def test_readsToWiggle_pysam_wiggles(self):
"""
Tests the ability of reads to wiggle to generate correct wiggle files
"""
reads = pysam.Samfile(
os.path.join(clipper.test_dir(), "allup_test.bam"))
reads = reads.fetch(region="chr15:91536649-91537641")
wiggle, jxns, pos_counts, lengths, allreads = readsToWiggle_pysam(
reads, 91537632, 91537675, '-', 'center', True)
wiggle_true = [
0.06060606060606061, 0.06060606060606061, 0.06060606060606061,
0.06060606060606061, 0.06060606060606061, 0.06060606060606061,
0.06060606060606061, 0.06060606060606061, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.33333333333333326, 0.33333333333333326, 0.33333333333333326,
0.2727272727272727, 0.2727272727272727, 0.2727272727272727,
0.2727272727272727, 0.2727272727272727, 0.2727272727272727,
0.2727272727272727, 0.2727272727272727, 0.0, 0.0, 0.0
]
print wiggle_true
print wiggle
for true, test in zip(wiggle_true, wiggle):
self.assertAlmostEqual(test, true, 4)
def test_readsToWiggle_pysam(self):
reads = pysam.Samfile(
os.path.join(clipper.test_dir(), "allup_test.bam"))
reads = reads.fetch(region="chr15:91536649-91537641")
wiggle, jxns, pos_counts, lengths, allreads = readsToWiggle_pysam(
reads, 91537632, 91537675, '-', 'center', False)
#wiggle, pos_counts, lengths = readsToWiggle_pysam(reads, 91537632, 91537675, '-', 'center', False)
wiggle_true = [
2., 2., 2., 2., 2., 2., 2., 2., 11., 11., 11., 11., 11., 11., 11.,
11., 11., 11., 11., 11., 11., 11., 11., 11., 11., 11., 11., 11.,
11., 11., 11., 11., 11., 9., 9., 9., 9., 9., 9., 9., 9., 0., 0., 0.
]
print wiggle
for true, test in zip(wiggle_true, wiggle):
self.assertEqual(test, true)
#
pos_counts_true = [
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 2.,
0., 0., 0., 0., 0., 0., 0., 9., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
]
for true, test in zip(pos_counts_true, pos_counts):
self.assertEqual(test, true)
assert lengths == [33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33]
def count_gene(bam_file, gene, flip):
"""
get read counts for genic regions in the gene specified by annotation in passed value 'keys'
bam_file - pysam bam file
"""
region_counts = {}
bam_file = pysam.Samfile(bam_file, 'rb')
# fetch reads from bam file for the gene referenced by keys (Ensembl ID)
subset_reads = bam_file.fetch(reference = gene['chrom'],
start = int(gene["start"]),
end = int(gene["stop"]))
# determine strand to keep based on flip option
keep_strand = gene["strand"]
if str(flip) == "flip":
if str(keep_strand) == '-':
keep_strand = '+'
elif str(keep_strand) == '+':
keep_strand = '-'
elif str(flip) == "both":
keep_strand = 0;
wig, jxns, nr_counts, read_lengths, reads = readsToWiggle_pysam(subset_reads,
int(gene["start"]),
int(gene["stop"]),
keep_strand,
'center', True)
gene_sum = 0
for region_start, region_stop in gene['regions']:
start = int(region_start) - gene["start"]
stop = int(region_stop) - gene["start"]
gene_sum += sum(wig[start:stop])
region_counts[gene['gene_id'] + ":" + str(region_start) + "-" + str(region_stop)] = sum(wig[start:stop])
bam_file.close()
return [(gene['gene_id'] + ":" + str(start) + "-" + str(stop), {"chrom" : gene['chrom'],
"start" : start,
"stop" : stop,
"strand" : gene["strand"],
"gene_id": gene['gene_id'],
'frea' : gene["frea"],
"counts" : count(gene_sum, region_counts[gene['gene_id'] + ":" + str(start) + "-" + str(stop)])}) for start, stop in gene['regions']]
def test_readsToWiggle_pysam_wiggles(self):
"""
Tests the ability of reads to wiggle to generate correct wiggle files
"""
reads = pysam.Samfile(os.path.join(clipper.test_dir(), "allup_test.bam"))
reads = reads.fetch(region="chr15:91536649-91537641")
wiggle, jxns, pos_counts, lengths, allreads = readsToWiggle_pysam(reads, 91537632, 91537675, '-', 'center', True)
wiggle_true = [0.06060606060606061, 0.06060606060606061, 0.06060606060606061, 0.06060606060606061, 0.06060606060606061, 0.06060606060606061, 0.06060606060606061, 0.06060606060606061, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.33333333333333326, 0.2727272727272727, 0.2727272727272727, 0.2727272727272727, 0.2727272727272727, 0.2727272727272727, 0.2727272727272727, 0.2727272727272727, 0.2727272727272727, 0.0, 0.0, 0.0]
print wiggle_true
print wiggle
for true, test in zip(wiggle_true, wiggle):
self.assertAlmostEqual(test, true, 4)
def test_readsToWiggle_pysam(self):
reads = pysam.Samfile(os.path.join(clipper.test_dir(), "allup_test.bam"))
reads = reads.fetch(region="chr15:91536649-91537641")
wiggle, jxns, pos_counts, lengths, allreads = readsToWiggle_pysam(reads, 91537632, 91537675, '-', 'center', False)
#wiggle, pos_counts, lengths = readsToWiggle_pysam(reads, 91537632, 91537675, '-', 'center', False)
wiggle_true = [ 2. , 2., 2. , 2. , 2. , 2. , 2. , 2. , 11. , 11., 11. , 11. ,11. , 11. , 11.,
11. , 11., 11., 11. , 11. ,11. , 11. , 11. , 11., 11. , 11. , 11. ,11. , 11. ,11.,
11. , 11., 11., 9. , 9. , 9. , 9. , 9., 9. , 9., 9. , 0. , 0., 0.]
