def go(species):
method_dir = r'%s/method/Ding' % my_constants.basePath
out_dir = r'%s/Ding' % my_constants.basePath
my_util.mkdir_p(out_dir)
# source_file = '%s/dataset/networks/%s' % (my_constants.basePath, my_constants.species_sbml[species])
source_file = '%s/method/Ding/Y2H-union.txt'
# S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts = importer.sbmlStoichiometricMatrix(source_file, True, read_species_compart=True, remove_biomass=False, normalize_stoich=False)
f = open('Ding.m', 'w')
write_line(f, 'addpath %s/src' % method_dir)
write_line(f, "model = readCbModel('%s')" % source_file)
# write_line(f, "model.c(%d) = 1;" % (rxns.index(biomass[0]) + 1))
# write_line(f, "changeCobraSolver('glpk');")
write_line(f, '[modules] = main( model );')
write_line(f, 'save Dingout.mat ')
f.close()
my_util.prepare_matlab_file_and_exec_and_wait_finish(
'Ding', 'Dingout.mat', False)
res_vars = my_util.try_load_matlab_result('Dingout.mat')
raw_modules = res_vars[
'modules'] # if matlab has failed, this will throw exception!
def read_module_hierarchy(file_path, mets, rxns):
res_vars = my_util.try_load_matlab_result(file_path)
raw_modules = res_vars['mods']
raw_modules_hierarchy = res_vars['mods_hier']
mods_rxns = []
mods_mets = []
for x, raw_mod in enumerate(raw_modules):
raw_mod = raw_mod[0]
mod_rxns_idxs = raw_mod[0][:, 0]
mod_mets_idxs = raw_mod[1][:, 0]
# mod_metname[0] = str(raw_mod[2][:, 0][1][0])
mod_s = raw_mod[3].toarray().tolist()
# mod_rxnname[0] = str(raw_mod[4][:, 0][1][0])
mod_bmatrix = raw_mod[5].tolist() if len(raw_mod[5][0]) > 0 else None
mod_q = raw_mod[6][0][0] if len(raw_mod[6][0]) > 0 else None
mod_s_eig = raw_mod[7][:, 0] if len(raw_mod[7][0]) > 0 else None
mod_rxns = [rxns[i - 1] for i in mod_rxns_idxs]
mod_mets = [mets[i - 1] for i in mod_mets_idxs]
mods_rxns.append(mod_rxns)
mods_mets.append(mod_mets)
raw_modules_hierarchy = convert_to_int_recursive(
raw_modules_hierarchy.toarray().tolist())
modules_hierarchy = [
[None, [], -1] for i in range(len(raw_modules))
] # map of module index to (super_index, [sub_indexes], depth). note that depth of root is 1
for ri, rm in enumerate(raw_modules_hierarchy):
for di, d in enumerate(rm):
if d != 0:
modules_hierarchy[ri][1].append(di)
modules_hierarchy[di][0] = ri
# note that tree_height - depth + 1 will give the height of each module. height of deepest modules will be 1! root will be the highest.
tree_height = -1
for mi, mh in enumerate(modules_hierarchy):
depth = len(get_path_to_root_recursive(modules_hierarchy, mi))
mh[2] = depth
if depth > tree_height:
tree_height = depth
# NOTE: this important hack causes leaf modules (which are real outputs of sridharan be placed on the bottom of the
# dendrogram, nearest place to their members (i.e. observations)
leaf_modules_idxs = []
for mi, mh in enumerate(modules_hierarchy):
if len(mh[1]) == 0:
leaf_modules_idxs.append(mi)
for mi in leaf_modules_idxs:
modules_hierarchy[mi][2] = tree_height
return modules_hierarchy, mods_mets, mods_rxns, tree_height
def compute_couplings(S, mets, rxns, revs, REACT_MAX_LEN, METAB_MAX_LEN):
global couplings_cache
for cc in couplings_cache:
if S == cc[0] and mets == cc[1] and rxns == cc[2] and revs == cc[
3] and REACT_MAX_LEN == cc[4] and METAB_MAX_LEN == cc[5]:
return cc[6], cc[7]
couplings_cache.append([S, mets, rxns, revs, REACT_MAX_LEN, METAB_MAX_LEN])
f = open('cohesion_coupling.m', 'w')
s_str = 'thenet.stoichiometricMatrix = zeros(%d,%d);' % (len(mets),
len(rxns))
write_line(f, s_str)
for i in xrange(len(S)):
s_str = 'thenet.stoichiometricMatrix(%d,:) = %s;' % (i + 1, str(S[i]))
write_line(f, s_str)
rev_str = 'thenet.reversibilityVector = [' + mat_rows(revs) + '];'
write_line(f, rev_str)
react_str = 'thenet.Reactions = [' + mat_rows(rxns, REACT_MAX_LEN) + '];'
write_line(f, react_str)
met_str = 'thenet.Metabolites = [' + mat_rows(mets, METAB_MAX_LEN) + '];'
write_line(f, met_str)
# mod_path = r'addpath %s/evaluation/fcf/ffca'
# func = 'FFCA'
mod_path = r'addpath %s/evaluation/fcf/f2c2'
func = 'F2C2'
fcf_tool_path = mod_path % my_constants.basePath
write_line(f, fcf_tool_path)
compute_str = "[cpls, blk] = %s('clp', thenet);" % func
write_line(f, compute_str)
save_str = 'save cohesion_coupling.mat cpls blk'
write_line(f, save_str)
f.close()
my_util.prepare_matlab_file_and_exec_and_wait_finish(
'cohesion_coupling', 'cohesion_coupling.mat', False)
res_vars = my_util.try_load_matlab_result('cohesion_coupling.mat')
