def buildSelectStatementfromPed(filter_type, pedfile, template):
'''Build a select statement from a template and a pedigree file'''
pedigree = csv.DictReader(
IOTools.open_file(pedfile),
delimiter='\t',
fieldnames=['family', 'sample', 'father', 'mother', 'sex', 'status'])
affecteds = []
unaffecteds = []
parents = []
select = None
# loop over pedigree file and establish relationships
for row in pedigree:
if row['status'] == '2':
if filter_type == "denovo":
father = row['father']
mother = row['mother']
proband = row['sample']
elif filter_type == "dominant" or filter_type == "recessive":
affecteds += [row['sample']]
if filter_type == "recessive":
parents += [row['father'], row['mother']]
if row['status'] == '1':
if filter_type == "dominant":
unaffecteds += [row['sample']]
elif filter_type == "recessive":
if row['sample'] not in parents:
unaffecteds += [row['sample']]
# Build select statement from template
if filter_type == "denovo":
select = template.replace("father", father)
select = select.replace("mother", mother)
select = select.replace("proband", proband)
elif filter_type == "dominant":
affecteds_exp = '").getPL().1==0&&vc.getGenotype("'.join(affecteds)
if len(unaffecteds) == 0:
unaffecteds_exp = ''
else:
unaffecteds_exp = '&&vc.getGenotype("' + \
('").isHomRef()&&vc.getGenotype("'.join(unaffecteds)) + \
'").isHomRef()'
select = template.replace("affecteds_exp", affecteds_exp)
select = select.replace("unaffecteds_exp", unaffecteds_exp)
elif filter_type == "recessive":
affecteds_exp = '").getPL().2==0&&vc.getGenotype("'.join(affecteds)
unaffecteds_exp = '").getPL().2!=0&&vc.getGenotype("'.join(unaffecteds)
if len(parents) == 0:
parents_exp = ''
else:
parents_exp = '&&vc.getGenotype("' + \
('").getPL().1==0&&vc.getGenotype("'.join(parents)) + \
'").getPL().1==0'
select = template.replace("affecteds_exp", affecteds_exp)
select = select.replace("unaffecteds_exp", unaffecteds_exp)
select = select.replace("parents_exp", parents_exp)
return select
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id",
usage=globals()["__doc__"])
parser.add_option("-u",
"--ucsc-genome",
dest="ucsc_genome",
type="string",
help="UCSC genome identifier [default=%default].")
parser.add_option("-g",
"--genome-file",
dest="genome_file",
type="string",
help="filename with genome [default=%default].")
parser.add_option("--extend",
dest="extension",
type="int",
help="extend tags by this number of bases "
"[default=%default].")
parser.add_option("--shift-size",
dest="shift",
type="int",
help="shift tags by this number of bases "
"[default=%default].")
parser.add_option("--window-size",
dest="window_size",
type="int",
help="window size to be used in the analysis"
"[default=%default].")
parser.add_option("--saturation-iterations",
dest="saturation_iterations",
type="int",
help="iterations for saturation analysis "
"[default=%default].")
parser.add_option("-t",
"--toolset",
dest="toolset",
type="choice",
action="append",
choices=("saturation", "coverage", "enrichment", "dmr",
"rms", "rpm", "all", "convert"),
help="actions to perform [default=%default].")
parser.add_option("-w",
"--bigwig-file",
dest="bigwig",
action="store_true",
help="store wig files as bigwig files - requires a "
"genome file [default=%default]")
parser.add_option("--treatment",
dest="treatment_files",
type="string",
action="append",
help="BAM files for treatment. At least one is required "
"[%default]")
parser.add_option("--control",
dest="control_files",
type="string",
action="append",
help="BAM files for control for differential "
"methylation analysis. Optional [%default].")
parser.add_option("--input",
dest="input_files",
type="string",
action="append",
help="BAM files for input correction. "
"Optional [%default].")
parser.add_option("--is-not-medip",
dest="is_medip",
action="store_false",
help="data is not MeDIP data and is not expected "
"to fit the calibration model. No CpG "
"density normalized rms data is computed"
"[default=%default].")
parser.add_option("--output-rdata",
dest="output_rdata",
action="store_true",
help="in dmr analysis, write R session to file. "
"The file name "
"is given by --ouptut-filename-pattern [%default].")
