def main(args):
# Checks if there is a control library
control_lib_exists = True
if (args.control_file is None):
control_lib_exists = False
# Create deep copy of the 'args' object for each treatment
args_1 = copy.deepcopy(args)
args_2 = copy.deepcopy(args)
# Format each args for SICER run
args_1.treatment_file = str(args.treatment_file[0])
args_2.treatment_file = str(args.treatment_file[1])
args_1.df = False
args_2.df = False
if (control_lib_exists):
args_1.control_file = str(args.control_file[0])
args_2.control_file = str(args.control_file[1])
# Execute run_SICER for each treatment library
temp_dir_1, library_size_file1 = run_SICER.main(args_1, True)
temp_dir_2, library_size_file2 = run_SICER.main(args_2, True)
try:
temp_dir = tempfile.mkdtemp()
# Change current working directory to temp_dir
os.chdir(temp_dir)
except:
sys.exit(
"Temporary directory required for SICER_df cannot be created. Check if directories can be created in %s."
% curr_path)
try:
num_chroms = len(args.species_chroms)
pool = mp.Pool(processes=min(args.cpu, num_chroms))
# Find the union island between two treatment files. It will generate a summary file
print("\n")
args.treatment_file[0] = os.path.basename(args.treatment_file[0])
args.treatment_file[1] = os.path.basename(args.treatment_file[1])
print("Finding all the union islands of ", args.treatment_file[0], "and ", args.treatment_file[1], "...")
find_union_islands.main(args, temp_dir_1, temp_dir_2, pool)
print("\n")
# Compare two treatment libraries
print("Comparing two treatment libraries...")
compare_two_libraries_on_islands.main(args, temp_dir_1, temp_dir_2, library_size_file1, library_size_file2, pool)
print("\n")
print("Identifying significantly increased islands using BH corrected p-value cutoff...")
filter_islands_by_significance.main(args, 9, pool)
print("\n")
print("Identifying significantly decreased islands using BH-corrected p-value cutoff...")
filter_islands_by_significance.main(args, 12, pool)
print("\n")
pool.close()
pool.join()
finally:
print("Removing all temporary directories and all files in it.")
shutil.rmtree(temp_dir)
shutil.rmtree(temp_dir_1)
shutil.rmtree(temp_dir_2)
def main(
args,
df_run=False
): # df_run indicates if run_RECOGNICER is being called by run_RECOGNICER_df function.
# Checks if there is a control library
control_lib_exists = True
if (args.control_file is None):
control_lib_exists = False
try:
temp_dir = tempfile.mkdtemp()
# Change current working directory to temp_dir
os.chdir(temp_dir)
except:
sys.exit(
"Temporary directory required for SICER cannot be created. Check if directories can be created in %s."
% curr_path)
try:
# Step 0: create Pool object for parallel-Processing
num_chroms = len(args.species_chroms)
pool = mp.Pool(processes=min(args.cpu, num_chroms))
# Step 1: Remove redundancy reads in input file according to input threshold
treatment_file_name = os.path.basename(args.treatment_file)
print("Preprocess the", treatment_file_name,
"file to remove redundancy with threshold of",
args.redundancy_threshold, "\n")
total_treatment_read_count = remove_redundant_reads.main(
args, args.treatment_file, pool)
args.treatment_file = treatment_file_name
print('\n')
# Step 2: Remove redundancy reads in control library according to input threshold
if (control_lib_exists):
control_file_name = os.path.basename(args.control_file)
print("Preprocess the", control_file_name,
"file to remove redundancy with threshold of",
args.redundancy_threshold, "\n")
total_control_read_count = remove_redundant_reads.main(
args, args.control_file, pool)
args.control_file = control_file_name
print('\n')
# Step 3: Partition the genome in windows and generate graph files for each chromsome
print(
"Partitioning the genome in windows and generate summary files... \n"
)
total_tag_in_windows = run_make_graph_file_by_chrom.main(args, pool)
print("\n")
# Step4+5: Normalize and generate WIG file
print(
"Normalizing graphs by total island filitered reads per million and generating summary WIG file...\n"
)
output_WIG_name = (treatment_file_name.replace('.bed', '') + "-W" +
str(args.window_size) + "-normalized.wig")
make_normalized_wig.main(args, output_WIG_name, pool)
# Step 6: Find condidate islands exhibiting clustering
print("Finding candidate islands exhibiting clustering... \n")
coarsegraining.main(args, total_tag_in_windows, pool)
print("\n")
# Running SICER with a control library
if (control_lib_exists):
# Step 7
print(
"Calculating significance of candidate islands using the control library... \n"
)
associate_tags_with_chip_and_control_w_fc_q.main(
args, total_treatment_read_count, total_control_read_count,
pool)
# Step 8:
print("Identifying significant islands using FDR criterion...")
significant_read_count = filter_islands_by_significance.main(
args, 7,
pool) # 7 represents the n-th column we want to filtered by
print("Out of the ", total_treatment_read_count, " reads in ",
treatment_file_name, ", ", significant_read_count,
" reads are in significant islands")
# Optional Outputs
if (args.significant_reads):
# Step 9: Filter treatment reads by the significant islands found from step 8
print("Filter reads with identified significant islands...\n")
filter_raw_tags_by_islands.main(args, pool)
# Step 10: Produce graph file based on the filtered reads from step 9
print("Make summary graph with filtered reads...\n")
run_make_graph_file_by_chrom.main(args, pool, True)
# Step 11: Produce Normalized WIG file
print(
"\nNormalizing graphs by total island filitered reads per million and generating summary WIG file \n"
)
output_WIG_name = (treatment_file_name.replace('.bed', '') + "-W" +
str(args.window_size) + "-FDR" +
str(args.false_discovery_rate) +
"-islandfiltered-normalized.wig")
make_normalized_wig.main(args, output_WIG_name, pool)
pool.close()
pool.join()
# Final Step
if (df_run == True):
return temp_dir, total_treatment_read_count
else:
print("End of SICER")
finally:
if df_run == False:
print("Removing temporary directory and all files in it.")
shutil.rmtree(temp_dir)