print wiggle
for true, test in zip(wiggle_true, wiggle):
self.assertEqual(test, true)
#
pos_counts_true = [ 0. , 0., 0. , 0. ,0. , 0., 0., 0., 0., 0., 0., 0., 0. , 0. ,
0. , 0. , 2., 0., 0. , 0., 0., 0., 0. , 0., 9., 0. , 0., 0. , 0. ,
0. , 0. , 0. , 0. , 0., 0., 0., 0. , 0., 0. , 0. , 0., 0., 0. , 0.]
for true, test in zip(pos_counts_true, pos_counts):
self.assertEqual(test, true)
assert lengths == [33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33]
def call_peaks(interval,
gene_length,
bam_file=None,
max_gap=25,
fdr_alpha=0.05,
user_threshold=None,
binom_alpha=0.05,
method="binomial",
min_reads=3,
poisson_cutoff=0.05,
plotit=False,
w_cutoff=10,
windowsize=1000,
SloP=False,
max_width=None,
min_width=None,
algorithm="spline",
reverse_strand=False,
input_bam=None):
"""
calls peaks for an individual gene
interval - gtf interval describing the gene to query
takes bam file or bam file object. Serial uses object parallel uses location (name)
max_gap - space between sections for calling new peaks
fdr_alpha - false discovery rate, p-value bonferoni correct from peaks script (called in setup)
user_threshold - user defined FDR thershold (probably should be factored into fdr_alpha
minreads - min reads in section to try and call peaks
poisson_cutoff - p-value for signifance cut off for number of reads in peak that gets called - might want to use ashifted distribution
plotit - makes figures
w_cutoff - width cutoff, peaks narrower than this are discarted
windowssize - for super local calculation distance left and right to look
SloP - super local p-value instead of gene-wide p-value
max_width - int maximum with of classic peak calling algorithm peak
min_width - int min width of classic peak calling algorithm peak
max_gap - int max gap of classic peak calling algorithm peak
"""
if plotit:
plt.rcParams['interactive'] = True
pass
bam_fileobj = pysam.Samfile(bam_file, 'rb')
#fixes non-standard chrom file names (without the chr)
if not interval.chrom.startswith("chr"):
interval.chrom = "chr" + interval.chrom
subset_reads = list(
bam_fileobj.fetch(reference=str(interval.chrom),
start=interval.start,
end=interval.stop))
strand = str(interval.strand)
if reverse_strand:
if strand == "+":
strand = "-"
elif strand == "-":
strand = "+"
(wiggle, jxns, pos_counts, lengths,
allreads) = readsToWiggle_pysam(subset_reads, interval.start,
interval.stop, strand, "start", False)
#This is the worst of hacks, need to factor out pysam eventually
bam_fileobj = Robust_BAM_Reader(bam_file)
subset_reads = list(
bam_fileobj.fetch(reference=str(interval.chrom),
start=interval.start,
end=interval.stop))
array_of_reads = read_array(subset_reads, interval.start, interval.stop)
if input_bam: #if not none
input_bam_fileobj = Robust_BAM_Reader(input_bam)
input_subset_reads = list(
input_bam_fileobj.fetch(reference=str(interval.chrom),
start=interval.start,
end=interval.stop))
input_array_of_reads = read_array(input_subset_reads, interval.start,
interval.stop)
nreads_in_gene = sum(pos_counts)
gene_length = int(gene_length)
lengths = [
gene_length - 1 if read >= gene_length else read for read in lengths
]
if user_threshold is None:
if method == "binomial": #Uses Binomial Distribution to get cutoff if specified by user
gene_threshold = get_FDR_cutoff_binom(lengths, gene_length,
binom_alpha)
elif method == "random":
gene_threshold = get_FDR_cutoff_mean(readlengths=lengths,
genelength=gene_length,
alpha=fdr_alpha)
else:
raise ValueError("Method %s does not exist" % (method))
else:
logging.info("using user threshold")
gene_threshold = user_threshold
if not isinstance(gene_threshold, int):
raise TypeError
#these are what is built in this dict, complicated enough that it might
#be worth turning into an object
peak_dict = {}
peak_dict['clusters'] = []
peak_dict['sections'] = {}
peak_dict['nreads'] = int(nreads_in_gene)
peak_dict['threshold'] = gene_threshold
peak_dict['loc'] = interval
peak_number = 0
sections = find_sections(wiggle, max_gap)
if plotit:
plot_sections(wiggle, sections, gene_threshold)
for sect in sections:
sectstart, sectstop = sect
sect_length = sectstop - sectstart + 1
data = wiggle[sectstart:(sectstop + 1)]
cur_interval = HTSeq.GenomicInterval(str(interval.chrom),
sectstart + interval.start,
sectstop + interval.start + 1,
strand)
Nreads = count_reads_in_interval(cur_interval, array_of_reads)
cts = pos_counts[sectstart:(sectstop + 1)]
xvals = arange(len(data))
peak_dict['sections'][sect] = {}
peak_dict['sections'][sect]['nreads'] = int(Nreads)
#makes sure there are enough reads
if Nreads < min_reads:
logging.info("""%d is not enough reads, skipping section: %s""" %
(Nreads, sect))
peak_dict['sections'][sect]['tried'] = False
continue
else:
logging.info("""Analyzing section %s with %d reads""" %
(sect, Nreads))
pass
if user_threshold is None:
if SloP:
half_width = 500
section_start = max(0, sectstart + interval.start - half_width)
section_stop = sectstop + interval.start + 1 + half_width
expanded_sect_length = section_stop - section_start
cur_interval = HTSeq.GenomicInterval(str(interval.chrom),
section_start,
