def batch_multi_dendrix(args):
"""Runs Multi-Dendrix for each parameter setting on the input
mutation data.
**Returns:**
A tuple containing the following:
* **collections** (*dictionary*) - mapping of t -> k -> output of Multi-Dendrix
* **mutation_data** (*tuple*) - mutation data tuple (see :func:`multi_dendrix.multi_dendrix` for details).
* **runtime** (*float*) - total runtime (in seconds) of Multi-Dendrix on all the parameter settings
"""
# Import required modules
from time import time
# Load mutation data used in each run
start = time()
include = Multi.white_and_blacklisting(args.patient_whitelist,
args.patient_blacklist, args.gene_whitelist, args.gene_blacklist)
gene2include, sample2include = include
mutation_data = Multi.load_mutation_data_w_cutoff(args.mutation_matrix,
sample2include, gene2include, args.cutoff)
m, n, genes, patients, mutation2patients, patient2mutations = mutation_data
if args.verbose: print "* Mutation data: %s genes x %s patients" % (m, n)
# Run Multi-Dendrix for the range of parameters
ts = range(args.min_num_gene_sets, args.max_num_gene_sets + 1)
ks = range(args.min_gene_set_size, args.max_gene_set_size + 1)
collections = dict( [(t, {}) for t in ts] )
for t, k_max in [(t, k) for t in ts for k in ks]:
multi_params = [ mutation_data, t, args.min_gene_set_size, k_max,
args.alpha, args.delta, args.lmbda ]
collection_w_weights = Multi.ILP(*multi_params, verbose=args.verbose)
collections[t][k_max] = zip(*collection_w_weights)
return collections, mutation_data, time() - start
def run():
"""
This script performs the matrix permutation test on a collection of gene
sets. It gives an example of how one could use various functions from
different modules in order to evaluate the collection without obtaining
the collection from the Multi-Dendrix pipeline. The program first loads
the GBM(2008) mutation data, permutes it the given number of times, and
then performs the matrix permutation test.
The default parameters permute the GBM(2008) data *ten times*.
On my machine, this script runs in 41.2 seconds.
"""
# Load required modules
import sys, os
sys.path.insert(1, os.path.abspath('../../'))
import multi_dendrix.permute.mutation_data as P
from multi_dendrix.evaluate.matrix import matrix_permutation_test
# Default parameters for this tutorial (CHANGE AT YOUR OWN RISK)
COLLECTION = [["CDKN2B", "RB1", "CDK4"], ["CDKN2A", "TP53", "DTX3"]]
MUTATION_MATRIX = "../mutation_data/GBM_2008/GBM_2008.m2"
GENE_BLACKLIST = "../mutation_data/fishy_genes.glst"
CUTOFF = 2
ALPHA = 1.0
NUM_PERMUTATIONS = 10
T, K_MIN, K_MAX, OVERLAPS, SETS_PER_GENE = 2, 3, 3, 0, 1
# Load the mutation data
import multi_dendrix as Multi
gene2include, patient2include = Multi.white_and_blacklisting(
None, None, None, GENE_BLACKLIST)
mutation_data = Multi.load_mutation_data_w_cutoff(MUTATION_MATRIX,
patient2include,
gene2include, CUTOFF)
m, n, genes, patients, mutation2patients, patient2mutations = mutation_data
# Permute the mutation data
G = P.construct_mutation_graph(mutation2patients, patient2mutations)
Hs = [
P.permute_mutation_data(G.copy(), genes, patients)
for i in range(NUM_PERMUTATIONS)
]
# Calculate the p-value and output the results
W_prime = sum(
[Multi.W(mutation2patients, module, ALPHA) for module in COLLECTION])
print "W' of collection:", W_prime
print 'P=', matrix_permutation_test(W_prime, Hs, T, K_MIN, K_MAX, ALPHA,
OVERLAPS, SETS_PER_GENE)
def run():
"""
This script performs the matrix permutation test on a collection of gene
sets. It gives an example of how one could use various functions from
different modules in order to evaluate the collection without obtaining
the collection from the Multi-Dendrix pipeline. The program first loads
the GBM(2008) mutation data, permutes it the given number of times, and
then performs the matrix permutation test.
