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Fix rank_mf for 'empty' trees
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@RJMW
RJMW committed Jul 16, 2020
1 parent 96d8dc0 commit 48ae2cc
Showing 1 changed file with 49 additions and 41 deletions.
90 changes: 49 additions & 41 deletions msnpy/annotation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -691,64 +691,72 @@ def rank_mf(trees: Sequence[nx.classes.ordered.OrderedDiGraph], rank_threshold:
df = pd.DataFrame(columns=columns)

def _scan_events(headers):
scan_events = ["ms1"]
scan_events = []
for header in headers:
info = re.findall(r'(ms\d+) ([\w\.-]+)@([a-zA-Z]+\d+\.\d+)', header)
if len(info) >= 1:
for match in info:
info_str = "{}@{}".format(match[0], match[2])
if info_str not in scan_events:
scan_events.append(info_str)
if " ms " in header:
scan_events.append("ms1")
else:
info = re.findall(r'(ms\d+) ([\w\.-]+)@([a-zA-Z]+\d+\.\d+)', header)
if len(info) >= 1:
for match in info:
info_str = "{}@{}".format(match[0], match[2])
if info_str not in scan_events:
scan_events.append(info_str)
return scan_events

annotated_trees = collections.OrderedDict()
for graph in trees:
for tree in trees:

if isinstance(graph.graph["id"], int) and "_" not in str(graph.graph["id"]):
group_id = str(graph.graph["id"]) # Tree without mf annotations
if isinstance(tree.graph["id"], int) and "_" not in str(tree.graph["id"]):
group_id = str(tree.graph["id"]) # Tree without mf annotations
else:
group_id = graph.graph["id"].split("_")[0]
annotated_trees.setdefault(group_id, []).append(graph)
group_id = tree.graph["id"].split("_")[0]
annotated_trees.setdefault(group_id, []).append(tree)

for i, graphs in enumerate(annotated_trees.values()):
for i, ats in enumerate(annotated_trees.values()):

df_subset = pd.DataFrame(columns=columns)

if len(graphs) == 1 and isinstance(graphs[0].graph["id"], int):
max_mslevel = max(nx.get_node_attributes(graph, 'mslevel').values())
headers = list(nx.get_node_attributes(graph, 'header').values())
scan_events = ",".join(map(str, _scan_events(headers)))
prec_node = list(graphs[0].nodes(data=True))[0]
if prec_node[1]["precursor"] and prec_node[1]["mslevel"] == 1:
mz = prec_node[1]["mz"]
if len(ats) == 1 and isinstance(ats[0].graph["id"], int):

max_mslevel = max(nx.get_node_attributes(ats[0], 'mslevel').values(), default=np.nan)
nodes = list(ats[0].nodes(data=True))

mz = np.nan
if nodes:
headers = list(nx.get_node_attributes(ats[0], 'header').values())
scan_events = ",".join(map(str, _scan_events(headers)))
if nodes[0][1]["precursor"] and nodes[0][1]["mslevel"] == 1:
mz = nodes[0][1]["mz"]
else:
mz = np.nan
values = [str(graphs[0].graph["id"]), graphs[0].graph["id"], mz, scan_events, max_mslevel,
scan_events = np.nan

values = [str(ats[0].graph["id"]), ats[0].graph["id"], mz, scan_events, max_mslevel,
np.nan, np.nan, np.nan, np.nan, np.nan,
0, 0, 0, len(graphs),
0, 0, 0, len(ats),
0]
d = collections.OrderedDict(zip(columns, values))
df_subset = df_subset.append(d, ignore_index=True)
df = pd.concat([df, df_subset], ignore_index=True)
else:
for graph in graphs:
for tree in ats:

mf_group_id = graph.graph["id"].split("_")
mf_group_id = tree.graph["id"].split("_")
mf_id = int(mf_group_id[1])
group_id = int(mf_group_id[0])
max_mslevel = max(nx.get_node_attributes(graph, 'mslevel').values())
headers = list(nx.get_node_attributes(graph, 'header').values())
max_mslevel = max(nx.get_node_attributes(tree, 'mslevel').values())
headers = list(nx.get_node_attributes(tree, 'header').values())
scan_events = ",".join(map(str, _scan_events(headers)))
prec_node = list(graph.nodes())[0]
mz = graph.nodes[prec_node]["mz"]
mf = str(graph.nodes[prec_node]["mf"][str(mf_id)]["mf"])
adduct = str(graph.nodes[prec_node]["mf"][str(mf_id)]["adduct"])
exact_mass = graph.nodes[prec_node]["mf"][str(mf_id)]["mass"]
prec_node = list(tree.nodes())[0]
mz = tree.nodes[prec_node]["mz"]
mf = str(tree.nodes[prec_node]["mf"][str(mf_id)]["mf"])
adduct = str(tree.nodes[prec_node]["mf"][str(mf_id)]["adduct"])
exact_mass = tree.nodes[prec_node]["mf"][str(mf_id)]["mass"]
ppm_error = float(mz - exact_mass) / (exact_mass * 0.000001)
nl_explained = len([e for e in graph.edges(data=True) if "mf" in e[2]])
values = [graph.graph["id"], group_id, mz, scan_events, max_mslevel,
nl_explained = len([e for e in tree.edges(data=True) if "mf" in e[2]])
values = [tree.graph["id"], group_id, mz, scan_events, max_mslevel,
mf_id, mf, adduct, exact_mass, ppm_error,
0, 0, 0, len(graphs),
0, 0, 0, len(ats),
nl_explained]
d = collections.OrderedDict(zip(columns, values))
df_subset = df_subset.append(d, ignore_index=True)
Expand All @@ -766,13 +774,13 @@ def _scan_events(headers):
"molecular_formula", "adduct", "mass", "ppm_error"]:
df[c] = df[c].astype('Int64')

for G in trees:
G.graph["rank"] = int(df[df["tree_id"] == str(G.graph["id"])]["rank"].iloc[0])
G.graph["mf_id"] = df[df["tree_id"] == str(G.graph["id"])]["mf_id"].iloc[0]
if pd.isna(G.graph["mf_id"]):
G.graph["mf_id"] = None
for tree in trees:
tree.graph["rank"] = int(df[df["tree_id"] == str(tree.graph["id"])]["rank"].iloc[0])
tree.graph["mf_id"] = df[df["tree_id"] == str(tree.graph["id"])]["mf_id"].iloc[0]
if pd.isna(tree.graph["mf_id"]):
tree.graph["mf_id"] = None
else:
G.graph["mf_id"] = int(G.graph["mf_id"])
tree.graph["mf_id"] = int(tree.graph["mf_id"])

trees_ranked = sorted(trees, key=lambda i: (int(str(i.graph['id']).split("_")[0]), i.graph['rank'], i.graph["mf_id"]))
if rank_threshold > 0:
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