def simulate_mixture(args):
nodelist = prmf.parse_nodelist(args.nodelist)
# first, sample subset of seed-lists
seed_lists_sample, seed_list_sizes, chosen_seed_fps = sample_pathways(args, nodelist)
# define multinomial parameters
prs = [(1 - args.noise_pr)/(len(seed_lists_sample) - 1)] * (len(seed_lists_sample) - 1)
prs.append(args.noise_pr)
# then, from the sampled seed list and the background <nodelist>, sample gene lists
gene_lists = []
for i in range(args.n_gene_lists):
# sample from multinomial to determine number of elements coming from each seed list
sample_sizes = nprand.multinomial(args.gene_list_size, prs)
# if we try to overdraw from a seed list, sample the remaining from background
for j in range(len(sample_sizes)-1):
seed_list_size = seed_list_sizes[j]
sample_size = sample_sizes[j]
diff = sample_size - seed_list_size
if diff > 0:
sample_sizes[j] -= diff
sample_sizes[-1] += diff
# then sample from seed lists uniformly at random
gene_list = set()
for j in range(len(seed_lists_sample)):
seed_list = seed_lists_sample[j]
sample_size = sample_sizes[j]
seed_list_inds = nprand.choice(len(seed_list), size=sample_size)
for seed_list_ind in seed_list_inds:
gene_list.add(seed_list[seed_list_ind])
gene_lists.append(sorted(gene_list))
# write gene lists to file
write_lists(args, gene_lists)
write_seeds(args, chosen_seed_fps)
def simulate_whole(args):
"""
In contrast to simulate_mixture, do not combine nodes from different pathways into a single gene list
"""
nodelist = prmf.parse_nodelist(args.nodelist)
# first, sample subset of seed-lists
seed_lists_sample, seed_list_sizes, chosen_seed_fps = sample_pathways(args, nodelist)
# then, from the sampled seed list and the background <nodelist>, sample gene lists
gene_lists = []
for i in range(args.n_gene_lists):
# sample from multinomial to determine number of elements coming from each seed list
sample_size = nprand.binomial(args.gene_list_size, 1 - args.noise_pr)
# then sample from seed list uniformly at random
gene_list = set()
seed_list = seed_lists_sample[i]
if sample_size < len(seed_list):
seed_list_inds = nprand.choice(len(seed_list), size=sample_size, replace=False)
for seed_list_ind in seed_list_inds:
gene_list.add(seed_list[seed_list_ind])
else:
# TODO warn?
for seed in seed_list:
gene_list.add(seed)
# sample remaining from background
# TODO catch error if size > len(nodelist)
nodelist_inds = nprand.choice(len(nodelist), size=(args.gene_list_size - sample_size), replace=False)
for nodelist_ind in nodelist_inds:
gene_list.add(nodelist[nodelist_ind])
gene_lists.append(sorted(gene_list))
# write gene lists to file
write_lists(args, gene_lists)
write_seeds(args, chosen_seed_fps)
def main():
parser = argparse.ArgumentParser(description="""
Evaluate NMF versus Pathway-Regularized Matrix Factorization by plotting PR curves on one figure.
""")
parser.add_argument("--gene-by-latent-csvs",
nargs="+",
help=".csv files",
required=True)
parser.add_argument("--labels",
nargs="+",
help="parallel to --gene-by-latent-csvs",
required=True)
parser.add_argument("--nodelist",
type=argparse.FileType('r'),
required=True)
parser.add_argument("--true-seeds",
type=argparse.FileType('r'),
required=True)
parser.add_argument("--outdir", type=str, required=True)
args = parser.parse_args()
# parse inputs - {{
W_mats = []
colors = [] # TODO
W_mats = list(
map(
lambda x: pd.read_csv(x, sep=",", header='infer', index_col=0).