The default parameters permute the GBM(2008) data *ten times*.
On my machine, this script runs in 41.2 seconds.
"""
# Load required modules
import sys, os
sys.path.insert(1, os.path.abspath('../../'))
import multi_dendrix.permute.mutation_data as P
from multi_dendrix.evaluate.matrix import matrix_permutation_test
# Default parameters for this tutorial (CHANGE AT YOUR OWN RISK)
COLLECTION = [["CDKN2B", "RB1", "CDK4"], ["CDKN2A", "TP53", "DTX3"]]
MUTATION_MATRIX="../mutation_data/GBM_2008/GBM_2008.m2"
GENE_BLACKLIST="../mutation_data/fishy_genes.glst"
CUTOFF=2
ALPHA=1.0
NUM_PERMUTATIONS=10
T, K_MIN, K_MAX, OVERLAPS, SETS_PER_GENE = 2, 3, 3, 0, 1
# Load the mutation data
import multi_dendrix as Multi
gene2include, patient2include = Multi.white_and_blacklisting(None, None,
None, GENE_BLACKLIST)
mutation_data = Multi.load_mutation_data_w_cutoff(MUTATION_MATRIX,
patient2include, gene2include, CUTOFF)
m, n, genes, patients, mutation2patients, patient2mutations = mutation_data
# Permute the mutation data
G = P.construct_mutation_graph(mutation2patients, patient2mutations)
Hs = [ P.permute_mutation_data(G.copy(), genes, patients)
for i in range(NUM_PERMUTATIONS) ]
# Calculate the p-value and output the results
W_prime = sum([ Multi.W(mutation2patients, module, ALPHA)
for module in COLLECTION])
print "W' of collection:", W_prime
print 'P=', matrix_permutation_test(W_prime, Hs, T, K_MIN, K_MAX, ALPHA,
OVERLAPS, SETS_PER_GENE)
def batch_multi_dendrix(args):
"""Runs Multi-Dendrix for each parameter setting on the input
mutation data.
**Returns:**
A tuple containing the following:
* **collections** (*dictionary*) - mapping of t -> k -> output of Multi-Dendrix
* **mutation_data** (*tuple*) - mutation data tuple (see :func:`multi_dendrix.multi_dendrix` for details).
* **runtime** (*float*) - total runtime (in seconds) of Multi-Dendrix on all the parameter settings
"""
# Import required modules
from time import time
# Load mutation data used in each run
start = time()
include = Multi.white_and_blacklisting(args.patient_whitelist,
args.patient_blacklist,
args.gene_whitelist,
args.gene_blacklist)
gene2include, sample2include = include
mutation_data = Multi.load_mutation_data_w_cutoff(args.mutation_matrix,
sample2include,
gene2include,
args.cutoff)
m, n, genes, patients, mutation2patients, patient2mutations = mutation_data
if args.verbose: print "* Mutation data: %s genes x %s patients" % (m, n)
# Run Multi-Dendrix for the range of parameters
ts = range(args.min_num_gene_sets, args.max_num_gene_sets + 1)
ks = range(args.min_gene_set_size, args.max_gene_set_size + 1)
collections = dict([(t, {}) for t in ts])
for t, k_max in [(t, k) for t in ts for k in ks]:
multi_params = [
mutation_data, t, args.min_gene_set_size, k_max, args.alpha,
args.delta, args.lmbda
]
collection_w_weights = Multi.ILP(*multi_params, verbose=args.verbose)
collections[t][k_max] = zip(*collection_w_weights)
return collections, mutation_data, time() - start
def run(args):
# Parse args into shorter variable handles
mutation_matrix = args.mutation_matrix
cutoff, gene_whitelist = args.cutoff, args.gene_whitelist
k, t = args.gene_set_size, args.num_gene_sets
N, s = args.num_iterations, args.step_length
npermutations = args.num_permutations
# Load mutation data
print "* Loading mutation data..."
include = Multi.white_and_blacklisting(args.sample_whitelist,
args.sample_blacklist,
args.gene_whitelist,
args.gene_blacklist)
gene2include, sample2include = include
mutation_data = Multi.load_mutation_data_w_cutoff(args.mutation_matrix,
sample2include,
gene2include,
args.cutoff)
m, n, genes, patients, gene2cases, sample2genes = mutation_data
# Convert the mutation data to integers for easy C parsing
gene2index, index2gene, gene2case_index = convert_mutation_data(
mutation_data)
# Run the MCMC algorithm
print "* Running MCMC algorithm..."
