def count_reads(regions_list, params):
""" Count reads from bam within regions (counts position of cutsite to prevent double-counting) """
bam_f = params.bam
read_shift = params.read_shift
bam_obj = pysam.AlignmentFile(bam_f, "rb")
log_q = params.log_q
logger = TobiasLogger("", params.verbosity,
log_q) #sending all logger calls to log_q
#Count per region
read_count = 0
logger.spam("Started counting region_chunk ({0} -> {1})".format(
"_".join([str(element) for element in regions_list[0]]),
"_".join([str(element) for element in regions_list[-1]])))
for region in regions_list:
read_lst = ReadList().from_bam(bam_obj, region)
for read in read_lst:
read.get_cutsite(read_shift)
if read.cutsite > region.start and read.cutsite < region.end: #only reads within borders
read_count += 1
logger.spam("Finished counting region_chunk ({0} -> {1})".format(
"_".join([str(element) for element in regions_list[0]]),
"_".join([str(element) for element in regions_list[-1]])))
bam_obj.close()
return (read_count)
def run_atacorrect(args):
"""
Function for bias correction of input .bam files
Calls functions in ATACorrect_functions and several internal classes
"""
#Test if required arguments were given:
if args.bam == None:
sys.exit("Error: No .bam-file given")
if args.genome == None:
sys.exit("Error: No .fasta-file given")
if args.peaks == None:
sys.exit("Error: No .peaks-file given")
#Adjust some parameters depending on input
args.prefix = os.path.splitext(os.path.basename(
args.bam))[0] if args.prefix == None else args.prefix
args.outdir = os.path.abspath(
args.outdir) if args.outdir != None else os.path.abspath(os.getcwd())
#Set output bigwigs based on input
tracks = ["uncorrected", "bias", "expected", "corrected"]
tracks = [track for track in tracks
if track not in args.track_off] # switch off printing
if args.split_strands == True:
strands = ["forward", "reverse"]
else:
strands = ["both"]
output_bws = {}
for track in tracks:
output_bws[track] = {}
for strand in strands:
elements = [args.prefix, track] if strand == "both" else [
args.prefix, track, strand
]
output_bws[track][strand] = {
"fn":
os.path.join(args.outdir, "{0}.bw".format("_".join(elements)))
}
#Set all output files
bam_out = os.path.join(args.outdir, args.prefix + "_atacorrect.bam")
bigwigs = [
output_bws[track][strand]["fn"]
for (track, strand) in itertools.product(tracks, strands)
]
figures_f = os.path.join(args.outdir,
"{0}_atacorrect.pdf".format(args.prefix))
output_files = bigwigs + [figures_f]
output_files = list(OrderedDict.fromkeys(
output_files)) #remove duplicates due to "both" option
strands = ["forward", "reverse"]
#----------------------------------------------------------------------------------------------------#
# Print info on run
#----------------------------------------------------------------------------------------------------#
logger = TobiasLogger("ATACorrect", args.verbosity)
logger.begin()
parser = add_atacorrect_arguments(argparse.ArgumentParser())
logger.arguments_overview(parser, args)
logger.output_files(output_files)
args.cores = check_cores(args.cores, logger)
#----------------------------------------------------------------------------------------------------#
# Test input file availability for reading
#----------------------------------------------------------------------------------------------------#
logger.info("----- Processing input data -----")
logger.debug("Testing input file availability")
check_files([args.bam, args.genome, args.peaks], "r")
logger.debug("Testing output directory/file writeability")
make_directory(args.outdir)
check_files(output_files, "w")
#Open pdf for figures
figure_pdf = PdfPages(figures_f, keep_empty=False)
#----------------------------------------------------------------------------------------------------#
# Read information in bam/fasta
#----------------------------------------------------------------------------------------------------#
logger.info("Reading info from .bam file")
bamfile = pysam.AlignmentFile(args.bam, "rb")
if bamfile.has_index() == False:
logger.warning("No index found for bamfile - creating one via pysam.")
pysam.index(args.bam)
bam_references = bamfile.references #chromosomes in correct order
bam_chrom_info = dict(zip(bamfile.references, bamfile.lengths))
logger.debug("bam_chrom_info: {0}".format(bam_chrom_info))
bamfile.close()
logger.info("Reading info from .fasta file")
fastafile = pysam.FastaFile(args.genome)
fasta_chrom_info = dict(zip(fastafile.references, fastafile.lengths))
logger.debug("fasta_chrom_info: {0}".format(fasta_chrom_info))
fastafile.close()
#Compare chrom lengths
chrom_in_common = set(bam_chrom_info.keys()).intersection(
fasta_chrom_info.keys())
for chrom in chrom_in_common:
bamlen = bam_chrom_info[chrom]
fastalen = fasta_chrom_info[chrom]
if bamlen != fastalen:
logger.warning("(Fastafile)\t{0} has length {1}".format(
chrom, fasta_chrom_info[chrom]))
logger.warning("(Bamfile)\t{0} has length {1}".format(
chrom, bam_chrom_info[chrom]))
sys.exit(
"Error: .bam and .fasta have different chromosome lengths. Please make sure the genome file is similar to the one used in mapping."
)
#Subset bam_references to those for which there are sequences in fasta
chrom_not_in_fasta = set(bam_references) - set(fasta_chrom_info.keys())
if len(chrom_not_in_fasta) > 1:
logger.warning(
"The following contigs in --bam did not have sequences in --fasta: {0}. NOTE: These contigs will be skipped in calculation and output."
.format(chrom_not_in_fasta))
bam_references = [ref for ref in bam_references if ref in fasta_chrom_info]
chrom_in_common = [ref for ref in chrom_in_common if ref in bam_references]
#----------------------------------------------------------------------------------------------------#
# Read regions from bedfiles
#----------------------------------------------------------------------------------------------------#
logger.info("Processing input/output regions")
#Chromosomes included in analysis
genome_regions = RegionList().from_list([
OneRegion([chrom, 0, bam_chrom_info[chrom]])
for chrom in bam_references if not "M" in chrom
]) #full genome length
chrom_in_common = [chrom for chrom in chrom_in_common if "M" not in chrom]
logger.debug("CHROMS\t{0}".format("; ".join(
["{0} ({1})".format(reg.chrom, reg.end) for reg in genome_regions])))
genome_bp = sum([region.get_length() for region in genome_regions])
# Process peaks
peak_regions = RegionList().from_bed(args.peaks)
peak_regions.merge()
for i in range(len(peak_regions) - 1, -1, -1):
region = peak_regions[i]
peak_regions[i] = region.check_boundary(
bam_chrom_info, "cut") #regions are cut/removed from list
if peak_regions[i] is None:
logger.warning(
"Peak region {0} was removed at it is either out of bounds or not in the chromosomes given in genome/bam."
