def split_gtf_by_category(infiles, outfiles, catname):
catfile, gtffile = infiles
categories = pd.read_csv(catfile, index_col=0, squeeze=True, sep="\t")
# create output filepool
outpool = IOTools.FilePool("{}_%s.gtf.gz".format(catname), force=True)
gtffile = IOTools.openFile(gtffile)
for gtfline in GTF.iterator(gtffile):
try:
transcript_id = gtfline.transcript_id
except AttributeError:
transcript_id = None
try:
gene_id = gtfline.gene_id
except AttributeError:
gene_id = None
if transcript_id in categories.index:
outpool.write(categories[transcript_id], str(gtfline) + "\n")
elif gene_id in categories.index:
outpool.write(categories[gene_id], str(gtfline) + "\n")
outpool.close()
def chunk_iterator_column(infile, args, prefix, use_header=False):
"""split at column.
The table need not be sorted by this column.
If num_files is given, files will randomly created
and tags according to column randomly assigned.
"""
column, max_files = args
files = IOTools.FilePool()
header = False
if max_files:
map_tag2file = {}
for line in infile:
if line[0] == "#":
continue
if not header and use_header:
files.setHeader(line)
header = True
continue
key = line[:-1].split("\t")[column]
if max_files:
if key in map_tag2file:
key = map_tag2file[key]
else:
n = "%010i" % (len(map_tag2file) % max_files)
map_tag2file[key] = n
key = n
files.write("%s/%s.in" % (prefix, key), line)
for filename, count in list(files.items()):
E.info("created file %s with %i items" % (filename, count))
yield filename
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: cgat_script_template.py 2871 2010-03-03 10:20:44Z andreas $",
usage=globals()["__doc__"])
parser.add_option("-o",
"--min-overlap",
dest="min_overlap",
type="int",
help="minimum overlap")
parser.add_option(
"-w",
"--pattern-window",
dest="pattern_window",
type="string",
help=
"regular expression to extract window coordinates from test id [%default]"
)
parser.add_option("-i",
"--invert",
dest="invert",
action="store_true",
help="invert direction of fold change [%default]")
parser.set_defaults(min_overlap=10,
invert=False,
pattern_window="(\S+):(\d+)-(\d+)"),
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv, add_output_options=True)
outfiles = IOTools.FilePool(options.output_filename_pattern)
if options.invert:
test_f = lambda l2fold: l2fold < 0
else:
test_f = lambda l2fold: l2fold > 0
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 = map(int, (start, end))
yield DATA._make(
(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 grouper(data, distance=10):
last = data.next()
entries = [last]
while 1:
d = data.next()
if d is None:
break
if d.contig == last.contig and d.start < last.start:
raise ValueError("error not sorted by start")
if ((d.contig != last.contig) or (d.start - last.end > distance)
or (d.status != last.status)
or (d.significant != last.significant)
or (d.l2fold * last.l2fold < 0)):
yield entries
entries = []
entries.append(d)
last = d
yield entries
counter = E.Counter()
options.stdout.write("\t".join(DATA._fields) + "\n")
# set of all sample names - used to create empty files
samples = set()
# need to sort by coordinate
all_data = list(read())
all_data.sort(key=lambda x: (x.contig, x.start))
for group in grouper(iter(all_data), distance=options.min_overlap):
start, end = group[0].start, group[-1].end
assert start < end, 'start > end: %s' % str(group)
n = float(len(group))
counter.input += n
g = group[0]
if g.l2fold < 0:
l2fold = max([x.l2fold for x in group])
fold = max([x.fold for x in group])
else:
l2fold = min([x.l2fold for x in group])
fold = min([x.fold for x in group])
outdata = DATA._make(
(g.contig, start, end, g.treatment_name,
sum([x.treatment_mean for x in group]) / n,
max([x.treatment_std for x in group]), g.control_name,
sum([x.control_mean
for x in group]) / n, max([x.control_std for x in group]),
max([x.pvalue for x in group]), max([x.qvalue for x in group]),
l2fold, fold, g.significant, g.status, int(n)))
samples.add(g.treatment_name)
samples.add(g.control_name)
if g.significant:
if test_f(g.l2fold):
# treatment lower methylation than control
outfiles.write(
g.treatment_name, "%s\t%i\t%i\t%s\t%f\n" %
(g.contig, g.start, g.end, g.treatment_name,
sum([x.treatment_mean for x in group]) / n))
else:
outfiles.write(
g.control_name, "%s\t%i\t%i\t%s\t%f\n" %
(g.contig, g.start, g.end, g.control_name,
sum([x.control_mean for x in group]) / n))
options.stdout.write("\t".join(map(str, outdata)) + "\n")
counter.output += 1
for sample in samples:
outfiles.write(sample, "")
outfiles.close()
E.info("%s" % counter)
# write footer and output benchmark information.
E.Stop()
def buildExpressionTracks(infile, outfiles, map_exp2columns, suffix):
'''build expression tracks.
read the analysis from FILENAME_EXPRESSION
..note::
The file A589_Data_RMA.csv does NOT always contain the probeset_id
in the first column, but instead it might be the transcript_cluster_id.
