def read():
rx_window = re.compile(options.pattern_window)
# filter any of the DESeq/EdgeR message that end up at the top of the
# output file
for data in IOTools.iterate(options.stdin):
contig, start, end = rx_window.match(data.test_id).groups()
start, end = list(map(int, (start, end)))
yield DATA._make((data.test_id,
contig, start, end,
data.treatment_name,
float(data.treatment_mean),
float(data.treatment_std),
data.control_name,
float(data.control_mean),
float(data.control_std),
float(data.pvalue),
float(data.qvalue),
float(data.l2fold),
float(data.fold),
int(data.significant),
data.status,
0))
def outputAllWindows( infile, outfile ):
'''output all Windows as a bed file with the l2fold change
as a score.
'''
outf = IOTools.openFile( outfile, "w" )
for line in IOTools.iterate( IOTools.openFile( infile ) ):
outf.write( "\t".join( (line.contig, line.start, line.end, "%6.4f" % float(line.l2fold ))) + "\n" )
outf.close()
def outputAllWindows(infile, outfile):
'''output all Windows as a bed file with the l2fold change
as a score.
'''
outf = IOTools.openFile(outfile, "w")
for line in IOTools.iterate(IOTools.openFile(infile)):
outf.write("\t".join((line.contig, line.start, line.end,
"%6.4f" % float(line.l2fold))) + "\n")
outf.close()
def buildDMRStats(infile, outfile, method):
'''build dmr summary statistics.
'''
results = collections.defaultdict(lambda: collections.defaultdict(int))
status = collections.defaultdict(lambda: collections.defaultdict(int))
x = 0
for line in IOTools.iterate(IOTools.openFile(infile)):
key = (line.treatment_name, line.control_name)
r, s = results[key], status[key]
r["tested"] += 1
s[line.status] += 1
is_significant = line.significant == "1"
up = float(line.l2fold) > 0
down = float(line.l2fold) < 0
fold2up = float(line.l2fold) > 1
fold2down = float(line.l2fold) < -1
fold2 = fold2up or fold2down
if up: r["up"] += 1
if down: r["down"] += 1
if fold2up: r["l2fold_up"] += 1
if fold2down: r["l2fold_down"] += 1
if is_significant:
r["significant"] += 1
if up: r["significant_up"] += 1
if down: r["significant_down"] += 1
if fold2: r["fold2"] += 1
if fold2up: r["significant_l2fold_up"] += 1
if fold2down: r["significant_l2fold_down"] += 1
header1, header2 = set(), set()
for r in results.values():
header1.update(r.keys())
for s in status.values():
header2.update(s.keys())
header = ["method", "treatment", "control"]
header1 = list(sorted(header1))
header2 = list(sorted(header2))
outf = IOTools.openFile(outfile, "w")
outf.write("\t".join(header + header1 + header2) + "\n")
for treatment, control in results.keys():
key = (treatment, control)
r = results[key]
s = status[key]
outf.write("%s\t%s\t%s\t" % (method, treatment, control))
outf.write("\t".join([str(r[x]) for x in header1]) + "\t")
outf.write("\t".join([str(s[x]) for x in header2]) + "\n")
def buildDMRStats( infile, outfile, method ):
'''build dmr summary statistics.
'''
results = collections.defaultdict( lambda : collections.defaultdict(int) )
status = collections.defaultdict( lambda : collections.defaultdict(int) )
x = 0
for line in IOTools.iterate( IOTools.openFile( infile ) ):
key = (line.treatment_name, line.control_name )
r,s = results[key], status[key]
r["tested"] += 1
s[line.status] += 1
is_significant = line.significant == "1"
up = float(line.l2fold) > 0
down = float(line.l2fold) < 0
fold2up = float(line.l2fold) > 1
fold2down = float(line.l2fold) < -1
fold2 = fold2up or fold2down
if up: r["up"] += 1
if down: r["down"] += 1
if fold2up: r["l2fold_up"] += 1
if fold2down: r["l2fold_down"] += 1
if is_significant:
r["significant"] += 1
if up: r["significant_up"] += 1
if down: r["significant_down"] += 1
if fold2: r["fold2"] += 1
if fold2up: r["significant_l2fold_up"] += 1
if fold2down: r["significant_l2fold_down"] += 1
header1, header2 = set(), set()
for r in results.values(): header1.update( r.keys() )
for s in status.values(): header2.update( s.keys() )
header = ["method", "treatment", "control" ]
header1 = list(sorted(header1))
header2 = list(sorted(header2))
outf = IOTools.openFile( outfile, "w" )
outf.write( "\t".join(header + header1 + header2) + "\n" )
for treatment,control in results.keys():
key = (treatment,control)
r = results[key]
s = status[key]
outf.write( "%s\t%s\t%s\t" % (method,treatment, control))
outf.write( "\t".join( [str(r[x]) for x in header1 ] ) + "\t" )
outf.write( "\t".join( [str(s[x]) for x in header2 ] ) + "\n" )
def outputAllWindows(infile, outfile):
'''output all windows as a bed file with the l2fold change
as a score.