.format(region.tup(), i + 1))
del peak_regions[i]
nonpeak_regions = deepcopy(genome_regions).subtract(peak_regions)
# Process specific input regions if given
if args.regions_in != None:
input_regions = RegionList().from_bed(args.regions_in)
input_regions.merge()
input_regions.apply_method(OneRegion.check_boundary, bam_chrom_info,
"cut")
else:
input_regions = nonpeak_regions
# Process specific output regions
if args.regions_out != None:
output_regions = RegionList().from_bed(args.regions_out)
else:
output_regions = deepcopy(peak_regions)
#Extend regions to make sure extend + flanking for window/flank are within boundaries
flank_extend = args.k_flank + int(args.window / 2.0)
output_regions.apply_method(OneRegion.extend_reg,
args.extend + flank_extend)
output_regions.merge()
output_regions.apply_method(OneRegion.check_boundary, bam_chrom_info,
"cut")
output_regions.apply_method(
OneRegion.extend_reg, -flank_extend
) #Cut to needed size knowing that the region will be extended in function
#Remove blacklisted regions and chromosomes not in common
blacklist_regions = RegionList().from_bed(
args.blacklist) if args.blacklist != None else RegionList(
[]) #fill in with regions from args.blacklist
regions_dict = {
"genome": genome_regions,
"input_regions": input_regions,
"output_regions": output_regions,
"peak_regions": peak_regions,
"nonpeak_regions": nonpeak_regions,
"blacklist_regions": blacklist_regions
}
for sub in [
"input_regions", "output_regions", "peak_regions",
"nonpeak_regions"
]:
regions_sub = regions_dict[sub]
regions_sub.subtract(blacklist_regions)
regions_sub = regions_sub.apply_method(OneRegion.split_region, 50000)
regions_sub.keep_chroms(chrom_in_common)
regions_dict[sub] = regions_sub
#write beds to look at in igv
#input_regions.write_bed(os.path.join(args.outdir, "input_regions.bed"))
#output_regions.write_bed(os.path.join(args.outdir, "output_regions.bed"))
#peak_regions.write_bed(os.path.join(args.outdir, "peak_regions.bed"))
#nonpeak_regions.write_bed(os.path.join(args.outdir, "nonpeak_regions.bed"))
#Sort according to order in bam_references:
output_regions.loc_sort(bam_references)
chrom_order = {bam_references[i]: i
for i in range(len(bam_references))
} #for use later when sorting output
#### Statistics about regions ####
genome_bp = sum([region.get_length() for region in regions_dict["genome"]])
for key in regions_dict:
total_bp = sum([region.get_length() for region in regions_dict[key]])
logger.stats("{0}: {1} regions | {2} bp | {3:.2f}% coverage".format(
key, len(regions_dict[key]), total_bp, total_bp / genome_bp * 100))
#Estallish variables for regions to be used
input_regions = regions_dict["input_regions"]
output_regions = regions_dict["output_regions"]
peak_regions = regions_dict["peak_regions"]
nonpeak_regions = regions_dict["nonpeak_regions"]
#Exit if no input/output regions were found
if len(input_regions) == 0 or len(output_regions) == 0 or len(
peak_regions) == 0 or len(nonpeak_regions) == 0:
logger.error("No regions found - exiting!")
sys.exit()
#----------------------------------------------------------------------------------------------------#
# Estimate normalization factors
#----------------------------------------------------------------------------------------------------#
#Setup logger queue
logger.debug("Setting up listener for log")
logger.start_logger_queue()
args.log_q = logger.queue
#----------------------------------------------------------------------------------------------------#
logger.comment("")
logger.info("----- Estimating normalization factors -----")
#If normalization is to be calculated
if not args.norm_off:
#Reads in peaks/nonpeaks
logger.info("Counting reads in peak regions")
peak_region_chunks = peak_regions.chunks(args.split)
reads_peaks = sum(
run_parallel(count_reads, peak_region_chunks, [args], args.cores,
logger))
logger.comment("")
logger.info("Counting reads in nonpeak regions")
nonpeak_region_chunks = nonpeak_regions.chunks(args.split)
reads_nonpeaks = sum(
run_parallel(count_reads, nonpeak_region_chunks, [args],
args.cores, logger))
reads_total = reads_peaks + reads_nonpeaks
logger.stats("TOTAL_READS\t{0}".format(reads_total))
logger.stats("PEAK_READS\t{0}".format(reads_peaks))
logger.stats("NONPEAK_READS\t{0}".format(reads_nonpeaks))
lib_norm = 10000000 / reads_total
frip = reads_peaks / reads_total
correct_factor = lib_norm * (1 / frip)
logger.stats("LIB_NORM\t{0:.5f}".format(lib_norm))
logger.stats("FRiP\t{0:.5f}".format(frip))
else:
logger.info("Normalization was switched off")
correct_factor = 1.0
logger.stats("CORRECTION_FACTOR:\t{0:.5f}".format(correct_factor))
#----------------------------------------------------------------------------------------------------#
# Estimate sequence bias
#----------------------------------------------------------------------------------------------------#
logger.comment("")
logger.info("Started estimation of sequence bias...")
input_region_chunks = input_regions.chunks(
args.split) #split to 100 chunks (also decides the step of output)
out_lst = run_parallel(bias_estimation, input_region_chunks, [args],
args.cores,
logger) #Output is list of AtacBias objects
#Join objects
estimated_bias = out_lst[0] #initialize object with first output
for output in out_lst[1:]:
estimated_bias.join(
output
) #bias object contains bias/background SequenceMatrix objects
logger.debug("Bias estimated\tno_reads: {0}".format(
estimated_bias.no_reads))
#----------------------------------------------------------------------------------------------------#
# Join estimations from all chunks of regions
#----------------------------------------------------------------------------------------------------#
bias_obj = estimated_bias
bias_obj.correction_factor = correct_factor
### Bias motif ###
logger.info("Finalizing bias motif for scoring")
for strand in strands:
bias_obj.bias[strand].prepare_mat()
logger.debug("Saving pssm to figure pdf")
fig = plot_pssm(bias_obj.bias[strand].pssm,
"Tn5 insertion bias of reads ({0})".format(strand))
figure_pdf.savefig(fig)
#Write bias motif to pickle
out_f = os.path.join(args.outdir, args.prefix + "_AtacBias.pickle")
logger.debug("Saving bias object to pickle ({0})".format(out_f))
bias_obj.to_pickle(out_f)
#----------------------------------------------------------------------------------------------------#
# Correct read bias and write to bigwig
#----------------------------------------------------------------------------------------------------#
logger.comment("")
logger.info("----- Correcting reads from .bam within output regions -----")
output_regions.loc_sort(bam_references) #sort in order of references
output_regions_chunks = output_regions.chunks(args.split)
no_tasks = float(len(output_regions_chunks))
chunk_sizes = [len(chunk) for chunk in output_regions_chunks]
logger.debug("All regions chunked: {0} ({1})".format(
len(output_regions), chunk_sizes))
### Create key-file linking for bigwigs
key2file = {}
for track in output_bws:
for strand in output_bws[track]:
filename = output_bws[track][strand]["fn"]
key = "{}:{}".format(track, strand)
key2file[key] = filename
#Start correction/write cores
n_bigwig = len(key2file.values())
writer_cores = min(n_bigwig, max(
1, int(args.cores *
0.1))) #at most one core per bigwig or 10% of cores (or 1)
worker_cores = max(1, args.cores - writer_cores)
logger.debug("Worker cores: {0}".format(worker_cores))
logger.debug("Writer cores: {0}".format(writer_cores))
worker_pool = mp.Pool(processes=worker_cores)
writer_pool = mp.Pool(processes=writer_cores)
manager = mp.Manager()
#Start bigwig file writers
writer_tasks = []
header = [(chrom, bam_chrom_info[chrom]) for chrom in bam_references]
key_chunks = [
list(key2file.keys())[i::writer_cores] for i in range(writer_cores)
