def runRMATS(gtffile, designfile, pvalue, strand, outdir, permute=0):
'''Module to generate rMATS statment
Module offers the option to permute group name labels and
calculates readlength, which must be identical in all reads.
Arguments
---------
gtffile: string
path to :term:`gtf` file
designfile: string
path to design file
pvalue: string
threshold for FDR testing
strand: string
strandedness option: can be 'fr-unstranded', 'fr-firststrand',
or 'fr-secondstrand'
outdir: string
directory path for rMATS results
permute : 1 or 0
option to activate random shuffling of sample groups
'''
design = Expression.ExperimentalDesign(designfile)
if permute == 1:
design.table.group = random.choice(
list(itertools.permutations(design.table.group)))
group1 = ",".join(
["%s.bam" % x for x in design.getSamplesInGroup(design.groups[0])])
with open(outdir + "/b1.txt", "w") as f:
f.write(group1)
group2 = ",".join(
["%s.bam" % x for x in design.getSamplesInGroup(design.groups[1])])
with open(outdir + "/b2.txt", "w") as f:
f.write(group2)
readlength = BamTools.estimateTagSize(design.samples[0] + ".bam")
statement = '''rMATS
--b1 %(outdir)s/b1.txt
--b2 %(outdir)s/b2.txt
--gtf <(gunzip -c %(gtffile)s)
--od %(outdir)s
--readLength %(readlength)s
--cstat %(pvalue)s
--libType %(strand)s
''' % locals()
# if Paired End Reads
if BamTools.isPaired(design.samples[0] + ".bam"):
statement += '''-t paired''' % locals()
statement += '''
> %(outdir)s/%(designfile)s.log
'''
P.run()
def loadZinba(infile, outfile, bamfile,
tablename=None,
controlfile=None):
'''load Zinba results in *tablename*
This method loads only positive peaks. It filters peaks by p-value,
q-value and fold change and loads the diagnostic data and
re-calculates peakcenter, peakval, ... using the supplied bamfile.
If *tablename* is not given, it will be :file:`<track>_intervals`
where track is derived from ``infile`` and assumed to end
in :file:`.zinba`.
If no peaks were predicted, an empty table is created.
This method creates :file:`<outfile>.tsv.gz` with the results
of the filtering.
This method uses the refined peak locations.
Zinba peaks can be overlapping. This method does not merge
overlapping intervals.
Zinba calls peaks in regions where there are many reads inside
the control. Thus this method applies a filtering step
removing all intervals in which there is a peak of
more than readlength / 2 height in the control.
.. note:
Zinba calls peaks that are overlapping.
'''
track = P.snip(os.path.basename(infile), ".zinba")
folder = os.path.dirname(infile)
infilename = infile + ".peaks"
outtemp = P.getTempFile(".")
tmpfilename = outtemp.name
outtemp.write("\t".join((
"interval_id",
"contig", "start", "end",
"npeaks", "peakcenter",
"length",
"avgval",
"peakval",
"nprobes",
"pvalue", "fold", "qvalue",
"macs_summit", "macs_nprobes",
)) + "\n")
counter = E.Counter()
if not os.path.exists(infilename):
E.warn("could not find %s" % infilename)
elif IOTools.isEmpty(infilename):
E.warn("no data in %s" % infilename)
else:
# filter peaks
shift = getPeakShiftFromZinba(infile)
assert shift is not None, \
"could not determine peak shift from Zinba file %s" % infile
E.info("%s: found peak shift of %i" % (track, shift))
samfiles = [pysam.Samfile(bamfile, "rb")]
offsets = [shift / 2]
if controlfile:
controlfiles = [pysam.Samfile(controlfile, "rb")]
readlength = BamTools.estimateTagSize(controlfile)
control_max_peakval = readlength // 2
E.info("removing intervals in which control has peak higher than %i reads" %
control_max_peakval)
else:
controlfiles = None
id = 0
# get thresholds
max_qvalue = float(PARAMS["zinba_fdr_threshold"])
with IOTools.openFile(infilename, "r") as ins:
for peak in WrapperZinba.iteratePeaks(ins):
# filter by qvalue
if peak.fdr > max_qvalue:
counter.removed_qvalue += 1
continue
assert peak.refined_start < peak.refined_end
# filter by control
if controlfiles:
npeaks, peakcenter, length, avgval, peakval, nreads = countPeaks(peak.contig,
peak.refined_start,
peak.refined_end,
controlfiles,
offsets)
if peakval > control_max_peakval:
counter.removed_control += 1
continue
# output peak
npeaks, peakcenter, length, avgval, peakval, nreads = countPeaks(peak.contig,
peak.refined_start,
peak.refined_end,
samfiles,
offsets)
outtemp.write("\t".join(map(str, (
id, peak.contig, peak.refined_start, peak.refined_end,
npeaks, peakcenter, length, avgval, peakval, nreads,
1.0 - peak.posterior, 1.0, peak.fdr,
peak.refined_start + peak.summit - 1,
peak.height))) + "\n")
id += 1
counter.output += 1
outtemp.close()
# output filtering summary
outf = IOTools.openFile("%s.tsv.gz" % outfile, "w")
outf.write("category\tcounts\n")
outf.write("%s\n" % counter.asTable())
outf.close()
E.info("%s filtering: %s" % (track, str(counter)))
if counter.output == 0:
E.warn("%s: no peaks found" % track)
# load data into table
if tablename is None:
tablename = "%s_intervals" % track
statement = '''
cgat csv2db %(csv2db_options)s
--allow-empty-file
--add-index=interval_id
--add-index=contig,start
--table=%(tablename)s
< %(tmpfilename)s
> %(outfile)s
'''
P.run()
os.unlink(tmpfilename)
def loadZinba(infile, outfile, bamfile,
tablename=None,
controlfile=None):
'''load Zinba results in *tablename*
This method loads only positive peaks. It filters peaks by p-value,
q-value and fold change and loads the diagnostic data and
re-calculates peakcenter, peakval, ... using the supplied bamfile.
