def run(path):
'''
'''
log("plugin [%s] start running" %key)
file_path_list = get_need_file_list(path , key)
save_dir = get_spec_dir(key)
preproces_filename = os.path.join(save_dir,s_process_file)
for file in file_path_list:
if os.path.exists(preproces_filename) == True:
os.unlink(preproces_filename)
ret = uncompress(file,key,save_dir)
if not ret:
return False
if not process(preproces_filename):
return False
return True
def process(self):
'''
#find pooled peaks that are in (rep1 AND rep2)
out, err = common.run_pipe([
'intersectBed -wa -f 0.50 -r -a %s -b %s' %(pooled_peaks_fn, rep1_peaks_fn),
'intersectBed -wa -f 0.50 -r -a stdin -b %s' %(rep2_peaks_fn)
], overlap_tr_fn)
print "%d peaks overlap with both true replicates" %(common.count_lines(overlap_tr_fn))
#pooled peaks that are in (pooledpseudorep1 AND pooledpseudorep2)
out, err = common.run_pipe([
'intersectBed -wa -f 0.50 -r -a %s -b %s' %(pooled_peaks_fn, pooledpr1_peaks_fn),
'intersectBed -wa -f 0.50 -r -a stdin -b %s' %(pooledpr2_peaks_fn)
], overlap_pr_fn)
print "%d peaks overlap with both pooled pseudoreplicates" %(common.count_lines(overlap_pr_fn))
#combined pooled peaks in (rep1 AND rep2) OR (pooledpseudorep1 AND pooledpseudorep2)
out, err = common.run_pipe([
'intersectBed -wa -a %s -b %s %s' %(pooled_peaks_fn, overlap_tr_fn, overlap_pr_fn),
'intersectBed -wa -u -a %s -b stdin' %(pooled_peaks_fn)
], overlapping_peaks_fn)
print "%d peaks overall with true replicates or with pooled pseudorepliates" %(common.count_lines(overlapping_peaks_fn))
'''
#the only difference between the peak_types is how the extra columns are handled
if self.peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif self.peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif self.peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
print "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak."
sys.exit()
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined 1bp
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %(self.pooled_peaks_fn, self.rep1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' %(self.rep2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], self.overlap_tr_fn)
print "%d peaks overlap with both true replicates" %(common.count_lines(self.overlap_tr_fn))
# Find pooled peaks that overlap PseudoRep1 and PseudoRep2 where overlap is defined as 1bp
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %(self.pooled_peaks_fn, self.pooledpr1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' %(self.pooledpr2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], self.overlap_pr_fn)
print "%d peaks overlap with both pooled pseudoreplicates" %(common.count_lines(self.overlap_pr_fn))
# Combine peak lists
out, err = common.run_pipe([
'cat %s %s' %(self.overlap_tr_fn, self.overlap_pr_fn),
'sort -u'
], self.overlapping_peaks_fn)
print "%d peaks overlap with true replicates or with pooled pseudorepliates" %(common.count_lines(self.overlapping_peaks_fn))
#rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' %(self.pooled_peaks_fn, self.overlapping_peaks_fn)
], self.rejected_peaks_fn)
print "%d peaks were rejected" %(common.count_lines(self.rejected_peaks_fn))
self.npeaks_in = common.count_lines(common.uncompress(self.pooled_peaks_fn))
self.npeaks_out = common.count_lines(self.overlapping_peaks_fn)
self.npeaks_rejected = common.count_lines(self.rejected_peaks_fn)
#make bigBed files for visualization
self.overlapping_peaks_bb_fn = common.bed2bb(self.overlapping_peaks_fn, self.chrom_sizes_fn, self.as_file_fn, bed_type=bed_type)
self.rejected_peaks_bb_fn = common.bed2bb(self.rejected_peaks_fn, self.chrom_sizes_fn, self.as_file_fn, bed_type=bed_type)
def main(experiment,
reps_peaks,
r1pr_peaks,
r2pr_peaks,
pooledpr_peaks,
chrom_sizes,
as_file,
blacklist=None):
#TODO for now just taking the peak files. This applet should actually call IDR instead of
#putting that in the workflow populator script
# Initialize the data object inputs on the platform into
# dxpy.DXDataObject instances.
reps_peaks_file = dxpy.DXFile(reps_peaks)
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
r2pr_peaks_file = dxpy.DXFile(r2pr_peaks)
pooledpr_peaks_file = dxpy.DXFile(pooledpr_peaks)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' % (blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Download the file inputs to the local file system.
#Need to prepend something to ensure the local filenames will be unique
reps_peaks_filename = 'true_%s' % (reps_peaks_file.name)
r1pr_peaks_filename = 'r1pr_%s' % (r1pr_peaks_file.name)
r2pr_peaks_filename = 'r2pr_%s' % (r2pr_peaks_file.name)
pooledpr_peaks_filename = 'pooledpr_%s' % (pooledpr_peaks_file.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(reps_peaks_file.get_id(), reps_peaks_filename)
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(r2pr_peaks_file.get_id(), r2pr_peaks_filename)
dxpy.download_dxfile(pooledpr_peaks_file.get_id(), pooledpr_peaks_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
print subprocess.check_output('ls -l', shell=True)
reps_peaks_filename = common.uncompress(reps_peaks_filename)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
r2pr_peaks_filename = common.uncompress(r2pr_peaks_filename)
pooledpr_peaks_filename = common.uncompress(pooledpr_peaks_filename)
Nt = common.count_lines(reps_peaks_filename)
print "%d peaks from true replicates" % (Nt)
N1 = common.count_lines(r1pr_peaks_filename)
print "%d peaks from rep1 self-pseudoreplicates" % (N1)
N2 = common.count_lines(r2pr_peaks_filename)
print "%d peaks from rep2 self-pseudoreplicates" % (N2)
Np = common.count_lines(pooledpr_peaks_filename)
print "%d peaks from pooled pseudoreplicates" % (Np)
conservative_set_filename = '%s_final_conservative.narrowPeak' % (
experiment)
if blacklist is not None:
blacklist_filter(reps_peaks_filename, conservative_set_filename,
blacklist_filename)
else:
conservative_set_filename = reps_peaks_filename
Ncb = common.count_lines(conservative_set_filename)
print "%d peaks blacklisted from the conservative set" % (Nt - Ncb)
if Nt >= Np:
peaks_to_filter_filename = reps_peaks_filename
No = Nt
else:
peaks_to_filter_filename = pooledpr_peaks_filename
No = Np
optimal_set_filename = '%s_final_optimal.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(peaks_to_filter_filename, optimal_set_filename,
blacklist_filename)
else:
optimal_set_filename = peaks_to_filter_filename
Nob = common.count_lines(optimal_set_filename)
print "%d peaks blacklisted from the optimal set" % (No - Nob)
rescue_ratio = float(max(Np, Nt)) / float(min(Np, Nt))
self_consistency_ratio = float(max(N1, N2)) / float(min(N1, N2))
if rescue_ratio > 2 and self_consistency_ratio > 2:
reproducibility = 'fail'
elif rescue_ratio > 2 or self_consistency_ratio > 2:
reproducibility = 'borderline'
else:
reproducibility = 'pass'
output = {}
#bedtobigbed often fails, so skip creating the bb if it does
conservative_set_bb_filename = common.bed2bb(conservative_set_filename,
chrom_sizes_filename,
as_file_filename)
optimal_set_bb_filename = common.bed2bb(optimal_set_filename,
chrom_sizes_filename,
as_file_filename)
if conservative_set_bb_filename:
conservative_set_bb_output = dxpy.upload_local_file(
conservative_set_bb_filename)
output.update(
{"conservative_set_bb": dxpy.dxlink(conservative_set_bb_output)})
if optimal_set_bb_filename:
optimal_set_bb_output = dxpy.upload_local_file(optimal_set_bb_filename)
output.update({"optimal_set_bb": dxpy.dxlink(optimal_set_bb_output)})
output.update({
"Nt":
Nt,
"N1":
N1,
"N2":
N2,
"Np":
Np,
"conservative_set":
dxpy.dxlink(
dxpy.upload_local_file(
common.compress(conservative_set_filename))),
"optimal_set":
dxpy.dxlink(
dxpy.upload_local_file(common.compress(optimal_set_filename))),
"rescue_ratio":
rescue_ratio,
"self_consistency_ratio":
self_consistency_ratio,
"reproducibility_test":
reproducibility
})
logging.info("Exiting with output: %s", output)
return output
def internal_pseudoreplicate_IDR(experiment, r1pr_peaks, rep1_ta, rep1_xcor,
paired_end, chrom_sizes, as_file, blacklist,
rep1_signal, fragment_length=None):
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
rep1_ta = dxpy.DXFile(rep1_ta)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' % (blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Need to prepend something to ensure the local filenames will be unique
r1pr_peaks_filename = 'r1pr_%s' % (r1pr_peaks_file.name)
rep1_ta_filename = 'r1ta_%s' % (rep1_ta.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(rep1_ta.get_id(), rep1_ta_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
# If fragment_length is given, override appropriate values.
# Calculate, or set the actually used fragment length value.
# Set the fragment_length_given_by_user flag appropriately.
if fragment_length is not None:
rep1_xcor_filename = None
fragment_length_used_rep1 = fragment_length
fragment_length_given_by_user = True
else:
rep1_xcor = dxpy.DXFile(rep1_xcor)
rep1_xcor_filename = 'r1xc_%s' % (rep1_xcor.name)
dxpy.download_dxfile(rep1_xcor.get_id(), rep1_xcor_filename)
fragment_length_used_rep1 = common.xcor_fraglen(rep1_xcor_filename)
fragment_length_given_by_user = False
subprocess.check_output('set -x; ls -l', shell=True)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
N1 = common.count_lines(r1pr_peaks_filename)
logger.info("%d peaks from rep1 self-pseudoreplicates (N1)" % (N1))
stable_set_filename = "%s_stable.narrowPeak" % (experiment)
if blacklist is not None:
blacklist_filter(r1pr_peaks_filename, stable_set_filename,
blacklist_filename)
Nsb = common.count_lines(stable_set_filename)
logger.info(
"%d peaks blacklisted from the stable set" % (N1-Nsb))
else:
subprocess.check_output(shlex.split(
'cp %s %s' % (r1pr_peaks_filename, stable_set_filename)))
Nsb = N1
logger.info("No blacklist filter applied to the stable set")
# calculate FRiP
n_reads, n_reads_in_peaks, frip_score = common.frip(
rep1_ta_filename, rep1_xcor_filename, stable_set_filename,
chrom_sizes_filename, fragment_length_used_rep1)
output = {
"rep1_frip_nreads": n_reads,
"rep1_frip_nreads_in_peaks": n_reads_in_peaks,
"F1": frip_score,
"fragment_length_used_rep1": fragment_length_used_rep1,
"fragment_length_given_by_user": fragment_length_given_by_user
}
# These are optional outputs to see what's being removed by the blacklist
if blacklist:
output.update({
"pre_bl_stable_set":
dxpy.dxlink(dxpy.upload_local_file(common.compress(
r1pr_peaks_filename)))}
)
# bedtobigbed often fails, so skip creating the bb if it does
stable_set_bb_filename = \
common.bed2bb(stable_set_filename, chrom_sizes_filename,
as_file_filename)
if stable_set_bb_filename:
stable_set_bb_output = \
dxpy.upload_local_file(stable_set_bb_filename)
output.update(
{"stable_set_bb": dxpy.dxlink(stable_set_bb_output)})
output.update({
"N1": N1,
"stable_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(stable_set_filename))),
"Ns": Nsb
})
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
return output
def replicated_IDR(experiment,
reps_peaks, r1pr_peaks, r2pr_peaks, pooledpr_peaks,
rep1_ta, rep1_xcor, rep2_ta, rep2_xcor,
paired_end, chrom_sizes, as_file, blacklist,
rep1_signal, rep2_signal, pooled_signal,
fragment_length=None):
# TODO for now just taking the peak files. This applet should actually
# call IDR instead of putting that in the workflow populator script
reps_peaks_file = dxpy.DXFile(reps_peaks)
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
r2pr_peaks_file = dxpy.DXFile(r2pr_peaks)
pooledpr_peaks_file = dxpy.DXFile(pooledpr_peaks)
rep1_ta_file = dxpy.DXFile(rep1_ta)
rep2_ta_file = dxpy.DXFile(rep2_ta)
rep1_xcor_file = dxpy.DXFile(rep1_xcor)
rep2_xcor_file = dxpy.DXFile(rep2_xcor)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' % (blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Need to prepend something to ensure the local filenames will be unique
reps_peaks_filename = 'true_%s' % (reps_peaks_file.name)
r1pr_peaks_filename = 'r1pr_%s' % (r1pr_peaks_file.name)
r2pr_peaks_filename = 'r2pr_%s' % (r2pr_peaks_file.name)
pooledpr_peaks_filename = 'pooledpr_%s' % (pooledpr_peaks_file.name)
rep1_ta_filename = 'r1ta_%s' % (rep1_ta_file.name)
rep2_ta_filename = 'r2ta_%s' % (rep2_ta_file.name)
rep1_xcor_filename = 'r1cc_%s' % (rep1_xcor_file.name)
rep2_xcor_filename = 'r2cc_%s' % (rep2_xcor_file.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(reps_peaks_file.get_id(), reps_peaks_filename)
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(r2pr_peaks_file.get_id(), r2pr_peaks_filename)
dxpy.download_dxfile(pooledpr_peaks_file.get_id(), pooledpr_peaks_filename)
dxpy.download_dxfile(rep1_ta_file.get_id(), rep1_ta_filename)
dxpy.download_dxfile(rep2_ta_file.get_id(), rep2_ta_filename)
dxpy.download_dxfile(rep1_xcor_file.get_id(), rep1_xcor_filename)
dxpy.download_dxfile(rep2_xcor_file.get_id(), rep2_xcor_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
reps_peaks_filename = common.uncompress(reps_peaks_filename)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
r2pr_peaks_filename = common.uncompress(r2pr_peaks_filename)
pooledpr_peaks_filename = common.uncompress(pooledpr_peaks_filename)
pool_applet = dxpy.find_one_data_object(
classname='applet',
name='pool',
project=dxpy.PROJECT_CONTEXT_ID,
zero_ok=False,
more_ok=False,
return_handler=True)
pool_replicates_subjob = \
pool_applet.run(
{"inputs": [rep1_ta, rep2_ta],
"prefix": 'pooled_reps'},
name='Pool replicates')