]
qs_list = []
qs = {}
for chunk in key_chunks:
logger.debug("Creating writer queue for {0}".format(chunk))
q = manager.Queue()
qs_list.append(q)
files = [key2file[key] for key in chunk]
writer_tasks.append(
writer_pool.apply_async(bigwig_writer,
args=(q, dict(zip(chunk, files)), header,
output_regions, args))
) #, callback = lambda x: finished.append(x) print("Writing time: {0}".format(x)))
for key in chunk:
qs[key] = q
args.qs = qs
writer_pool.close() #no more jobs applied to writer_pool
#Start correction
logger.debug("Starting correction")
task_list = [
worker_pool.apply_async(bias_correction, args=[chunk, args, bias_obj])
for chunk in output_regions_chunks
]
worker_pool.close()
monitor_progress(task_list, logger, "Correction progress:"
) #does not exit until tasks in task_list finished
results = [task.get() for task in task_list]
#Get all results
pre_bias = results[0][0] #initialize with first result
post_bias = results[0][1] #initialize with first result
for result in results[1:]:
pre_bias_chunk = result[0]
post_bias_chunk = result[1]
for direction in strands:
pre_bias[direction].add_counts(pre_bias_chunk[direction])
post_bias[direction].add_counts(post_bias_chunk[direction])
#Stop all queues for writing
logger.debug("Stop all queues by inserting None")
for q in qs_list:
q.put((None, None, None))
#Fetch error codes from bigwig writers
logger.debug("Fetching possible errors from bigwig_writer tasks")
results = [task.get()
for task in writer_tasks] #blocks until writers are finished
logger.debug("Joining bigwig_writer queues")
qsum = sum([q.qsize() for q in qs_list])
while qsum != 0:
qsum = sum([q.qsize() for q in qs_list])
logger.spam("- Queue sizes {0}".format([(key, qs[key].qsize())
for key in qs]))
time.sleep(0.5)
#Waits until all queues are closed
writer_pool.join()
worker_pool.terminate()
worker_pool.join()
#Stop multiprocessing logger
logger.stop_logger_queue()
#----------------------------------------------------------------------------------------------------#
# Information and verification of corrected read frequencies
#----------------------------------------------------------------------------------------------------#
logger.comment("")
logger.info("Verifying bias correction")
#Calculating variance per base
for strand in strands:
#Invert negative counts
abssum = np.abs(np.sum(post_bias[strand].neg_counts, axis=0))
post_bias[strand].neg_counts = post_bias[strand].neg_counts + abssum
#Join negative/positive counts
post_bias[strand].counts += post_bias[strand].neg_counts #now pos
pre_bias[strand].prepare_mat()
post_bias[strand].prepare_mat()
pre_var = np.mean(np.var(pre_bias[strand].bias_pwm,
axis=1)[:4]) #mean of variance per nucleotide
post_var = np.mean(np.var(post_bias[strand].bias_pwm, axis=1)[:4])
logger.stats("BIAS\tpre-bias variance {0}:\t{1:.7f}".format(
strand, pre_var))
logger.stats("BIAS\tpost-bias variance {0}:\t{1:.7f}".format(
strand, post_var))
#Plot figure
fig_title = "Nucleotide frequencies in corrected reads\n({0} strand)".format(
strand)
figure_pdf.savefig(
plot_correction(pre_bias[strand].bias_pwm,
post_bias[strand].bias_pwm, fig_title))
#----------------------------------------------------------------------------------------------------#
# Finish up
#----------------------------------------------------------------------------------------------------#
plt.close('all')
figure_pdf.close()
logger.end()
def run_bindetect(args):
""" Main function to run bindetect algorithm with input files and parameters given in args """
#Checking input and setting cond_names
check_required(args, ["signals", "motifs", "genome", "peaks"])
args.cond_names = [
os.path.basename(os.path.splitext(bw)[0]) for bw in args.signals
] if args.cond_names is None else args.cond_names
args.outdir = os.path.abspath(args.outdir)
#Set output files
states = ["bound", "unbound"]
outfiles = [
os.path.abspath(
os.path.join(args.outdir, "*", "beds",
"*_{0}_{1}.bed".format(condition, state)))
for (condition, state) in itertools.product(args.cond_names, states)
]
outfiles.append(
os.path.abspath(os.path.join(args.outdir, "*", "beds", "*_all.bed")))
outfiles.append(
os.path.abspath(
os.path.join(args.outdir, "*", "plots", "*_log2fcs.pdf")))
outfiles.append(
os.path.abspath(os.path.join(args.outdir, "*", "*_overview.txt")))
outfiles.append(
os.path.abspath(os.path.join(args.outdir, "*", "*_overview.xlsx")))
outfiles.append(
os.path.abspath(
os.path.join(args.outdir, args.prefix + "_distances.txt")))
outfiles.append(
os.path.abspath(os.path.join(args.outdir,
args.prefix + "_results.txt")))
outfiles.append(
os.path.abspath(
os.path.join(args.outdir, args.prefix + "_results.xlsx")))
outfiles.append(
os.path.abspath(os.path.join(args.outdir,
args.prefix + "_figures.pdf")))
#-------------------------------------------------------------------------------------------------------------#
#-------------------------------------------- Setup logger and pool ------------------------------------------#
#-------------------------------------------------------------------------------------------------------------#
logger = TobiasLogger("BINDetect", args.verbosity)
logger.begin()
parser = add_bindetect_arguments(argparse.ArgumentParser())
logger.arguments_overview(parser, args)
logger.output_files(outfiles)
# Setup pool
args.cores = check_cores(args.cores, logger)
writer_cores = max(1, int(args.cores * 0.1))
worker_cores = max(1, args.cores - writer_cores)
logger.debug("Worker cores: {0}".format(worker_cores))
logger.debug("Writer cores: {0}".format(writer_cores))
pool = mp.Pool(processes=worker_cores)
writer_pool = mp.Pool(processes=writer_cores)
#-------------------------------------------------------------------------------------------------------------#
#-------------------------- Pre-processing data: Reading motifs, sequences, peaks ----------------------------#
#-------------------------------------------------------------------------------------------------------------#
logger.info("----- Processing input data -----")
#Check opening/writing of files
logger.info("Checking reading/writing of files")
check_files([args.signals, args.motifs, args.genome, args.peaks],
action="r")
check_files(outfiles[-3:], action="w")
make_directory(args.outdir)
#Comparisons between conditions
no_conditions = len(args.signals)
if args.time_series:
comparisons = list(zip(args.cond_names[:-1], args.cond_names[1:]))
args.comparisons = comparisons
else:
comparisons = list(itertools.combinations(args.cond_names,
2)) #all-against-all
args.comparisons = comparisons
#Open figure pdf and write overview
fig_out = os.path.abspath(
os.path.join(args.outdir, args.prefix + "_figures.pdf"))
figure_pdf = PdfPages(fig_out, keep_empty=True)
plt.figure()
plt.axis('off')
plt.text(0.5,
0.8,
"BINDETECT FIGURES",
ha="center",
va="center",
fontsize=20)
#output and order
titles = []
titles.append("Raw score distributions")
titles.append("Normalized score distributions")
if args.debug:
for (cond1, cond2) in comparisons:
titles.append("Background log2FCs ({0} / {1})".format(
cond1, cond2))
for (cond1, cond2) in comparisons:
titles.append("BINDetect plot ({0} / {1})".format(cond1, cond2))
plt.text(0.1,
0.6,
"\n".join([
"Page {0}) {1}".format(i + 2, titles[i])
for i in range(len(titles))
]) + "\n\n",
va="top")
figure_pdf.savefig(bbox_inches='tight')
plt.close()
################# Read peaks ################
#Read peak and peak_header
logger.info("Reading peaks")
peaks = RegionList().from_bed(args.peaks)
logger.info("- Found {0} regions in input peaks".format(len(peaks)))
peaks = peaks.merge() #merge overlapping peaks
logger.info("- Merged to {0} regions".format(len(peaks)))
if len(peaks) == 0:
logger.error("Input --peaks file is empty!")