section_stop, strand)
expanded_Nreads = get_reads_in_interval(
cur_interval, array_of_reads)
sect_read_lengths = read_lengths_from_htseq(expanded_Nreads)
sect_read_lengths = [
sect_length - 1 if read > sect_length else read
for read in sect_read_lengths
]
peak_dict['sections'][sect]['expanded_Nreads'] = len(
expanded_Nreads)
if method == "binomial": #Uses Binomial Distribution to get cutoff if specified by user
threshold = max(
gene_threshold,
get_FDR_cutoff_binom(sect_read_lengths,
expanded_sect_length,
binom_alpha))
elif method == "random":
#use the minimum FDR cutoff between superlocal and gene-wide calculations
threshold = max(
gene_threshold,
get_FDR_cutoff_mean(readlengths=sect_read_lengths,
genelength=expanded_sect_length,
alpha=fdr_alpha))
else:
raise ValueError("Method %s does not exist" % (method))
logging.info("Using super-local threshold %d" % (threshold))
else:
threshold = gene_threshold
else:
threshold = user_threshold
#saves threshold for each individual section
peak_dict['sections'][sect]['threshold'] = threshold
peak_dict['sections'][sect]['nreads'] = int(Nreads)
peak_dict['sections'][sect]['tried'] = True
peak_dict['sections'][sect]['nPeaks'] = 0
if max(data) < threshold:
logging.info("data does not excede threshold, stopping")
continue
if algorithm == "spline":
data = map(float, data)
#Magic number for initial smoothing, but it works
initial_smoothing_value = (
(sectstop - sectstart + 1)**(1 / 3)) + 10
peak_dict['sections'][sect][
'smoothing_factor'] = initial_smoothing_value
logging.info("initial smoothing value: %.2f" %
initial_smoothing_value)
fitter = SmoothingSpline(
xvals,
data,
smoothing_factor=initial_smoothing_value,
lossFunction="get_turn_penalized_residuals",
threshold=threshold,
num_reads=Nreads)
elif algorithm == "gaussian":
cts = map(float, cts)
fitter = GaussMix(xvals, cts)
elif algorithm == "classic":
data = map(float, data)
fitter = Classic(xvals, data, max_width, min_width, max_gap)
try:
peak_definitions = fitter.peaks()
logging.info("optimized smoothing value: %.2f" %
fitter.smoothing_factor)
peak_dict['sections'][sect][
'final_smoothing_factor'] = fitter.smoothing_factor
if peak_definitions is None:
numpeaks = 0
else:
numpeaks = len(peak_definitions)
logging.info("I identified %d potential peaks" % (numpeaks))
except Exception as error:
logging.error("peak finding failed:, %s, %s" %
(interval.name, error))
raise error
#subsections that are above threshold
#peak center is actually the location where we think binding should
#occur, not the average of start and stop
#Need to get all ranges, count number of reads in each range and compute from there
for peak_start, peak_stop, peak_center in peak_definitions:
genomic_start = interval.start + sectstart + peak_start
genomic_stop = interval.start + sectstart + peak_stop
cur_interval = HTSeq.GenomicInterval(str(interval.chrom),
genomic_start, genomic_stop,
strand)
number_reads_in_peak = count_reads_in_interval(
cur_interval, array_of_reads)
if input_bam:
input_number_reads_in_peak = count_reads_in_interval(
cur_interval, input_array_of_reads)
else:
input_number_reads_in_peak = 0
peak_length = genomic_stop - genomic_start + 1
logging.info("""Peak %d (%d - %d) has %d
reads""" % (peak_number, peak_start,
(peak_stop + 1), number_reads_in_peak))
#highest point in start stop
genomic_center = interval.start + sectstart + peak_center
#makes it thicker so we can see on the browser
#error checking logic to keep bed files from breaking
thick_start = max(genomic_center - 2, genomic_start)
thick_stop = min(genomic_center + 2, genomic_stop)
#super local logic
area_start = max(0, (peak_center + sectstart) - windowsize)
area_stop = min((peak_center + sectstart) + windowsize,
len(wiggle))
cur_interval = HTSeq.GenomicInterval(str(interval.chrom),
interval.start + area_start,
interval.start + area_stop,
strand)
number_reads_in_area = count_reads_in_interval(
cur_interval, array_of_reads)
area_length = area_stop - area_start + 1
peak_dict['clusters'].append(
Peak(
chrom=interval.chrom,
genomic_start=genomic_start,
genomic_stop=genomic_stop,
gene_name=interval.attrs['gene_id'],
strand=interval.strand,
thick_start=thick_start,
thick_stop=thick_stop,
peak_number=peak_number,
number_reads_in_peak=number_reads_in_peak,
size=peak_length,
p=0,
effective_length=int(interval.attrs['effective_length']),
peak_length=peak_length,
area_reads=number_reads_in_area,
area_size=area_length,
nreads_in_gene=nreads_in_gene,
#nreads_in_input=input_number_reads_in_peak,
))
peak_number += 1
peak_dict['sections'][sect]['nPeaks'] += 1
peak_dict['Nclusters'] = peak_number
if plotit:
import sys
plt.show()
v = sys.stdin.read(1)
return peak_dict
def call_peaks(interval, gene_length, bam_file=None, max_gap=25,
fdr_alpha=0.05, user_threshold=None, binom_alpha=0.05, method="binomial",
min_reads=3, poisson_cutoff=0.05,
plotit=False, w_cutoff=10, windowsize=1000,
SloP=False, max_width=None, min_width=None,
algorithm="spline", reverse_strand=False, input_bam=None):
"""
calls peaks for an individual gene
interval - gtf interval describing the gene to query