# next call could be on 267 and save time?
pool_replicates_subjob.wait_on_done()
# If fragment_length is not given, calculate the fragment_length
# using crosscorrelation. Else use the overridevalue. Set the
# pool_xcor_filename to None to accommodate common.frip calls.
# Calculate, or set, actually used fragment lengths for different
# cases. Set the flag indicating whether the fragment length
# was given by the user.
if fragment_length is not None:
pool_xcor_filename = None
fragment_length_used_rep1 = fragment_length
fragment_length_used_rep2 = fragment_length
fragment_length_used_pool = fragment_length
fragment_length_given_by_user = True
else:
pooled_replicates_xcor_subjob = \
xcor_only(
pool_replicates_subjob.get_output_ref("pooled"),
paired_end,
spp_version=None,
name='Pool cross-correlation')
pooled_replicates_xcor_subjob.wait_on_done()
pool_xcor_link = pooled_replicates_xcor_subjob.describe()['output'].get("CC_scores_file")
pool_xcor_file = dxpy.get_handler(pool_xcor_link)
pool_xcor_filename = 'poolcc_%s' % (pool_xcor_file.name)
dxpy.download_dxfile(pool_xcor_file.get_id(), pool_xcor_filename)
fragment_length_used_rep1 = common.xcor_fraglen(rep1_xcor_filename)
fragment_length_used_rep2 = common.xcor_fraglen(rep2_xcor_filename)
fragment_length_used_pool = common.xcor_fraglen(pool_xcor_filename)
fragment_length_given_by_user = False
pool_ta_link = pool_replicates_subjob.describe()['output'].get("pooled")
pool_ta_file = dxpy.get_handler(pool_ta_link)
pool_ta_filename = 'poolta_%s' % (pool_ta_file.name)
dxpy.download_dxfile(pool_ta_file.get_id(), pool_ta_filename)
logger.info(subprocess.check_output('set -x; ls -l', shell=True))
Nt = common.count_lines(reps_peaks_filename)
logger.info("%d peaks from true replicates (Nt)" % (Nt))
N1 = common.count_lines(r1pr_peaks_filename)
logger.info("%d peaks from rep1 self-pseudoreplicates (N1)" % (N1))
N2 = common.count_lines(r2pr_peaks_filename)
logger.info("%d peaks from rep2 self-pseudoreplicates (N2)" % (N2))
Np = common.count_lines(pooledpr_peaks_filename)
logger.info("%d peaks from pooled pseudoreplicates (Np)" % (Np))
# generate the conservative set, which is always based on the IDR peaks
# from true replicates
conservative_set_filename = \
'%s_final_conservative.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(reps_peaks_filename, conservative_set_filename,
blacklist_filename)
Ncb = common.count_lines(conservative_set_filename)
logger.info(
"%d peaks blacklisted from the conservative set" % (Nt-Ncb))
else:
subprocess.check_output(shlex.split(
'cp %s %s' % (reps_peaks_filename, conservative_set_filename)))
Ncb = Nt
logger.info("No blacklist filter applied to the conservative set")
# generate the optimal set, which is based on the longest of IDR peaks
# list from true reps or the IDR peaks from the pseudoreplicates of the
# pool
if Nt >= Np:
peaks_to_filter_filename = reps_peaks_filename
No = Nt
else:
peaks_to_filter_filename = pooledpr_peaks_filename
No = Np
optimal_set_filename = '%s_final_optimal.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(peaks_to_filter_filename, optimal_set_filename,
blacklist_filename)
Nob = common.count_lines(optimal_set_filename)
logger.info("%d peaks blacklisted from the optimal set" % (No-Nob))
else:
subprocess.check_output(shlex.split(
'cp %s %s' % (peaks_to_filter_filename, optimal_set_filename)))
Nob = No
logger.info("No blacklist filter applied to the optimal set")
rescue_ratio = float(max(Np, Nt)) / float(min(Np, Nt))
self_consistency_ratio = float(max(N1, N2)) / float(min(N1, N2))
if rescue_ratio > 2 and self_consistency_ratio > 2:
reproducibility = 'fail'
elif rescue_ratio > 2 or self_consistency_ratio > 2:
reproducibility = 'borderline'
else:
reproducibility = 'pass'
# FRiP (fraction reads in peaks)
# rep1 stable peaks comparing internal pseudoreplicates
rep1_n_reads, rep1_n_reads_in_peaks, rep1_frip_score = common.frip(
rep1_ta_filename, rep1_xcor_filename, r1pr_peaks_filename,
chrom_sizes_filename, fragment_length)
# rep2 stable peaks comparing internal pseudoreplicates
rep2_n_reads, rep2_n_reads_in_peaks, rep2_frip_score = common.frip(
rep2_ta_filename, rep2_xcor_filename, r2pr_peaks_filename,
chrom_sizes_filename, fragment_length)
# comparing true reps
true_n_reads, true_n_reads_in_peaks, true_frip_score = common.frip(
pool_ta_filename, pool_xcor_filename, reps_peaks_filename,
chrom_sizes_filename, fragment_length)
# comparing pooled pseudoreplicates
pr_n_reads, pr_n_reads_in_peaks, pr_frip_score = common.frip(
pool_ta_filename, pool_xcor_filename, pooledpr_peaks_filename,
chrom_sizes_filename, fragment_length)
output = {
"rep1_frip_nreads" : rep1_n_reads,
"rep1_frip_nreads_in_peaks" : rep1_n_reads_in_peaks,
"F1" : rep1_frip_score,
"rep2_frip_nreads" : rep2_n_reads,
"rep2_frip_nreads_in_peaks" : rep2_n_reads_in_peaks,
"F2" : rep2_frip_score,
"true_frip_nreads" : true_n_reads,
"true_frip_nreads_in_peaks" : true_n_reads_in_peaks,
"Ft" : true_frip_score,
"pr_frip_nreads" : pr_n_reads,
"pr_frip_nreads_in_peaks" : pr_n_reads_in_peaks,
"Fp" : pr_frip_score,
"fragment_length_used_rep1": fragment_length_used_rep1,
"fragment_length_used_rep2": fragment_length_used_rep2,
"fragment_length_used_pool": fragment_length_used_pool,
"fragment_length_given_by_user": fragment_length_given_by_user
}
# These are optional outputs to see what's being removed by the blacklist
if blacklist:
output.update({
"pre_bl_conservative_set":
dxpy.dxlink(dxpy.upload_local_file(common.compress(
reps_peaks_filename))),
"pre_bl_optimal_set":
dxpy.dxlink(dxpy.upload_local_file(common.compress(
peaks_to_filter_filename)))}
)
# bedtobigbed often fails, so skip creating the bb if it does
conservative_set_bb_filename = \
common.bed2bb(conservative_set_filename, chrom_sizes_filename,
as_file_filename)
optimal_set_bb_filename = \
common.bed2bb(optimal_set_filename, chrom_sizes_filename,
as_file_filename)
if conservative_set_bb_filename:
conservative_set_bb_output = \
dxpy.upload_local_file(conservative_set_bb_filename)
output.update(
{"conservative_set_bb": dxpy.dxlink(conservative_set_bb_output)})
if optimal_set_bb_filename:
optimal_set_bb_output = dxpy.upload_local_file(optimal_set_bb_filename)
output.update(
{"optimal_set_bb": dxpy.dxlink(optimal_set_bb_output)})
output.update({
"Nt": Nt,
"N1": N1,
"N2": N2,
"Np": Np,
"conservative_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(conservative_set_filename))),
"optimal_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(optimal_set_filename))),
"rescue_ratio": rescue_ratio,
"self_consistency_ratio": self_consistency_ratio,
"reproducibility_test": reproducibility,
"No": Nob,
"Nc": Ncb
})
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
if rep2_signal:
output.update({"rep2_signal": rep2_signal})
if pooled_signal:
output.update({"pooled_signal": pooled_signal})
return output
def main(rep1_peaks,
rep2_peaks,
pooled_peaks,
pooledpr1_peaks,
pooledpr2_peaks,
chrom_sizes,
as_file,
peak_type,
prefix=None,
rep1_signal=None,
rep2_signal=None,
pooled_signal=None):
# Initialize data object inputs on the platform
# into dxpy.DXDataObject instances
rep1_peaks = dxpy.DXFile(rep1_peaks)
rep2_peaks = dxpy.DXFile(rep2_peaks)
pooled_peaks = dxpy.DXFile(pooled_peaks)
pooledpr1_peaks = dxpy.DXFile(pooledpr1_peaks)
pooledpr2_peaks = dxpy.DXFile(pooledpr2_peaks)
chrom_sizes = dxpy.DXFile(chrom_sizes)
as_file = dxpy.DXFile(as_file)
#Input filenames - necessary to define each explicitly because input files could have the same name, in which case subsequent
#file would overwrite previous file
rep1_peaks_fn = 'rep1-%s' % (rep1_peaks.name)
rep2_peaks_fn = 'rep2-%s' % (rep2_peaks.name)
pooled_peaks_fn = 'pooled-%s' % (pooled_peaks.name)
pooledpr1_peaks_fn = 'pooledpr1-%s' % (pooledpr1_peaks.name)
pooledpr2_peaks_fn = 'pooledpr2-%s' % (pooledpr2_peaks.name)
chrom_sizes_fn = 'chrom.sizes'
as_file_fn = '%s.as' % (peak_type)
# Output filenames
if prefix:
basename = prefix
else:
m = re.match(
'(.*)(\.%s)+(\.((gz)|(Z)|(bz)|(bz2)))' % (peak_type),
pooled_peaks.name) #strip off the peak and compression extensions
if m:
basename = m.group(1)
else:
basename = pooled_peaks.name
overlapping_peaks_fn = '%s.replicated.%s' % (basename, peak_type)
overlapping_peaks_bb_fn = overlapping_peaks_fn + '.bb'
rejected_peaks_fn = '%s.rejected.%s' % (basename, peak_type)
rejected_peaks_bb_fn = rejected_peaks_fn + '.bb'
# Intermediate filenames
overlap_tr_fn = 'replicated_tr.%s' % (peak_type)
overlap_pr_fn = 'replicated_pr.%s' % (peak_type)
# Download file inputs to the local file system with local filenames
dxpy.download_dxfile(rep1_peaks.get_id(), rep1_peaks_fn)
dxpy.download_dxfile(rep2_peaks.get_id(), rep2_peaks_fn)
dxpy.download_dxfile(pooled_peaks.get_id(), pooled_peaks_fn)
dxpy.download_dxfile(pooledpr1_peaks.get_id(), pooledpr1_peaks_fn)