sys.exit()
#Read header and check match with number of peak columns
peak_columns = len(peaks[0]) #number of columns
if args.peak_header != None:
content = open(args.peak_header, "r").read()
args.peak_header_list = content.split()
logger.debug("Peak header: {0}".format(args.peak_header_list))
#Check whether peak header fits with number of peak columns
if len(args.peak_header_list) != peak_columns:
logger.error(
"Length of --peak_header ({0}) does not fit number of columns in --peaks ({1})."
.format(len(args.peak_header_list), peak_columns))
sys.exit()
else:
args.peak_header_list = ["peak_chr", "peak_start", "peak_end"] + [
"additional_" + str(num + 1) for num in range(peak_columns - 3)
]
logger.debug("Peak header list: {0}".format(args.peak_header_list))
################# Check for match between peaks and fasta/bigwig #################
logger.info(
"Checking for match between --peaks and --fasta/--signals boundaries")
logger.info("- Comparing peaks to {0}".format(args.genome))
fasta_obj = pysam.FastaFile(args.genome)
fasta_boundaries = dict(zip(fasta_obj.references, fasta_obj.lengths))
fasta_obj.close()
logger.debug("Fasta boundaries: {0}".format(fasta_boundaries))
peaks = peaks.apply_method(OneRegion.check_boundary, fasta_boundaries,
"exit") #will exit if peaks are outside borders
#Check boundaries of each bigwig signal individually
for signal in args.signals:
logger.info("- Comparing peaks to {0}".format(signal))
pybw_obj = pybw.open(signal)
pybw_header = pybw_obj.chroms()
pybw_obj.close()
logger.debug("Signal boundaries: {0}".format(pybw_header))
peaks = peaks.apply_method(OneRegion.check_boundary, pybw_header,
"exit")
##### GC content for motif scanning ######
#Make chunks of regions for multiprocessing
logger.info("Estimating GC content from peak sequences")
peak_chunks = peaks.chunks(args.split)
gc_content_pool = pool.starmap(
get_gc_content, itertools.product(peak_chunks, [args.genome]))
gc_content = np.mean(gc_content_pool) #fraction
args.gc = gc_content
bg = np.array([(1 - args.gc) / 2.0, args.gc / 2.0, args.gc / 2.0,
(1 - args.gc) / 2.0])
logger.info("- GC content estimated at {0:.2f}%".format(gc_content * 100))
################ Get motifs ################
logger.info("Reading motifs from file")
motif_list = MotifList()
args.motifs = expand_dirs(args.motifs)
for f in args.motifs:
motif_list += MotifList().from_file(f) #List of OneMotif objects
no_pfms = len(motif_list)
logger.info("- Read {0} motifs".format(no_pfms))
logger.debug("Getting motifs ready")
motif_list.bg = bg
logger.debug("Getting reverse motifs")
motif_list.extend([motif.get_reverse() for motif in motif_list])
logger.spam(motif_list)
#Set prefixes
for motif in motif_list: #now with reverse motifs as well
motif.set_prefix(args.naming)
motif.bg = bg
logger.spam("Getting pssm for motif {0}".format(motif.name))
motif.get_pssm()
motif_names = list(set([motif.prefix for motif in motif_list]))
#Get threshold for motifs
logger.debug("Getting match threshold per motif")
outlist = pool.starmap(OneMotif.get_threshold,
itertools.product(motif_list, [args.motif_pvalue]))
motif_list = MotifList(outlist)
for motif in motif_list:
logger.debug("Motif {0}: threshold {1}".format(motif.name,
motif.threshold))
logger.info("Creating folder structure for each TF")
for TF in motif_names:
logger.spam("Creating directories for {0}".format(TF))
make_directory(os.path.join(args.outdir, TF))
make_directory(os.path.join(args.outdir, TF, "beds"))
make_directory(os.path.join(args.outdir, TF, "plots"))
#-------------------------------------------------------------------------------------------------------------#
#----------------------------------------- Plot logos for all motifs -----------------------------------------#
#-------------------------------------------------------------------------------------------------------------#
plus_motifs = [motif for motif in motif_list if motif.strand == "+"]
logo_filenames = {
motif.prefix: os.path.join(args.outdir, motif.prefix,
motif.prefix + ".png")
for motif in plus_motifs
}
logger.info("Plotting sequence logos for each motif")
task_list = [
pool.apply_async(OneMotif.logo_to_file, (
motif,
logo_filenames[motif.prefix],
)) for motif in plus_motifs
]
monitor_progress(task_list, logger)
results = [task.get() for task in task_list]
logger.comment("")
logger.debug("Getting base64 strings per motif")
for motif in motif_list:
if motif.strand == "+":
#motif.get_base()
with open(logo_filenames[motif.prefix], "rb") as png:
motif.base = base64.b64encode(png.read()).decode("utf-8")
#-------------------------------------------------------------------------------------------------------------#
#--------------------- Motif scanning: Find binding sites and match to footprint scores ----------------------#
#-------------------------------------------------------------------------------------------------------------#
logger.comment("")
logger.start_logger_queue(
) #start process for listening and handling through the main logger queue
args.log_q = logger.queue #queue for multiprocessing logging
manager = mp.Manager()
logger.info("Scanning for motifs and matching to signals...")
#Create writer queues for bed-file output
logger.debug("Setting up writer queues")
qs_list = []
writer_qs = {}
#writer_queue = create_writer_queue(key2file, writer_cores)
#writer_queue.stop() #wait until all are done
manager = mp.Manager()
TF_names_chunks = [
motif_names[i::writer_cores] for i in range(writer_cores)
]
for TF_names_sub in TF_names_chunks:
logger.debug("Creating writer queue for {0}".format(TF_names_sub))
files = [
os.path.join(args.outdir, TF, "beds", TF + ".tmp")
for TF in TF_names_sub
]
q = manager.Queue()
qs_list.append(q)
writer_pool.apply_async(
file_writer, args=(q, dict(zip(TF_names_sub, files)), args)
) #, callback = lambda x: finished.append(x) print("Writing time: {0}".format(x)))
for TF in TF_names_sub:
writer_qs[TF] = q
writer_pool.close() #no more jobs applied to writer_pool
#todo: use run_parallel
#Start working on data
if worker_cores == 1:
logger.debug("Running with cores = 1")
results = []
for chunk in peak_chunks:
results.append(
scan_and_score(chunk, motif_list, args, args.log_q, writer_qs))
else:
logger.debug("Sending jobs to worker pool")
task_list = [
pool.apply_async(scan_and_score, (
chunk,
motif_list,
args,
args.log_q,
writer_qs,
)) for chunk in peak_chunks
]
monitor_progress(task_list, logger)
results = [task.get() for task in task_list]
logger.info("Done scanning for TFBS across regions!")