takes bam file or bam file object. Serial uses object parallel uses location (name)
max_gap - space between sections for calling new peaks
fdr_alpha - false discovery rate, p-value bonferoni correct from peaks script (called in setup)
user_threshold - user defined FDR thershold (probably should be factored into fdr_alpha
minreads - min reads in section to try and call peaks
poisson_cutoff - p-value for signifance cut off for number of reads in peak that gets called - might want to use ashifted distribution
plotit - makes figures
w_cutoff - width cutoff, peaks narrower than this are discarted
windowssize - for super local calculation distance left and right to look
SloP - super local p-value instead of gene-wide p-value
max_width - int maximum with of classic peak calling algorithm peak
min_width - int min width of classic peak calling algorithm peak
max_gap - int max gap of classic peak calling algorithm peak
"""
if plotit:
plt.rcParams['interactive'] = True
pass
bam_fileobj = pysam.Samfile(bam_file, 'rb')
#fixes non-standard chrom file names (without the chr)
if not interval.chrom.startswith("chr"):
interval.chrom = "chr" + interval.chrom
subset_reads = list(bam_fileobj.fetch(reference=str(interval.chrom), start=interval.start, end=interval.stop))
strand = str(interval.strand)
if reverse_strand:
if strand == "+":
strand = "-"
elif strand == "-":
strand = "+"
(wiggle, jxns, pos_counts,
lengths, allreads) = readsToWiggle_pysam(subset_reads, interval.start,
interval.stop, strand, "start", False)
#This is the worst of hacks, need to factor out pysam eventually
bam_fileobj = Robust_BAM_Reader(bam_file)
subset_reads = list(bam_fileobj.fetch(reference=str(interval.chrom), start=interval.start, end=interval.stop))
array_of_reads = read_array(subset_reads, interval.start, interval.stop)
if input_bam: #if not none
input_bam_fileobj = Robust_BAM_Reader(input_bam)
input_subset_reads = list(input_bam_fileobj.fetch(reference=str(interval.chrom), start=interval.start, end=interval.stop))
input_array_of_reads = read_array(input_subset_reads, interval.start, interval.stop)
nreads_in_gene = sum(pos_counts)
gene_length = int(gene_length)
lengths = [gene_length - 1 if read >= gene_length else read for read in lengths]
if user_threshold is None:
if method == "binomial": #Uses Binomial Distribution to get cutoff if specified by user
gene_threshold = get_FDR_cutoff_binom(lengths, gene_length, binom_alpha)
elif method == "random":
gene_threshold = get_FDR_cutoff_mean(readlengths=lengths,
genelength=gene_length,
alpha=fdr_alpha)
else:
raise ValueError("Method %s does not exist" % (method))
else:
logging.info("using user threshold")
gene_threshold = user_threshold
if not isinstance(gene_threshold, int):
raise TypeError
#these are what is built in this dict, complicated enough that it might
#be worth turning into an object
peak_dict = {}
peak_dict['clusters'] = []
peak_dict['sections'] = {}
peak_dict['nreads'] = int(nreads_in_gene)
peak_dict['threshold'] = gene_threshold
peak_dict['loc'] = interval
peak_number = 0
sections = find_sections(wiggle, max_gap)
if plotit:
plot_sections(wiggle, sections, gene_threshold)
for sect in sections:
sectstart, sectstop = sect
sect_length = sectstop - sectstart + 1
data = wiggle[sectstart:(sectstop + 1)]
cur_interval = HTSeq.GenomicInterval(str(interval.chrom), sectstart + interval.start, sectstop + interval.start + 1,
strand)
Nreads = count_reads_in_interval(cur_interval, array_of_reads)
cts = pos_counts[sectstart:(sectstop + 1)]
xvals = arange(len(data))
peak_dict['sections'][sect] = {}
peak_dict['sections'][sect]['nreads'] = int(Nreads)
#makes sure there are enough reads
if Nreads < min_reads:
logging.info("""%d is not enough reads, skipping section: %s""" % (Nreads, sect))
peak_dict['sections'][sect]['tried'] = False
continue
else:
logging.info("""Analyzing section %s with %d reads""" % (sect, Nreads))
pass
if user_threshold is None:
if SloP:
half_width = 500
section_start = max(0, sectstart + interval.start - half_width)
section_stop = sectstop + interval.start + 1 + half_width
expanded_sect_length = section_stop - section_start
cur_interval = HTSeq.GenomicInterval(str(interval.chrom), section_start, section_stop,strand )
expanded_Nreads = get_reads_in_interval(cur_interval, array_of_reads)
sect_read_lengths = read_lengths_from_htseq(expanded_Nreads)
sect_read_lengths = [sect_length - 1 if read > sect_length else read for read in sect_read_lengths]
if method == "binomial": #Uses Binomial Distribution to get cutoff if specified by user
threshold = max(gene_threshold, get_FDR_cutoff_binom(sect_read_lengths, expanded_sect_length, binom_alpha))
elif method == "random":
#use the minimum FDR cutoff between superlocal and gene-wide calculations
threshold = max(gene_threshold, get_FDR_cutoff_mean(readlengths=sect_read_lengths, genelength=expanded_sect_length, alpha=fdr_alpha))
else:
raise ValueError("Method %s does not exist" % (method))
logging.info("Using super-local threshold %d" %(threshold))
else:
threshold = gene_threshold
else:
threshold = user_threshold
#saves threshold for each individual section