dxpy.download_dxfile(pooledpr2_peaks.get_id(), pooledpr2_peaks_fn)
dxpy.download_dxfile(chrom_sizes.get_id(), chrom_sizes_fn)
dxpy.download_dxfile(as_file.get_id(), as_file_fn)
'''
#find pooled peaks that are in (rep1 AND rep2)
out, err = common.run_pipe([
'intersectBed -wa -f 0.50 -r -a %s -b %s' %(pooled_peaks_fn, rep1_peaks_fn),
'intersectBed -wa -f 0.50 -r -a stdin -b %s' %(rep2_peaks_fn)
], overlap_tr_fn)
print "%d peaks overlap with both true replicates" %(common.count_lines(overlap_tr_fn))
#pooled peaks that are in (pooledpseudorep1 AND pooledpseudorep2)
out, err = common.run_pipe([
'intersectBed -wa -f 0.50 -r -a %s -b %s' %(pooled_peaks_fn, pooledpr1_peaks_fn),
'intersectBed -wa -f 0.50 -r -a stdin -b %s' %(pooledpr2_peaks_fn)
], overlap_pr_fn)
print "%d peaks overlap with both pooled pseudoreplicates" %(common.count_lines(overlap_pr_fn))
#combined pooled peaks in (rep1 AND rep2) OR (pooledpseudorep1 AND pooledpseudorep2)
out, err = common.run_pipe([
'intersectBed -wa -a %s -b %s %s' %(pooled_peaks_fn, overlap_tr_fn, overlap_pr_fn),
'intersectBed -wa -u -a %s -b stdin' %(pooled_peaks_fn)
], overlapping_peaks_fn)
print "%d peaks overall with true replicates or with pooled pseudorepliates" %(common.count_lines(overlapping_peaks_fn))
'''
#the only difference between the peak_types is how the extra columns are handled
if peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
assert peak_type in [
'narrowPeak', 'gappedPeak', 'broadPeak'
], "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak." % (
peak_type)
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined as the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %
(pooled_peaks_fn, rep1_peaks_fn), awk_command, cut_command, 'sort -u',
'intersectBed -wo -a stdin -b %s' %
(rep2_peaks_fn), awk_command, cut_command, 'sort -u'
], overlap_tr_fn)
print "%d peaks overlap with both true replicates" % (
common.count_lines(overlap_tr_fn))
# Find pooled peaks that overlap PseudoRep1 and PseudoRep2 where overlap is defined as the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' % (pooled_peaks_fn, pooledpr1_peaks_fn),
awk_command, cut_command, 'sort -u',
'intersectBed -wo -a stdin -b %s' %
(pooledpr2_peaks_fn), awk_command, cut_command, 'sort -u'
], overlap_pr_fn)
print "%d peaks overlap with both pooled pseudoreplicates" % (
common.count_lines(overlap_pr_fn))
# Combine peak lists
out, err = common.run_pipe(
['cat %s %s' % (overlap_tr_fn, overlap_pr_fn), 'sort -u'],
overlapping_peaks_fn)
print "%d peaks overlap with true replicates or with pooled pseudorepliates" % (
common.count_lines(overlapping_peaks_fn))
#rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' %
(pooled_peaks_fn, overlapping_peaks_fn)
], rejected_peaks_fn)
print "%d peaks were rejected" % (common.count_lines(rejected_peaks_fn))
npeaks_in = common.count_lines(common.uncompress(pooled_peaks_fn))
npeaks_out = common.count_lines(overlapping_peaks_fn)
npeaks_rejected = common.count_lines(rejected_peaks_fn)
#make bigBed files for visualization
overlapping_peaks_bb_fn = common.bed2bb(overlapping_peaks_fn,
chrom_sizes_fn,
as_file_fn,
bed_type=bed_type)
rejected_peaks_bb_fn = common.bed2bb(rejected_peaks_fn,
chrom_sizes_fn,
as_file_fn,
bed_type=bed_type)
# overlapping_peaks_bb_fn = common.bed2bb(common.slop_clip(overlapping_peaks_fn, chrom_sizes_fn, "gappedPeak"), chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# rejected_peaks_bb_fn = common.bed2bb(common.slop_clip(rejected_peaks_fn, chrom_sizes_fn, "gappedPeak"), chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# Upload file outputs from the local file system.
overlapping_peaks = dxpy.upload_local_file(
common.compress(overlapping_peaks_fn))
overlapping_peaks_bb = dxpy.upload_local_file(overlapping_peaks_bb_fn)
rejected_peaks = dxpy.upload_local_file(common.compress(rejected_peaks_fn))
rejected_peaks_bb = dxpy.upload_local_file(rejected_peaks_bb_fn)
# The following line fills in some basic dummy output and assumes
# that you have created variables to represent your output with
# the same name as your output fields.
output = {
"overlapping_peaks": dxpy.dxlink(overlapping_peaks),
"overlapping_peaks_bb": dxpy.dxlink(overlapping_peaks_bb),
"rejected_peaks": dxpy.dxlink(rejected_peaks),
"rejected_peaks_bb": dxpy.dxlink(rejected_peaks_bb),
"npeaks_in": npeaks_in,
"npeaks_out": npeaks_out,
'npeaks_rejected': npeaks_rejected
}
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
if rep2_signal:
output.update({"rep2_signal": rep2_signal})
if pooled_signal:
output.update({"pooled_signal": pooled_signal})
return output
def internal_pseudoreplicate_overlap(rep1_peaks,
rep2_peaks,
pooled_peaks,
rep1_ta,
rep1_xcor,
paired_end,
chrom_sizes,
as_file,
peak_type,
prefix,
fragment_length=None):
rep1_peaks_file = dxpy.DXFile(rep1_peaks)
rep2_peaks_file = dxpy.DXFile(rep2_peaks)
pooled_peaks_file = dxpy.DXFile(pooled_peaks)
rep1_ta_file = dxpy.DXFile(rep1_ta)
rep1_xcor_file = dxpy.DXFile(rep1_xcor)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
# Input filenames - necessary to define each explicitly because input files
# could have the same name, in which case subsequent
# file would overwrite previous file
rep1_peaks_fn = 'rep1-%s' % (rep1_peaks_file.name)
rep2_peaks_fn = 'rep2-%s' % (rep2_peaks_file.name)
pooled_peaks_fn = 'pooled-%s' % (pooled_peaks_file.name)
rep1_ta_fn = 'r1ta_%s' % (rep1_ta_file.name)
rep1_xcor_fn = 'r1xc_%s' % (rep1_xcor_file.name)
chrom_sizes_fn = 'chrom.sizes'
as_file_fn = '%s.as' % (peak_type)
# Output filenames
if prefix:
basename = prefix
else:
# strip off the peak and compression extensions
m = re.match('(.*)(\.%s)+(\.((gz)|(Z)|(bz)|(bz2)))' % (peak_type),
pooled_peaks.name)
if m:
basename = m.group(1)
else:
basename = pooled_peaks.name
overlapping_peaks_fn = '%s.replicated.%s' % (basename, peak_type)
overlapping_peaks_bb_fn = overlapping_peaks_fn + '.bb'
rejected_peaks_fn = '%s.rejected.%s' % (basename, peak_type)
rejected_peaks_bb_fn = rejected_peaks_fn + '.bb'
# Intermediate filenames
overlap_tr_fn = 'replicated_tr.%s' % (peak_type)
overlap_pr_fn = 'replicated_pr.%s' % (peak_type)
# Download file inputs to the local file system with local filenames
dxpy.download_dxfile(rep1_peaks_file.get_id(), rep1_peaks_fn)
dxpy.download_dxfile(rep2_peaks_file.get_id(), rep2_peaks_fn)
dxpy.download_dxfile(pooled_peaks_file.get_id(), pooled_peaks_fn)
dxpy.download_dxfile(rep1_ta_file.get_id(), rep1_ta_fn)
dxpy.download_dxfile(rep1_xcor_file.get_id(), rep1_xcor_fn)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_fn)
dxpy.download_dxfile(as_file_file.get_id(), as_file_fn)
logger.info(subprocess.check_output('set -x; ls -l', shell=True))
# the only difference between the peak_types is how the extra columns are
# handled
if peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
assert peak_type in [
'narrowPeak', 'gappedPeak', 'broadPeak'
], "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak." % (
peak_type)
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined as
# the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %
(pooled_peaks_fn, rep1_peaks_fn), awk_command, cut_command, 'sort -u',
'intersectBed -wo -a stdin -b %s' %
(rep2_peaks_fn), awk_command, cut_command, 'sort -u'
], overlap_tr_fn)
print("%d peaks overlap with both true replicates" %
(common.count_lines(overlap_tr_fn)))
# this is a simplicate analysis
# overlapping peaks are just based on pseudoreps of the one pool
out, err = common.run_pipe(['cat %s' % (overlap_tr_fn), 'sort -u'],
overlapping_peaks_fn)
print("%d peaks overlap" % (common.count_lines(overlapping_peaks_fn)))
# rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' %
(pooled_peaks_fn, overlapping_peaks_fn)
], rejected_peaks_fn)
print("%d peaks were rejected" % (common.count_lines(rejected_peaks_fn)))
# calculate FRiP (Fraction of Reads in Peaks)
# Extract the fragment length estimate from column 3 of the
# cross-correlation scores file or use the user-defined
# fragment_length if given.
if fragment_length is not None:
fraglen = fragment_length
fragment_length_given_by_user = True
else:
fraglen = common.xcor_fraglen(rep1_xcor_fn)
fragment_length_given_by_user = False
# FRiP
reads_in_peaks_fn = 'reads_in_%s.ta' % (peak_type)
n_reads, n_reads_in_peaks, frip_score = common.frip(
rep1_ta_fn,
rep1_xcor_fn,
overlapping_peaks_fn,
chrom_sizes_fn,
fraglen,
reads_in_peaks_fn=reads_in_peaks_fn)
# count peaks
npeaks_in = common.count_lines(common.uncompress(pooled_peaks_fn))
npeaks_out = common.count_lines(overlapping_peaks_fn)
npeaks_rejected = common.count_lines(rejected_peaks_fn)
# make bigBed files for visualization
overlapping_peaks_bb_fn = common.bed2bb(overlapping_peaks_fn,
chrom_sizes_fn,
as_file_fn,
bed_type=bed_type)
rejected_peaks_bb_fn = common.bed2bb(rejected_peaks_fn,
chrom_sizes_fn,
as_file_fn,
bed_type=bed_type)