# if matlab has failed, this will throw exception!
couplings = res_vars['cpls'].tolist()
blocks = [b[0] for b in res_vars['blk'].tolist()]
couplings_cache[-1].append(couplings)
couplings_cache[-1].append(blocks)
return couplings, blocks
def go(species):
method_dir = r'%s/method/muller2_new' % my_constants.basePath
out_dir = r'%s/%s/muller2_new' % (my_constants.resultPath, species)
my_util.mkdir_p(out_dir)
source_file = '%s/dataset/networks/%s' % (
my_constants.basePath, my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts = importer.sbmlStoichiometricMatrix(
source_file,
True,
read_species_compart=True,
remove_biomass=False,
normalize_stoich=False)
f = open('muller2_new.m', 'w')
write_line(f, 'addpath %s/code' % method_dir)
write_line(f, "model = readCbModel('%s')" % source_file)
write_line(f, "model.c(%d) = 1;" % (rxns.index(biomass[0]) + 1))
write_line(f, "changeCobraSolver('glpk');")
write_line(f, '[modules, var, flux] = computeModulesOpt( model );')
write_line(f, 'save muller2_newout.mat modules var flux')
f.close()
my_util.prepare_matlab_file_and_exec_and_wait_finish(
'muller2_new', 'muller2_newout.mat', False)
res_vars = my_util.try_load_matlab_result('muller2_newout.mat')
raw_modules = res_vars[
'modules'] # if matlab has failed, this will throw exception!
shutil.copy('muller2_newout.mat', out_dir)
raw_modules = raw_modules.T.tolist(
) # eacho row will be a module where reactions are marked
modules = []
for raw_module in raw_modules:
modules.append([])
for rIdx, in_module in enumerate(raw_module):
if in_module == 1:
modules[-1].append(rxns[rIdx])
out = open('%s/final_modules.txt' % out_dir, 'w')
out.write(
"#each row is a module of reactions. not all reactions are specified (nature of this method only select some reaction to be modules)\n"
)
for m in modules:
out.write(' '.join(m))
out.write('\n')
out.close()
def read_module_hierarchy_incorrect(file_path, mets, rxns):
res_vars = my_util.try_load_matlab_result(file_path)
raw_modules = res_vars['mods']
raw_modules_hierarchy = res_vars['mods_hier']
mods_rxns = []
mods_mets = []
for x, raw_mod in enumerate(raw_modules):
raw_mod = raw_mod[0]
mod_rxns_idxs = raw_mod[0][:, 0]
mod_mets_idxs = raw_mod[1][:, 0]
# mod_metname[0] = str(raw_mod[2][:, 0][1][0])
mod_s = raw_mod[3].toarray().tolist()
# mod_rxnname[0] = str(raw_mod[4][:, 0][1][0])
mod_bmatrix = raw_mod[5].tolist() if len(raw_mod[5][0]) > 0 else None
mod_q = raw_mod[6][0][0] if len(raw_mod[6][0]) > 0 else None
mod_s_eig = raw_mod[7][:, 0] if len(raw_mod[7][0]) > 0 else None
mod_rxns = [rxns[i - 1] for i in mod_rxns_idxs]
mod_mets = [mets[i - 1] for i in mod_mets_idxs]
mods_rxns.append(mod_rxns)
mods_mets.append(mod_mets)
raw_modules_hierarchy = convert_to_int_recursive(
raw_modules_hierarchy.toarray().tolist())
modules_hierarchy = [
[None, [], -1] for i in range(len(raw_modules))
] # map of module index to (super_index, [sub_indexes], depth). note that depth of root is 1
for ri, rm in enumerate(raw_modules_hierarchy):
for di, d in enumerate(rm):
if d != 0:
modules_hierarchy[ri][1].append(di)
modules_hierarchy[di][0] = ri
# note that tree_height - depth + 1 will give the height of each module. height of deepest modules will be 1! root will be the highest.
tree_height = -1
for mi, mh in enumerate(modules_hierarchy):
depth = len(get_path_to_root_recursive(modules_hierarchy, mi))
mh[2] = depth
if depth > tree_height:
tree_height = depth
return modules_hierarchy, mods_mets, mods_rxns, tree_height