A possible explanation is that if several probesets map to the same
transcript cluster, the transcript cluster is normalized.
The set of cluster_id and probeset ids are completely non-overlapping.
Hence, the :term:`cluster_id` will be used.
'''
E.info("importing expression data from %s" % infile)
dbhandle = sqlite3.connect(PARAMS["database"])
cc = dbhandle.cursor()
statement = "SELECT DISTINCT probeset, cluster_id, transcript_id FROM probeset2transcript"
cc.execute(statement)
map_cluster2transcript, map_probeset2cluster = {}, {}
for probeset, cluster, transcript_id in cc.fetchall():
map_probeset2cluster[probeset] = cluster
map_cluster2transcript[cluster] = transcript_id
reader = csv.reader(open(infile, "rU"))
first = True
# do not delete old files as this function is called several times
output_files = IOTools.FilePool(output_pattern="exp%s.data", force=False)
headers = (("Probe Set ID", "cluster_id"), ("Gene Symbol", "genesymbol"),
("mRna - Description", "description"), ('mRNA Accession',
'mrna_id'),
('mRNA Source', 'source'), ('mRNA - xhyb', 'xhyb'),
('GO Biological Process ID',
'go_biol_id'), ('GO Biological Process Term', 'go_biol_term'),
('GO Cellular Component ID',
'go_cell_id'), ('GO Cellular Component Term', 'go_cell_term'),
('GO Molecular Function ID',
'go_mol_id'), ('GO Molecular Function Term', 'go_mol_term'),
('Pathway Source', 'pw_source'), ('Pathway Name', 'pw_name'))
old_headers = set([x[0] for x in headers])
new_headers = [x[1] for x in headers]
take = []
index_soure, index_accession, index_probeset = None, None, None
counts = E.Counter()
found = set()
outf = open(outfiles[0] + suffix, "w")
outf.write("# %s\n" % infile)
outs = open(outfiles[1] + suffix, "w")
outs.write("# %s\n" % infile)
writer = csv.writer(outf)
for row in reader:
if first:
first = False
writer.writerow(row)
for x, old_header in enumerate(row):
if old_header == "mRNA Source": index_source = len(take)
if old_header == "mRNA Accession": index_accession = len(take)
if old_header == "Probe Set ID": index_probeset = len(take)
if old_header in old_headers: take.append(x)
# write headers to all files
outs.write("\t".join(new_headers) + "\n")
for exp, columns in map_exp2columns.items():
output_files.write(
exp, "\t".join(
("cluster_id", Stats.Summary().getHeader(), "\t".join(
["R%i" % i for i in range(len(columns))]))) + "\n")
else:
new_row = []
for x in take:
if row[x].strip() != "---":
new_row.append(row[x].strip())
else:
new_row.append("")
probeset = new_row[index_probeset].strip()
if probeset in map_probeset2cluster:
probeset = map_probeset2cluster[probeset]
counter.mapped_to_cluster += 1
if probeset not in map_cluster2transcript:
writer.writerow(row)
counts.skipped += 1
continue
else:
if probeset in found:
counts.duplicates += 1
counts.output += 1
found.add(probeset)
outs.write("\t".join(new_row) + "\n")
for exp, cols in map_exp2columns.items():
data = [row[x] for x in cols]
output_files.write(
exp, "\t".join(
(probeset, str(Stats.Summary(
[float(x)
for x in data])), "\t".join(data))) + "\n")
outf.close()
if counts.duplicates > 0:
P.warn("duplicate probeset/clusters")
P.info("probeset source information: %s" % str(counts))
output_files.close()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if DISABLE:
print "# tophat_segment_juncs.py disabled"
argv[0] = "segment_juncs.original"
runCommand(argv, "segment_juncs.log")
return 0
E.Start(no_parsing=True)
# collect arguments
parser = argparse.ArgumentParser(description='Process tophat options.')