solns = mcmc(k, t, m, n, gene2case_index, N, s, int(not args.quiet))
# Collate the results and sort them descending by sampling frequency
solns_w_weights = convert_solns(index2gene, solns)
results = collate_results(solns_w_weights)
# Flatten the results
print "* Flattening the results..."
flat_results = [[S, data["freq"], data["W_prime"], data["Ws"]]
for S, data in results.items()]
flat_results.sort(key=lambda (S, F, W_prime, Ws): F, reverse=True)
# Perform the permutation test (if necessary)
if args.num_permutations > 0:
permute_pval = run_matrix_permutation_test(npermutations,
args.permuted_matrices_dir,
mutation_data, k, t, N, s,
flat_results, args.parallel)
# Output results
print "* Outputting results..."
if args.num_permutations > 0:
output = ["#Sampling Frequency\tWeight W'\tP-value ({} permutations)"\
"\tGene set\tWeight W".format(args.num_permutations)]
else:
output = ["#Sampling Frequency\tWeight W'\tGene set\tWeight W"]
for S, freq, W_prime, Ws in flat_results:
for i, M in enumerate(S):
if i == 0:
row = [freq, W_prime, ", ".join(sorted(M)), Ws[i]]
if args.num_permutations > 0:
row.insert(2, permute_pval[S])
else:
row = ["", "", "", ", ".join(sorted(M)), Ws[i]]
output.append("\t".join(map(str, row)))
open(args.output_file, "w").write("\n".join(output))
def run(args):
# Parse args into shorter variable handles
mutation_matrix = args.mutation_matrix
cutoff, gene_whitelist = args.cutoff, args.gene_whitelist
k, t = args.gene_set_size, args.num_gene_sets
N, s = args.num_iterations, args.step_length
npermutations = args.num_permutations
# Load mutation data
print "* Loading mutation data..."
include = Multi.white_and_blacklisting(
args.sample_whitelist, args.sample_blacklist, args.gene_whitelist, args.gene_blacklist
)
gene2include, sample2include = include
mutation_data = Multi.load_mutation_data_w_cutoff(args.mutation_matrix, sample2include, gene2include, args.cutoff)
m, n, genes, patients, gene2cases, sample2genes = mutation_data
# Convert the mutation data to integers for easy C parsing
gene2index, index2gene, gene2case_index = convert_mutation_data(mutation_data)
# Run the MCMC algorithm
print "* Running MCMC algorithm..."
solns = mcmc(k, t, m, n, gene2case_index, N, s, int(not args.quiet))
# Collate the results and sort them descending by sampling frequency
solns_w_weights = convert_solns(index2gene, solns)
results = collate_results(solns_w_weights)
# Flatten the results
print "* Flattening the results..."
flat_results = [[S, data["freq"], data["W_prime"], data["Ws"]] for S, data in results.items()]
flat_results.sort(key=lambda (S, F, W_prime, Ws): F, reverse=True)
# Perform the permutation test (if necessary)
if args.num_permutations > 0:
permute_pval = run_matrix_permutation_test(
npermutations, args.permuted_matrices_dir, mutation_data, k, t, N, s, flat_results, args.parallel
)
# Output results
print "* Outputting results..."
if args.num_permutations > 0:
output = [
"#Sampling Frequency\tWeight W'\tP-value ({} permutations)"
"\tGene set\tWeight W".format(args.num_permutations)
]
else:
output = ["#Sampling Frequency\tWeight W'\tGene set\tWeight W"]
for S, freq, W_prime, Ws in flat_results:
for i, M in enumerate(S):
if i == 0:
row = [freq, W_prime, ", ".join(sorted(M)), Ws[i]]
if args.num_permutations > 0:
row.insert(2, permute_pval[S])
else:
row = ["", "", "", ", ".join(sorted(M)), Ws[i]]
output.append("\t".join(map(str, row)))
open(args.output_file, "w").write("\n".join(output))
