def run( args ):
# Set up the arguments for a general CoMEt run on real data
realOutputDir = "{}/comet-results".format(args.output_directory)
realCometArgs = []
permuteFlags = ["-np", "--parallel", "--keep_temp_files", "-o"]
for i, arg in enumerate(sys.argv[1:]):
if arg not in permuteFlags and sys.argv[i] not in permuteFlags:
realCometArgs.append( arg )
realCometArgs += [ "-o", realOutputDir, "--noviz"]
# perform simple run without viz first.
results = runComet(realCometArgs)
# Load mutation data using Multi-Dendrix and output as a temporary file
realMutations = C.load_mutation_data(args.mutation_matrix, args.patient_file,
args.gene_file, args.min_freq, args.subtype)
m, n, genes, patients, geneToCases, patientToGenes, subtypes = realMutations
if args.verbose:
print('* Mutation data: %s genes x %s patients' % (m, n))
# Construct bipartite graph from mutation data
if args.verbose: print("* Creating bipartite graph...")
G = C.construct_mutation_graph(geneToCases, patientToGenes)
if args.verbose:
print('\t- Graph has', len( G.edges() ), 'edges among', len( G.nodes() ), 'nodes.')
# reset the arguments for a general CoMEt run on permuted matrices
cometArgs = []
permuteFlags = ["-np", "--parallel", "--keep_temp_files", "-m", "-o"]
for i, arg in enumerate(sys.argv[1:]):
if arg not in permuteFlags and sys.argv[i] not in permuteFlags:
cometArgs.append( arg )
cometArgs.append('--noviz')
# Create a permuted matrix, and then run it through CoMEt
import tempfile
arguments = []
if args.keep_temp_files:
directory = args.output_directory
else:
directory = tempfile.mkdtemp(dir=".", prefix=".tmp")
# Generate random seeds for each permutation
random.seed(args.seed)
seeds = [ random.randint(0, 2**31-1) for _ in range(args.num_permutations) ]
for i, seed in enumerate(seeds):
# Print simple progress bar
sys.stdout.write("* Running CoMEt on permuted matrices... {}/{}\r".format(i+1, args.num_permutations))
sys.stdout.flush()
# Create a permuted dataset and save it a temporary file
mutations = C.permute_mutation_data(G, genes, patients, seed, args.Q)
_, _, _, _, geneToCases, patientToGenes = mutations
adj_list = [ p + "\t" + "\t".join( sorted(patientToGenes[p]) ) for p in patients ]
permutation_file = "{}/permuted-matrix-{}.m2".format(directory, i+1)
with open(permutation_file, 'w') as outfile: outfile.write('\n'.join(adj_list))
# Add the new arguments
permuteArgs = list(map(str, cometArgs))
permuteArgs += [ "-m", permutation_file ]
permuteArgs += [ "-o", "{}/comet-results-on-permutation-{}".format(directory, i+1)]
arguments.append( permuteArgs )
if args.parallel:
pool = mp.Pool(25)
results = pool.map(runComet, arguments)
pool.close()
pool.join()
else:
results = [ runComet(permuteArgs) for permuteArgs in arguments ]
# Find the maximum test statistic on the permuted datasets
from itertools import islice
maxStat = 0
for rf in [ rf for rf in os.listdir(directory) if rf.startswith("comet-results-on-permutation") ]:
for df in [df for df in os.listdir("{}/{}/results".format(directory, rf) ) if df.endswith(".tsv")]:
with open("{}/{}/results/{}".format(directory, rf, df)) as infile:
for line in islice(infile, 1, 2):
score = float(line.split("\t")[1])
if score > maxStat:
maxStat = score
print("*" * 80)
print("Number of permutations:", args.num_permutations)
print("Max statistic:", maxStat)
# Prepare comet results on real, mutation data, and output directory for viz
for rf in [rf for rf in os.listdir( "{}/results/".format(realOutputDir) ) if rf.endswith(".tsv")]:
resultsTable = [l.rstrip() for l in open( "{}/results/{}".format(realOutputDir, rf))]
realMutations = (m, n, genes, patients, geneToCases, patientToGenes )
outputDirViz = realOutputDir + "/viz/"
C.ensure_dir(outputDirViz)
# Perform visualization
C.output_comet_viz(RC.get_parser().parse_args(realCometArgs), realMutations,
resultsTable, maxStat, args.num_permutations)
# Destroy the temporary directory if necessary
if not args.keep_temp_files:
import shutil
shutil.rmtree(directory)
def run( args ):
# Load mutation data using Multi-Dendrix and output as a temporary file
mutations = C.load_mutation_data(args.mutation_matrix, args.patient_file,
args.gene_file, args.min_freq)
m, n, genes, patients, geneToCases, patientToGenes = mutations
if args.verbose:
print '* Mutation data: %s genes x %s patients' % (m, n)
# Construct bipartite graph from mutation data
if args.verbose: print "* Creating bipartite graph..."
