def main(argv=sys.argv):
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--method",
dest="methods",
type="choice",
action="append",
choices=("reorder-samples", ),
help="method to apply [default=%default]")
parser.add_option("--sort-order",
dest="sort_order",
help="sort order for sample names. Give column names as "
"comma-separated list or specify ``alphabetical`` "
"[default=%default]")
parser.set_defaults(
methods=[],
sort_order="alphabetical",
)
(options, args) = E.start(parser, add_pipe_options=True)
if not options.methods:
raise ValueError("no method specified")
infile = VCF.VCFFile(options.stdin)
sort_order = False
if "reorder-samples" in options.methods:
if options.sort_order:
sort_order = options.sort_order.split(",")
if "alphabetical" in sort_order:
sort_order = sorted(infile.samples)
infile.writeHeader(options.stdout, order=sort_order)
for vcf in infile:
if sort_order:
vcf.order = sort_order
options.stdout.write(str(vcf) + "\n")
E.stop()
def main(argv=sys.argv):
parser = E.OptionParser(
version=
"%prog version: $Id: bed2bed.py 2861 2010-02-23 17:36:32Z andreas $",
usage=globals()["__doc__"])
parser.add_option(
"--reorder",
dest="reorder",
type="string",
help=
"reorder columns. Give column names as comma-separated list or specify ``alphabetical`` [default=%default]"
)
parser.set_defaults(reorder=None)
(options, args) = E.Start(parser, add_pipe_options=True)
noutput = 0
infile = VCF.VCFFile(options.stdin)
if options.reorder:
order = options.reorder.split(",")
if "alphabetical" in order:
order = sorted(infile.samples)
else:
order = False
infile.writeHeader(options.stdout, order=order)
for vcf in infile:
if order:
vcf.order = order
options.stdout.write(str(vcf) + "\n")
E.Stop()
def extractEBioinfo(eBio_ids, vcfs, outfile):
'''find the number of mutations identitified in previous studies (eBio_ids)
for the mutated genes in the vcfs'''
genes = set()
for vcf in vcfs:
infile = VCF.VCFFile(IOTools.openFile(vcf))
for vcf_entry in infile:
# assumes all vcf entries without "REJECT" are "PASS"
if vcf_entry.filter != "REJECT":
info_entries = vcf_entry.info.split(";")
for entry in info_entries:
if "SNPEFF_GENE_NAME" in entry:
genes.update((entry.split("=")[1], ))
eBio_ids = IOTools.openFile(eBio_ids, "r")
tissue_counts = collections.defaultdict(
lambda: collections.defaultdict(lambda: collections.defaultdict(int)))
def chunks(l, n):
''' Yield successive n-sized chunks from l '''
for i in range(0, len(l), n):
yield l[i:i + n]
# delete me
E.info("number of genes: %i" % len(list(genes)))
for line in eBio_ids:
tissue, study, table = line.strip().split("\t")
n = 0
for i in range(0, len(list(genes)), 250):
genes_chunk = list(genes)[i:i + 250]
# TS sporadic error when querying with a single gene at a time
# "urllib2.URLError: <urlopen error [Errno 110] Connection timed out>"
# max URL length appears to be 8200 characters,
# try doing 250 genes at a time?
gene_list = "+".join(list(genes_chunk))
n += len(genes_chunk)
E.info("number of genes processed: %i" % n)
url = (
"http://www.cbioportal.org/webservice.do?cmd=getProfileData&"
"case_set_id=%(study)s_all&genetic_profile_id=%(table)s&"
"gene_list=%(gene_list)s" % locals())
df = pd.io.parsers.read_csv(url,
comment="#",
sep="\t",
index_col=0)
for gene in genes_chunk:
tmp_df = df[df['COMMON'] == gene]
# check dataframe contains data!
if tmp_df.shape[0] != 0:
# seem to be having issues with gene set containing duplicates!
# --> dataframe with repeated instances of gene after selection
# so splice to first row and recreate dataframe from series
if tmp_df.shape[0] > 1:
tmp_df = pd.DataFrame(tmp_df.iloc[0]).T
tissue_counts[tissue][gene]["total"] += tmp_df.shape[1] - 2
tissue_counts[tissue][gene]["mutations"] += int(
tmp_df.count(1)) - 1
out = IOTools.openFile(outfile, "w")
tissues = list(tissue_counts.keys())
out.write(
"gene\t%s\n" %
"\t".join(["%s_frequency" % x.replace(" ", "_") for x in tissues]))
for gene in genes:
freq_values = []
for tissue in tissues:
total = tissue_counts[tissue][gene]["total"]
mutations = tissue_counts[tissue][gene]["mutations"]
freq_values.append(round(np.divide(float(mutations), total), 4))
out.write("%s\t%s\n" % (gene, "\t".join(map(str, freq_values))))
out.close()