def run_main(args):
# Now we run the relevant stage of processing
project_folder = create_folder(args.base_folder, args.project)
# Add a logger specific to the project and processing stage
log_file = os.path.join(project_folder, 'genewalk_%s.log' % args.stage)
formatter = logging.Formatter(default_logger_format,
datefmt=default_date_format)
project_log_handler = logging.FileHandler(log_file)
project_log_handler.setFormatter(formatter)
root_logger.addHandler(project_log_handler)
if args.random_seed:
logger.info('Running with random seed %d' % args.random_seed)
random.seed(a=int(args.random_seed))
# Make sure we have all the resource files
rm = ResourceManager(base_folder=args.base_folder)
rm.download_all()
if args.stage in ('all', 'node_vectors'):
genes = read_gene_list(args.genes, args.id_type, rm)
save_pickle(genes, project_folder, 'genes')
MG = load_network(args.network_source, args.network_file, genes,
resource_manager=rm)
save_pickle(MG.graph, project_folder, 'multi_graph')
for i in range(args.nreps_graph):
logger.info('%s/%s' % (i + 1, args.nreps_graph))
DW = run_walks(MG.graph, workers=args.nproc, size=args.dim_rep)
# Pickle the node vectors (embeddings) and DW object
if args.save_dw:
save_pickle(DW, project_folder, 'deepwalk_%d' % (i + 1))
nv = copy.deepcopy(DW.model.wv)
save_pickle(nv, project_folder,
'deepwalk_node_vectors_%d' % (i + 1))
# Delete the DeepWalk object to clear memory
del DW, nv
gc.collect()
if args.stage in ('all', 'null_distribution'):
MG = load_pickle(project_folder, 'multi_graph')
srd = []
for i in range(args.nreps_null):
logger.info('%s/%s' % (i + 1, args.nreps_null))
RG = get_rand_graph(MG)
DW = run_walks(RG, workers=args.nproc, size=args.dim_rep)
# Pickle the node vectors (embeddings) and DW object
if args.save_dw:
save_pickle(DW, project_folder, 'deepwalk_rand_%d' % (i + 1))
nv = copy.deepcopy(DW.model.wv)
save_pickle(nv, project_folder, 'deepwalk_node_vectors_rand_%d'
% (i + 1))
# Delete the DeepWalk object to clear memory
del DW
gc.collect()
# Calculate the null distributions
srd += get_null_distributions(RG, nv)
del nv
gc.collect()
srd = np.asarray(sorted(srd))
save_pickle(srd, project_folder, 'genewalk_rand_simdists')
if args.stage in ('all', 'statistics'):
MG = load_pickle(project_folder, 'multi_graph')
genes = load_pickle(project_folder, 'genes')
nvs = [load_pickle(project_folder,
'deepwalk_node_vectors_%d' % (i + 1))
for i in range(args.nreps_graph)]
null_dist = load_pickle(project_folder, 'genewalk_rand_simdists')
GW = GeneWalk(MG, genes, nvs, null_dist)
df = GW.generate_output(alpha_fdr=args.alpha_fdr,
base_id_type=args.id_type)
fname = os.path.join(project_folder, 'genewalk_results.csv')
logger.info('Saving final results into %s' % fname)
df.to_csv(fname, index=False, float_format='%.3e')
if args.stage in ('all', 'visual'):
fname = os.path.join(project_folder, 'genewalk_results.csv')
dGW = pd.read_csv(fname)
figure_folder = create_folder(project_folder, 'figures')
create_folder(figure_folder, 'barplots')
GWp = GW_Plotter(figure_folder, dGW, args.alpha_fdr)
GWp.generate_plots()
def main():
parser = argparse.ArgumentParser(
description='Run GeneWalk on a list of genes provided in a text '
'file.')
parser.add_argument('--version',
action='version',
version='GeneWalk %s' % __version__,
help='Print the version of GeneWalk and exit.')
parser.add_argument('--project',
help='A name for the project which '
'determines the folder within the '
'base folder in which the '
'intermediate and final results '
'are written. Must contain only '
'characters that are valid in '
'folder names.',
required=True)
parser.add_argument('--genes',
help='Path to a text file with a list of '
'genes of interest, for example'
'differentially expressed genes. '
'The type of gene identifiers used in '
'the text file are provided in the '
'id_type argument.',
required=True)
parser.add_argument('--id_type',
help='The type of gene IDs provided in the text file '
'in the genes argument. Possible values are: '
'hgnc_symbol, hgnc_id, ensembl_id, and mgi_id.',
choices=[
'hgnc_symbol', 'hgnc_id', 'ensembl_id', 'mgi_id',
'entrez_human', 'entrez_mouse'
],
required=True)
parser.add_argument(
'--stage',
default='all',
help='The stage of processing to run. Default: '
'%(default)s',
choices=['all', 'node_vectors', 'null_distribution', 'statistics'])
parser.add_argument('--base_folder',
default=default_base_folder,
help='The base folder used to store GeneWalk '
'temporary and result files for a given project.'
' Default: %(default)s')
parser.add_argument('--network_source',
default='pc',
help='The source of the network to be used.'