peak_dict['sections'][sect]['threshold'] = threshold
peak_dict['sections'][sect]['nreads'] = int(Nreads)
peak_dict['sections'][sect]['expanded_Nreads'] = len(expanded_Nreads)
peak_dict['sections'][sect]['tried'] = True
peak_dict['sections'][sect]['nPeaks'] = 0
if max(data) < threshold:
logging.info("data does not excede threshold, stopping")
continue
if algorithm == "spline":
data = map(float, data)
#Magic number for initial smoothing, but it works
initial_smoothing_value = ((sectstop - sectstart + 1)**(1/3)) + 10
peak_dict['sections'][sect]['smoothing_factor'] = initial_smoothing_value
logging.info("initial smoothing value: %.2f" % initial_smoothing_value)
fitter = SmoothingSpline(xvals, data, smoothing_factor=initial_smoothing_value,
lossFunction="get_turn_penalized_residuals",
threshold=threshold,
num_reads=Nreads)
elif algorithm == "gaussian":
cts = map(float, cts)
fitter = GaussMix(xvals, cts)
elif algorithm == "classic":
data = map(float, data)
fitter = Classic(xvals, data, max_width, min_width, max_gap)
try:
peak_definitions = fitter.peaks()
logging.info("optimized smoothing value: %.2f" % fitter.smoothing_factor)
peak_dict['sections'][sect]['final_smoothing_factor'] = fitter.smoothing_factor
if peak_definitions is None:
numpeaks = 0
else:
numpeaks = len(peak_definitions)
logging.info("I identified %d potential peaks" % (numpeaks))
except Exception as error:
logging.error("peak finding failed:, %s, %s" % (interval.name, error))
raise error
#subsections that are above threshold
#peak center is actually the location where we think binding should
#occur, not the average of start and stop
#Need to get all ranges, count number of reads in each range and compute from there
for peak_start, peak_stop, peak_center in peak_definitions:
genomic_start = interval.start + sectstart + peak_start
genomic_stop = interval.start + sectstart + peak_stop
cur_interval = HTSeq.GenomicInterval(str(interval.chrom), genomic_start, genomic_stop,
strand)
number_reads_in_peak = count_reads_in_interval(cur_interval, array_of_reads)
if input_bam:
input_number_reads_in_peak = count_reads_in_interval(cur_interval, input_array_of_reads)
else:
input_number_reads_in_peak = 0
peak_length = genomic_stop - genomic_start + 1
logging.info("""Peak %d (%d - %d) has %d
reads""" % (peak_number, peak_start,
(peak_stop + 1), number_reads_in_peak))
#highest point in start stop
genomic_center = interval.start + sectstart + peak_center
#makes it thicker so we can see on the browser
#error checking logic to keep bed files from breaking
thick_start = max(genomic_center - 2, genomic_start)
thick_stop = min(genomic_center + 2, genomic_stop)
#super local logic
area_start = max(0, (peak_center + sectstart) - windowsize)
area_stop = min((peak_center + sectstart) + windowsize, len(wiggle))
cur_interval = HTSeq.GenomicInterval(str(interval.chrom), interval.start + area_start, interval.start + area_stop,
strand)
number_reads_in_area = count_reads_in_interval(cur_interval, array_of_reads)
area_length = area_stop - area_start + 1
peak_dict['clusters'].append(Peak(chrom=interval.chrom,
genomic_start=genomic_start,
genomic_stop=genomic_stop,
gene_name=interval.attrs['gene_id'],
strand=interval.strand,
thick_start=thick_start,
thick_stop=thick_stop,
peak_number=peak_number,
number_reads_in_peak=number_reads_in_peak,
size=peak_length,
p=0,
effective_length=int(interval.attrs['effective_length']),
peak_length=peak_length,
area_reads=number_reads_in_area,
area_size=area_length,
nreads_in_gene=nreads_in_gene,
#nreads_in_input=input_number_reads_in_peak,
))
peak_number += 1
peak_dict['sections'][sect]['nPeaks'] += 1
peak_dict['Nclusters'] = peak_number
if plotit:
import sys
plt.show()
v = sys.stdin.read(1)
return peak_dict
def call_peaks(interval,
gene_length,
bam_file=None,
max_gap=25,
fdr_alpha=0.05,
user_threshold=None,
binom_alpha=0.05,
method="binomial",
min_reads=3,
poisson_cutoff=0.05,
plotit=False,
w_cutoff=10,
windowsize=1000,
SloP=False,
max_width=None,
min_width=None,
algorithm="spline",
reverse_strand=False,
exons=None):
"""
calls peaks for an individual gene
interval - gtf interval describing the gene to query
takes bam file or bam file object. Serial uses object parallel uses location (name)
max_gap - space between sections for calling new peaks
fdr_alpha - false discovery rate, p-value bonferoni correct from peaks script (called in setup)
user_threshold - user defined FDR thershold (probably should be factored into fdr_alpha
minreads - min reads in section to try and call peaks
poisson_cutoff - p-value for signifance cut off for number of reads in peak that gets called - might want to use ashifted distribution
plotit - makes figures
w_cutoff - width cutoff, peaks narrower than this are discarded
windowssize - for super local calculation distance left and right to look
SloP - super local p-value instead of gene-wide p-value (+/- 500 b.p. of each section)
max_width - int maximum with of classic peak calling algorithm peak
min_width - int min width of classic peak calling algorithm peak
max_gap - int max gap of classic peak calling algorithm peak