G = C.construct_mutation_graph(geneToCases, patientToGenes)
if args.verbose:
print '\t- Graph has', len( G.edges() ), 'edges among', len( G.nodes() ), 'nodes.'
# Set up the arguments for a general CoMEt run
cometArgs = []
permuteFlags = ["-np", "--parallel", "--keep_temp_files", "-m", "-o"]
for i, arg in enumerate(sys.argv[1:]):
if arg not in permuteFlags and sys.argv[i] not in permuteFlags:
cometArgs.append( arg )
# Create a permuted matrix, and then run it through CoMEt
import tempfile
arguments = []
if args.keep_temp_files:
directory = args.output_directory
else:
directory = tempfile.mkdtemp(dir=".", prefix=".tmp")
for i in range(args.num_permutations):
# Print simple progress bar
sys.stdout.write("* Running CoMEt on permuted matrices... {}/{}\r".format(i+1, n))
sys.stdout.flush()
# Create a permuted dataset and save it a temporary file
mutations = C.permute_mutation_data(G, genes, patients, args.seed, args.Q)
_, _, _, _, geneToCases, patientToGenes = mutations
adj_list = [ p + "\t" + "\t".join( sorted(patientToGenes[p]) ) for p in patients ]
permutation_file = "{}/permuted-matrix-{}.m2".format(directory, i+1)
with open(permutation_file, 'w') as outfile: outfile.write('\n'.join(adj_list))
# Add the new arguments
permuteArgs = map(str, cometArgs)
permuteArgs += [ "-m", permutation_file ]
permuteArgs += [ "-o", "{}/comet-results-on-permutation-{}".format(directory, i+1)]
arguments.append( permuteArgs )
if args.parallel:
pool = mp.Pool(25)
results = pool.map(runComet, arguments)
pool.close()
pool.join()
else:
results = [ runComet(permuteArgs) for permuteArgs in arguments ]
# Find the maximum test statistic on the permuted datasets
from itertools import islice
maxStat = 0
for rf in [ rf for rf in os.listdir(directory) if rf.startswith("comet-results") ]:
with open("{}/{}".format(directory, rf)) as infile:
for line in islice(infile, 1, 2):
score = float(line.split("\t")[1])
if score > maxStat:
maxStat = score
print "*" * 80
print "Number of permutations:", args.num_permutations
print "Max statistic:", maxStat
# Output the results to files
with open("{}/comet-stats.json".format(args.output_directory), "w") as outfile:
output = dict(maxPermutedWeight=maxStat,
numPermutations=args.num_permutations,
keepTempFiles=args.keep_temp_files,
mutationNatrix=args.mutation_matrix,
geneFile=args.gene_file, patientFile=args.patient_file,
minFreq=args.min_freq, Q=args.Q)
json.dump( output, outfile, sort_keys=True, indent=4)
# Destroy the temporary directory if necessary
if not args.keep_temp_files:
import shutil
shutil.rmtree(directory)
def run(args):
# Load mutation data using Multi-Dendrix and output as a temporary file
mutations = C.load_mutation_data(args.mutation_matrix, args.patient_file,
args.gene_file, args.min_freq)
m, n, genes, patients, geneToCases, patientToGenes = mutations
if args.verbose:
print '* Mutation data: %s genes x %s patients' % (m, n)
# Construct bipartite graph from mutation data
if args.verbose: print "* Creating bipartite graph..."
