def broadpeaks_wo_control(bam_path, WINDOW_SIZE, GAP, EFFECTIVE_PROPORTION, ISLAND_SCORE_THRESHOLD, p0):
chromosomes_info = pre_counting.get_chromosomes_info(bam_path)
logging.info("\nStep 1 of 4\nCOUNTING UNIQUE READS\n")
input_unique_reads_count = pre_counting.count_unique_reads(bam_path, chromosomes_info)
# Effective genome length (L)
effective_length = pre_counting.count_effective_length(EFFECTIVE_PROPORTION, chromosomes_info)
# Lambda for poisson distribution
lambdaa = pre_counting.count_lambda(input_unique_reads_count, WINDOW_SIZE, effective_length)
# Minimum #reads in a window for eligibility
# Formula (1), finding l0
l0 = scipy.stats.poisson.ppf(1 - p0, lambdaa)
NORMALIZATION_CONSTANT = 1
logging.info("\nWindow read threshold is {} reads, \ni.e. {} is minimum number of reads in window "
"to consider this window `eligible` with Poisson distribution p-value {}".format(l0, l0, p0))
logging.info("\nStep 2 of 4\nMAKING WINDOW LIST\n")
(window_list, window_list_dict) = islands.make_windows_list(bam_path, chromosomes_info, l0, WINDOW_SIZE, GAP,
input_unique_reads_count, NORMALIZATION_CONSTANT)
logging.info("\nStep 3 of 4\nMAKING ISLAND LIST\n")
island_list = islands.make_islands_list(window_list, lambdaa, WINDOW_SIZE, l0, chromosomes_info,
ISLAND_SCORE_THRESHOLD)
return island_list
def broadpeaks_with_control(bam_path, control_path, window_size, gap, EFFECTIVE_PROPORTION, ISLAND_SCORE_THRESHOLD, p0):
chromosomes_info = pre_counting.get_chromosomes_info(bam_path)
control_chromosomes_info = pre_counting.get_chromosomes_info(control_path)
logging.info("\nStep 1 of 4\nCOUNTING UNIQUE READS\n")
logging.info("\nFor input file\n")
# input_unique_reads_count = 6300518
input_unique_reads_count = pre_counting.count_unique_reads(bam_path, chromosomes_info)
logging.info("\nFor control file\n")
# control_unique_reads_count = 3758349
control_unique_reads_count = pre_counting.count_unique_reads(control_path, control_chromosomes_info)
# Effective genome length (L)
# effective_length = 2383684366.91
effective_length = pre_counting.count_effective_length(EFFECTIVE_PROPORTION, chromosomes_info)
# Lambda for poisson distribution
# input_lambda = 0.53
input_lambda = pre_counting.count_lambda(input_unique_reads_count, window_size, effective_length)
# control_lambda = 0.32
control_lambda = pre_counting.count_lambda(control_unique_reads_count, window_size, effective_length)
# Minimum #reads in a window for eligibility
# Formula (1), finding l0
input_l0 = scipy.stats.poisson.ppf(1 - p0, input_lambda)
control_l0 = scipy.stats.poisson.ppf(1 - p0, control_lambda)
logging.info("\nWindow read threshold is {} reads, \ni.e. {} is minimum number of reads in window "
"to consider this window `eligible` with Poisson distribution p-value {}".format(input_l0, input_l0, p0))
logging.info("\nStep 2 of 4\nMAKING WINDOW LIST\n")
# for two libraries are independent, we do not scale them here
NORMALIZATION_CONSTANT = 1
logging.info("\nFor input file\n")
(window_list_input, window_list_input_dict) = islands.make_windows_list(bam_path, chromosomes_info, input_l0, window_size, gap, input_unique_reads_count, NORMALIZATION_CONSTANT)
logging.info("\nFor control file\n")
(window_list_control, window_list_control_dict) = islands.make_windows_list(control_path, chromosomes_info, control_l0, window_size, gap, control_unique_reads_count, NORMALIZATION_CONSTANT)
#window_list = islands.modify_window_list_based_on_control(control_path, chromosomes_info, l0, window_size, gap, input_unique_reads_count, control_unique_reads_count, window_list_temp)
logging.info("\nStep 3 of 4\nMAKING ISLAND LIST\n")
logging.info("\nFor input file\n")
island_list_input = islands.make_islands_list(window_list_input, input_lambda, window_size, input_l0, chromosomes_info, ISLAND_SCORE_THRESHOLD)
logging.info("\nFor control file\n")
island_list_control = islands.make_islands_list(window_list_control, control_lambda, window_size, control_l0, control_chromosomes_info, ISLAND_SCORE_THRESHOLD)
"""
# with switching tracks
calculate_fdr(island_list_input, window_list_control)
calculate_fdr(island_list_control, window_list_input)
island_list = islands.find_unintersected_islands(island_list_input,island_list_control)
# calculate FDR
FDR = (len(island_list_control) - (len(island_list_input)-len(island_list)))/len(island_list)
logging.info("\nFDR is {} reads, \n".format(FDR))
"""
# appending FDR to island_list_input
# normalization to smaller dataset
if input_unique_reads_count >= control_unique_reads_count:
NORMALIZATION_CONSTANT = float(control_unique_reads_count) / input_unique_reads_count
FDR_calculation.calculate_and_append_score_for_fdr(island_list_input, window_list_control_dict, input_lambda, window_size, NORMALIZATION_CONSTANT, 1)
FDR_calculation.calculate_and_append_score_for_fdr(island_list_control, window_list_input_dict, control_lambda, window_size, 1, NORMALIZATION_CONSTANT)
else:
NORMALIZATION_CONSTANT = float(input_unique_reads_count) / control_unique_reads_count
FDR_calculation.calculate_and_append_score_for_fdr(island_list_input, window_list_control_dict, input_lambda, window_size, 1, NORMALIZATION_CONSTANT)
FDR_calculation.calculate_and_append_score_for_fdr(island_list_control, window_list_input_dict, control_lambda, window_size, NORMALIZATION_CONSTANT, 1)
FDR_calculation.calculate_and_append_fdr(island_list_input, island_list_control, 7)
FDR_calculation.calculate_and_append_fdr(island_list_input, island_list_control, 8)
return island_list_input