returns peak_dict, dictionary containing
peak_dict['clusters']: list of Peak objects
peak_dict['sections']: key: section
['nreads'] how many reads in this section
['threshold'] = threshold // either be suerlocal threshold, mRNA threshold or pre-mRNA threshold
['tried'] = True
['nPeaks'] = number of peaks
peak_dict['nreads']: No. reads in gene
peak_dict['threshold']
peak_dict['loc']: interval
peak_dict['Nclusters']: total peaks in transcript
"""
###########################################################################
# print("starting call_peaks on gene_no:", gene_no, "interval:", interval)
# genecallpeaksloggingperiode = 100
# should_log_gene_call_peaks_this_time = (gene_no % genecallpeaksloggingperiode == 0)
###########################################################################
# if should_log_gene_call_peaks_this_time:
# logging.info(" starting call_peaks on gene_no {}".format(gene_no))
###########################################################################
if plotit:
plt.rcParams['interactive'] = True
pass
bam_fileobj = pysam.Samfile(bam_file, 'rb')
# fixes non-standard chrom file names (without the chr)
if not interval.chrom.startswith("chr") and not interval.chrom.startswith(
"ERCC") and not interval.chrom.startswith("phiX"):
interval.chrom = "chr" + interval.chrom
# fetch reads in the genomic region
subset_reads = list(
bam_fileobj.fetch(reference=str(interval.chrom),
start=interval.start,
end=interval.stop))
strand = str(interval.strand)
if reverse_strand:
if strand == "+":
strand = "-"
elif strand == "-":
strand = "+"
# convert pysam to a wiggle vector, junction, positional count(coverage), read lengths, all_reads, location
(wiggle, jxns, pos_counts, lengths, allreads,
read_locations) = readsToWiggle_pysam(subset_reads, interval.start,
interval.stop, strand, "start",
False)
nreads_in_gene = sum(pos_counts)
gene_length = int(gene_length)
lengths = [
gene_length - 1 if read >= gene_length else read for read in lengths
]
# pre-mRNA Threshold
if user_threshold is None:
if method == "binomial": # Uses Binomial Distribution to get cutoff if specified by user
# print(len(lengths), gene_length, binom_alpha)
premRNA_threshold = get_FDR_cutoff_binom(lengths, gene_length,
binom_alpha)
# print(premRNA_threshold)
elif method == "random":
premRNA_threshold = get_FDR_cutoff_mean(readlengths=lengths,
genelength=gene_length,
alpha=fdr_alpha)
else:
raise ValueError("Method %s does not exist" % (method))
else:
logging.info("using user threshold")
premRNA_threshold = user_threshold
# mRNA Threshold
exons = pybedtools.BedTool(exons)
exons = exons.filter(
lambda x: x.name == interval.attrs['gene_id']).saveas()
total_exonic_reads = []
total_exonic_length = 0
htseq_exons = HTSeq.GenomicArrayOfSets(chroms="auto", stranded=False)
for exon, exon_interval in zip(exons, bed_to_genomic_interval(exons)):
exon.stop += 1
exonic_reads = get_reads_in_interval_pysam(exon, interval.start,
read_locations)
exon_read_lengths = read_lengths_from_pysam(exonic_reads)
exon_read_lengths = [
exon_interval.length - 1 if read > exon_interval.length else read
for read in exon_read_lengths
]
total_exonic_reads += exon_read_lengths
total_exonic_length += exon_interval.length
htseq_exons[exon_interval] += 'exon'
mRNA_threshold = get_FDR_cutoff_binom(total_exonic_reads,
total_exonic_length, binom_alpha)
if not isinstance(premRNA_threshold, int):
raise TypeError
# these are what is built in this dict, complicated enough that it might
# be worth turning into an object
peak_dict = {}
peak_dict['clusters'] = []
peak_dict['sections'] = {}
peak_dict['nreads'] = int(nreads_in_gene)
peak_dict['threshold'] = premRNA_threshold
peak_dict['loc'] = interval
peak_number = 0
sections = find_sections(
wiggle,
max_gap) # return list of base with contiguous read > 0 (gap allowed)
if plotit:
plot_sections(wiggle, sections, premRNA_threshold)
# for each section, call peaks
for sect in sections:
sectstart, sectstop = sect
sect_length = sectstop - sectstart + 1
data = wiggle[sectstart:(sectstop + 1)]
# make interval for teh section
cur_interval = HTSeq.GenomicInterval(str(interval.chrom),
sectstart + interval.start,
sectstop + interval.start + 1,
strand)
# Logic to use variable thresholds for exons or introns, still superseded by superLocal logic
overlaps_exon = len(
reduce(set.union,
(val for iv, val in htseq_exons[cur_interval].steps()))) > 0
gene_threshold = mRNA_threshold if overlaps_exon else premRNA_threshold
# maybe make a function that takes a genomic interval and converts it into a pybedtools interval
bed_format = [
interval.chrom, sectstart + interval.start,
sectstop + interval.start + 1, interval.name, interval.score,
strand
]
bed_format = list(map(str, bed_format))
cur_pybedtools_interval = pybedtools.create_interval_from_list(
bed_format)
Nreads = count_reads_in_interval_pysam(cur_pybedtools_interval,
interval.start, read_locations)
cts = pos_counts[sectstart:(sectstop + 1)]
xvals = arange(len(data))
peak_dict['sections'][sect] = {}
peak_dict['sections'][sect]['nreads'] = int(Nreads)