#logger.stop_logger_queue() #stop the listening process (wait until all was written)
#--------------------------------------#
logger.info("Waiting for bedfiles to write")
#Stop all queues for writing
logger.debug("Stop all queues by inserting None")
for q in qs_list:
q.put((None, None))
logger.debug("Joining bed_writer queues")
for i, q in enumerate(qs_list):
logger.debug("- Queue {0} (size {1})".format(i, q.qsize()))
#Waits until all queues are closed
writer_pool.join()
#-------------------------------------------------------------------------------------------------------------#
#---------------------------- Process information on background scores and overlaps --------------------------#
#-------------------------------------------------------------------------------------------------------------#
logger.info("Merging results from subsets")
background = merge_dicts([result[0] for result in results])
TF_overlaps = merge_dicts([result[1] for result in results])
results = None
#Add missing TF overlaps (if some TFs had zero sites)
for TF1 in plus_motifs:
if TF1.prefix not in TF_overlaps:
TF_overlaps[TF1.prefix] = 0
for TF2 in plus_motifs:
tup = (TF1.prefix, TF2.prefix)
if tup not in TF_overlaps:
TF_overlaps[tup] = 0
#Collect sampled background values
for bigwig in args.cond_names:
background["signal"][bigwig] = np.array(background["signal"][bigwig])
#Check how many values were fetched from background
n_bg_values = len(background["signal"][args.cond_names[0]])
logger.debug("Collected {0} values from background".format(n_bg_values))
if n_bg_values < 1000:
err_str = "Number of background values collected from peaks is low (={0}) ".format(
n_bg_values)
err_str += "- this affects estimation of the bound/unbound threshold and the normalization between conditions. "
err_str += "To improve this estimation, please run BINDetect with --peaks = the full peak set across all conditions."
logger.warning(err_str)
logger.comment("")
logger.info("Estimating score distribution per condition")
fig = plot_score_distribution(
[background["signal"][bigwig] for bigwig in args.cond_names],
labels=args.cond_names,
title="Raw scores per condition")
figure_pdf.savefig(fig, bbox_inches='tight')
plt.close()
logger.info("Normalizing scores")
list_of_vals = [background["signal"][bigwig] for bigwig in args.cond_names]
normed, norm_objects = quantile_normalization(list_of_vals)
args.norm_objects = dict(zip(args.cond_names, norm_objects))
for bigwig in args.cond_names:
background["signal"][bigwig] = args.norm_objects[bigwig].normalize(
background["signal"][bigwig])
fig = plot_score_distribution(
[background["signal"][bigwig] for bigwig in args.cond_names],
labels=args.cond_names,
title="Normalized scores per condition")
figure_pdf.savefig(fig, bbox_inches='tight')
plt.close()
###########################################################
logger.info("Estimating bound/unbound threshold")
#Prepare scores (remove 0's etc.)
bg_values = np.array(normed).flatten()
bg_values = bg_values[np.logical_not(np.isclose(
bg_values, 0.0))] #only non-zero counts
x_max = np.percentile(bg_values, [99])
bg_values = bg_values[bg_values < x_max]
if len(bg_values) == 0:
logger.error(
"Error processing bigwig scores from background. It could be that there are no scores in the bigwig (=0) assigned for the peaks. Please check your input files."
)
sys.exit()
#Fit mixture of normals
lowest_bic = np.inf
for n_components in [2]: #2 components
gmm = sklearn.mixture.GaussianMixture(n_components=n_components,
random_state=1)
gmm.fit(np.log(bg_values).reshape(-1, 1))
bic = gmm.bic(np.log(bg_values).reshape(-1, 1))
logger.debug("n_compontents: {0} | bic: {1}".format(n_components, bic))
if bic < lowest_bic:
lowest_bic = bic
best_gmm = gmm
gmm = best_gmm
#Extract most-right gaussian
means = gmm.means_.flatten()
sds = np.sqrt(gmm.covariances_).flatten()
chosen_i = np.argmax(means) #Mixture with largest mean
log_params = scipy.stats.lognorm.fit(bg_values[bg_values < x_max],
f0=sds[chosen_i],
fscale=np.exp(means[chosen_i]))
#all_log_params[bigwig] = log_params
#Mode of distribution
mode = scipy.optimize.fmin(
lambda x: -scipy.stats.lognorm.pdf(x, *log_params), 0, disp=False)[0]
logger.debug("- Mode estimated at: {0}".format(mode))
pseudo = mode / 2.0 #pseudo is half the mode
args.pseudo = pseudo
logger.debug("Pseudocount estimated at: {0}".format(round(args.pseudo, 5)))
# Estimate theoretical normal for threshold
leftside_x = np.linspace(
scipy.stats.lognorm(*log_params).ppf([0.01]), mode, 100)
leftside_pdf = scipy.stats.lognorm.pdf(leftside_x, *log_params)
#Flip over
mirrored_x = np.concatenate([leftside_x,
np.max(leftside_x) + leftside_x]).flatten()
mirrored_pdf = np.concatenate([leftside_pdf, leftside_pdf[::-1]]).flatten()
popt, cov = scipy.optimize.curve_fit(
lambda x, std, sc: sc * scipy.stats.norm.pdf(x, mode, std), mirrored_x,
mirrored_pdf)
norm_params = (mode, popt[0])
logger.debug("Theoretical normal parameters: {0}".format(norm_params))
#Set threshold for bound/unbound
threshold = round(
scipy.stats.norm.ppf(1 - args.bound_pvalue, *norm_params), 5)
args.thresholds = {bigwig: threshold for bigwig in args.cond_names}
logger.stats("- Threshold estimated at: {0}".format(threshold))
#Only plot if args.debug is True
if args.debug:
#Plot fit
fig, ax = plt.subplots(1, 1)
ax.hist(bg_values[bg_values < x_max],
bins='auto',
density=True,
label="Observed score distribution")
xvals = np.linspace(0, x_max, 1000)
log_probas = scipy.stats.lognorm.pdf(xvals, *log_params)
ax.plot(xvals, log_probas, label="Log-normal fit", color="orange")
#Theoretical normal
norm_probas = scipy.stats.norm.pdf(xvals, *norm_params)
ax.plot(xvals,
norm_probas * (np.max(log_probas) / np.max(norm_probas)),
color="grey",
linestyle="--",
label="Theoretical normal")
ax.axvline(threshold, color="black", label="Bound/unbound threshold")
ymax = plt.ylim()[1]
ax.text(threshold, ymax, "\n {0:.3f}".format(threshold), va="top")
#Decorate plot
plt.title("Score distribution")
plt.xlabel("Bigwig score")
plt.ylabel("Density")
plt.legend(fontsize=8)
plt.xlim((0, x_max))
figure_pdf.savefig(fig)
plt.close(fig)
############ Foldchanges between conditions ################
logger.comment("")
log2fc_params = {}
if len(args.signals) > 1:
logger.info(
"Calculating background log2 fold-changes between conditions")
for (bigwig1, bigwig2) in comparisons: #cond1, cond2
logger.info("- {0} / {1}".format(bigwig1, bigwig2))
#Estimate background log2fc
scores1 = np.copy(background["signal"][bigwig1])
scores2 = np.copy(background["signal"][bigwig2])
included = np.logical_or(scores1 > 0, scores2 > 0)
scores1 = scores1[included]
scores2 = scores2[included]
#Calculate background log2fc normal disitribution
log2fcs = np.log2(
np.true_divide(scores1 + args.pseudo, scores2 + args.pseudo))
lower, upper = np.percentile(log2fcs, [1, 99])
log2fcs_fit = log2fcs[np.logical_and(log2fcs >= lower,
log2fcs <= upper)]
norm_params = scipy.stats.norm.fit(log2fcs_fit)
logger.debug(
"({0} / {1}) Background log2fc normal distribution: {2}".