values, args.gene_by_latent_csvs))
label_strs = list(map(lambda x: x + "; AUC={:0.3f}", args.labels))
nodelist = prmf.parse_nodelist(args.nodelist)
true_seed_fps = []
for line in args.true_seeds:
line = line.rstrip()
true_seed_fps.append(line)
true_seed_lists = []
for true_seed_fp in true_seed_fps:
seed_list = prmf.parse_seedlist(true_seed_fp)
true_seed_lists.append(seed_list)
pathways_mat = prmf.nodelists_to_mat(true_seed_lists, nodelist)
# }} - parse inputs
# reorganize <matching> so we can find each method's latent factor that best matches the ground truth
pathway_to_latent_maps = []
for i in range(len(W_mats)):
matching = prmf.match(W_mats[i], pathways_mat)
pathway_to_latent_map = {}
for match in matching:
factor_id_match, pathway_id_match, auc = match
factor_id = matching_id_to_ind(factor_id_match)
pathway_id = matching_id_to_ind(pathway_id_match)
pathway_to_latent_map[pathway_id] = (factor_id, auc)
pathway_to_latent_maps.append(pathway_to_latent_map)
# plot Precision-Recall curves
match_ind = 0
method_to_avg_precision_vals = {}
for i in range(len(W_mats)):
method_to_avg_precision_vals[i] = []
for pathway_id in range(pathways_mat.shape[1]):
plt.clf()
y_true = pathways_mat[:, pathway_id]
true_fraction = np.sum(y_true) / y_true.shape[0]
for i in range(len(pathway_to_latent_maps)):
pathway_to_latent_map = pathway_to_latent_maps[i]
factor_id, auc = pathway_to_latent_map[pathway_id]
y_score = W_mats[i][:, factor_id]
precision, recall, thresholds = sklearn.metrics.precision_recall_curve(
y_true, y_score)
method_to_avg_precision_vals[i].append(
sklearn.metrics.average_precision_score(y_true, y_score))
plt.plot(recall,
precision,
label=label_strs[i].format(auc),
linewidth=2.0)
#plt.plot(recall, precision, color=colors[i], label=label_strs[i].format(auc), linewidth=2.0)
plt.plot(np.linspace(0, 1, num=50),
np.repeat(true_fraction, 50),
label="Random; AUC={:0.3f}".format(true_fraction),
linewidth=2.0)
plt.xlabel('Recall', fontsize='x-large')
plt.ylabel('Precision', fontsize='x-large')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall of PRMF and Friends', fontsize='xx-large')
plt.legend()
ofp = os.path.join(args.outdir, "fig{}.png".format(match_ind))
plt.savefig(ofp, bbox_inches='tight')
match_ind += 1
# report the average of average precision for each method
with open(os.path.join(args.outdir, 'avg_precision.txt'), 'w') as ofh:
for i in range(len(W_mats)):
avg_of_avg = np.mean(method_to_avg_precision_vals[i])
label = args.labels[i]
ofh.write("{}\t{}\n".format(label, avg_of_avg))
def main():
parser = argparse.ArgumentParser(description="""
Python implementation of Pathway-Regularized NMF.
Solve an optimization problem of the form
min ||X - UV^T|| +
gamma * sum_k min_i V[:,k]^T Ls[i] V[:,k] +
delta * sum_k sum_{i | i in G_k} 1 / V[i,k] +
||U||_F^2
where Ls[i] is the Laplacian matrix associated with Gs[i],
G_k is the manifold associated with latent factor k
X has shape (n_obs, n_features),
U has shape (n_obs, n_latent),
V has shape (n_feature, n_latent)
References
----------
Cai 2008. Non-negative Matrix Factorization on Manifold
""",
formatter_class=RawTextHelpFormatter)
prmf_args.add_prmf_arguments(parser)
args = parser.parse_args()
OUTDIR = args.outdir
# tradeoff, gamma, and delta
tradeoff = args.tradeoff
if tradeoff == -1:
tradeoff = None
# TODO update gamma default
manifold_fps = []
if args.manifolds is None and args.manifolds_file is None:
sys.stderr.write(
"Exactly one of --manifolds or --manifolds-file is required.\n")
sys.exit(22)
elif args.manifolds is None and args.manifolds_file is not None:
with open(args.manifolds_file, 'r') as fh:
for line in fh:
line = line.rstrip()
manifold_fps.append(line)
elif args.manifolds is not None and args.manifolds_file is None:
manifold_fps = args.manifolds
else:
sys.stderr.write(
"Exactly one of --manifolds or --manifolds-file is required.\n")
sys.exit(23)
G_fp_pairs = parse_pathways(manifold_fps)
fp_to_G = {}
for G, fp in G_fp_pairs:
fp_to_G[fp] = G
Gs = list(map(lambda x: x[0], G_fp_pairs))
# TODO warn if --node-attribute is not found
if args.seed is not None:
seed = int(args.seed)
np.random.seed(seed)
random.seed(seed)
has_header = check_header(args.data, args.delimiter)
has_row_names = check_row_names(args.data, args.delimiter, has_header)
# load data
X = None
# pd.read_csv defaults updated by CLI arguments
nrows = None
if args.m_samples is not None:
n_rows = args.m_samples
header = 'infer'
if not has_header:
header = None
index_col = None
if has_row_names:
index_col = 0
X = pd.read_csv(args.data,
sep=args.delimiter,
header=header,
nrows=nrows,
index_col=index_col)
samples = list(X.index)
# transpose data if desired
m, n = X.shape
if args.high_dimensional:
if m > n:
X = X.transpose()
else:
if m < n:
X = X.transpose()
# finalize data prep for nmf_pathway:
# parse nodelist if provided or infer it from X as a dataframe
# convert data frame to numpy
nodelist = None
if args.nodelist is not None:
nodelist = prmf.parse_nodelist(open(args.nodelist))
X = X.to_numpy()
else:
if has_header:
# use the header to construct a nodelist
nodelist = list(X.columns)
nodelist_set = set(nodelist)
for G in Gs:
for node in G:
if node not in nodelist_set:
nodelist.append(node)
nodelist_set.add(node)
X = prmf.embed_arr(nodelist, list(X.columns), X.to_numpy())
else:
sys.stderr.write(
"--nodelist is not provided and there is no header in <--data>\n"
)
sys.exit(25)
# check node identifiers in G against nodelist
# TODO rework this test for inferred nodelist
nodelist_set = set(nodelist)
G_index_to_frac = {}
all_zero = True
for i, G in enumerate(Gs):
count = 0
for node in G.nodes_iter():
if node in nodelist_set:
count += 1
frac = count / G.order()
G_index_to_frac[i] = frac
if count != 0:
all_zero = False
if all_zero:
sys.stderr.write(
"Invalid manifolds. Check that the node identifiers of the manifolds are present in the nodelist. Try setting --node-attribute if the node identifier is in a graphml attribute rather than the XML node attribute 'id'\n"
)
sys.exit(24)
sys.stdout.write("Printing manifold node representation in nodelist:\n")
for i, G_fp_pair in enumerate(G_fp_pairs):
sys.stdout.write("{}: {:2.1f}%\n".format(G_fp_pair[1],
G_index_to_frac[i] * 100))
U_fp = os.path.join(args.outdir, "U.csv")
V_fp = os.path.join(args.outdir, "V.csv")
obj_fp = os.path.join(args.outdir, "obj.txt")
# cross validation
# TODO use other folds
X_test = None
if args.cross_validation is not None:
kf = KFold(n_splits=round(1 / args.cross_validation))
for train_index, test_index in kf.split(X):
X_train = X[train_index]
X_test = X[test_index]
X = X_train
samples = [samples[i] for i in train_index]
break
# normalize data if desired
# data at this stage is assumed to be observations x features
# normalization is done for each feature value
# e.g. the sample with the highest read count for gene X gets the value 1 in the gene X column
if not args.no_normalize:
X = quantile_transform(X)
# --manifolds-init - {{
pathway_init_fp = os.path.join(args.outdir, 'init_pathways.txt')
U_init = None
V_init = None
init_fps = []
if args.manifolds_init is not None:
Gs_init = list(map(lambda fp: fp_to_G[fp], args.manifolds_init))
if len(args.manifolds_init) < args.k_latent:
# then extend Gs_init with a random sample from the pathway population
non_init_fps = list(set(manifold_fps) - set(args.manifolds_init))
chosen_fps = random.sample(
non_init_fps, args.k_latent - len(args.manifolds_init))
init_fps = copy.copy(args.manifolds_init)
