible_kmers)
for i_alteration in range(0, len(g_patient.alteration_list)):
g_random_data = g_random.check_path(
g_patient.alteration_list[i_alteration].reference_path,
g_patient.alteration_list[i_alteration].alternative_path,
g_patient.alteration_list[i_alteration].min_coverage)
g_patient.alteration_list[i_alteration].random_ratio_list.append(
g_random_data[0])
g_patient.alteration_list[
i_alteration].random_reference_count_list.append(
g_random_data[1])
g_patient.alteration_list[
i_alteration].random_alternative_count_list.append(
g_random_data[2])
logger.info("Will generate p-values for %d possible alterations",
len(g_patient.alteration_list))
for i_alteration in range(0, len(g_patient.alteration_list)):
g_patient.alteration_list[i_alteration].pvalue_init()
g_patient.significant_alteration_list_init(
p_value_threshold=p_value_threshold)
# If more than one significant alteration, check if they are not in "spike" (en épis)
if len(g_patient.significant_alteration_list) > 1:
g_patient.multiple_alternative_path_filter()
## Stat
# alteration stat
alt_stat_file = open(dir_stat + "/alt_stat_file" + sample_key + ".tsv",
'w')
for i_alteration in range(0, len(g_patient.alteration_list)):
if g_patient.alteration_list[i_alteration].pvalue_ratio <= 1:
alt_stat_file.write(
"%d\t%s\t%d\t%s\t%s\t%s\t%s\t%f\t%f\t%s\t%s\n" %
(i_alteration + 1, sample_key, g_patient.coverage['total'],
g_patient.alteration_list[i_alteration].reference_sequence,
g_patient.alteration_list[i_alteration].alternative_sequence,
g_patient.alteration_list[i_alteration].reference_read_count,
g_patient.alteration_list[i_alteration].
alternative_read_count,
g_patient.alteration_list[i_alteration].ratio_read_count,
g_patient.alteration_list[i_alteration].pvalue_ratio,
str(g_patient.alteration_list[i_alteration].zscore),
"\t".join(
map(
str, g_patient.alteration_list[i_alteration].
random_ratio_list))))
# For visualisation
graph_name = "G_%s_" % sample_key
merged_graph_name = "G_%s_merged_" % sample_key
cleaned_graph_name = graph_name + "clean%d_" % min_support_percentage
merged_cleaned_graph_name = graph_name + "clean%d_merged_" % min_support_percentage
if export_gml:
logger.info("Will save viz graph for %s with k==%d", sample_key,
kmer_length)
get_or_create_dir(destination_directory)
# for the refrence graph
g_reference_merge = VISU.merge_reference_graph(g_reference.dbg.copy())
g_reference_visu = VISU.reference_graph_visualization_formatting(
g_reference.dbg.copy())
g_reference_merge_visu = VISU.reference_graph_merged_visualization_formatting(
g_reference_merge.copy())
nx.write_gml(
g_reference_visu, destination_directory + "/g_reference_visu" +
str(kmer_length) + ".gml")
nx.write_gml(
g_reference_merge_visu, destination_directory +
"/g_reference_merge_visu" + str(kmer_length) + ".gml")
# for the patient graph
g_patient_visu = VISU.individu_graph_visualization_formating(
g_patient.dbg.copy(), g_reference.dbg.copy())
g_patient_clean_visu = VISU.individu_graph_visualization_formating(
g_patient.dbgclean.copy(), g_reference.dbg.copy())
g_patient_merged = VISU.merge_individu_graph(g_patient.dbg.copy(),
g_reference.dbg.copy())
g_patient_merged_visu = VISU.individu_graph_merged_visualization_formating(
g_patient_merged.copy(), g_reference.dbg.copy())
g_patient_clean_merged = VISU.merge_individu_graph(
g_patient.dbgclean.copy(), g_reference.dbg.copy())
g_patient_clean_merged_visu = VISU.individu_graph_merged_visualization_formating(
g_patient_clean_merged.copy(), g_reference.dbg.copy())
nx.write_gml(
g_patient_visu, destination_directory + "/" + graph_name +
str(kmer_length) + ".gml")
nx.write_gml(
g_patient_clean_visu, destination_directory + "/" +
cleaned_graph_name + str(kmer_length) + ".gml")
nx.write_gml(
g_patient_merged_visu, destination_directory + "/" +
merged_graph_name + str(kmer_length) + ".gml")
nx.write_gml(
g_patient_clean_merged_visu, destination_directory + "/" +
merged_cleaned_graph_name + str(kmer_length) + ".gml")
# Annotation
if experiment_name == "TP53":
annotate_and_output_results(g_patient, g_reference, output_results)
# SNP
dir_stat = get_or_create_dir("output/snp")
# graph stat
graph_snp = open(dir_stat + "/snp_" + sample_key + ".tsv", 'w')
for snp_id in g_reference.snp.keys():
if g_reference.snp[snp_id][1] in g_patient.dbgclean:
if g_reference.snp[snp_id][0] in g_patient.dbgclean:
graph_snp.write("%s\t%s\t%d\t%d\n" %
(sample_key, snp_id,
len(g_patient.dbg.node[g_reference.snp[snp_id]
[0]]['read_list_n']),
len(g_patient.dbg.node[g_reference.snp[snp_id]
[1]]['read_list_n'])))
else:
graph_snp.write("%s\t%s\t0\t%d\n" %
(sample_key, snp_id,
len(g_patient.dbg.node[g_reference.snp[snp_id]
[1]]['read_list_n'])))