format(bigwig1, bigwig2, norm_params))
log2fc_params[(bigwig1, bigwig2)] = norm_params
#Plot background log2fc to figures
fig, ax = plt.subplots(1, 1)
plt.hist(log2fcs,
density=True,
bins='auto',
label="Background log2fc ({0} / {1})".format(
bigwig1, bigwig2))
xvals = np.linspace(plt.xlim()[0], plt.xlim()[1], 100)
pdf = scipy.stats.norm.pdf(xvals,
*log2fc_params[(bigwig1, bigwig2)])
plt.plot(xvals, pdf, label="Normal distribution fit")
plt.title("Background log2FCs ({0} / {1})".format(
bigwig1, bigwig2))
plt.xlabel("Log2 fold change")
plt.ylabel("Density")
if args.debug:
figure_pdf.savefig(fig, bbox_inches='tight')
f = open(
os.path.join(
args.outdir,
"{0}_{1}_log2fcs.txt".format(bigwig1, bigwig2)), "w")
f.write("\n".join([str(val) for val in log2fcs]))
f.close()
plt.close()
background = None #free up space
#-------------------------------------------------------------------------------------------------------------#
#----------------------------- Read total sites per TF to estimate bound/unbound -----------------------------#
#-------------------------------------------------------------------------------------------------------------#
logger.comment("")
logger.info("Processing scanned TFBS individually")
#Getting bindetect table ready
info_columns = ["total_tfbs"]
info_columns.extend([
"{0}_{1}".format(cond, metric)
for (cond, metric
) in itertools.product(args.cond_names, ["threshold", "bound"])
])
info_columns.extend([
"{0}_{1}_{2}".format(comparison[0], comparison[1], metric)
for (comparison,
metric) in itertools.product(comparisons, ["change", "pvalue"])
])
cols = len(info_columns)
rows = len(motif_names)
info_table = pd.DataFrame(np.zeros((rows, cols)),
columns=info_columns,
index=motif_names)
#Starting calculations
results = []
if args.cores == 1:
for name in motif_names:
logger.info("- {0}".format(name))
results.append(process_tfbs(name, args, log2fc_params))
else:
logger.debug("Sending jobs to worker pool")
task_list = [
pool.apply_async(process_tfbs, (
name,
args,
log2fc_params,
)) for name in motif_names
]
monitor_progress(task_list,
logger) #will not exit before all jobs are done
results = [task.get() for task in task_list]
logger.info("Concatenating results from subsets")
info_table = pd.concat(results) #pandas tables
pool.terminate()
pool.join()
logger.stop_logger_queue()
#-------------------------------------------------------------------------------------------------------------#
#------------------------------------------------ Cluster TFBS -----------------------------------------------#
#-------------------------------------------------------------------------------------------------------------#
clustering = RegionCluster(TF_overlaps)
clustering.cluster()
#Convert full ids to alt ids
convert = {motif.prefix: motif.name for motif in motif_list}
for cluster in clustering.clusters:
for name in convert:
clustering.clusters[cluster]["cluster_name"] = clustering.clusters[
cluster]["cluster_name"].replace(name, convert[name])
#Write out distance matrix
matrix_out = os.path.join(args.outdir, args.prefix + "_distances.txt")
clustering.write_distance_mat(matrix_out)
#-------------------------------------------------------------------------------------------------------------#
#----------------------------------------- Write all_bindetect file ------------------------------------------#
#-------------------------------------------------------------------------------------------------------------#
logger.comment("")
logger.info("Writing all_bindetect files")
#Add columns of name / motif_id / prefix
names = []
ids = []
for prefix in info_table.index:
motif = [motif for motif in motif_list if motif.prefix == prefix]
names.append(motif[0].name)
ids.append(motif[0].id)
info_table.insert(0, "output_prefix", info_table.index)
info_table.insert(1, "name", names)
info_table.insert(2, "motif_id", ids)
#info_table.insert(3, "motif_logo", [os.path.join("motif_logos", os.path.basename(logo_filenames[prefix])) for prefix in info_table["output_prefix"]]) #add relative path to logo
#Add cluster to info_table
cluster_names = []
for name in info_table.index:
for cluster in clustering.clusters:
if name in clustering.clusters[cluster]["member_names"]:
cluster_names.append(
clustering.clusters[cluster]["cluster_name"])
info_table.insert(3, "cluster", cluster_names)
#Cluster table on motif clusters
info_table_clustered = info_table.groupby(
"cluster").mean() #mean of each column
info_table_clustered.reset_index(inplace=True)
#Map correct type
info_table["total_tfbs"] = info_table["total_tfbs"].map(int)
for condition in args.cond_names:
info_table[condition + "_bound"] = info_table[condition +
"_bound"].map(int)
#### Write excel ###
bindetect_excel = os.path.join(args.outdir, args.prefix + "_results.xlsx")
writer = pd.ExcelWriter(bindetect_excel, engine='xlsxwriter')
#Tables
info_table.to_excel(writer, index=False, sheet_name="Individual motifs")
info_table_clustered.to_excel(writer,
index=False,
sheet_name="Motif clusters")
for sheet in writer.sheets:
worksheet = writer.sheets[sheet]
n_rows = worksheet.dim_rowmax
n_cols = worksheet.dim_colmax
worksheet.autofilter(0, 0, n_rows, n_cols)
writer.save()
#Format comparisons
for (cond1, cond2) in comparisons:
base = cond1 + "_" + cond2
info_table[base + "_change"] = info_table[base + "_change"].round(5)
info_table[base + "_pvalue"] = info_table[base + "_pvalue"].map(
"{:.5E}".format, na_action="ignore")
#Write bindetect results tables
#info_table.insert(0, "TF_name", info_table.index) #Set index as first column
bindetect_out = os.path.join(args.outdir, args.prefix + "_results.txt")
info_table.to_csv(bindetect_out,
sep="\t",
index=False,
header=True,
na_rep="NA")
#-------------------------------------------------------------------------------------------------------------#
#------------------------------------------- Make BINDetect plot ---------------------------------------------#
#-------------------------------------------------------------------------------------------------------------#
if no_conditions > 1:
logger.info("Creating BINDetect plot(s)")
#Fill NAs from info_table to enable plotting of log2fcs (NA -> 0 change)
change_cols = [col for col in info_table.columns if "_change" in col]
pvalue_cols = [col for col in info_table.columns if "_pvalue" in col]
info_table[change_cols] = info_table[change_cols].fillna(0)
info_table[pvalue_cols] = info_table[pvalue_cols].fillna(1)
#Plotting bindetect per comparison
for (cond1, cond2) in comparisons:
logger.info("- {0} / {1}".format(cond1, cond2))
base = cond1 + "_" + cond2
#Define which motifs to show
xvalues = info_table[base + "_change"].astype(float)
yvalues = info_table[base + "_pvalue"].astype(float)
y_min = np.percentile(yvalues[yvalues > 0],
5) #5% smallest pvalues
x_min, x_max = np.percentile(
xvalues, [5, 95]) #5% smallest and largest changes
#Make copy of motifs and fill in with metadata
comparison_motifs = [
motif for motif in motif_list if motif.strand == "+"
] #copy.deepcopy(motif_list) - swig pickle error, just overwrite motif_list
for motif in comparison_motifs:
name = motif.prefix
motif.change = float(info_table.at[name, base + "_change"])
motif.pvalue = float(info_table.at[name, base + "_pvalue"])
motif.logpvalue = -np.log10(
motif.pvalue) if motif.pvalue > 0 else -np.log10(1e-308)
#Assign each motif to group
if motif.change < x_min or motif.change > x_max or motif.pvalue < y_min:
if motif.change < 0:
motif.group = cond2 + "_up"
if motif.change > 0:
motif.group = cond1 + "_up"
else:
motif.group = "n.s."