for chosen_fp in chosen_fps:
Gs_init.append(fp_to_G[chosen_fp])
init_fps.append(chosen_fp)
elif len(args.manifolds_init) == args.k_latent:
# no modification to Gs_init is needed
init_fps = args.manifolds_init
else: # len(args.manifolds_init) > args.k_latent
# then sample from Gs_init
inds = np.random.choice(len(Gs_init), args.k_latent)
Gs_init_new = []
for ind in inds:
Gs_init_new.append(Gs_init[ind])
init_fps.append(args.manifolds_init[ind])
Gs_init = Gs_init_new
vs = []
us = []
for G in Gs_init:
v, pathway_ind = pathway_to_vec(X, G, nodelist)
v_pathway_signal = v[pathway_ind]
u, res = nmf_init_u(X, v)
v_new, res = nmf_init_v(X, u)
v_new[pathway_ind] = v_pathway_signal
vs.append(v_new)
us.append(u)
V_init = np.concatenate(vs, axis=1)
U_init = np.concatenate(us, axis=1)
sys.stdout.write(
"Using the following manifolds for initialization:\n{}\n".format(
"\n".join(init_fps)))
# also write these to their own file
with open(pathway_init_fp, 'w') as pathway_init_fh:
pathway_init_fh.write("\n".join(init_fps))
# }} - --manifolds-init
# TODO other arguments
U, V, obj_data = nmf_pathway(X,
Gs,
nodelist=nodelist,
gamma=args.gamma,
tradeoff=tradeoff,
k_latent=args.k_latent,
U_init=U_init,
V_init=V_init,
verbose=args.verbose)
U = pd.DataFrame(U,
index=samples,
columns=list(
map(lambda x: "LV{}".format(x),
range(args.k_latent))))
V = pd.DataFrame(V,
index=nodelist,
columns=list(
map(lambda x: "LV{}".format(x),
range(args.k_latent))))
U.to_csv(U_fp, sep=",", index=has_row_names, quoting=csv.QUOTE_NONNUMERIC)
V.to_csv(V_fp, sep=",", index=True, quoting=csv.QUOTE_NONNUMERIC)
# cross validation
if args.cross_validation is not None:
normalized_test_errors = prmf.measure_cv_performance(V, X_test)
avg_normalized_test_error = np.mean(normalized_test_errors)
error_fp = os.path.join(args.outdir, 'test_error.csv')
np.savetxt(error_fp, normalized_test_errors, delimiter=",")
obj_data['average_normalized_test_error'] = avg_normalized_test_error
with open(obj_fp, 'w') as obj_fh:
latent_to_pathway_data = obj_data.pop('latent_to_pathway_data', {})
for k, v in obj_data.items():
obj_fh.write("{} = {:0.5f}\n".format(k, v))
# write which manifold file was used for each latent factor
ks = sorted(latent_to_pathway_data.keys())
for k in ks:
lapl_inds = list(map(lambda x: x[0], latent_to_pathway_data[k]))
# TODO pick first, assumes convergence
lapl_ind = lapl_inds[0]
G, fp = G_fp_pairs[lapl_ind]
obj_fh.write("{} -> {}\n".format(k, fp))
parser = argparse.ArgumentParser()
parser.add_argument('--nodelist', required=True)
parser.add_argument('--gene-by-latent', required=True)
parser.add_argument('--opt-outfile', required=True)
parser.add_argument('--ppi-network',
help="PPI and pathway union graph stored as graphml",
required=True)
parser.add_argument(
'--latent',
default=None,
help="If provided, only run script on this latent factor",
type=int)
parser.add_argument('--outdir', required=True)
args = parser.parse_args()
nodelist = prmf.parse_nodelist(open(args.nodelist))
ppi_network = nx.read_graphml(args.ppi_network)
gene_by_latent = np.genfromtxt(args.gene_by_latent, delimiter=",")
k_to_pathway_fp = prmf.parse_pathway_obj(args.opt_outfile)
if (args.latent is not None):
k_to_pathway_fp = {args.latent: k_to_pathway_fp[args.latent]}
ofp = os.path.join(args.outdir, 'pathway_extension.out')
ofh = open(ofp, 'w')
for k, fp in k_to_pathway_fp.items():
pathway = nx.read_graphml(fp)
vec = gene_by_latent[:, k]
node_to_score = score_pathway_neighbors(ppi_network, pathway, nodelist,
vec)
node_to_score = filter_pathway_neighbors(vec, node_to_score)
def main():
parser = argparse.ArgumentParser(description="""
Diffuse node scores over a network. The diffused matrix is of shape (n_nodes, n_gene_lists) === (n_feature x n_obs).