G = C.construct_mutation_graph(geneToCases, patientToGenes)
if args.verbose:
print '\t- Graph has', len(G.edges()), 'edges among', len(
G.nodes()), 'nodes.'
# Set up the arguments for a general CoMEt run
cometArgs = []
permuteFlags = ["-np", "--parallel", "--keep_temp_files", "-m", "-o"]
for i, arg in enumerate(sys.argv[1:]):
if arg not in permuteFlags and sys.argv[i] not in permuteFlags:
cometArgs.append(arg)
# Create a permuted matrix, and then run it through CoMEt
import tempfile
arguments = []
if args.keep_temp_files:
directory = args.output_directory
else:
directory = tempfile.mkdtemp(dir=".", prefix=".tmp")
# Generate random seeds for each permutation
random.seed(args.seed)
seeds = [
random.randint(0, 2**31 - 1) for _ in range(args.num_permutations)
]
for i, seed in enumerate(seeds):
# Print simple progress bar
sys.stdout.write(
"* Running CoMEt on permuted matrices... {}/{}\r".format(
i + 1, args.num_permutations))
sys.stdout.flush()
# Create a permuted dataset and save it a temporary file
mutations = C.permute_mutation_data(G, genes, patients, seed, args.Q)
_, _, _, _, geneToCases, patientToGenes = mutations
adj_list = [
p + "\t" + "\t".join(sorted(patientToGenes[p])) for p in patients
]
permutation_file = "{}/permuted-matrix-{}.m2".format(directory, i + 1)
with open(permutation_file, 'w') as outfile:
outfile.write('\n'.join(adj_list))
# Add the new arguments
permuteArgs = map(str, cometArgs)
permuteArgs += ["-m", permutation_file]
permuteArgs += [
"-o",
"{}/comet-results-on-permutation-{}".format(directory, i + 1)
]
arguments.append(permuteArgs)
if args.parallel:
pool = mp.Pool(25)
results = pool.map(runComet, arguments)
pool.close()
pool.join()
else:
results = [runComet(permuteArgs) for permuteArgs in arguments]
# Find the maximum test statistic on the permuted datasets
from itertools import islice
maxStat = 0
for rf in [
rf for rf in os.listdir(directory)
if rf.startswith("comet-results")
]:
with open("{}/{}".format(directory, rf)) as infile:
for line in islice(infile, 1, 2):
score = float(line.split("\t")[1])
if score > maxStat:
maxStat = score
print "*" * 80
print "Number of permutations:", args.num_permutations
print "Max statistic:", maxStat
# Output the results to files
with open("{}/comet-stats.json".format(args.output_directory),
"w") as outfile:
output = dict(maxPermutedWeight=maxStat,
numPermutations=args.num_permutations,
keepTempFiles=args.keep_temp_files,
mutationNatrix=args.mutation_matrix,
geneFile=args.gene_file,
patientFile=args.patient_file,
minFreq=args.min_freq,
Q=args.Q)
json.dump(output, outfile, sort_keys=True, indent=4)
# Destroy the temporary directory if necessary
if not args.keep_temp_files:
import shutil
shutil.rmtree(directory)
def run(args):
# Set up the arguments for a general CoMEt run on real data
realOutputDir = "{}/comet-results".format(args.output_directory)
realCometArgs = []
permuteFlags = ["-np", "--parallel", "--keep_temp_files", "-o"]
for i, arg in enumerate(sys.argv[1:]):
if arg not in permuteFlags and sys.argv[i] not in permuteFlags:
realCometArgs.append(arg)
realCometArgs += ["-o", realOutputDir, "--noviz"]
# perform simple run without viz first.
results = runComet(realCometArgs)
# Load mutation data using Multi-Dendrix and output as a temporary file
realMutations = C.load_mutation_data(args.mutation_matrix,
args.patient_file, args.gene_file,
args.min_freq, args.subtype)
m, n, genes, patients, geneToCases, patientToGenes, subtypes = realMutations
if args.verbose:
print(f'* Mutation data: {m} genes x {n} patients')
# Construct bipartite graph from mutation data
if args.verbose: print('* Creating bipartite graph...')
