def between_correls(args):
"""TABLES MUST SORT SO THAT SAMPLES ARE IN THE SAME ORDER """
logger = general.Logger("SCNIC_log.txt")
logger["SCNIC analysis type"] = "between"
# correlation and p-value adjustment methods
correl_methods = {'spearman': spearmanr, 'pearson': pearsonr}
p_methods = {'bh': general.bh_adjust, 'bon': general.bonferroni_adjust}
correl_method = correl_methods[args.correl_method]
if args.p_adjust is not None:
p_adjust = p_methods[args.p_adjust]
else:
p_adjust = None
# load tables
table1 = load_table(args.table1)
table2 = load_table(args.table2)
logger["input table 1"] = args.table1
logger["input table 1"] = args.table2
table1 = table1.sort()
table2 = table2.sort()
if not np.array_equal(table1.ids(), table2.ids()):
raise ValueError("Tables have different sets of samples present")
# make new output directory and change to it
if args.output is not None:
os.makedirs(args.output)
os.chdir(args.output)
logger["output directory"] = args.output
# filter tables
if args.min_sample is not None:
table1 = general.filter_table(table1, args.min_sample)
metadata = general.get_metadata_from_table(table1)
table2 = general.filter_table(table2, args.min_sample)
metadata.update(general.get_metadata_from_table(table2))
else:
metadata = general.get_metadata_from_table(table1)
metadata.update(general.get_metadata_from_table(table2))
# make correlations
logger["correlation metric"] = args.correl_method
logger["p adjustment method"] = args.p_adjust
correls = between_correls_from_tables(table1, table2, correl_method)
correls.sort_values(correls.columns[-1], inplace=True)
correls.to_csv(open('correls.txt', 'w'), sep='\t', index=False)
# adjust p-values
correls['p_adj'] = p_adjust(correls['p'])
# make network
net = general.correls_to_net(correls, metadata=metadata, min_p=args.min_p, min_r=args.min_r)
logger["number of nodes"] = net.number_of_nodes()
logger["number of edges"] = net.number_of_edges()
nx.write_gml(net, 'crossnet.gml')
logger.output_log()
print '\a'
def test_between_correls_from_tables_too_many_procs(biom_table1, biom_table2):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
correls = between_correls_from_tables(biom_table1,
biom_table2,
nprocs=1000)
assert len(w) == 1
assert isinstance(correls, pd.DataFrame)
assert correls.shape[0] == biom_table1.shape[0] * biom_table2.shape[0]
def between_correls(args):
"""TABLES MUST SORT SO THAT SAMPLES ARE IN THE SAME ORDER """
logger = general.Logger("SCNIC_log.txt")
logger["SCNIC analysis type"] = "between"
# correlation and p-value adjustment methods
correl_methods = {'spearman': spearmanr, 'pearson': pearsonr}
correl_method = correl_methods[args.correl_method]
# load tables
table1 = load_table(args.table1)
table2 = load_table(args.table2)
logger["input table 1"] = args.table1
logger["input table 1"] = args.table2
table1 = table1.sort()
table2 = table2.sort()
# make new output directory and change to it
if args.force and args.output is not None:
shutil.rmtree(args.output, ignore_errors=True)
if args.output is not None:
os.makedirs(args.output)
os.chdir(args.output)
logger["output directory"] = args.output
# filter tables
if args.sparcc_filter is True:
table1 = general.sparcc_paper_filter(table1)
table2 = general.sparcc_paper_filter(table2)
print("Table 1 filtered: %s observations" % str(table1.shape[0]))
print("Table 2 filtered: %s observations" % str(table2.shape[0]))
logger["sparcc paper filter"] = True
logger["number of observations present in table 1 after filter"] = table1.shape[0]
logger["number of observations present in table 2 after filter"] = table2.shape[0]
if args.min_sample is not None:
table1 = general.filter_table(table1, args.min_sample)
table2 = general.filter_table(table2, args.min_sample)
if not np.array_equal(table1.ids(), table2.ids()):
raise ValueError("Tables have different sets of samples present")
metadata = general.get_metadata_from_table(table1)
metadata.update(general.get_metadata_from_table(table2))
# make correlations
logger["correlation metric"] = args.correl_method
logger["p adjustment method"] = args.p_adjust
correls = ca.between_correls_from_tables(table1, table2, correl_method, nprocs=args.procs)
correls.sort_values(correls.columns[-1], inplace=True)
correls['p_adj'] = general.p_adjust(correls['p'])
correls.to_csv(open('correls.txt', 'w'), sep='\t', index=True)
# make network
correls_filt = general.filter_correls(correls, min_p=args.min_p, min_r=args.min_r)
net = general.correls_to_net(correls_filt, metadata=metadata)
logger["number of nodes"] = net.number_of_nodes()
logger["number of edges"] = net.number_of_edges()
nx.write_gml(net, 'crossnet.gml')
logger.output_log()
def test_between_correls_from_tables_multi(biom_table1, biom_table2):
correls = between_correls_from_tables(biom_table1, biom_table2, nprocs=2)
assert isinstance(correls, pd.DataFrame)
assert correls.shape[0] == biom_table1.shape[0] * biom_table2.shape[0]
def test_between_correls_from_tables(biom_table1, biom_table2):
correls = between_correls_from_tables(biom_table1, biom_table2)
assert type(correls) is pd.DataFrame
assert correls.shape[0] == biom_table1.shape[0] * biom_table2.shape[0]