#Bindetect plot
fig = plot_bindetect(comparison_motifs, clustering, [cond1, cond2],
args)
figure_pdf.savefig(fig, bbox_inches='tight')
plt.close(fig)
#Interactive BINDetect plot
html_out = os.path.join(args.outdir, "bindetect_" + base + ".html")
plot_interactive_bindetect(comparison_motifs, [cond1, cond2],
html_out)
#-------------------------------------------------------------------------------------------------------------#
#----------------------------- Make heatmap across conditions (for debugging)---------------------------------#
#-------------------------------------------------------------------------------------------------------------#
if args.debug:
mean_columns = [cond + "_mean_score" for cond in args.cond_names]
heatmap_table = info_table[mean_columns]
heatmap_table.index = info_table["output_prefix"]
#Decide fig size
rows, cols = heatmap_table.shape
figsize = (7 + cols, max(10, rows / 8.0))
cm = sns.clustermap(
heatmap_table,
figsize=figsize,
z_score=0, #zscore for rows
col_cluster=False, #do not cluster condition columns
yticklabels=True, #show all row annotations
xticklabels=True,
cbar_pos=(0, 0, .4, .005),
dendrogram_ratio=(0.3, 0.01),
cbar_kws={
"orientation": "horizontal",
'label': 'Row z-score'
},
method="single")
#Adjust width of columns
#hm = cm.ax_heatmap.get_position()
#cm.ax_heatmap.set_position([hm.x0, hm.y0, cols * 3 * hm.height / rows, hm.height]) #aspect should be equal
plt.setp(cm.ax_heatmap.get_xticklabels(),
fontsize=8,
rotation=45,
ha="right")
plt.setp(cm.ax_heatmap.get_yticklabels(), fontsize=5)
cm.ax_col_dendrogram.set_title('Mean scores across conditions',
fontsize=20)
cm.ax_heatmap.set_ylabel("Transcription factor motifs",
fontsize=15,
rotation=270)
#cm.ax_heatmap.set_title('Conditions')
#cm.fig.suptitle('Mean scores across conditions')
#cm.cax.set_visible(False)
#Save to output pdf
plt.tight_layout()
figure_pdf.savefig(cm.fig, bbox_inches='tight')
plt.close(cm.fig)
#-------------------------------------------------------------------------------------------------------------#
#-------------------------------------------------- Wrap up---------------------------------------------------#
#-------------------------------------------------------------------------------------------------------------#
figure_pdf.close()
logger.end()
def bias_correction(regions_list, params, bias_obj):
""" Corrects bias in cutsites (from bamfile) using estimated bias """
logger = TobiasLogger("", params.verbosity, params.log_q)
bam_f = params.bam
fasta_f = params.genome
k_flank = params.k_flank
read_shift = params.read_shift
L = 2 * k_flank + 1
w = params.window
f = int(w / 2.0)
qs = params.qs
f_extend = k_flank + f
strands = ["forward", "reverse"]
pre_bias = {strand: SequenceMatrix.create(L, "PWM") for strand in strands}
post_bias = {strand: SequenceMatrix.create(L, "PWM") for strand in strands}
#Open bamfile and fasta
bam_obj = pysam.AlignmentFile(bam_f, "rb")
fasta_obj = pysam.FastaFile(fasta_f)
out_signals = {}
#Go through each region
for region_obj in regions_list:
region_obj.extend_reg(f_extend)
reg_len = region_obj.get_length() #length including flanking
reg_key = (region_obj.chrom, region_obj.start + f_extend,
region_obj.end - f_extend) #output region
out_signals[reg_key] = {
"uncorrected": {},
"bias": {},
"expected": {},
"corrected": {}
}
################################
####### Uncorrected reads ######
################################
#Get cutsite positions for each read
read_lst = ReadList().from_bam(bam_obj, region_obj)
for read in read_lst:
read.get_cutsite(read_shift)
logger.spam("Read {0} reads from region {1}".format(
len(read_lst), region_obj))
#Exclude reads with cutsites outside region
read_lst = ReadList([
read for read in read_lst if read.cutsite > region_obj.start
and read.cutsite < region_obj.end
])
for_lst, rev_lst = read_lst.split_strands()
read_lst_strand = {"forward": for_lst, "reverse": rev_lst}
for strand in strands:
out_signals[reg_key]["uncorrected"][strand] = read_lst_strand[
strand].signal(region_obj)
out_signals[reg_key]["uncorrected"][strand] = np.round(
out_signals[reg_key]["uncorrected"][strand], 5)
################################
###### Estimation of bias ######
################################
#Get sequence in this region
sequence_obj = GenomicSequence(region_obj).from_fasta(fasta_obj)
#Score sequence using forward/reverse motifs
for strand in strands:
if strand == "forward":
seq = sequence_obj.sequence
bias = bias_obj.bias[strand].score_sequence(seq)
elif strand == "reverse":
seq = sequence_obj.revcomp
bias = bias_obj.bias[strand].score_sequence(seq)[::-1] #3'-5'
out_signals[reg_key]["bias"][strand] = np.nan_to_num(
bias) #convert any nans to 0
#################################
###### Correction of reads ######
#################################
reg_end = reg_len - k_flank
step = 10
overlaps = int(params.window / step)
window_starts = list(range(k_flank, reg_end - params.window, step))
window_ends = list(range(k_flank + params.window, reg_end, step))
window_ends[-1] = reg_len
windows = list(zip(window_starts, window_ends))
for strand in strands:
########### Estimate bias threshold ###########
bias_predictions = np.zeros((overlaps, reg_len))
row = 0
for window in windows:
signal_w = out_signals[reg_key]["uncorrected"][strand][
window[0]:window[1]]
bias_w = out_signals[reg_key]["bias"][strand][
window[0]:window[1]]
signalmax = np.max(signal_w)
#biasmin = np.min(bias_w)
#biasmax = np.max(bias_w)
if signalmax > 0:
try:
popt, pcov = curve_fit(relu, bias_w, signal_w)
bias_predict = relu(bias_w, *popt)
except (OptimizeWarning, RuntimeError):
cut_positions = np.logical_not(np.isclose(signal_w, 0))
bias_min = np.min(bias_w[cut_positions])
bias_predict = bias_w - bias_min
bias_predict[bias_predict < 0] = 0
if np.max(bias_predict) > 0:
bias_predict = bias_predict / np.max(bias_predict)
else:
bias_predict = np.zeros(window[1] - window[0])
bias_predictions[row, window[0]:window[1]] = bias_predict
row += 1 if row < overlaps - 1 else 0
bias_prediction = np.mean(bias_predictions, axis=0)
bias = bias_prediction
######## Calculate expected signal ######
signal_sum = fast_rolling_math(
out_signals[reg_key]["uncorrected"][strand], w, "sum")