# makes sure there are enough reads
if Nreads < min_reads:
logging.info("""%d is not enough reads, skipping section: %s""" %
(Nreads, sect))
peak_dict['sections'][sect]['tried'] = False
continue
else:
logging.info("""Analyzing section %s with %d reads""" %
(sect, Nreads))
pass
if user_threshold is None:
if SloP:
# super local p-value: section +/- 500 b.p.'; instead of using whole gene's length and read, use this extended region
half_width = 500
section_start = max(
0, sectstart + interval.start -
half_width) # aim at -500 offset from section start
section_stop = sectstop + interval.start + 1 + half_width # aim at _500 from section stop
expanded_sect_length = section_stop - section_start
bed_format = [
interval.chrom, section_start, section_stop, interval.name,
interval.score, strand
]
bed_format = list(map(str, bed_format))
cur_pybedtools_interval = pybedtools.create_interval_from_list(
bed_format)
expanded_Nreads = get_reads_in_interval_pysam(
cur_pybedtools_interval, interval.start, read_locations)
sect_read_lengths = read_lengths_from_pysam(expanded_Nreads)
sect_read_lengths = [
sect_length - 1 if read > sect_length else read
for read in sect_read_lengths
]
peak_dict['sections'][sect]['expanded_Nreads'] = len(
expanded_Nreads)
if method == "binomial": # Uses Binomial Distribution to get cutoff if specified by user
slop_threshold = get_FDR_cutoff_binom(
readlengths=sect_read_lengths,
genelength=expanded_sect_length,
alpha=binom_alpha)
elif method == "random":
# use the minimum FDR cutoff between superlocal and gene-wide calculations
slop_threshold = get_FDR_cutoff_mean(
readlengths=sect_read_lengths,
genelength=expanded_sect_length,
alpha=fdr_alpha)
else:
raise ValueError("Method %s does not exist" % (method))
threshold = max(gene_threshold, slop_threshold)
logging.info("Using super-local threshold %d" % (threshold))
else:
# if not use super local threshold (+/- 500 bp), use mRNA_threshold for exon; premRNA_threshold if section does not overlap with exon
threshold = gene_threshold
else:
threshold = user_threshold
# saves threshold for each individual section
peak_dict['sections'][sect]['threshold'] = threshold
peak_dict['sections'][sect]['nreads'] = int(Nreads)
peak_dict['sections'][sect]['tried'] = True
peak_dict['sections'][sect]['nPeaks'] = 0
if max(data) < threshold:
logging.info("data does not excede threshold, stopping")
continue
if algorithm == "spline":
data = list(map(float, data))
# Magic number for initial smoothing, but it works
initial_smoothing_value = (
(sectstop - sectstart + 1)**(1 / 3)) + 10
peak_dict['sections'][sect][
'smoothing_factor'] = initial_smoothing_value
logging.info("initial smoothing value: %.2f" %
initial_smoothing_value)
fitter = SmoothingSpline(
xvals,
data,
smoothing_factor=initial_smoothing_value,
lossFunction="get_turn_penalized_residuals",
threshold=threshold,
num_reads=Nreads)
elif algorithm == "gaussian":
cts = list(map(float, cts))
fitter = GaussMix(xvals, cts)
elif algorithm == "classic":
data = list(map(float, data))
fitter = Classic(xvals, data, max_width, min_width, max_gap)
try:
peak_definitions = fitter.peaks()
logging.info("optimized smoothing value: %.2f" %
fitter.smoothing_factor)
peak_dict['sections'][sect][
'final_smoothing_factor'] = fitter.smoothing_factor
if peak_definitions is None:
numpeaks = 0
else:
numpeaks = len(peak_definitions)
logging.info("I identified %d potential peaks" % (numpeaks))
except Exception as error:
logging.error("peak finding failed:, %s, %s" %
(interval.name, error))
raise error
# subsections that are above threshold
# peak center is actually the location where we think binding should
# occur, not the average of start and stop
# Need to get all ranges, count number of reads in each range and compute from there
for peak_start, peak_stop, peak_center in peak_definitions:
genomic_start = interval.start + sectstart + peak_start
genomic_stop = interval.start + sectstart + peak_stop
# save to bedtool
bed_format = [
interval.chrom, genomic_start, genomic_stop, interval.name,
interval.score, strand
]
bed_format = list(map(str,
bed_format)) # create_interval_only_take_str
cur_pybedtools_interval = pybedtools.create_interval_from_list(
bed_format)
number_reads_in_peak = count_reads_in_interval_pysam(
cur_pybedtools_interval, interval.start, read_locations)
peak_length = genomic_stop - genomic_start + 1
logging.info("""Peak %d (%d - %d) has %d
reads""" % (peak_number, peak_start,
(peak_stop + 1), number_reads_in_peak))
# highest point in start stop
genomic_center = interval.start + sectstart + peak_center
# makes it thicker so we can see on the browser
# error checking logic to keep bed files from breaking
thick_start = max(genomic_center - 2, genomic_start)
thick_stop = min(genomic_center + 2, genomic_stop)
# super local logic
area_start = max(0, (peak_center + sectstart) - windowsize)
area_stop = min((peak_center + sectstart) + windowsize,
len(wiggle))
bed_format = [
interval.chrom, interval.start + area_start,
interval.start + area_stop, interval.name, interval.score,
strand
]
bed_format = list(map(str, bed_format))
