def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("--results-directory", dest="res_dir",
type="string", help="directory to write results"
"tables to")
parser.add_option("--alpha", dest="alpha", type="string",
help="statistical significance p-value threshold")
parser.add_option("--method", dest="method", type="string",
help="analysis design. "
"either timepoint or condition")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
try:
infile = argv[-1]
open(infile, "r")
# check for compression state
if infile.split(".")[-1] == "gz":
comp = "gzip"
else:
comp = None
except IOError:
infile = options.stdin
# check for compression state
if infile.name.split(".")[-1] == "gz":
comp = "gzip"
else:
comp = None
alpha = float(options.alpha)
res_dir = options.res_dir
count_table = pd.read_table(infile,
sep="\t",
index_col=0,
header=0,
compression=comp)
columns = count_table.columns
conditions = set([x.split(".")[0] for x in columns])
times = set([x.split(".")[1] for x in columns])
data_dict = {}
cond_times = [x for x in itertools.product(conditions, times)]
base_col = {}
time_dict = {}
if options.method == "timepoint":
# assumes all column names are in the form
# `condition`:`time`:`replicate`
# use `condition`.`time` as dictionary keys
for x in cond_times:
c_t = "%s.%s" % (x[0], x[1])
cols = [k for k in count_table.columns if re.search(c_t, k)]
if x[1] == '000':
base_col[c_t] = count_table[cols]
else:
time_dict[c_t] = count_table[cols]
for bt in itertools.product(base_col.keys(),
time_dict.keys()):
df = pd.merge(left=base_col[bt[0]],
right=time_dict[bt[1]],
how='outer',
left_index=True,
right_index=True)
time = int(bt[1].split(".")[1])
data_dict["%s_0_%i" % (bt[0].split(".")[0],
time)] = df
for each in data_dict.keys():
df_ = data_dict[each]
outfile = "%s/%s-time.tsv" % (res_dir,
each)
res_frame = TS.timepointDESeq2(df_,
each,
alpha,
res_dir)
res_frame.to_csv(outfile,
sep="\t",
index_label="gene_id")
elif options.method == "condition":
# assumes all column names are in the form
# `condition`:`time`:`replicate`
# use `condition`.`time` as dictionary keys
for x in cond_times:
c_t = "%s.%s" % (x[0], x[1])
cols = [k for k in count_table.columns if re.search(c_t, k)]
if int(x[1]) == 0:
base_col[c_t] = count_table[cols]
else:
time_dict[c_t] = count_table[cols]
# make a dataframe for each 0:time point combination
# for all conditions, index on `condition:0_time`
base_keys = base_col.keys()
time_keys = time_dict.keys()
for k in conditions:
for x in itertools.product(base_keys, time_keys):
if re.search(k, x[0]) and re.search(k, x[1]):
df = pd.merge(left=base_col[x[0]],
right=time_dict[x[1]],
how='outer',
left_index=True,
right_index=True)
time = int(x[1].split(".")[1])
data_dict["%s.0_%i" % (x[0].split(".")[0],
time)] = df
else:
pass
time_span = set([x.split(".")[1] for x in data_dict.keys()])
all_dict = {}
for cond in itertools.combinations(conditions, 2):
c1 = cond[0]
c2 = cond[1]
for x in time_span:
key1 = "%s.%s" % (c1, x)
key2 = "%s.%s" % (c2, x)
df = pd.merge(left=data_dict[key1],
right=data_dict[key2],
how='outer',
left_index=True,
right_index=True)
all_dict["%s_%s.%s-diff" % (c1, c2, x)] = df
for each in all_dict.keys():
df = all_dict[each]
outfile = "%s/%s-cond.tsv" % (res_dir,
each)
res_frame = TS.conditionDESeq2(df,
each,
alpha,
res_dir)
res_frame.to_csv(outfile, sep="\t", index_label="gene_id")
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t",
"--test",