'Possible values are: pc, indra, edge_list, and '
'sif. In case of indra, edge_list, and sif, '
'the network_file argument must be specified.'
' Default: %(default)s',
choices=['pc', 'indra', 'edge_list', 'sif'])
parser.add_argument('--network_file',
default=None,
help='If network_source is indra, this argument '
'points to a Python pickle file in which a list '
'of INDRA Statements constituting the network '
'is contained. In case network_source is '
'edge_list or sif, '
'the network_file argument points to a text file '
'representing the network.')
parser.add_argument('--nproc',
default=1,
type=int,
help='The number of processors to use in a '
'multiprocessing environment. Default: '
'%(default)s')
parser.add_argument('--nreps_graph',
default=3,
type=int,
help='The number of repeats to run when calculating '
'node vectors on the GeneWalk graph. '
'Default: %(default)s')
parser.add_argument('--nreps_null',
default=3,
type=int,
help='The number of repeats to run when calculating '
'node vectors on the random network graphs '
'for constructing the null distribution. '
'Default: %(default)s')
parser.add_argument('--alpha_fdr',
default=1,
type=float,
help='The false discovery rate to use when '
'outputting the final statistics table. '
'If 1 (default), all similarities are output, '
'otherwise only the ones whose false discovery '
'rate are below this parameter are included. '
'Default: %(default)s')
parser.add_argument('--save_dw',
default=False,
type=bool,
help='If True, the full DeepWalk object for each '
'repeat is saved in the project folder. This can '
'be useful for debugging but the files are '
'typically very large. Default: %(default)s')
parser.add_argument('--random_seed',
default=None,
type=int,
help='If provided, the random number generator is '
'seeded with the given value. This should only '
'be used if the goal is to deterministically '
'reproduce a prior result obtained with the same '
'random seed.')
args = parser.parse_args()
# Now we run the relevant stage of processing
project_folder = create_project_folder(args.base_folder, args.project)
# Add a logger specific to the project and processing stage
log_file = os.path.join(project_folder, 'genewalk_%s.log' % args.stage)
formatter = logging.Formatter(default_logger_format,
datefmt=default_date_format)
project_log_handler = logging.FileHandler(log_file)
project_log_handler.setFormatter(formatter)
root_logger.addHandler(project_log_handler)
if args.random_seed:
logger.info('Running with random seed %d' % args.random_seed)
random.seed(a=int(args.random_seed))
# Make sure we have all the resource files
rm = ResourceManager(base_folder=args.base_folder)
rm.download_all()
if args.stage in ('all', 'node_vectors'):
genes = read_gene_list(args.genes, args.id_type, rm)
save_pickle(genes, project_folder, 'genes')
MG = load_network(args.network_source,
args.network_file,
genes,
resource_manager=rm)
save_pickle(MG.graph, project_folder, 'multi_graph')
for i in range(args.nreps_graph):
logger.info('%s/%s' % (i + 1, args.nreps_graph))
DW = run_walks(MG.graph, workers=args.nproc)
# Pickle the node vectors (embeddings) and DW object
if args.save_dw:
save_pickle(DW, project_folder, 'deepwalk_%d' % (i + 1))
nv = copy.deepcopy(DW.model.wv)
save_pickle(nv, project_folder,
'deepwalk_node_vectors_%d' % (i + 1))
# Delete the DeepWalk object to clear memory
del DW, nv
gc.collect()
if args.stage in ('all', 'null_distribution'):
MG = load_pickle(project_folder, 'multi_graph')
srd = []
for i in range(args.nreps_null):
logger.info('%s/%s' % (i + 1, args.nreps_null))
RG = get_rand_graph(MG)
DW = run_walks(RG, workers=args.nproc)
# Pickle the node vectors (embeddings) and DW object
if args.save_dw:
save_pickle(DW, project_folder, 'deepwalk_rand_%d' % (i + 1))
nv = copy.deepcopy(DW.model.wv)
save_pickle(nv, project_folder,
'deepwalk_node_vectors_rand_%d' % (i + 1))
# Delete the DeepWalk object to clear memory
del DW
gc.collect()
# Calculate the null distributions
srd += get_null_distributions(RG, nv)
del nv
gc.collect()
srd = np.asarray(sorted(srd))
save_pickle(srd, project_folder, 'genewalk_rand_simdists')
if args.stage in ('all', 'statistics'):
MG = load_pickle(project_folder, 'multi_graph')
genes = load_pickle(project_folder, 'genes')
nvs = [
load_pickle(project_folder, 'deepwalk_node_vectors_%d' % (i + 1))
for i in range(args.nreps_graph)
]
null_dist = load_pickle(project_folder, 'genewalk_rand_simdists')
GW = GeneWalk(MG, genes, nvs, null_dist)
df = GW.generate_output(alpha_fdr=args.alpha_fdr,
base_id_type=args.id_type)
fname = os.path.join(project_folder, 'genewalk_results.csv')
logger.info('Saving final results into %s' % fname)
df.to_csv(fname, index=False, float_format='%.3e')