# Upload file outputs from the local file system.
overlapping_peaks = dxpy.upload_local_file(
common.compress(overlapping_peaks_fn))
overlapping_peaks_bb = dxpy.upload_local_file(overlapping_peaks_bb_fn)
rejected_peaks = dxpy.upload_local_file(common.compress(rejected_peaks_fn))
rejected_peaks_bb = dxpy.upload_local_file(rejected_peaks_bb_fn)
output = {
"overlapping_peaks": dxpy.dxlink(overlapping_peaks),
"overlapping_peaks_bb": dxpy.dxlink(overlapping_peaks_bb),
"rejected_peaks": dxpy.dxlink(rejected_peaks),
"rejected_peaks_bb": dxpy.dxlink(rejected_peaks_bb),
"npeaks_in": npeaks_in,
"npeaks_out": npeaks_out,
"npeaks_rejected": npeaks_rejected,
"frip_nreads": n_reads,
"frip_nreads_in_peaks": n_reads_in_peaks,
"frip_score": frip_score,
"fragment_length_used": fraglen,
"fragment_length_given_by_user": fragment_length_given_by_user
}
return output
def replicated_overlap(rep1_peaks,
rep2_peaks,
pooled_peaks,
pooledpr1_peaks,
pooledpr2_peaks,
rep1_ta,
rep1_xcor,
rep2_ta,
rep2_xcor,
paired_end,
chrom_sizes,
as_file,
peak_type,
prefix,
fragment_length=None):
rep1_peaks_file = dxpy.DXFile(rep1_peaks)
rep2_peaks_file = dxpy.DXFile(rep2_peaks)
pooled_peaks_file = dxpy.DXFile(pooled_peaks)
pooledpr1_peaks_file = dxpy.DXFile(pooledpr1_peaks)
pooledpr2_peaks_file = dxpy.DXFile(pooledpr2_peaks)
rep1_ta_file = dxpy.DXFile(rep1_ta)
rep2_ta_file = dxpy.DXFile(rep2_ta)
rep1_xcor_file = dxpy.DXFile(rep1_xcor)
rep2_xcor_file = dxpy.DXFile(rep2_xcor)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
# Input filenames - necessary to define each explicitly because input files
# could have the same name, in which case subsequent
# file would overwrite previous file
rep1_peaks_fn = 'rep1-%s' % (rep1_peaks_file.name)
rep2_peaks_fn = 'rep2-%s' % (rep2_peaks_file.name)
pooled_peaks_fn = 'pooled-%s' % (pooled_peaks_file.name)
pooledpr1_peaks_fn = 'pooledpr1-%s' % (pooledpr1_peaks_file.name)
pooledpr2_peaks_fn = 'pooledpr2-%s' % (pooledpr2_peaks_file.name)
rep1_ta_fn = 'r1ta_%s' % (rep1_ta_file.name)
rep2_ta_fn = 'r2ta_%s' % (rep2_ta_file.name)
rep1_xcor_fn = 'r1cc_%s' % (rep1_xcor_file.name)
rep2_xcor_fn = 'r2cc_%s' % (rep2_xcor_file.name)
chrom_sizes_fn = 'chrom.sizes'
as_file_fn = '%s.as' % (peak_type)
# Output filenames
if prefix:
basename = prefix
else:
# strip off the peak and compression extensions
m = re.match('(.*)(\.%s)+(\.((gz)|(Z)|(bz)|(bz2)))' % (peak_type),
pooled_peaks.name)
if m:
basename = m.group(1)
else:
basename = pooled_peaks.name
overlapping_peaks_fn = '%s.replicated.%s' % (basename, peak_type)
overlapping_peaks_bb_fn = overlapping_peaks_fn + '.bb'
rejected_peaks_fn = '%s.rejected.%s' % (basename, peak_type)
rejected_peaks_bb_fn = rejected_peaks_fn + '.bb'
# Intermediate filenames
overlap_tr_fn = 'replicated_tr.%s' % (peak_type)
overlap_pr_fn = 'replicated_pr.%s' % (peak_type)
# Download file inputs to the local file system with local filenames
dxpy.download_dxfile(rep1_peaks_file.get_id(), rep1_peaks_fn)
dxpy.download_dxfile(rep2_peaks_file.get_id(), rep2_peaks_fn)
dxpy.download_dxfile(pooled_peaks_file.get_id(), pooled_peaks_fn)
dxpy.download_dxfile(pooledpr1_peaks_file.get_id(), pooledpr1_peaks_fn)
dxpy.download_dxfile(pooledpr2_peaks_file.get_id(), pooledpr2_peaks_fn)
dxpy.download_dxfile(rep1_ta_file.get_id(), rep1_ta_fn)
dxpy.download_dxfile(rep2_ta_file.get_id(), rep2_ta_fn)
dxpy.download_dxfile(rep1_xcor_file.get_id(), rep1_xcor_fn)
dxpy.download_dxfile(rep2_xcor_file.get_id(), rep2_xcor_fn)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_fn)
dxpy.download_dxfile(as_file_file.get_id(), as_file_fn)
pool_applet = dxpy.find_one_data_object(classname='applet',
name='pool',
project=dxpy.PROJECT_CONTEXT_ID,
zero_ok=False,
more_ok=False,
return_handler=True)
pool_replicates_subjob = \
pool_applet.run(
{"inputs": [rep1_ta, rep2_ta],
"prefix": 'pooled_reps'},
name='Pool replicates')
# If fragment length was given by user we skip pooled_replicates
# _xcor_subjob, set the pool_xcor_filename to None, and update
# the flag fragment_length_given_by_user. Otherwise, run the subjob
# to be able to extract the fragment length fron cross-correlations.
if fragment_length is not None:
pool_xcor_filename = None
fraglen = fragment_length
fragment_length_given_by_user = True
else:
pooled_replicates_xcor_subjob = \
xcor_only(
pool_replicates_subjob.get_output_ref("pooled"),
paired_end,
spp_version=None,
name='Pool cross-correlation')
pooled_replicates_xcor_subjob.wait_on_done()
pool_xcor_link = pooled_replicates_xcor_subjob.describe(
)['output'].get("CC_scores_file")
pool_xcor_file = dxpy.get_handler(pool_xcor_link)
pool_xcor_filename = 'poolcc_%s' % (pool_xcor_file.name)
dxpy.download_dxfile(pool_xcor_file.get_id(), pool_xcor_filename)
fraglen = common.xcor_fraglen(pool_xcor_filename)
fragment_length_given_by_user = False
pool_replicates_subjob.wait_on_done()
pool_ta_link = pool_replicates_subjob.describe()['output'].get("pooled")
pool_ta_file = dxpy.get_handler(pool_ta_link)
pool_ta_filename = 'poolta_%s' % (pool_ta_file.name)
dxpy.download_dxfile(pool_ta_file.get_id(), pool_ta_filename)
logger.info(subprocess.check_output('set -x; ls -l', shell=True))
# the only difference between the peak_types is how the extra columns are
# handled
if peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
assert peak_type in [
'narrowPeak', 'gappedPeak', 'broadPeak'
], "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak." % (
peak_type)
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined as
# the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %
(pooled_peaks_fn, rep1_peaks_fn), awk_command, cut_command, 'sort -u',
'intersectBed -wo -a stdin -b %s' %
(rep2_peaks_fn), awk_command, cut_command, 'sort -u'
], overlap_tr_fn)
print("%d peaks overlap with both true replicates" %
(common.count_lines(overlap_tr_fn)))
# Find pooled peaks that overlap PseudoRep1 and PseudoRep2 where
# overlap is defined as the fractional overlap wrt any one of the
# overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' % (pooled_peaks_fn, pooledpr1_peaks_fn),
awk_command, cut_command, 'sort -u',
'intersectBed -wo -a stdin -b %s' %
(pooledpr2_peaks_fn), awk_command, cut_command, 'sort -u'
], overlap_pr_fn)
print("%d peaks overlap with both pooled pseudoreplicates" %
(common.count_lines(overlap_pr_fn)))
# Combine peak lists
out, err = common.run_pipe(
['cat %s %s' % (overlap_tr_fn, overlap_pr_fn), 'sort -u'],
overlapping_peaks_fn)
print(
"%d peaks overlap with true replicates or with pooled pseudoreplicates"
% (common.count_lines(overlapping_peaks_fn)))
# rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' %
(pooled_peaks_fn, overlapping_peaks_fn)
], rejected_peaks_fn)
print("%d peaks were rejected" % (common.count_lines(rejected_peaks_fn)))
# calculate FRiP (Fraction of Reads in Peaks)
reads_in_peaks_fn = 'reads_in_%s.ta' % (peak_type)
n_reads, n_reads_in_peaks, frip_score = common.frip(
pool_ta_filename,
pool_xcor_filename,
overlapping_peaks_fn,
chrom_sizes_fn,
fraglen,
reads_in_peaks_fn=reads_in_peaks_fn)
# count peaks
npeaks_in = common.count_lines(common.uncompress(pooled_peaks_fn))
npeaks_out = common.count_lines(overlapping_peaks_fn)
npeaks_rejected = common.count_lines(rejected_peaks_fn)
# make bigBed files for visualization
overlapping_peaks_bb_fn = common.bed2bb(overlapping_peaks_fn,
chrom_sizes_fn,
as_file_fn,
bed_type=bed_type)
rejected_peaks_bb_fn = common.bed2bb(rejected_peaks_fn,
chrom_sizes_fn,
as_file_fn,
bed_type=bed_type)
# Upload file outputs from the local file system.
overlapping_peaks = dxpy.upload_local_file(
common.compress(overlapping_peaks_fn))
overlapping_peaks_bb = dxpy.upload_local_file(overlapping_peaks_bb_fn)
rejected_peaks = dxpy.upload_local_file(common.compress(rejected_peaks_fn))
rejected_peaks_bb = dxpy.upload_local_file(rejected_peaks_bb_fn)
output = {
"overlapping_peaks": dxpy.dxlink(overlapping_peaks),
"overlapping_peaks_bb": dxpy.dxlink(overlapping_peaks_bb),
"rejected_peaks": dxpy.dxlink(rejected_peaks),
"rejected_peaks_bb": dxpy.dxlink(rejected_peaks_bb),
"npeaks_in": npeaks_in,
"npeaks_out": npeaks_out,
"npeaks_rejected": npeaks_rejected,
"frip_nreads": n_reads,
"frip_nreads_in_peaks": n_reads_in_peaks,
"frip_score": frip_score,
"fragment_length_used": fraglen,
"fragment_length_given_by_user": fragment_length_given_by_user
}
return output
def main(experiment, reps_peaks, r1pr_peaks, r2pr_peaks, pooledpr_peaks, chrom_sizes, as_file, blacklist=None):
#TODO for now just taking the peak files. This applet should actually call IDR instead of
#putting that in the workflow populator script
# Initialize the data object inputs on the platform into
# dxpy.DXDataObject instances.
reps_peaks_file = dxpy.DXFile(reps_peaks)
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
r2pr_peaks_file = dxpy.DXFile(r2pr_peaks)
pooledpr_peaks_file = dxpy.DXFile(pooledpr_peaks)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' %(blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Download the file inputs to the local file system.
