def main():
parser = OptionParser()
parser.add_option("-i","--input_heats",dest="input_heats",action="store",default=None,help="\
File with columns as samples, rows as pathway features")
parser.add_option("-r", "--real-only", action="store_true", default=False, help="Report only observed values")
parser.add_option("-k","--kernel",dest="kernel",action="store",default=None,help="\
Pre-computed heat diffusion kernel in tab-delimited form. Should have both a header and row labels")
parser.add_option("-o","--output",dest="output",action="store",default="diffused.tab")
parser.add_option("-n","--network",dest="network",action="store",default=None,help="\
.sif network file for the curated pathway to search. <source> <(-a>,-a|,-t>,-t|,-component>)> <target>")
parser.add_option("--pagerank",dest="pagerank",action="store_true",default=False,help="Use Personalized PageRank to Diffuse")
(opts, args) = parser.parse_args()
if opts.input_heats is None:
print "Please define input_heat"
return
input_heats = DataMatrix(opts.input_heats)
#
# Diffusion Step:
# Load the heat diffusion kernel and perform a kernel-multiply, or alternatively use supplied
# page-rank diffused vectors
#
if opts.network is None:
print "Please define network"
return
print "Parsing Network File..."
network = parseNet(opts.network)
network_nodes = getNetworkNodes(network)
diffuser = None
if opts.pagerank:
from ppr import PPrDiffuser
diffuser = PPrDiffuser(network)
else:
print "Loading Heat Diffusion Kernel.."
if not opts.kernel:
# requires SCIPY installation
from kernel_scipy import SciPYKernel
diffuser = SciPYKernel(opts.network)
else:
diffuser = Kernel(opts.kernel)
print "Finished Loading..."
if opts.real_only:
print "Report Real only..."
feature_list = input_heats.getFeatureList()
else:
feature_list = diffuser.getKernelLabels()
# negative values require dual-diffusion and a merge/sum step
m_diffuser = MultiDiffuser(diffuser)
out = open(opts.output, 'w')
out.write("Sample\t"+"\t".join(feature_list)+"\n")
for sample in input_heats.getSampleList():
in_heat_vec = input_heats.getSampleValues(sample)
diffused_heat_vec = m_diffuser.diffuse(in_heat_vec, reverse=False)
# print diffused heats in order, to line up with feature columns
out.write(sample+"\t"+"\t".join([str(diffused_heat_vec.get(feature, "NA")) for feature in feature_list])+"\n")
out.close()
def main():
parser = OptionParser()
parser.add_option("-i",
"--input_heats",
dest="input_heats",
action="store",
default=None,
help="\
File with columns as samples, rows as pathway features")
parser.add_option("-r",
"--real-only",
action="store_true",
default=False,
help="Report only observed values")
parser.add_option("-k",
"--kernel",
dest="kernel",
action="store",
default=None,
help="\
Pre-computed heat diffusion kernel in tab-delimited form. Should have both a header and row labels"
)
parser.add_option("-o",
"--output",
dest="output",
action="store",
default="diffused.tab")
parser.add_option("-n",
"--network",
dest="network",
action="store",
default=None,
help="\
.sif network file for the curated pathway to search. <source> <(-a>,-a|,-t>,-t|,-component>)> <target>"
)
parser.add_option("--pagerank",
dest="pagerank",
action="store_true",
default=False,
help="Use Personalized PageRank to Diffuse")
(opts, args) = parser.parse_args()
if opts.input_heats is None:
print "Please define input_heat"
return
input_heats = DataMatrix(opts.input_heats)
#
# Diffusion Step:
# Load the heat diffusion kernel and perform a kernel-multiply, or alternatively use supplied
# page-rank diffused vectors
#
if opts.network is None:
print "Please define network"
return
print "Parsing Network File..."
network = parseNet(opts.network)
network_nodes = getNetworkNodes(network)
diffuser = None
if opts.pagerank:
from ppr import PPrDiffuser
diffuser = PPrDiffuser(network)
else:
print "Loading Heat Diffusion Kernel.."
if not opts.kernel:
# requires SCIPY installation
from kernel_scipy import SciPYKernel
diffuser = SciPYKernel(opts.network)
else:
diffuser = Kernel(opts.kernel)
print "Finished Loading..."
if opts.real_only:
print "Report Real only..."
feature_list = input_heats.getFeatureList()
else:
feature_list = diffuser.getKernelLabels()
# negative values require dual-diffusion and a merge/sum step
m_diffuser = MultiDiffuser(diffuser)
out = open(opts.output, 'w')
out.write("Sample\t" + "\t".join(feature_list) + "\n")
for sample in input_heats.getSampleList():
in_heat_vec = input_heats.getSampleValues(sample)
diffused_heat_vec = m_diffuser.diffuse(in_heat_vec, reverse=False)
# print diffused heats in order, to line up with feature columns
out.write(sample + "\t" + "\t".join([
str(diffused_heat_vec.get(feature, "NA"))
for feature in feature_list
]) + "\n")
out.close()