dest="test",
type="string",
help="supply help")
parser.add_option("--results-directory",
dest="res_dir",
type="string",
help="directory to write results"
"tables to")
parser.add_option("--alpha",
dest="alpha",
type="string",
help="statistical significance p-value threshold")
parser.add_option("--method",
dest="method",
type="string",
help="analysis design. "
"either timepoint or condition")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
try:
infile = argv[-1]
open(infile, "r")
# check for compression state
if infile.split(".")[-1] == "gz":
comp = "gzip"
else:
comp = None
except IOError:
infile = options.stdin
# check for compression state
if infile.name.split(".")[-1] == "gz":
comp = "gzip"
else:
comp = None
alpha = float(options.alpha)
res_dir = options.res_dir
count_table = pd.read_table(infile,
sep="\t",
index_col=0,
header=0,
compression=comp)
columns = count_table.columns
conditions = set([x.split(".")[0] for x in columns])
times = set([x.split(".")[1] for x in columns])
data_dict = {}
cond_times = [x for x in itertools.product(conditions, times)]
base_col = {}
time_dict = {}
if options.method == "timepoint":
# assumes all column names are in the form
# `condition`:`time`:`replicate`
# use `condition`.`time` as dictionary keys
for x in cond_times:
c_t = "%s.%s" % (x[0], x[1])
cols = [k for k in count_table.columns if re.search(c_t, k)]
if x[1] == '000':
base_col[c_t] = count_table[cols]
else:
time_dict[c_t] = count_table[cols]
for bt in itertools.product(base_col.keys(), time_dict.keys()):
df = pd.merge(left=base_col[bt[0]],
right=time_dict[bt[1]],
how='outer',
left_index=True,
right_index=True)
time = int(bt[1].split(".")[1])
data_dict["%s_0_%i" % (bt[0].split(".")[0], time)] = df
for each in data_dict.keys():
df_ = data_dict[each]
outfile = "%s/%s-time.tsv" % (res_dir, each)
res_frame = TS.timepointDESeq2(df_, each, alpha, res_dir)
res_frame.to_csv(outfile, sep="\t", index_label="gene_id")
elif options.method == "condition":
# assumes all column names are in the form
# `condition`:`time`:`replicate`
# use `condition`.`time` as dictionary keys
for x in cond_times:
c_t = "%s.%s" % (x[0], x[1])
cols = [k for k in count_table.columns if re.search(c_t, k)]
if int(x[1]) == 0:
base_col[c_t] = count_table[cols]
else:
time_dict[c_t] = count_table[cols]
# make a dataframe for each 0:time point combination
# for all conditions, index on `condition:0_time`
base_keys = base_col.keys()
time_keys = time_dict.keys()
for k in conditions:
for x in itertools.product(base_keys, time_keys):
if re.search(k, x[0]) and re.search(k, x[1]):
df = pd.merge(left=base_col[x[0]],
right=time_dict[x[1]],
how='outer',
left_index=True,
right_index=True)
time = int(x[1].split(".")[1])
data_dict["%s.0_%i" % (x[0].split(".")[0], time)] = df
else:
pass
time_span = set([x.split(".")[1] for x in data_dict.keys()])
all_dict = {}
for cond in itertools.combinations(conditions, 2):
c1 = cond[0]
c2 = cond[1]
for x in time_span:
key1 = "%s.%s" % (c1, x)
key2 = "%s.%s" % (c2, x)
df = pd.merge(left=data_dict[key1],
right=data_dict[key2],
how='outer',
left_index=True,
right_index=True)
all_dict["%s_%s.%s-diff" % (c1, c2, x)] = df
for each in all_dict.keys():
df = all_dict[each]
outfile = "%s/%s-cond.tsv" % (res_dir, each)
res_frame = TS.conditionDESeq2(df, each, alpha, res_dir)
res_frame.to_csv(outfile, sep="\t", index_label="gene_id")
# write footer and output benchmark information.
E.Stop()