If *tablename* is not given, it will be :file:`<track>_intervals`
where track is derived from ``infile`` and assumed to end
in :file:`.zinba`.
If no peaks were predicted, an empty table is created.
This method creates :file:`<outfile>.tsv.gz` with the results
of the filtering.
This method uses the refined peak locations.
Zinba peaks can be overlapping. This method does not merge
overlapping intervals.
Zinba calls peaks in regions where there are many reads inside
the control. Thus this method applies a filtering step
removing all intervals in which there is a peak of
more than readlength / 2 height in the control.
.. note:
Zinba calls peaks that are overlapping.
'''
track = P.snip(os.path.basename(infile), ".zinba")
folder = os.path.dirname(infile)
infilename = infile + ".peaks"
outtemp = P.getTempFile(".")
tmpfilename = outtemp.name
outtemp.write("\t".join((
"interval_id",
"contig", "start", "end",
"npeaks", "peakcenter",
"length",
"avgval",
"peakval",
"nprobes",
"pvalue", "fold", "qvalue",
"macs_summit", "macs_nprobes",
)) + "\n")
counter = E.Counter()
if not os.path.exists(infilename):
E.warn("could not find %s" % infilename)
elif IOTools.isEmpty(infilename):
E.warn("no data in %s" % infilename)
else:
# filter peaks
shift = getPeakShiftFromZinba(infile)
assert shift is not None, \
"could not determine peak shift from Zinba file %s" % infile
E.info("%s: found peak shift of %i" % (track, shift))
samfiles = [pysam.Samfile(bamfile, "rb")]
offsets = [shift / 2]
if controlfile:
controlfiles = [pysam.Samfile(controlfile, "rb")]
readlength = BamTools.estimateTagSize(controlfile)
control_max_peakval = readlength // 2
E.info("removing intervals in which control has peak higher than %i reads" %
control_max_peakval)
else:
controlfiles = None
id = 0
# get thresholds
max_qvalue = float(PARAMS["zinba_fdr_threshold"])
with IOTools.openFile(infilename, "r") as ins:
for peak in WrapperZinba.iteratePeaks(ins):
# filter by qvalue
if peak.fdr > max_qvalue:
counter.removed_qvalue += 1
continue
assert peak.refined_start < peak.refined_end
# filter by control
if controlfiles:
npeaks, peakcenter, length, avgval, peakval, nreads = countPeaks(peak.contig,
peak.refined_start,
peak.refined_end,
controlfiles,
offsets)
if peakval > control_max_peakval:
counter.removed_control += 1
continue
# output peak
npeaks, peakcenter, length, avgval, peakval, nreads = countPeaks(peak.contig,
peak.refined_start,
peak.refined_end,
samfiles,
offsets)
outtemp.write("\t".join(map(str, (
id, peak.contig, peak.refined_start, peak.refined_end,
npeaks, peakcenter, length, avgval, peakval, nreads,
1.0 - peak.posterior, 1.0, peak.fdr,
peak.refined_start + peak.summit - 1,
peak.height))) + "\n")
id += 1
counter.output += 1
outtemp.close()
# output filtering summary
outf = IOTools.openFile("%s.tsv.gz" % outfile, "w")
outf.write("category\tcounts\n")
outf.write("%s\n" % counter.asTable())
outf.close()
E.info("%s filtering: %s" % (track, str(counter)))
if counter.output == 0:
E.warn("%s: no peaks found" % track)
# load data into table
if tablename is None:
tablename = "%s_intervals" % track
statement = '''
cgat csv2db %(csv2db_options)s
--allow-empty-file
--add-index=interval_id
--add-index=contig,start
--table=%(tablename)s
< %(tmpfilename)s
> %(outfile)s
'''
P.run()
os.unlink(tmpfilename)