parser.add_option("--rdata-file",
dest="input_rdata",
type="string",
help="in dmr analysis, read saved R session from "
"file. This can be used to apply different "
"filters [%default]")
parser.add_option("--fdr-threshold",
dest="fdr_threshold",
type="float",
help="FDR threshold to apply for selecting DMR "
"[default=%default].")
parser.add_option("--fdr-method",
dest="fdr_method",
type="choice",
choices=("bonferroni", "BH", "holm", "hochberg",
"hommel", "BY", "fdr", "none"),
help="FDR method to apply for selecting DMR "
"[default=%default].")
parser.add_option("--bwa",
dest="bwa",
action="store_true",
help="alignment generated with bwa"
"[default=%default].")
parser.add_option("--unique",
dest="unique",
type="float",
help="Threshold p-value to determine which read pile\
ups are the result of PCR overamplification"
"[default=%default].")
parser.add_option("--chroms",
dest="chroms",
type="str",
help="Comma delimited list of chromosomes to include"
"[default=%default].")
parser.set_defaults(input_format="bam",
ucsc_genome="Hsapiens.UCSC.hg19",
genome_file=None,
extend=0,
shift=0,
window_size=300,
saturation_iterations=10,
toolset=[],
bigwig=False,
treatment_files=[],
control_files=[],
input_files=[],
output_rdata=False,
input_rdata=None,
is_medip=True,
fdr_threshold=0.1,
fdr_method="BH",
bwa=False,
unique=0.001,
chroms=None)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.start(parser, argv=argv, add_output_options=True)
if "convert" in options.toolset:
results = []
for line in CSV.DictReader(options.stdin, dialect="excel-tab"):
if line['edgeR.p.value'] == "NA":
continue
# assumes only a single treatment/control
treatment_name = options.treatment_files[0]
control_name = options.control_files[0]
status = "OK"
try:
results.append(
Expression.GeneExpressionResult._make((
"%s:%i-%i" %
(line['chr'], int(line['start']), int(line['stop'])),
treatment_name,
float(line['MSets1.rpkm.mean']),
0,
control_name,
float(line['MSets2.rpkm.mean']),
0,
float(line['edgeR.p.value']),
float(line['edgeR.adj.p.value']),
float(line['edgeR.logFC']),
math.pow(2.0, float(line['edgeR.logFC'])),
float(line['edgeR.logFC']), # no transform
["0", "1"][float(line['edgeR.adj.p.value']) <
options.fdr_threshold],
status)))
except ValueError as msg:
raise ValueError("parsing error %s in line: %s" % (msg, line))
Expression.writeExpressionResults(options.stdout, results)
return
if len(options.treatment_files) < 1:
raise ValueError("please specify a filename with sample data")
if options.bigwig and not options.genome_file:
raise ValueError("please provide a genome file when outputting bigwig")
if options.genome_file:
fasta = IndexedFasta.IndexedFasta(options.genome_file)
contig_sizes = fasta.getContigSizes()
if len(options.toolset) == 0:
options.toolset = ["all"]
do_all = "all" in options.toolset
if options.chroms is None:
chrstring = ""
else:
chroms = options.chroms.split(",")
chrstring = ' chr.select=c(\"%s\"), ' % '\",\"'.join(chroms)
# load MEDIPS
R.library('MEDIPS')
genome_file = 'BSgenome.%s' % options.ucsc_genome
R.library(genome_file)
window_size = options.window_size
extend = options.extend
shift = options.shift
saturation_iterations = options.saturation_iterations
uniq = float(options.unique)
if options.bwa is True:
BWA = "TRUE"
else:
BWA = "FALSE"
if "saturation" in options.toolset or do_all:
E.info("saturation analysis")
for fn in options.treatment_files + options.control_files:
paired = isPaired(fn)
R('''sr = MEDIPS.saturation(
file='%(fn)s',
BSgenome='%(genome_file)s',
shift=%(shift)i,
extend=%(extend)i,
window_size=%(window_size)i,
uniq=%(uniq)s,