#Need to prepend something to ensure the local filenames will be unique
reps_peaks_filename = 'true_%s' %(reps_peaks_file.name)
r1pr_peaks_filename = 'r1pr_%s' %(r1pr_peaks_file.name)
r2pr_peaks_filename = 'r2pr_%s' %(r2pr_peaks_file.name)
pooledpr_peaks_filename = 'pooledpr_%s' %(pooledpr_peaks_file.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(reps_peaks_file.get_id(), reps_peaks_filename)
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(r2pr_peaks_file.get_id(), r2pr_peaks_filename)
dxpy.download_dxfile(pooledpr_peaks_file.get_id(), pooledpr_peaks_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
print subprocess.check_output('ls -l', shell=True)
reps_peaks_filename = common.uncompress(reps_peaks_filename)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
r2pr_peaks_filename = common.uncompress(r2pr_peaks_filename)
pooledpr_peaks_filename = common.uncompress(pooledpr_peaks_filename)
Nt = common.count_lines(reps_peaks_filename)
print "%d peaks from true replicates" %(Nt)
N1 = common.count_lines(r1pr_peaks_filename)
print "%d peaks from rep1 self-pseudoreplicates" %(N1)
N2 = common.count_lines(r2pr_peaks_filename)
print "%d peaks from rep2 self-pseudoreplicates" %(N2)
Np = common.count_lines(pooledpr_peaks_filename)
print "%d peaks from pooled pseudoreplicates" %(Np)
conservative_set_filename = '%s_final_conservative.narrowPeak' %(experiment)
if blacklist is not None:
blacklist_filter(reps_peaks_filename, conservative_set_filename, blacklist_filename)
else:
conservative_set_filename = reps_peaks_filename
Ncb = common.count_lines(conservative_set_filename)
print "%d peaks blacklisted from the conservative set" %(Nt-Ncb)
if Nt >= Np:
peaks_to_filter_filename = reps_peaks_filename
No = Nt
else:
peaks_to_filter_filename = pooledpr_peaks_filename
No = Np
optimal_set_filename = '%s_final_optimal.narrowPeak' %(experiment)
if blacklist is not None:
blacklist_filter(peaks_to_filter_filename, optimal_set_filename, blacklist_filename)
else:
optimal_set_filename = peaks_to_filter_filename
Nob = common.count_lines(optimal_set_filename)
print "%d peaks blacklisted from the optimal set" %(No-Nob)
rescue_ratio = float(max(Np,Nt)) / float(min(Np,Nt))
self_consistency_ratio = float(max(N1,N2)) / float(min(N1,N2))
if rescue_ratio > 2 and self_consistency_ratio > 2:
reproducibility = 'fail'
elif rescue_ratio > 2 or self_consistency_ratio > 2:
reproducibility = 'borderline'
else:
reproducibility = 'pass'
output = {}
#bedtobigbed often fails, so skip creating the bb if it does
conservative_set_bb_filename = common.bed2bb(conservative_set_filename, chrom_sizes_filename, as_file_filename)
optimal_set_bb_filename = common.bed2bb(optimal_set_filename, chrom_sizes_filename, as_file_filename)
if conservative_set_bb_filename:
conservative_set_bb_output = dxpy.upload_local_file(conservative_set_bb_filename)
output.update({"conservative_set_bb": dxpy.dxlink(conservative_set_bb_output)})
if optimal_set_bb_filename:
optimal_set_bb_output = dxpy.upload_local_file(optimal_set_bb_filename)
output.update({"optimal_set_bb": dxpy.dxlink(optimal_set_bb_output)})
output.update({
"Nt": Nt,
"N1": N1,
"N2": N2,
"Np": Np,
"conservative_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(conservative_set_filename))),
"optimal_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(optimal_set_filename))),
"rescue_ratio": rescue_ratio,
"self_consistency_ratio": self_consistency_ratio,
"reproducibility_test": reproducibility,
"No": Nob,
"Nc": Ncb
})
logging.info("Exiting with output: %s", output)
return output
def main(experiment, reps_peaks, r1pr_peaks, r2pr_peaks, pooledpr_peaks,
chrom_sizes, as_file, blacklist=None,
rep1_signal=None, rep2_signal=None, pooled_signal=None):
# TODO for now just taking the peak files. This applet should actually
# call IDR instead of putting that in the workflow populator script
reps_peaks_file = dxpy.DXFile(reps_peaks)
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
r2pr_peaks_file = dxpy.DXFile(r2pr_peaks)
pooledpr_peaks_file = dxpy.DXFile(pooledpr_peaks)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' % (blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Need to prepend something to ensure the local filenames will be unique
reps_peaks_filename = 'true_%s' % (reps_peaks_file.name)
r1pr_peaks_filename = 'r1pr_%s' % (r1pr_peaks_file.name)
r2pr_peaks_filename = 'r2pr_%s' % (r2pr_peaks_file.name)
pooledpr_peaks_filename = 'pooledpr_%s' % (pooledpr_peaks_file.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(reps_peaks_file.get_id(), reps_peaks_filename)
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(r2pr_peaks_file.get_id(), r2pr_peaks_filename)
dxpy.download_dxfile(pooledpr_peaks_file.get_id(), pooledpr_peaks_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
subprocess.check_output('set -x; ls -l', shell=True)
reps_peaks_filename = common.uncompress(reps_peaks_filename)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
r2pr_peaks_filename = common.uncompress(r2pr_peaks_filename)
pooledpr_peaks_filename = common.uncompress(pooledpr_peaks_filename)
Nt = common.count_lines(reps_peaks_filename)
logger.info("%d peaks from true replicates (Nt)" % (Nt))
N1 = common.count_lines(r1pr_peaks_filename)
logger.info("%d peaks from rep1 self-pseudoreplicates (N1)" % (N1))
N2 = common.count_lines(r2pr_peaks_filename)
logger.info("%d peaks from rep2 self-pseudoreplicates (N2)" % (N2))
Np = common.count_lines(pooledpr_peaks_filename)
logger.info("%d peaks from pooled pseudoreplicates (Np)" % (Np))
# generate the conservative set, which is always based on the IDR peaks
# from true replicates
conservative_set_filename = \
'%s_final_conservative.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(reps_peaks_filename, conservative_set_filename,
blacklist_filename)
Ncb = common.count_lines(conservative_set_filename)
logger.info(
"%d peaks blacklisted from the conservative set" % (Nt-Ncb))
else:
subprocess.check_output(shlex.split(
'cp %s %s' % (reps_peaks_filename, conservative_set_filename)))
Ncb = Nt
logger.info("No blacklist filter applied to the conservative set")
# generate the optimal set, which is based on the longest of IDR peaks
# list from true reps or the IDR peaks from the pseudoreplicates of the
# pool
if Nt >= Np:
peaks_to_filter_filename = reps_peaks_filename
No = Nt
else:
peaks_to_filter_filename = pooledpr_peaks_filename
No = Np
optimal_set_filename = '%s_final_optimal.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(peaks_to_filter_filename, optimal_set_filename,
blacklist_filename)
Nob = common.count_lines(optimal_set_filename)
logger.info("%d peaks blacklisted from the optimal set" % (No-Nob))
else:
subprocess.check_output(shlex.split(
'cp %s %s' % (peaks_to_filter_filename, optimal_set_filename)))
Nob = No
logger.info("No blacklist filter applied to the optimal set")
rescue_ratio = float(max(Np, Nt)) / float(min(Np, Nt))
self_consistency_ratio = float(max(N1, N2)) / float(min(N1, N2))
if rescue_ratio > 2 and self_consistency_ratio > 2:
reproducibility = 'fail'
elif rescue_ratio > 2 or self_consistency_ratio > 2:
reproducibility = 'borderline'
else:
reproducibility = 'pass'
output = {}
# These are optional outputs to see what's being removed by the blacklist
if blacklist:
output.update({
"pre_bl_conservative_set":
dxpy.dxlink(dxpy.upload_local_file(common.compress(
reps_peaks_filename))),
"pre_bl_optimal_set":
dxpy.dxlink(dxpy.upload_local_file(common.compress(
peaks_to_filter_filename)))}
)
# bedtobigbed often fails, so skip creating the bb if it does
conservative_set_bb_filename = \
common.bed2bb(conservative_set_filename, chrom_sizes_filename,
as_file_filename)
optimal_set_bb_filename = \
common.bed2bb(optimal_set_filename, chrom_sizes_filename,
as_file_filename)
if conservative_set_bb_filename:
conservative_set_bb_output = \
dxpy.upload_local_file(conservative_set_bb_filename)
output.update(
{"conservative_set_bb": dxpy.dxlink(conservative_set_bb_output)})
if optimal_set_bb_filename:
optimal_set_bb_output = dxpy.upload_local_file(optimal_set_bb_filename)
output.update(
{"optimal_set_bb": dxpy.dxlink(optimal_set_bb_output)})
output.update({
"Nt": Nt,
"N1": N1,
"N2": N2,
"Np": Np,
"conservative_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(conservative_set_filename))),
"optimal_set": dxpy.dxlink(dxpy.upload_local_file(common.compress(optimal_set_filename))),
"rescue_ratio": rescue_ratio,
"self_consistency_ratio": self_consistency_ratio,
"reproducibility_test": reproducibility,
"No": Nob,
"Nc": Ncb
})
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
if rep2_signal:
output.update({"rep2_signal": rep2_signal})
if pooled_signal:
output.update({"pooled_signal": pooled_signal})
logging.info("Exiting with output: %s", output)
return output
def main(rep1_peaks, rep2_peaks, pooled_peaks, pooledpr1_peaks, pooledpr2_peaks,
chrom_sizes, as_file, peak_type, prefix=None,
rep1_signal=None, rep2_signal=None, pooled_signal=None):
# Initialize data object inputs on the platform
# into dxpy.DXDataObject instances
rep1_peaks = dxpy.DXFile(rep1_peaks)
rep2_peaks = dxpy.DXFile(rep2_peaks)
pooled_peaks = dxpy.DXFile(pooled_peaks)
pooledpr1_peaks = dxpy.DXFile(pooledpr1_peaks)
pooledpr2_peaks = dxpy.DXFile(pooledpr2_peaks)
chrom_sizes = dxpy.DXFile(chrom_sizes)
as_file = dxpy.DXFile(as_file)
#Input filenames - necessary to define each explicitly because input files could have the same name, in which case subsequent
#file would overwrite previous file
rep1_peaks_fn = 'rep1-%s' %(rep1_peaks.name)
rep2_peaks_fn = 'rep2-%s' %(rep2_peaks.name)
pooled_peaks_fn = 'pooled-%s' %(pooled_peaks.name)
pooledpr1_peaks_fn = 'pooledpr1-%s' %(pooledpr1_peaks.name)
pooledpr2_peaks_fn = 'pooledpr2-%s' %(pooledpr2_peaks.name)
chrom_sizes_fn = 'chrom.sizes'
as_file_fn = '%s.as' %(peak_type)
# Output filenames
if prefix:
basename = prefix
else:
m = re.match('(.*)(\.%s)+(\.((gz)|(Z)|(bz)|(bz2)))' %(peak_type), pooled_peaks.name) #strip off the peak and compression extensions
if m:
basename = m.group(1)
else:
basename = pooled_peaks.name
overlapping_peaks_fn = '%s.replicated.%s' %(basename, peak_type)
overlapping_peaks_bb_fn = overlapping_peaks_fn + '.bb'
rejected_peaks_fn = '%s.rejected.%s' %(basename, peak_type)
rejected_peaks_bb_fn = rejected_peaks_fn + '.bb'
# Intermediate filenames
overlap_tr_fn = 'replicated_tr.%s' %(peak_type)
overlap_pr_fn = 'replicated_pr.%s' %(peak_type)
# Download file inputs to the local file system with local filenames
dxpy.download_dxfile(rep1_peaks.get_id(), rep1_peaks_fn)
dxpy.download_dxfile(rep2_peaks.get_id(), rep2_peaks_fn)
dxpy.download_dxfile(pooled_peaks.get_id(), pooled_peaks_fn)
dxpy.download_dxfile(pooledpr1_peaks.get_id(), pooledpr1_peaks_fn)
dxpy.download_dxfile(pooledpr2_peaks.get_id(), pooledpr2_peaks_fn)
dxpy.download_dxfile(chrom_sizes.get_id(), chrom_sizes_fn)
dxpy.download_dxfile(as_file.get_id(), as_file_fn)
'''
#find pooled peaks that are in (rep1 AND rep2)
out, err = common.run_pipe([
'intersectBed -wa -f 0.50 -r -a %s -b %s' %(pooled_peaks_fn, rep1_peaks_fn),
'intersectBed -wa -f 0.50 -r -a stdin -b %s' %(rep2_peaks_fn)
], overlap_tr_fn)
print "%d peaks overlap with both true replicates" %(common.count_lines(overlap_tr_fn))
#pooled peaks that are in (pooledpseudorep1 AND pooledpseudorep2)
out, err = common.run_pipe([
'intersectBed -wa -f 0.50 -r -a %s -b %s' %(pooled_peaks_fn, pooledpr1_peaks_fn),
'intersectBed -wa -f 0.50 -r -a stdin -b %s' %(pooledpr2_peaks_fn)
], overlap_pr_fn)
print "%d peaks overlap with both pooled pseudoreplicates" %(common.count_lines(overlap_pr_fn))
#combined pooled peaks in (rep1 AND rep2) OR (pooledpseudorep1 AND pooledpseudorep2)
out, err = common.run_pipe([
'intersectBed -wa -a %s -b %s %s' %(pooled_peaks_fn, overlap_tr_fn, overlap_pr_fn),
'intersectBed -wa -u -a %s -b stdin' %(pooled_peaks_fn)
], overlapping_peaks_fn)
print "%d peaks overall with true replicates or with pooled pseudorepliates" %(common.count_lines(overlapping_peaks_fn))
'''
#the only difference between the peak_types is how the extra columns are handled
if peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
assert peak_type in ['narrowPeak', 'gappedPeak', 'broadPeak'], "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak." % (peak_type)
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined as the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %(pooled_peaks_fn, rep1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' %(rep2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], overlap_tr_fn)
print "%d peaks overlap with both true replicates" %(common.count_lines(overlap_tr_fn))
# Find pooled peaks that overlap PseudoRep1 and PseudoRep2 where overlap is defined as the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' %(pooled_peaks_fn, pooledpr1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' %(pooledpr2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], overlap_pr_fn)
print "%d peaks overlap with both pooled pseudoreplicates" %(common.count_lines(overlap_pr_fn))
# Combine peak lists
out, err = common.run_pipe([
'cat %s %s' %(overlap_tr_fn, overlap_pr_fn),
'sort -u'
], overlapping_peaks_fn)
print "%d peaks overlap with true replicates or with pooled pseudorepliates" %(common.count_lines(overlapping_peaks_fn))
#rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' %(pooled_peaks_fn, overlapping_peaks_fn)
], rejected_peaks_fn)
print "%d peaks were rejected" %(common.count_lines(rejected_peaks_fn))
npeaks_in = common.count_lines(common.uncompress(pooled_peaks_fn))
npeaks_out = common.count_lines(overlapping_peaks_fn)
npeaks_rejected = common.count_lines(rejected_peaks_fn)
#make bigBed files for visualization
overlapping_peaks_bb_fn = common.bed2bb(overlapping_peaks_fn, chrom_sizes_fn, as_file_fn, bed_type=bed_type)
rejected_peaks_bb_fn = common.bed2bb(rejected_peaks_fn, chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# overlapping_peaks_bb_fn = common.bed2bb(common.slop_clip(overlapping_peaks_fn, chrom_sizes_fn, "gappedPeak"), chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# rejected_peaks_bb_fn = common.bed2bb(common.slop_clip(rejected_peaks_fn, chrom_sizes_fn, "gappedPeak"), chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# Upload file outputs from the local file system.