Arguments
---------
infile : string
Input filename in :term:`tsv` format. Typically the output
from :mod:`scripts/runExpression`.
outfile : string
Output filename in :term:`bed` format.
'''
outf = IOTools.openFile(outfile, "w")
for line in IOTools.iterate(IOTools.openFile(infile)):
outf.write("\t".join((line.contig, line.start, line.end,
"%6.4f" % float(line.l2fold))) + "\n")
outf.close()
def outputAllWindows(infile, outfile):
'''output all windows as a bed file with the l2fold change
as a score.
Arguments
---------
infile : string
Input filename in :term:`tsv` format. Typically the output
from :mod:`scripts/runExpression`.
outfile : string
Output filename in :term:`bed` format.
'''
outf = IOTools.openFile(outfile, "w")
for line in IOTools.iterate(IOTools.openFile(infile)):
outf.write("\t".join(
(line.contig, line.start, line.end,
"%6.4f" % float(line.l2fold))) + "\n")
outf.close()
def buildDMRStats(infiles, outfile, method, fdr_threshold=None):
'''build dmr summary statistics.
This method works from output files created by Expression.py
(method="deseq" or method="edger") or runMEDIPS (method="medips")
This method counts the number of up/down, 2fold up/down, etc.
genes in output from (:mod:`scripts/runExpression`).
This method also creates diagnostic plots in the
<exportdir>/<method> directory.
Arguments
---------
infiles ; list
List of tabs with DMR output
outfile : string
Output filename. Tab separated file summarizing
method : string
Method name
fdr_threshold : float
FDR threshold to apply. Currently unused.
'''
results = collections.defaultdict(lambda: collections.defaultdict(int))
status = collections.defaultdict(lambda: collections.defaultdict(int))
# deseq/edger
def f_significant(x):
return x.significant == "1"
def f_up(x):
return float(x.l2fold) > 0
def f_down(x):
return float(x.l2fold) < 0
def f_fold2up(x):
return float(x.l2fold) > 1
def f_fold2down(x):
return float(x.l2fold) < -1
def f_key(x):
return (x.treatment_name, x.control_name)
def f_status(x):
return x.status
outf = IOTools.openFile(outfile, "w")
is_first = True
for infile in infiles:
xx = 0
for line in IOTools.iterate(IOTools.openFile(infile)):
key = f_key(line)
r, s = results[key], status[key]
r["tested"] += 1
ss = f_status(line)
s[ss] += 1
if ss != "OK":
continue
is_significant = f_significant(line)
up = f_up(line)
down = f_down(line)
fold2up = f_fold2up(line)
fold2down = f_fold2down(line)
fold2 = fold2up or fold2down
if up:
r["up"] += 1
if down:
r["down"] += 1
if fold2up:
r["l2fold_up"] += 1
if fold2down:
r["l2fold_down"] += 1
if is_significant:
r["significant"] += 1
if up:
r["significant_up"] += 1
if down:
r["significant_down"] += 1
if fold2:
r["fold2"] += 1
if fold2up:
r["significant_l2fold_up"] += 1
if fold2down:
r["significant_l2fold_down"] += 1
if xx > 10000:
break
if is_first:
is_first = False
header1, header2 = set(), set()
for r in results.values():
header1.update(r.keys())
for s in status.values():
header2.update(s.keys())
header = ["method", "treatment", "control"]
header1 = list(sorted(header1))
header2 = list(sorted(header2))
outf.write("\t".join(header + header1 + header2) + "\n")
for treatment, control in results.keys():
key = (treatment, control)
r = results[key]
s = status[key]
outf.write("%s\t%s\t%s\t" % (method, treatment, control))
outf.write("\t".join([str(r[x]) for x in header1]) + "\t")
outf.write("\t".join([str(s[x]) for x in header2]) + "\n")
def buildDMRStats(infiles, outfile, method, fdr_threshold=None):
'''build dmr summary statistics.
This method works from output files created by Expression.py
(method="deseq" or method="edger") or runMEDIPS (method="medips")
This method counts the number of up/down, 2fold up/down, etc.
genes in output from (:mod:`scripts/runExpression`).
This method also creates diagnostic plots in the
<exportdir>/<method> directory.
Arguments
---------
infiles ; list
List of tabs with DMR output
outfile : string
Output filename. Tab separated file summarizing
method : string
Method name
fdr_threshold : float
FDR threshold to apply. Currently unused.