cur_pybedtools_interval = pybedtools.create_interval_from_list(
bed_format)
number_reads_in_area = count_reads_in_interval_pysam(
cur_pybedtools_interval, interval.start, read_locations)
area_length = area_stop - area_start + 1
peak_dict['clusters'].append(
Peak(
chrom=interval.chrom,
genomic_start=genomic_start,
genomic_stop=genomic_stop,
gene_name=interval.attrs['gene_id'],
strand=interval.strand,
thick_start=thick_start,
thick_stop=thick_stop,
peak_number=peak_number,
number_reads_in_peak=number_reads_in_peak,
size=peak_length,
p=0,
effective_length=int(interval.attrs['effective_length']),
peak_length=peak_length,
area_reads=number_reads_in_area,
area_size=area_length,
nreads_in_gene=nreads_in_gene,
# nreads_in_input=input_number_reads_in_peak,
))
peak_number += 1
peak_dict['sections'][sect]['nPeaks'] += 1
peak_dict['Nclusters'] = peak_number
if plotit:
import sys
plt.show()
v = sys.stdin.read(1)
###################################################
# print("returning gene_no:", gene_no, "peak_dict:", peak_dict)
####################################################
return peak_dict
def call_peaks(interval, gene_length, bam_fileobj=None, bam_file=None,
max_gap=25, fdr_alpha=0.05, user_threshold=None, binom_alpha=0.001, method="random",
minreads=20, poisson_cutoff=0.05,
plotit=False, w_cutoff=10, windowsize=1000,
SloP=False, correct_p=False, max_width=None, min_width=None,
algorithm="spline"):
"""
calls peaks for an individual gene
interval - gtf interval describing the gene to query
takes bam file or bam file object. Serial uses object parallel uses location (name)
max_gap - space between sections for calling new peaks
fdr_alpha - false discovery rate, p-value bonferoni correct from peaks script (called in setup)
user_threshold - user defined FDR thershold (probably should be factored into fdr_alpha
minreads - min reads in section to try and call peaks
poisson_cutoff - p-value for signifance cut off for number of reads in peak that gets called - might want to use ashifted distribution
plotit - makes figures
w_cutoff - width cutoff, peaks narrower than this are discarted
windowssize - for super local calculation distance left and right to look
SloP - super local p-value instead of gene-wide p-value
correct_p - boolean bonferoni correction of p-values from poisson
max_width - int maximum with of classic peak calling algorithm peak
min_width - int min width of classic peak calling algorithm peak
max_gap - int max gap of classic peak calling algorithm peak
"""
#sys.stderr.write("plotit foo" + str(plotit))
if plotit:
plt.rcParams['interactive']=True
pass
logging.info("running on gene %s" % (str(interval)))
bam_fileobj = pysam.Samfile(bam_file, 'rb')
#fixes non-standard chrom file names (without the chr)
if not interval.chrom.startswith("chr"):
interval.chrom = "chr" + interval.chrom
subset_reads = bam_fileobj.fetch(reference=interval.chrom, start=interval.start, end=interval.stop)
#need to document reads to wiggle
wiggle, jxns, pos_counts, read_lengths, allreads = readsToWiggle_pysam(subset_reads, interval.start, interval.stop, interval.strand, "center", False)
#TODO have a check to kill this if there aren't any reads in a region
result = peaks_from_info(bam_fileobj= bam_fileobj,
wiggle=list(wiggle),
pos_counts=pos_counts,
lengths=read_lengths,
interval=interval,
gene_length=gene_length,
max_gap=max_gap,
fdr_alpha=fdr_alpha,
binom_alpha=binom_alpha,
method=method,
user_threshold=user_threshold,
minreads=minreads,
poisson_cutoff=poisson_cutoff,
plotit=plotit,
width_cutoff=w_cutoff,
windowsize=windowsize,
SloP=SloP,
correct_p=correct_p,
max_width=max_width,
min_width= min_width,
algorithm=algorithm)
return result
def count_gene(bam_file, gene, flip):
"""
get read counts for genic regions in the gene specified by annotation in passed value 'keys'
bam_file - pysam bam file
"""
region_counts = {}
bam_file = pysam.Samfile(bam_file, 'rb')
# fetch reads from bam file for the gene referenced by keys (Ensembl ID)
subset_reads = bam_file.fetch(reference=gene['chrom'],
start=int(gene["start"]),
end=int(gene["stop"]))
# determine strand to keep based on flip option
keep_strand = gene["strand"]
if str(flip) == "flip":
if str(keep_strand) == '-':
keep_strand = '+'
elif str(keep_strand) == '+':
keep_strand = '-'
elif str(flip) == "both":
keep_strand = 0
wig, jxns, nr_counts, read_lengths, reads = readsToWiggle_pysam(
subset_reads, int(gene["start"]), int(gene["stop"]), keep_strand,
'center', True)
gene_sum = 0
for region_start, region_stop in gene['regions']:
start = int(region_start) - gene["start"]
stop = int(region_stop) - gene["start"]
gene_sum += sum(wig[start:stop])
region_counts[gene['gene_id'] + ":" + str(region_start) + "-" +
str(region_stop)] = sum(wig[start:stop])
bam_file.close()
return [(gene['gene_id'] + ":" + str(start) + "-" + str(stop), {
"chrom":
gene['chrom'],
"start":
start,
"stop":
stop,
"strand":
gene["strand"],
"gene_id":
gene['gene_id'],
'frea':
gene["frea"],
"counts":
count(
gene_sum, region_counts[gene['gene_id'] + ":" + str(start) + "-" +
str(stop)])
}) for start, stop in gene['regions']]