overlapping_peaks = dxpy.upload_local_file(common.compress(overlapping_peaks_fn))
overlapping_peaks_bb = dxpy.upload_local_file(overlapping_peaks_bb_fn)
rejected_peaks = dxpy.upload_local_file(common.compress(rejected_peaks_fn))
rejected_peaks_bb = dxpy.upload_local_file(rejected_peaks_bb_fn)
# The following line fills in some basic dummy output and assumes
# that you have created variables to represent your output with
# the same name as your output fields.
output = {
"overlapping_peaks" : dxpy.dxlink(overlapping_peaks),
"overlapping_peaks_bb" : dxpy.dxlink(overlapping_peaks_bb),
"rejected_peaks" : dxpy.dxlink(rejected_peaks),
"rejected_peaks_bb" : dxpy.dxlink(rejected_peaks_bb),
"npeaks_in" : npeaks_in,
"npeaks_out" : npeaks_out,
'npeaks_rejected' : npeaks_rejected
}
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
if rep2_signal:
output.update({"rep2_signal": rep2_signal})
if pooled_signal:
output.update({"pooled_signal": pooled_signal})
return output
def internal_pseudoreplicate_IDR(experiment,
r1pr_peaks,
rep1_ta,
rep1_xcor,
paired_end,
chrom_sizes,
as_file,
blacklist,
rep1_signal,
fragment_length=None):
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
rep1_ta = dxpy.DXFile(rep1_ta)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' % (blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Need to prepend something to ensure the local filenames will be unique
r1pr_peaks_filename = 'r1pr_%s' % (r1pr_peaks_file.name)
rep1_ta_filename = 'r1ta_%s' % (rep1_ta.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(rep1_ta.get_id(), rep1_ta_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
# If fragment_length is given, override appropriate values.
# Calculate, or set the actually used fragment length value.
# Set the fragment_length_given_by_user flag appropriately.
if fragment_length is not None:
rep1_xcor_filename = None
fragment_length_used_rep1 = fragment_length
fragment_length_given_by_user = True
else:
rep1_xcor = dxpy.DXFile(rep1_xcor)
rep1_xcor_filename = 'r1xc_%s' % (rep1_xcor.name)
dxpy.download_dxfile(rep1_xcor.get_id(), rep1_xcor_filename)
fragment_length_used_rep1 = common.xcor_fraglen(rep1_xcor_filename)
fragment_length_given_by_user = False
subprocess.check_output('set -x; ls -l', shell=True)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
N1 = common.count_lines(r1pr_peaks_filename)
logger.info("%d peaks from rep1 self-pseudoreplicates (N1)" % (N1))
stable_set_filename = "%s_stable.narrowPeak" % (experiment)
if blacklist is not None:
blacklist_filter(r1pr_peaks_filename, stable_set_filename,
blacklist_filename)
Nsb = common.count_lines(stable_set_filename)
logger.info("%d peaks blacklisted from the stable set" % (N1 - Nsb))
else:
subprocess.check_output(
shlex.split('cp %s %s' %
(r1pr_peaks_filename, stable_set_filename)))
Nsb = N1
logger.info("No blacklist filter applied to the stable set")
# calculate FRiP
n_reads, n_reads_in_peaks, frip_score = common.frip(
rep1_ta_filename, rep1_xcor_filename, stable_set_filename,
chrom_sizes_filename, fragment_length_used_rep1)
output = {
"rep1_frip_nreads": n_reads,
"rep1_frip_nreads_in_peaks": n_reads_in_peaks,
"F1": frip_score,
"fragment_length_used_rep1": fragment_length_used_rep1,
"fragment_length_given_by_user": fragment_length_given_by_user
}
# These are optional outputs to see what's being removed by the blacklist
if blacklist:
output.update({
"pre_bl_stable_set":
dxpy.dxlink(
dxpy.upload_local_file(common.compress(r1pr_peaks_filename)))
})
# bedtobigbed often fails, so skip creating the bb if it does
stable_set_bb_filename = \
common.bed2bb(stable_set_filename, chrom_sizes_filename,
as_file_filename)
if stable_set_bb_filename:
stable_set_bb_output = \
dxpy.upload_local_file(stable_set_bb_filename)
output.update({"stable_set_bb": dxpy.dxlink(stable_set_bb_output)})
output.update({
"N1":
N1,
"stable_set":
dxpy.dxlink(
dxpy.upload_local_file(common.compress(stable_set_filename))),
"Ns":
Nsb
})
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
return output
def replicated_IDR(experiment,
reps_peaks,
r1pr_peaks,
r2pr_peaks,
pooledpr_peaks,
rep1_ta,
rep1_xcor,
rep2_ta,
rep2_xcor,
paired_end,
chrom_sizes,
as_file,
blacklist,
rep1_signal,
rep2_signal,
pooled_signal,
fragment_length=None):
# TODO for now just taking the peak files. This applet should actually
# call IDR instead of putting that in the workflow populator script
reps_peaks_file = dxpy.DXFile(reps_peaks)
r1pr_peaks_file = dxpy.DXFile(r1pr_peaks)
r2pr_peaks_file = dxpy.DXFile(r2pr_peaks)
pooledpr_peaks_file = dxpy.DXFile(pooledpr_peaks)
rep1_ta_file = dxpy.DXFile(rep1_ta)
rep2_ta_file = dxpy.DXFile(rep2_ta)
rep1_xcor_file = dxpy.DXFile(rep1_xcor)
rep2_xcor_file = dxpy.DXFile(rep2_xcor)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
if blacklist is not None:
blacklist_file = dxpy.DXFile(blacklist)
blacklist_filename = 'blacklist_%s' % (blacklist_file.name)
dxpy.download_dxfile(blacklist_file.get_id(), blacklist_filename)
blacklist_filename = common.uncompress(blacklist_filename)
# Need to prepend something to ensure the local filenames will be unique
reps_peaks_filename = 'true_%s' % (reps_peaks_file.name)
r1pr_peaks_filename = 'r1pr_%s' % (r1pr_peaks_file.name)
r2pr_peaks_filename = 'r2pr_%s' % (r2pr_peaks_file.name)
pooledpr_peaks_filename = 'pooledpr_%s' % (pooledpr_peaks_file.name)
rep1_ta_filename = 'r1ta_%s' % (rep1_ta_file.name)
rep2_ta_filename = 'r2ta_%s' % (rep2_ta_file.name)
rep1_xcor_filename = 'r1cc_%s' % (rep1_xcor_file.name)
rep2_xcor_filename = 'r2cc_%s' % (rep2_xcor_file.name)
chrom_sizes_filename = chrom_sizes_file.name
as_file_filename = as_file_file.name
dxpy.download_dxfile(reps_peaks_file.get_id(), reps_peaks_filename)
dxpy.download_dxfile(r1pr_peaks_file.get_id(), r1pr_peaks_filename)
dxpy.download_dxfile(r2pr_peaks_file.get_id(), r2pr_peaks_filename)
dxpy.download_dxfile(pooledpr_peaks_file.get_id(), pooledpr_peaks_filename)
dxpy.download_dxfile(rep1_ta_file.get_id(), rep1_ta_filename)
dxpy.download_dxfile(rep2_ta_file.get_id(), rep2_ta_filename)
dxpy.download_dxfile(rep1_xcor_file.get_id(), rep1_xcor_filename)
dxpy.download_dxfile(rep2_xcor_file.get_id(), rep2_xcor_filename)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_filename)
dxpy.download_dxfile(as_file_file.get_id(), as_file_filename)
reps_peaks_filename = common.uncompress(reps_peaks_filename)
r1pr_peaks_filename = common.uncompress(r1pr_peaks_filename)
r2pr_peaks_filename = common.uncompress(r2pr_peaks_filename)
pooledpr_peaks_filename = common.uncompress(pooledpr_peaks_filename)
pool_applet = dxpy.find_one_data_object(classname='applet',
name='pool',
project=dxpy.PROJECT_CONTEXT_ID,
zero_ok=False,
more_ok=False,
return_handler=True)
pool_replicates_subjob = \
pool_applet.run(
{"inputs": [rep1_ta, rep2_ta],
"prefix": 'pooled_reps'},
name='Pool replicates')
# next call could be on 267 and save time?
pool_replicates_subjob.wait_on_done()