parser.add_argument('-p',
'--num-threads',
metavar='N',
type=int,
dest='nthreads',
help='number of threads')
parser.add_argument('--version', action='version', version='%(prog)s')
options, args = parser.parse_known_args(argv[1:])
E.info("parallelizing segment juncs with %i threads" % options.nthreads)
x = argv.index("--ium-reads") + 1
all_options = argv[1:x]
(input_missing_reads, input_genome, output_junctions, output_insertions,
output_deletions, input_left_all_reads, input_left_all_map,
input_left_segments_maps) = argv[x:x + 8]
input_left_segments_maps = input_left_segments_maps.split(",")
if len(argv) > x + 8:
(input_right_all_reads, input_right_all_map,
input_right_segments_maps) = argv[x + 8:x + 11]
input_right_segments_maps = input_right_segments_maps.split(",")
else:
input_right_all_reads = ""
input_right_all_map = ""
input_right_segments_maps = []
keys = set()
# some filenames might appear multiple times
files_to_split = set([input_left_all_map, \
input_right_all_map ] +\
input_left_segments_maps +\
input_right_segments_maps )
E.info("splitting %i files" % len(files_to_split))
## split all map files by chromosome
for filename in files_to_split:
if filename == "": continue
E.info("splitting %s" % filename)
base, ext = os.path.splitext(filename)
f = glob.glob("%s.input.*%s" % (filename, ext))
if f:
E.info("files already exist - skipping")
keys.update([
re.match("%s.input.(\S+)%s" % (filename, ext), x).groups()[0]
for x in f
])
continue
infile = IOTools.openFile(filename)
outfiles = IOTools.FilePool(filename + ".input.%s" + ext)
for line in infile:
key = line.split("\t")[2]
keys.add(key)
outfiles.write(key, line)
outfiles.close()
# keys = set( ["chr1", "chr2", "chr3", "chr4", "chr5",
# "chr6", "chr7", "chr8", "chr9", "chr10",
# "chr11", "chr12", "chr13", "chr14", "chr15",
# "chr16", "chr17", "chr18", "chr19", "chr20",
# "chr21", "chr22", "chrX", "chrY", "chrM" ] )
E.info("working on %i contigs: %s" % (len(keys), list(keys)))
pool = multiprocessing.pool.ThreadPool(options.nthreads)
#pool = threadpool.ThreadPool( THREADS )
tmpdir = os.path.dirname(input_left_all_reads)
logdir = os.path.join(tmpdir[:-len("tmp")], "logs")
if not os.path.exists(logdir):
raise IOError("can not find logdir %s" % logdir)
args = []
for key in keys:
def modout(old, key):
if not old: return ""
_, ext = os.path.splitext(old)
return old + ".output.%s%s" % (key, ext)
def modin(old, key):
if not old: return ""
_, ext = os.path.splitext(old)
return old + ".input.%s%s" % (key, ext)
def modgenome(old, key):
dirname, filename = os.path.split(old)
genome, ext = os.path.splitext(filename)
if genome.lower().endswith("_cs"): genome = genome[:-3]
new = os.path.join(dirname, genome + ".perchrom", key + ext)
if not os.path.exists(new):
raise ValueError("can not find chromoseme file %s" % new)
return new
cmd = ["segment_juncs"] +\
all_options +\
[input_missing_reads, \
modgenome(input_genome,key), \
modout(output_junctions,key),\
modout(output_insertions,key),\
modout(output_deletions,key),\
input_left_all_reads,\
modin( input_left_all_map, key ),\
",".join( [ modin( x, key ) for x in input_left_segments_maps ] ),\
input_right_all_reads,\
modin( input_right_all_map, key ),\
",".join( [ modin( x, key ) for x in input_right_segments_maps ] ) ]
logfile = os.path.join(logdir, "segment_juncs_%s.log" % key)
args.append((cmd, logfile))
E.info("submitting %i jobs" % len(keys))
pool.map(runCommand, args, chunksize=1)
pool.close()
pool.join()
E.info("all jobs finished successfully")
E.info("merging results")
## merge results
for filename in (output_junctions, output_insertions, output_deletions):
outfile = open(filename, "w")
for inf in glob.glob(filename + ".output.*"):
infile = open(inf, "r")
outfile.write(infile.read())
infile.close()
outfile.close()
E.info("results merged")
## cleaning up is done automatically by tophat
E.info("cleaning up")
for f in glob.glob( os.path.join( tmpdir, "*.output.*") ) +\
glob.glob( os.path.join( tmpdir, "*.input.*") ):
os.remove(f)
## write footer and output benchmark information.
E.Stop()