signal_sum[np.isnan(signal_sum)] = 0 #f-width ends of region
bias_sum = fast_rolling_math(bias, w, "sum") #ends of arr are nan
nulls = np.logical_or(np.isclose(bias_sum, 0), np.isnan(bias_sum))
bias_sum[nulls] = 1 # N-regions will give stretches of 0-bias
bias_probas = bias / bias_sum
bias_probas[nulls] = 0 #nan to 0
out_signals[reg_key]["expected"][strand] = signal_sum * bias_probas
######## Correct signal ########
out_signals[reg_key]["uncorrected"][
strand] *= bias_obj.correction_factor
out_signals[reg_key]["expected"][
strand] *= bias_obj.correction_factor
out_signals[reg_key]["corrected"][
strand] = out_signals[reg_key]["uncorrected"][
strand] - out_signals[reg_key]["expected"][strand]
######## Rescale signal to fit uncorrected sum ########
uncorrected_sum = fast_rolling_math(
out_signals[reg_key]["uncorrected"][strand], w, "sum")
uncorrected_sum[np.isnan(uncorrected_sum)] = 0
corrected_sum = fast_rolling_math(
np.abs(out_signals[reg_key]["corrected"][strand]), w,
"sum") #negative values count as positive
corrected_sum[np.isnan(corrected_sum)] = 0
#Positive signal left after correction
corrected_pos = np.copy(out_signals[reg_key]["corrected"][strand])
corrected_pos[corrected_pos < 0] = 0
corrected_pos_sum = fast_rolling_math(corrected_pos, w, "sum")
corrected_pos_sum[np.isnan(corrected_pos_sum)] = 0
corrected_neg_sum = corrected_sum - corrected_pos_sum
#The corrected sum is less than the signal sum, so scale up positive cuts
zero_sum = corrected_pos_sum == 0
corrected_pos_sum[zero_sum] = np.nan #allow for zero division
scale_factor = (uncorrected_sum -
corrected_neg_sum) / corrected_pos_sum
scale_factor[
zero_sum] = 1 #Scale factor is 1 (which will be multiplied to the 0 values)
scale_factor[scale_factor < 1] = 1 #Only scale up if needed
pos_bool = out_signals[reg_key]["corrected"][strand] > 0
out_signals[reg_key]["corrected"][strand][
pos_bool] *= scale_factor[pos_bool]
#######################################
######## Verify correction ########
#######################################
#Verify correction across all reads
for strand in strands:
for idx in range(k_flank, reg_len - k_flank - 1):
if idx > k_flank and idx < reg_len - k_flank:
orig = out_signals[reg_key]["uncorrected"][strand][idx]
correct = out_signals[reg_key]["corrected"][strand][idx]
if orig != 0 or correct != 0: #if both are 0, don't add to pre/post bias
if strand == "forward":
kmer = sequence_obj.sequence[idx - k_flank:idx +
k_flank + 1]
else:
kmer = sequence_obj.revcomp[reg_len - idx -
k_flank - 1:reg_len -
idx + k_flank]
#Save kmer for bias correction verification
pre_bias[strand].add_sequence(kmer, orig)
post_bias[strand].add_sequence(kmer, correct)
#######################################
######## Write to queue #########
#######################################
#Set size back to original
for track in out_signals[reg_key]:
for strand in out_signals[reg_key][track]:
out_signals[reg_key][track][strand] = out_signals[reg_key][
track][strand][f_extend:-f_extend]
#Calculate "both" if split_strands == False
if params.split_strands == False:
for track in out_signals[reg_key]:
out_signals[reg_key][track]["both"] = out_signals[reg_key][
track]["forward"] + out_signals[reg_key][track]["reverse"]
#Send to queue
strands_to_write = ["forward", "reverse"
] if params.split_strands == True else ["both"]
for track in out_signals[reg_key]:
#Send to writer per strand
for strand in strands_to_write:
key = "{0}:{1}".format(track, strand)
logger.spam(
"Sending {0} signal from region {1} to writer queue".
format(key, reg_key))
qs[key].put(
(key, reg_key, out_signals[reg_key][track][strand]))
#Sent to qs - delete from this process
out_signals[reg_key] = None
bam_obj.close()
fasta_obj.close()
gc.collect()
return ([pre_bias, post_bias])
def bias_estimation(regions_list, params):
""" Estimates bias of insertions within regions """
#Info on run
bam_f = params.bam
fasta_f = params.genome
k_flank = params.k_flank
bg_shift = params.bg_shift
read_shift = params.read_shift
L = 2 * k_flank + 1
logger = TobiasLogger("", params.verbosity,
params.log_q) #sending all logger calls to log_q
#Open objects for reading
bam_obj = pysam.AlignmentFile(bam_f, "rb")
fasta_obj = pysam.FastaFile(fasta_f)
chrom_lengths = dict(
zip(bam_obj.references,
bam_obj.lengths)) #Chromosome boundaries from bam_obj
bias_obj = AtacBias(L, params.score_mat)
strands = ["forward", "reverse"]
#Estimate bias at each region
for region in regions_list:
read_lst = ReadList().from_bam(bam_obj, region)
for read in read_lst:
read.get_cutsite(read_shift)
## Kmer cutting bias ##
if len(read_lst) > 0:
#Extract sequence
extended_region = region.extend_reg(
k_flank + bg_shift) #Extend to allow full kmers
extended_region.check_boundary(chrom_lengths, "cut")
sequence_obj = GenomicSequence(extended_region).from_fasta(
fasta_obj)
#Split reads forward/reverse
for_lst, rev_lst = read_lst.split_strands()
read_lst_strand = {"forward": for_lst, "reverse": rev_lst}
logger.spam(
"Region: {0}. Forward reads: {1}. Reverse reads: {2}".format(
region, len(for_lst), len(rev_lst)))
for strand in strands:
#Map reads to positions
read_per_pos = {}
for read in read_lst_strand[strand]:
if read.cigartuples is not None:
first_tuple = read.cigartuples[
-1] if read.is_reverse else read.cigartuples[0]
if first_tuple[0] == 0 and first_tuple[
1] > params.k_flank + max(
np.abs(params.read_shift)
): #Only include non-clipped reads
read_per_pos[read.cutsite] = read_per_pos.get(
read.cutsite, []) + [read]
#Get kmer for each position
for cutsite in read_per_pos:
if cutsite > region.start and cutsite < region.end: #only reads within borders
read = read_per_pos[cutsite][
0] #use first read in list to establish kmer
no_cut = min(
len(read_per_pos[cutsite]), 10
) #put cap on number of cuts to limit influence of outliers
read.get_kmer(sequence_obj, k_flank)
bias_obj.bias[strand].add_sequence(read.kmer, no_cut)
read.shift_cutsite(
-bg_shift
) #upstream of read; ensures that bg is not within fragment
read.get_kmer(
sequence_obj,
k_flank) #kmer updated to kmer for shifted read
bias_obj.bias[strand].add_background(read.kmer, no_cut)
bias_obj.no_reads += no_cut
bam_obj.close()
fasta_obj.close()
return (bias_obj) #object containing information collected on bias