G = C.construct_mutation_graph(geneToCases, patientToGenes)
if args.verbose:
print('\t- Graph has', len(G.edges()), 'edges among', len(G.nodes()),
'nodes.')
# reset the arguments for a general CoMEt run on permuted matrices
cometArgs = []
permuteFlags = ["-np", "--parallel", "--keep_temp_files", "-m", "-o"]
for i, arg in enumerate(sys.argv[1:]):
if arg not in permuteFlags and sys.argv[i] not in permuteFlags:
cometArgs.append(arg)
cometArgs.append('--noviz')
# Create a permuted matrix, and then run it through CoMEt
import tempfile
arguments = []
if args.keep_temp_files:
directory = args.output_directory
else:
directory = tempfile.mkdtemp(dir=".", prefix=".tmp")
# Generate random seeds for each permutation
random.seed(args.seed)
seeds = [
random.randint(0, 2**31 - 1) for _ in range(args.num_permutations)
]
for i, seed in enumerate(seeds):
# Print simple progress bar
sys.stdout.write(
"* Running CoMEt on permuted matrices... {}/{}\r".format(
i + 1, args.num_permutations))
sys.stdout.flush()
# Create a permuted dataset and save it a temporary file
mutations = C.permute_mutation_data(G, genes, patients, seed, args.Q)
_, _, _, _, geneToCases, patientToGenes = mutations
adj_list = [
p + "\t" + "\t".join(sorted(patientToGenes[p])) for p in patients
]
permutation_file = "{}/permuted-matrix-{}.m2".format(directory, i + 1)
with open(permutation_file, 'w') as outfile:
outfile.write('\n'.join(adj_list))
# Add the new arguments
permuteArgs = list(map(str, cometArgs))
permuteArgs += ["-m", permutation_file]
permuteArgs += [
"-o",
"{}/comet-results-on-permutation-{}".format(directory, i + 1)
]
arguments.append(permuteArgs)
if args.parallel:
pool = mp.Pool(25)
results = pool.map(runComet, arguments)
pool.close()
pool.join()
else:
results = [runComet(permuteArgs) for permuteArgs in arguments]
# Find the maximum test statistic on the permuted datasets
from itertools import islice
maxStat = 0
for rf in [
rf for rf in os.listdir(directory)
if rf.startswith("comet-results-on-permutation")
]:
for df in [
df for df in os.listdir("{}/{}/results".format(directory, rf))
if df.endswith(".tsv")
]:
with open("{}/{}/results/{}".format(directory, rf, df)) as infile:
for line in islice(infile, 1, 2):
score = float(line.split("\t")[1])
if score > maxStat:
maxStat = score
print("*" * 80)
print("Number of permutations:", args.num_permutations)
print("Max statistic:", maxStat)
# Prepare comet results on real, mutation data, and output directory for viz
for rf in [
rf for rf in os.listdir("{}/results/".format(realOutputDir))
if (not rf.startswith('.') and rf.endswith(".tsv"))
]:
resultsTable = [
l.rstrip() for l in open("{}/results/{}".format(realOutputDir, rf))
]
realMutations = (m, n, genes, patients, geneToCases, patientToGenes)
outputDirViz = realOutputDir + "/viz/"
C.ensure_dir(outputDirViz)
# Perform visualization
C.output_comet_viz(RC.get_parser().parse_args(realCometArgs), realMutations, \
resultsTable, maxStat, args.num_permutations)
# Destroy the temporary directory if necessary
if not args.keep_temp_files:
import shutil
shutil.rmtree(directory)