nit = %(saturation_iterations)i,
paired = %(paired)s,
bwa = %(BWA)s,
%(chrstring)s
nrit = 1)''' % locals())
R.png(E.get_output_file("%s_saturation.png" % fn))
R('''MEDIPS.plotSaturation(sr)''')
R('''dev.off()''')
R('''write.table(sr$estimation, file ='%s', sep='\t')''' %
E.get_output_file("%s_saturation_estimation.tsv" % fn))
outfile = IOTools.open_file(
E.get_output_file("%s_saturation.tsv" % fn), "w")
outfile.write("category\tvalues\n")
outfile.write("estimated_correlation\t%s\n" %
",".join(["%f" % x for x in R('''sr$maxEstCor''')]))
outfile.write("true_correlation\t%s\n" %
",".join(["%f" % x for x in R('''sr$maxTruCor''')]))
outfile.write("nreads\t%s\n" %
",".join(["%i" % x
for x in R('''sr$numberReads''')]))
outfile.close()
if "coverage" in options.toolset or do_all:
E.info("CpG coverage analysis")
for fn in options.treatment_files + options.control_files:
paired = isPaired(fn)
R('''cr = MEDIPS.seqCoverage(
file='%(fn)s',
BSgenome='%(genome_file)s',
pattern='CG',
shift=%(shift)i,
extend=%(extend)i,
paired=%(paired)s,
bwa=%(BWA)s,
%(chrstring)s
uniq=%(uniq)s)''' % locals())
R.png(E.get_output_file("%s_cpg_coverage_pie.png" % fn))
R('''MEDIPS.plotSeqCoverage(seqCoverageObj=cr,
type = "pie", cov.level = c(0, 1, 2, 3, 4, 5))''')
R('''dev.off()''')
R.png(E.get_output_file("%s_cpg_coverage_hist.png" % fn))
R('''MEDIPS.plotSeqCoverage(seqCoverageObj=cr,
type = "hist", t=15)''')
R('''dev.off()''')
# note: this file is large
R('''write.table(cr$cov.res, file=gzfile('%s','w'),
sep='\t')''' %
E.get_output_file("%s_saturation_coveredpos.tsv.gz" % fn))
if 'enrichment' in options.toolset or do_all:
E.info("CpG enrichment analysis")
outfile = IOTools.open_file(E.get_output_file("enrichment.tsv.gz"),
"w")
slotnames = (("regions.CG", "regions_CG",
"%i"), ("regions.C", "regions_C",
"%s"), ("regions.G", "regions_G", "%f"),
("regions.relH", "regions_relH",
"%i"), ("regions.GoGe", "regions_GoGe",
"%i"), ("genome.CG", "genome_CG",
"%s"), ("genome.C", "genome_C", "%s"),
("genome.G", "genome_G", "%i"), ("genome.relH",
"genome_relH", "%i"),
("enrichment.score.relH", "enrichment_relH", "%s"),
("enrichment.score.GoGe", "enrichment_GoGe", "%s"))
outfile.write("\t".join(['sample'] + [x[1] for x in slotnames]) + "\n")
for fn in options.treatment_files + options.control_files:
paired = isPaired(fn)
R('''ce = MEDIPS.CpGenrich(
file='%(fn)s',
BSgenome='%(genome_file)s',
shift=%(shift)i,
extend=%(extend)i,
paired=%(paired)s,
bwa=%(BWA)s,
%(chrstring)s
uniq=%(uniq)s)''' % locals())
outfile.write("%s" % fn)
for slotname, label, pattern in slotnames:
value = tuple(R('''ce$%s''' % slotname))
if len(value) == 0:
value = ""
outfile.write("\t%s" % pattern % value[0])
outfile.write("\n")
outfile.close()
if options.input_rdata:
E.info("reading R session info from '%s'" % options.input_rdata)
R('''load('%s')''' % options.input_rdata)
else:
if "dmr" in options.toolset or "correlation" in options.toolset \
or do_all:
# build four sets
for x, fn in enumerate(options.treatment_files):
paired = isPaired(fn)
E.info("loading '%s'" % fn)
R('''treatment_R%(x)i = MEDIPS.createSet(
file='%(fn)s',
BSgenome='%(genome_file)s',
shift=%(shift)i,
extend=%(extend)i,
window_size=%(window_size)i,
paired=%(paired)s,
bwa=%(BWA)s,
%(chrstring)s
uniq=%(uniq)s)''' % locals())
R('''treatment_set = c(%s)''' % ",".join([
"treatment_R%i" % x
for x in range(len(options.treatment_files))
]))
if options.control_files:
for x, fn in enumerate(options.control_files):
paired = isPaired(fn)
E.info("loading '%s'" % fn)
R('''control_R%(x)i = MEDIPS.createSet(
file='%(fn)s',
BSgenome='%(genome_file)s',
shift=%(shift)i,
extend=%(extend)i,