""")
parser.add_argument("--network",
type=str,
help="graphml network file to run diffusion on",
required=True)
parser.add_argument(
"--nodelist",
type=argparse.FileType("r"),
required=True,
help=
"Association between gene identifier and matrix index provided as a whitespace delimited list"
)
parser.add_argument(
"--gene-lists",
nargs="+",
help="one or more files with an node identifiers on each line")
parser.add_argument(
"--gene-csv",
help=
"One csv file with genes along columns and observations along rows; must contain column names but not row names"
)
parser.add_argument("--diffused",
"-d",
type=argparse.FileType("w"),
required=True,
help="Diffused matrix")
parser.add_argument("--alpha",
"-a",
type=float,
default=0.7,
help="Diffusion rate parameter")
parser.add_argument(
"--tolerance",
"-t",
type=float,
default=10e-6,
help=
"Tolerance threshold for diffusion; stop when change in diffused matrix crosses below threshold"
)
parser.add_argument(
"--string-edge-type",
default="combined_score",
help=
"\"experimental\" for edges supported by experimental evidence only; \"combined_score\" for the entire stringdb network; default=\"combined_score\""
)
parser.add_argument(
"--diffused-format",
type=str,
default='csv',
help=
"Either \"ampl\" or \"csv\"; default=\"csv\" which is short for MatrixMarket, a sparse matrix file format"
)
args = parser.parse_args()
# TODO ampl only right now
# fail fast on --diffused-format
#if args.diffused_format not in ['ampl', 'mm']:
# sys.stderr.write("invalid --diffused-format={}\n".format(args.diffused_format))
# sys.exit(22)
if args.gene_lists is None and args.gene_csv is None:
sys.stderr.write(
"Exactly one of --gene-lists or --gene-csv is required")
sys.exit(23)
# TODO edge confidence threshold, edge_type in other script
G_ppi = nx.read_graphml(args.network)
nodelist = prmf.parse_nodelist(args.nodelist)
# NOTE if G_ppi has 'weight' attribute on edges, its value is used; otherwise a value of
# 1 is populated in the ij entry for an edge (i, j)
adj = nx.to_scipy_sparse_matrix(G_ppi, nodelist=nodelist, dtype=bool)
mat = None
if args.gene_lists is not None:
# parse gene lists
gene_lists = []
for gene_path in args.gene_lists:
with open(gene_path) as fh:
gene_lists.append(prmf.parse_ws_delim(fh))
# verify gene lists present in ppi_db
def get_row_vec_for_gene_list(gene_list):
row_vec, missing = prmf.embed_ids(nodelist, gene_list)
sys.stderr.write("missing {}/{} node identifiers: {}\n".format(
len(missing), len(gene_list), ", ".join(missing)))
return row_vec
row_vecs = map(get_row_vec_for_gene_list, gene_lists)
mat = sp.vstack(row_vecs)
else:
mat = sp.csc_matrix(np.genfromtxt(args.gene_csv, delimiter=","))
# do diffusion
smoothed_mat = prmf.diffusion(mat,
adj,
alpha=args.alpha,
tol=args.tolerance)
# write results
if args.diffused_format == "ampl":
# TODO does this work with 'wb'?
prmf.ampl_write_sparse_arr(smoothed_mat, args.diffused, len(nodelist))
else:
index = list(
map(lambda x: "sample{}".format(x + 1),
range(len(args.gene_lists))))
smoothed_mat_df = pd.DataFrame(smoothed_mat.todense(),
index=index,
columns=nodelist)
smoothed_mat_df.to_csv(args.diffused,
sep=",",
index=True,
quoting=csv.QUOTE_NONNUMERIC)