# If fragment_length is not given, calculate the fragment_length
# using crosscorrelation. Else use the overridevalue. Set the
# pool_xcor_filename to None to accommodate common.frip calls.
# Calculate, or set, actually used fragment lengths for different
# cases. Set the flag indicating whether the fragment length
# was given by the user.
if fragment_length is not None:
pool_xcor_filename = None
fragment_length_used_rep1 = fragment_length
fragment_length_used_rep2 = fragment_length
fragment_length_used_pool = fragment_length
fragment_length_given_by_user = True
else:
pooled_replicates_xcor_subjob = \
xcor_only(
pool_replicates_subjob.get_output_ref("pooled"),
paired_end,
spp_version=None,
name='Pool cross-correlation')
pooled_replicates_xcor_subjob.wait_on_done()
pool_xcor_link = pooled_replicates_xcor_subjob.describe(
)['output'].get("CC_scores_file")
pool_xcor_file = dxpy.get_handler(pool_xcor_link)
pool_xcor_filename = 'poolcc_%s' % (pool_xcor_file.name)
dxpy.download_dxfile(pool_xcor_file.get_id(), pool_xcor_filename)
fragment_length_used_rep1 = common.xcor_fraglen(rep1_xcor_filename)
fragment_length_used_rep2 = common.xcor_fraglen(rep2_xcor_filename)
fragment_length_used_pool = common.xcor_fraglen(pool_xcor_filename)
fragment_length_given_by_user = False
pool_ta_link = pool_replicates_subjob.describe()['output'].get("pooled")
pool_ta_file = dxpy.get_handler(pool_ta_link)
pool_ta_filename = 'poolta_%s' % (pool_ta_file.name)
dxpy.download_dxfile(pool_ta_file.get_id(), pool_ta_filename)
logger.info(subprocess.check_output('set -x; ls -l', shell=True))
Nt = common.count_lines(reps_peaks_filename)
logger.info("%d peaks from true replicates (Nt)" % (Nt))
N1 = common.count_lines(r1pr_peaks_filename)
logger.info("%d peaks from rep1 self-pseudoreplicates (N1)" % (N1))
N2 = common.count_lines(r2pr_peaks_filename)
logger.info("%d peaks from rep2 self-pseudoreplicates (N2)" % (N2))
Np = common.count_lines(pooledpr_peaks_filename)
logger.info("%d peaks from pooled pseudoreplicates (Np)" % (Np))
# generate the conservative set, which is always based on the IDR peaks
# from true replicates
conservative_set_filename = \
'%s_final_conservative.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(reps_peaks_filename, conservative_set_filename,
blacklist_filename)
Ncb = common.count_lines(conservative_set_filename)
logger.info("%d peaks blacklisted from the conservative set" %
(Nt - Ncb))
else:
subprocess.check_output(
shlex.split('cp %s %s' %
(reps_peaks_filename, conservative_set_filename)))
Ncb = Nt
logger.info("No blacklist filter applied to the conservative set")
# generate the optimal set, which is based on the longest of IDR peaks
# list from true reps or the IDR peaks from the pseudoreplicates of the
# pool
if Nt >= Np:
peaks_to_filter_filename = reps_peaks_filename
No = Nt
else:
peaks_to_filter_filename = pooledpr_peaks_filename
No = Np
optimal_set_filename = '%s_final_optimal.narrowPeak' % (experiment)
if blacklist is not None:
blacklist_filter(peaks_to_filter_filename, optimal_set_filename,
blacklist_filename)
Nob = common.count_lines(optimal_set_filename)
logger.info("%d peaks blacklisted from the optimal set" % (No - Nob))
else:
subprocess.check_output(
shlex.split('cp %s %s' %
(peaks_to_filter_filename, optimal_set_filename)))
Nob = No
logger.info("No blacklist filter applied to the optimal set")
rescue_ratio = float(max(Np, Nt)) / float(min(Np, Nt))
self_consistency_ratio = float(max(N1, N2)) / float(min(N1, N2))
if rescue_ratio > 2 and self_consistency_ratio > 2:
reproducibility = 'fail'
elif rescue_ratio > 2 or self_consistency_ratio > 2:
reproducibility = 'borderline'
else:
reproducibility = 'pass'
# FRiP (fraction reads in peaks)
# rep1 stable peaks comparing internal pseudoreplicates
rep1_n_reads, rep1_n_reads_in_peaks, rep1_frip_score = common.frip(
rep1_ta_filename, rep1_xcor_filename, r1pr_peaks_filename,
chrom_sizes_filename, fragment_length)
# rep2 stable peaks comparing internal pseudoreplicates
rep2_n_reads, rep2_n_reads_in_peaks, rep2_frip_score = common.frip(
rep2_ta_filename, rep2_xcor_filename, r2pr_peaks_filename,
chrom_sizes_filename, fragment_length)
# comparing true reps
true_n_reads, true_n_reads_in_peaks, true_frip_score = common.frip(
pool_ta_filename, pool_xcor_filename, reps_peaks_filename,
chrom_sizes_filename, fragment_length)
# comparing pooled pseudoreplicates
pr_n_reads, pr_n_reads_in_peaks, pr_frip_score = common.frip(
pool_ta_filename, pool_xcor_filename, pooledpr_peaks_filename,
chrom_sizes_filename, fragment_length)
output = {
"rep1_frip_nreads": rep1_n_reads,
"rep1_frip_nreads_in_peaks": rep1_n_reads_in_peaks,
"F1": rep1_frip_score,
"rep2_frip_nreads": rep2_n_reads,
"rep2_frip_nreads_in_peaks": rep2_n_reads_in_peaks,
"F2": rep2_frip_score,
"true_frip_nreads": true_n_reads,
"true_frip_nreads_in_peaks": true_n_reads_in_peaks,
"Ft": true_frip_score,
"pr_frip_nreads": pr_n_reads,
"pr_frip_nreads_in_peaks": pr_n_reads_in_peaks,
"Fp": pr_frip_score,
"fragment_length_used_rep1": fragment_length_used_rep1,
"fragment_length_used_rep2": fragment_length_used_rep2,
"fragment_length_used_pool": fragment_length_used_pool,
"fragment_length_given_by_user": fragment_length_given_by_user
}
# These are optional outputs to see what's being removed by the blacklist
if blacklist:
output.update({
"pre_bl_conservative_set":
dxpy.dxlink(
dxpy.upload_local_file(common.compress(reps_peaks_filename))),
"pre_bl_optimal_set":
dxpy.dxlink(
dxpy.upload_local_file(
common.compress(peaks_to_filter_filename)))
})
# bedtobigbed often fails, so skip creating the bb if it does
conservative_set_bb_filename = \
common.bed2bb(conservative_set_filename, chrom_sizes_filename,
as_file_filename)
optimal_set_bb_filename = \
common.bed2bb(optimal_set_filename, chrom_sizes_filename,
as_file_filename)
if conservative_set_bb_filename:
conservative_set_bb_output = \
dxpy.upload_local_file(conservative_set_bb_filename)
output.update(
{"conservative_set_bb": dxpy.dxlink(conservative_set_bb_output)})
if optimal_set_bb_filename:
optimal_set_bb_output = dxpy.upload_local_file(optimal_set_bb_filename)
output.update({"optimal_set_bb": dxpy.dxlink(optimal_set_bb_output)})
output.update({
"Nt":
Nt,
"N1":
N1,
"N2":
N2,
"Np":
Np,
"conservative_set":
dxpy.dxlink(
dxpy.upload_local_file(
common.compress(conservative_set_filename))),
"optimal_set":
dxpy.dxlink(
dxpy.upload_local_file(common.compress(optimal_set_filename))),
"rescue_ratio":
rescue_ratio,
"self_consistency_ratio":
self_consistency_ratio,
"reproducibility_test":
reproducibility,
"No":
Nob,
"Nc":
Ncb
})
# These are just passed through for convenience so that signals and tracks
# are available in one place. Both input and output are optional.
if rep1_signal:
output.update({"rep1_signal": rep1_signal})
if rep2_signal:
output.update({"rep2_signal": rep2_signal})
if pooled_signal:
output.update({"pooled_signal": pooled_signal})
return output
def internal_pseudoreplicate_overlap(rep1_peaks, rep2_peaks, pooled_peaks,
rep1_ta, rep1_xcor,
paired_end, chrom_sizes, as_file,
peak_type, prefix, fragment_length=None):
rep1_peaks_file = dxpy.DXFile(rep1_peaks)
rep2_peaks_file = dxpy.DXFile(rep2_peaks)
pooled_peaks_file = dxpy.DXFile(pooled_peaks)
rep1_ta_file = dxpy.DXFile(rep1_ta)
rep1_xcor_file = dxpy.DXFile(rep1_xcor)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
# Input filenames - necessary to define each explicitly because input files
# could have the same name, in which case subsequent
# file would overwrite previous file
rep1_peaks_fn = 'rep1-%s' % (rep1_peaks_file.name)
rep2_peaks_fn = 'rep2-%s' % (rep2_peaks_file.name)
pooled_peaks_fn = 'pooled-%s' % (pooled_peaks_file.name)
rep1_ta_fn = 'r1ta_%s' % (rep1_ta_file.name)
rep1_xcor_fn = 'r1xc_%s' % (rep1_xcor_file.name)
chrom_sizes_fn = 'chrom.sizes'
as_file_fn = '%s.as' % (peak_type)
# Output filenames
if prefix:
basename = prefix
else:
# strip off the peak and compression extensions
m = re.match(
'(.*)(\.%s)+(\.((gz)|(Z)|(bz)|(bz2)))' % (peak_type),
pooled_peaks.name)
if m:
basename = m.group(1)
else:
basename = pooled_peaks.name
overlapping_peaks_fn = '%s.replicated.%s' % (basename, peak_type)
overlapping_peaks_bb_fn = overlapping_peaks_fn + '.bb'
rejected_peaks_fn = '%s.rejected.%s' % (basename, peak_type)
rejected_peaks_bb_fn = rejected_peaks_fn + '.bb'
# Intermediate filenames
overlap_tr_fn = 'replicated_tr.%s' % (peak_type)
overlap_pr_fn = 'replicated_pr.%s' % (peak_type)
# Download file inputs to the local file system with local filenames
dxpy.download_dxfile(rep1_peaks_file.get_id(), rep1_peaks_fn)
dxpy.download_dxfile(rep2_peaks_file.get_id(), rep2_peaks_fn)
dxpy.download_dxfile(pooled_peaks_file.get_id(), pooled_peaks_fn)
dxpy.download_dxfile(rep1_ta_file.get_id(), rep1_ta_fn)
dxpy.download_dxfile(rep1_xcor_file.get_id(), rep1_xcor_fn)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_fn)
dxpy.download_dxfile(as_file_file.get_id(), as_file_fn)
logger.info(subprocess.check_output('set -x; ls -l', shell=True))
# the only difference between the peak_types is how the extra columns are
# handled
if peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
assert peak_type in ['narrowPeak', 'gappedPeak', 'broadPeak'], "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak." % (peak_type)
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined as
# the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' % (pooled_peaks_fn, rep1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' % (rep2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], overlap_tr_fn)
print(
"%d peaks overlap with both true replicates"
% (common.count_lines(overlap_tr_fn)))
# this is a simplicate analysis
# overlapping peaks are just based on pseudoreps of the one pool
out, err = common.run_pipe([
'cat %s' % (overlap_tr_fn),
'sort -u'
], overlapping_peaks_fn)
print(
"%d peaks overlap"
% (common.count_lines(overlapping_peaks_fn)))
# rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' % (pooled_peaks_fn, overlapping_peaks_fn)
], rejected_peaks_fn)
print("%d peaks were rejected" % (common.count_lines(rejected_peaks_fn)))
# calculate FRiP (Fraction of Reads in Peaks)
# Extract the fragment length estimate from column 3 of the
# cross-correlation scores file or use the user-defined
# fragment_length if given.
if fragment_length is not None:
fraglen = fragment_length
fragment_length_given_by_user = True
else:
fraglen = common.xcor_fraglen(rep1_xcor_fn)
fragment_length_given_by_user = False
# FRiP
reads_in_peaks_fn = 'reads_in_%s.ta' % (peak_type)
n_reads, n_reads_in_peaks, frip_score = common.frip(
rep1_ta_fn, rep1_xcor_fn, overlapping_peaks_fn,
chrom_sizes_fn, fraglen,
reads_in_peaks_fn=reads_in_peaks_fn)
# count peaks
npeaks_in = common.count_lines(common.uncompress(pooled_peaks_fn))
npeaks_out = common.count_lines(overlapping_peaks_fn)
npeaks_rejected = common.count_lines(rejected_peaks_fn)
# make bigBed files for visualization
overlapping_peaks_bb_fn = common.bed2bb(
overlapping_peaks_fn, chrom_sizes_fn, as_file_fn, bed_type=bed_type)
rejected_peaks_bb_fn = common.bed2bb(
rejected_peaks_fn, chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# Upload file outputs from the local file system.