'''
results = collections.defaultdict(lambda: collections.defaultdict(int))
status = collections.defaultdict(lambda: collections.defaultdict(int))
# deseq/edger
def f_significant(x):
return x.significant == "1"
def f_up(x):
return float(x.l2fold) > 0
def f_down(x):
return float(x.l2fold) < 0
def f_fold2up(x):
return float(x.l2fold) > 1
def f_fold2down(x):
return float(x.l2fold) < -1
def f_key(x):
return (x.treatment_name, x.control_name)
def f_status(x):
return x.status
outf = IOTools.openFile(outfile, "w")
is_first = True
for infile in infiles:
xx = 0
for line in IOTools.iterate(IOTools.openFile(infile)):
key = f_key(line)
r, s = results[key], status[key]
r["tested"] += 1
ss = f_status(line)
s[ss] += 1
if ss != "OK":
continue
is_significant = f_significant(line)
up = f_up(line)
down = f_down(line)
fold2up = f_fold2up(line)
fold2down = f_fold2down(line)
fold2 = fold2up or fold2down
if up:
r["up"] += 1
if down:
r["down"] += 1
if fold2up:
r["l2fold_up"] += 1
if fold2down:
r["l2fold_down"] += 1
if is_significant:
r["significant"] += 1
if up:
r["significant_up"] += 1
if down:
r["significant_down"] += 1
if fold2:
r["fold2"] += 1
if fold2up:
r["significant_l2fold_up"] += 1
if fold2down:
r["significant_l2fold_down"] += 1
if xx > 10000:
break
if is_first:
is_first = False
header1, header2 = set(), set()
for r in results.values():
header1.update(r.keys())
for s in status.values():
header2.update(s.keys())
header = ["method", "treatment", "control"]
header1 = list(sorted(header1))
header2 = list(sorted(header2))
outf.write("\t".join(header + header1 + header2) + "\n")
for treatment, control in results.keys():
key = (treatment, control)
r = results[key]
s = status[key]
outf.write("%s\t%s\t%s\t" % (method, treatment, control))
outf.write("\t".join([str(r[x]) for x in header1]) + "\t")
outf.write("\t".join([str(s[x]) for x in header2]) + "\n")
def buildDMRStats(infiles, outfile, method, fdr_threshold=None):
'''build dmr summary statistics.
This method works from output files created by Expression.py
(method="deseq" or method="edger") or runMEDIPS (method="medips")
'''
results = collections.defaultdict(lambda: collections.defaultdict(int))
status = collections.defaultdict(lambda: collections.defaultdict(int))
# deseq/edger
def f_significant(x):
return x.significant == "1"
def f_up(x):
return float(x.l2fold) > 0
def f_down(x):
return float(x.l2fold) < 0
def f_fold2up(x):
return float(x.l2fold) > 1
def f_fold2down(x):
return float(x.l2fold) < -1
def f_key(x):
return (x.treatment_name, x.control_name)
def f_status(x):
return x.status
outf = IOTools.openFile(outfile, "w")
is_first = True
for infile in infiles:
xx = 0
for line in IOTools.iterate(IOTools.openFile(infile)):
key = f_key(line)
r, s = results[key], status[key]
r["tested"] += 1
ss = f_status(line)
s[ss] += 1
if ss != "OK":
continue
is_significant = f_significant(line)
up = f_up(line)
down = f_down(line)
fold2up = f_fold2up(line)
fold2down = f_fold2down(line)
fold2 = fold2up or fold2down
if up:
r["up"] += 1
if down:
r["down"] += 1
if fold2up:
r["l2fold_up"] += 1
if fold2down:
r["l2fold_down"] += 1
if is_significant:
r["significant"] += 1
if up:
r["significant_up"] += 1
if down:
r["significant_down"] += 1
if fold2:
r["fold2"] += 1
if fold2up:
r["significant_l2fold_up"] += 1
if fold2down:
r["significant_l2fold_down"] += 1
if xx > 10000:
break
if is_first:
is_first = False
header1, header2 = set(), set()
for r in results.values():
header1.update(r.keys())
for s in status.values():
header2.update(s.keys())
header = ["method", "treatment", "control"]
header1 = list(sorted(header1))
header2 = list(sorted(header2))
outf.write("\t".join(header + header1 + header2) + "\n")
for treatment, control in results.keys():
key = (treatment, control)
r = results[key]
s = status[key]
outf.write("%s\t%s\t%s\t" % (method, treatment, control))
outf.write("\t".join([str(r[x]) for x in header1]) + "\t")
outf.write("\t".join([str(s[x]) for x in header2]) + "\n")