window_size=%(window_size)i,
paired=%(paired)s,
bwa=%(BWA)s,
%(chrstring)s
uniq=%(uniq)s)''' % locals())
R('''control_set = c(%s)''' % ",".join([
"control_R%i" % x
for x in range(len(options.control_files))
]))
# build coupling vector
R('''CS = MEDIPS.couplingVector(pattern="CG",
refObj = treatment_set[[1]])''')
if "correlation" in options.toolset or do_all:
R('''cor.matrix = MEDIPS.correlation(
c(treatment_set, control_set))''')
R('''write.table(cor.matrix,
file='%s',
sep="\t")''' % E.get_output_file("correlation"))
if "dmr" in options.toolset or do_all:
# Data that does not fit the model causes
# "Error in 1:max_signal_index : argument of length 0"
# The advice is to set MeDIP=FALSE
# See: http://comments.gmane.org/
# gmane.science.biology.informatics.conductor/52319
if options.is_medip:
medip = "TRUE"
else:
medip = "FALSE"
fdr_method = options.fdr_method
E.info("applying test for differential methylation")
R('''meth = MEDIPS.meth(
MSet1 = treatment_set,
MSet2 = control_set,
CSet = CS,
ISet1 = NULL,
ISet2 = NULL,
p.adj = "%(fdr_method)s",
diff.method = "edgeR",
MeDIP = %(medip)s,
CNV = F,
minRowSum = 1)''' % locals())
# Note: several Gb in size
# Output full methylation data table
R('''write.table(meth,
file=gzfile('%s', 'w'),
sep="\t",
row.names=F,
quote=F)''' % E.get_output_file("data.tsv.gz"))
# save R session
if options.output_rdata:
R('''save.image(file='%s', safe=FALSE)''' %
E.get_output_file("session.RData"))
# DMR analysis - test for windows and output
if "dmr" in options.toolset:
E.info("selecting differentially methylated windows")
# test windows for differential methylation
fdr_threshold = options.fdr_threshold
R('''tested = MEDIPS.selectSig(meth,
adj=T,
ratio=NULL,
p.value=%(fdr_threshold)f,
bg.counts=NULL,
CNV=F)''' % locals())
R('''write.table(tested,
file=gzfile('%s', 'w'),
sep="\t",
quote=F)''' % E.get_output_file("significant_windows.gz"))
# select gain and merge adjacent windows
try:
R('''gain = tested[which(tested[, grep("logFC", colnames(tested))] > 0),];
gain_merged = MEDIPS.mergeFrames(frames=gain, distance=1)''')
E.info('gain output: %s, merged: %s' %
(str(R('''dim(gain)''')), str(R('''dim(gain_merged)'''))))
R('''of=gzfile('%s', 'w');
write.table(gain_merged,
file=of,
sep="\t",
quote=F,
row.names=FALSE,
col.names=FALSE); close(of)''' % E.get_output_file("gain.bed.gz"))
except rpy2.rinterface.RRuntimeError as msg:
E.warn("could not compute gain windows: msg=%s" % msg)
# select loss and merge adjacent windows
try:
R('''loss = tested[which(tested[, grep("logFC", colnames(tested))] < 0),];
loss_merged = MEDIPS.mergeFrames(frames=loss, distance=1)''')
E.info('loss output: %s, merged: %s' %
(str(R('''dim(loss)''')), str(R('''dim(loss_merged)'''))))
R('''of=gzfile('%s', 'w');
write.table(loss_merged,
file=of,
sep="\t",
quote=F,
row.names=F,
col.names=F); close(of)''' % E.get_output_file("loss.bed.gz"))
except rpy2.rinterface.RRuntimeError as msg:
E.warn("could not compute loss windows: msg=%s" % msg)
# if "rpm" in options.toolset or do_all:
# outputfile = E.get_output_file("rpm.wig")
# R('''MEDIPS.exportWIG(file = '%(outputfile)s',
# data = CONTROL.SET, raw = T, descr = "rpm")''' %
# locals())
# if options.bigwig:
# bigwig(outputfile, contig_sizes)
# else:
# compress(outputfile)
# if "rms" in options.toolset or do_all:
# outputfile = E.get_output_file("rms.wig")
# R('''MEDIPS.exportWIG(file = '%(outputfile)s',
# data = CONTROL.SET, raw = F, descr = "rms")''' %
# locals())
# if options.bigwig:
# bigwig(outputfile, contig_sizes)
# else:
# compress(outputfile)
# write footer and output benchmark information.
E.stop()