overlapping_peaks = dxpy.upload_local_file(common.compress(overlapping_peaks_fn))
overlapping_peaks_bb = dxpy.upload_local_file(overlapping_peaks_bb_fn)
rejected_peaks = dxpy.upload_local_file(common.compress(rejected_peaks_fn))
rejected_peaks_bb = dxpy.upload_local_file(rejected_peaks_bb_fn)
output = {
"overlapping_peaks" : dxpy.dxlink(overlapping_peaks),
"overlapping_peaks_bb" : dxpy.dxlink(overlapping_peaks_bb),
"rejected_peaks" : dxpy.dxlink(rejected_peaks),
"rejected_peaks_bb" : dxpy.dxlink(rejected_peaks_bb),
"npeaks_in" : npeaks_in,
"npeaks_out" : npeaks_out,
"npeaks_rejected" : npeaks_rejected,
"frip_nreads" : n_reads,
"frip_nreads_in_peaks" : n_reads_in_peaks,
"frip_score" : frip_score,
"fragment_length_used" : fraglen,
"fragment_length_given_by_user": fragment_length_given_by_user
}
return output
def replicated_overlap(rep1_peaks, rep2_peaks, pooled_peaks,
pooledpr1_peaks, pooledpr2_peaks,
rep1_ta, rep1_xcor, rep2_ta, rep2_xcor,
paired_end, chrom_sizes, as_file, peak_type, prefix,
fragment_length=None):
rep1_peaks_file = dxpy.DXFile(rep1_peaks)
rep2_peaks_file = dxpy.DXFile(rep2_peaks)
pooled_peaks_file = dxpy.DXFile(pooled_peaks)
pooledpr1_peaks_file = dxpy.DXFile(pooledpr1_peaks)
pooledpr2_peaks_file = dxpy.DXFile(pooledpr2_peaks)
rep1_ta_file = dxpy.DXFile(rep1_ta)
rep2_ta_file = dxpy.DXFile(rep2_ta)
rep1_xcor_file = dxpy.DXFile(rep1_xcor)
rep2_xcor_file = dxpy.DXFile(rep2_xcor)
chrom_sizes_file = dxpy.DXFile(chrom_sizes)
as_file_file = dxpy.DXFile(as_file)
# Input filenames - necessary to define each explicitly because input files
# could have the same name, in which case subsequent
# file would overwrite previous file
rep1_peaks_fn = 'rep1-%s' % (rep1_peaks_file.name)
rep2_peaks_fn = 'rep2-%s' % (rep2_peaks_file.name)
pooled_peaks_fn = 'pooled-%s' % (pooled_peaks_file.name)
pooledpr1_peaks_fn = 'pooledpr1-%s' % (pooledpr1_peaks_file.name)
pooledpr2_peaks_fn = 'pooledpr2-%s' % (pooledpr2_peaks_file.name)
rep1_ta_fn = 'r1ta_%s' % (rep1_ta_file.name)
rep2_ta_fn = 'r2ta_%s' % (rep2_ta_file.name)
rep1_xcor_fn = 'r1cc_%s' % (rep1_xcor_file.name)
rep2_xcor_fn = 'r2cc_%s' % (rep2_xcor_file.name)
chrom_sizes_fn = 'chrom.sizes'
as_file_fn = '%s.as' % (peak_type)
# Output filenames
if prefix:
basename = prefix
else:
# strip off the peak and compression extensions
m = re.match(
'(.*)(\.%s)+(\.((gz)|(Z)|(bz)|(bz2)))' % (peak_type),
pooled_peaks.name)
if m:
basename = m.group(1)
else:
basename = pooled_peaks.name
overlapping_peaks_fn = '%s.replicated.%s' % (basename, peak_type)
overlapping_peaks_bb_fn = overlapping_peaks_fn + '.bb'
rejected_peaks_fn = '%s.rejected.%s' % (basename, peak_type)
rejected_peaks_bb_fn = rejected_peaks_fn + '.bb'
# Intermediate filenames
overlap_tr_fn = 'replicated_tr.%s' % (peak_type)
overlap_pr_fn = 'replicated_pr.%s' % (peak_type)
# Download file inputs to the local file system with local filenames
dxpy.download_dxfile(rep1_peaks_file.get_id(), rep1_peaks_fn)
dxpy.download_dxfile(rep2_peaks_file.get_id(), rep2_peaks_fn)
dxpy.download_dxfile(pooled_peaks_file.get_id(), pooled_peaks_fn)
dxpy.download_dxfile(pooledpr1_peaks_file.get_id(), pooledpr1_peaks_fn)
dxpy.download_dxfile(pooledpr2_peaks_file.get_id(), pooledpr2_peaks_fn)
dxpy.download_dxfile(rep1_ta_file.get_id(), rep1_ta_fn)
dxpy.download_dxfile(rep2_ta_file.get_id(), rep2_ta_fn)
dxpy.download_dxfile(rep1_xcor_file.get_id(), rep1_xcor_fn)
dxpy.download_dxfile(rep2_xcor_file.get_id(), rep2_xcor_fn)
dxpy.download_dxfile(chrom_sizes_file.get_id(), chrom_sizes_fn)
dxpy.download_dxfile(as_file_file.get_id(), as_file_fn)
pool_applet = dxpy.find_one_data_object(
classname='applet',
name='pool',
project=dxpy.PROJECT_CONTEXT_ID,
zero_ok=False,
more_ok=False,
return_handler=True)
pool_replicates_subjob = \
pool_applet.run(
{"inputs": [rep1_ta, rep2_ta],
"prefix": 'pooled_reps'},
name='Pool replicates')
# If fragment length was given by user we skip pooled_replicates
# _xcor_subjob, set the pool_xcor_filename to None, and update
# the flag fragment_length_given_by_user. Otherwise, run the subjob
# to be able to extract the fragment length fron cross-correlations.
if fragment_length is not None:
pool_xcor_filename = None
fraglen = fragment_length
fragment_length_given_by_user = True
else:
pooled_replicates_xcor_subjob = \
xcor_only(
pool_replicates_subjob.get_output_ref("pooled"),
paired_end,
spp_version=None,
name='Pool cross-correlation')
pooled_replicates_xcor_subjob.wait_on_done()
pool_xcor_link = pooled_replicates_xcor_subjob.describe()['output'].get("CC_scores_file")
pool_xcor_file = dxpy.get_handler(pool_xcor_link)
pool_xcor_filename = 'poolcc_%s' % (pool_xcor_file.name)
dxpy.download_dxfile(pool_xcor_file.get_id(), pool_xcor_filename)
fraglen = common.xcor_fraglen(pool_xcor_filename)
fragment_length_given_by_user = False
pool_replicates_subjob.wait_on_done()
pool_ta_link = pool_replicates_subjob.describe()['output'].get("pooled")
pool_ta_file = dxpy.get_handler(pool_ta_link)
pool_ta_filename = 'poolta_%s' % (pool_ta_file.name)
dxpy.download_dxfile(pool_ta_file.get_id(), pool_ta_filename)
logger.info(subprocess.check_output('set -x; ls -l', shell=True))
# the only difference between the peak_types is how the extra columns are
# handled
if peak_type == "narrowPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$13-$12; if (($21/s1 >= 0.5) || ($21/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-10'
bed_type = 'bed6+4'
elif peak_type == "gappedPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$18-$17; if (($31/s1 >= 0.5) || ($31/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-15'
bed_type = 'bed12+3'
elif peak_type == "broadPeak":
awk_command = r"""awk 'BEGIN{FS="\t";OFS="\t"}{s1=$3-$2; s2=$12-$11; if (($19/s1 >= 0.5) || ($19/s2 >= 0.5)) {print $0}}'"""
cut_command = 'cut -f 1-9'
bed_type = 'bed6+3'
else:
assert peak_type in ['narrowPeak', 'gappedPeak', 'broadPeak'], "%s is unrecognized. peak_type should be narrowPeak, gappedPeak or broadPeak." % (peak_type)
# Find pooled peaks that overlap Rep1 and Rep2 where overlap is defined as
# the fractional overlap wrt any one of the overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' % (pooled_peaks_fn, rep1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' % (rep2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], overlap_tr_fn)
print(
"%d peaks overlap with both true replicates"
% (common.count_lines(overlap_tr_fn)))
# Find pooled peaks that overlap PseudoRep1 and PseudoRep2 where
# overlap is defined as the fractional overlap wrt any one of the
# overlapping peak pairs > 0.5
out, err = common.run_pipe([
'intersectBed -wo -a %s -b %s' % (pooled_peaks_fn, pooledpr1_peaks_fn),
awk_command,
cut_command,
'sort -u',
'intersectBed -wo -a stdin -b %s' % (pooledpr2_peaks_fn),
awk_command,
cut_command,
'sort -u'
], overlap_pr_fn)
print(
"%d peaks overlap with both pooled pseudoreplicates"
% (common.count_lines(overlap_pr_fn)))
# Combine peak lists
out, err = common.run_pipe([
'cat %s %s' % (overlap_tr_fn, overlap_pr_fn),
'sort -u'
], overlapping_peaks_fn)
print(
"%d peaks overlap with true replicates or with pooled pseudoreplicates"
% (common.count_lines(overlapping_peaks_fn)))
# rejected peaks
out, err = common.run_pipe([
'intersectBed -wa -v -a %s -b %s' % (pooled_peaks_fn, overlapping_peaks_fn)
], rejected_peaks_fn)
print("%d peaks were rejected" % (common.count_lines(rejected_peaks_fn)))
# calculate FRiP (Fraction of Reads in Peaks)
reads_in_peaks_fn = 'reads_in_%s.ta' % (peak_type)
n_reads, n_reads_in_peaks, frip_score = common.frip(
pool_ta_filename, pool_xcor_filename, overlapping_peaks_fn,
chrom_sizes_fn, fraglen, reads_in_peaks_fn=reads_in_peaks_fn)
# count peaks
npeaks_in = common.count_lines(common.uncompress(pooled_peaks_fn))
npeaks_out = common.count_lines(overlapping_peaks_fn)
npeaks_rejected = common.count_lines(rejected_peaks_fn)
# make bigBed files for visualization
overlapping_peaks_bb_fn = common.bed2bb(
overlapping_peaks_fn, chrom_sizes_fn, as_file_fn, bed_type=bed_type)
rejected_peaks_bb_fn = common.bed2bb(
rejected_peaks_fn, chrom_sizes_fn, as_file_fn, bed_type=bed_type)
# Upload file outputs from the local file system.
overlapping_peaks = dxpy.upload_local_file(common.compress(overlapping_peaks_fn))
overlapping_peaks_bb = dxpy.upload_local_file(overlapping_peaks_bb_fn)
rejected_peaks = dxpy.upload_local_file(common.compress(rejected_peaks_fn))
rejected_peaks_bb = dxpy.upload_local_file(rejected_peaks_bb_fn)
output = {
"overlapping_peaks" : dxpy.dxlink(overlapping_peaks),
"overlapping_peaks_bb" : dxpy.dxlink(overlapping_peaks_bb),
"rejected_peaks" : dxpy.dxlink(rejected_peaks),
"rejected_peaks_bb" : dxpy.dxlink(rejected_peaks_bb),
"npeaks_in" : npeaks_in,
"npeaks_out" : npeaks_out,
"npeaks_rejected" : npeaks_rejected,
"frip_nreads" : n_reads,
"frip_nreads_in_peaks" : n_reads_in_peaks,
"frip_score" : frip_score,
"fragment_length_used" : fraglen,
"fragment_length_given_by_user": fragment_length_given_by_user
}
return output
