evaluate.py 22 KB
Newer Older
1
2
3
4
5
6
7
#!/usr/bin/env python3


import sys
import argparse
from termcolor import colored
import networkx as nx
8
sys.setrecursionlimit(10000)
9
10
11


def parse_args():
12
    parser = argparse.ArgumentParser(description='Process a d2 graph (complete graph or path) to evaluate its quality.')
13
    parser.add_argument('filename', type=str,
Yoann Dufresne's avatar
Yoann Dufresne committed
14
                        help='The file to evalute')
15
    parser.add_argument('--type', '-t', choices=["d2", "path", "d2-2annotate", "dgraphs"], default="path", required=True,
16
                        help="Define the data type to evaluate. Must be 'd2' or 'path' or 'd2-2annotate' (Rayan's hack).")
Yoann Dufresne's avatar
Yoann Dufresne committed
17
18
    parser.add_argument('--light-print', '-l', action='store_true',
                        help='Print only wrong nodes and paths')
19
    parser.add_argument('--max_gap', '-g', type=int, default=0, help="Allow to jump over max_gap nodes during the increasing path search")
Yoann Dufresne's avatar
Yoann Dufresne committed
20
    parser.add_argument('--barcode_graph', '-b', help="Path to the barcode graph corresponding to the d2_graph to analyse.")
21
    parser.add_argument('--optimization_file', '-o',
Yoann Dufresne's avatar
Yoann Dufresne committed
22
                        help="If the main file is a d2, a file formatted for optimization can be set. This file will be used to compute the coverage of the longest path on the barcode graph.")
23
24
25
26
27
28
29
30
31
32
33
34
35
36

    args = parser.parse_args()
    return args


def load_graph(filename):
    if filename.endswith('.graphml'):
        return nx.read_graphml(filename)
    elif filename.endswith('.gexf'):
        return nx.read_gexf(filename)
    else:
        print("Wrong file format. Require graphml or gefx format", file=sys.stderr)
        exit()

37
38
39
40
41
42
43
44
45
46
47
def save_graph(g, filename):
    if filename.endswith('.graphml'):
        return nx.write_graphml(g,filename)
    elif filename.endswith('.gexf'):
        return nx.write_gexf(g,filename)
    else:
        print("Wrong file format. Require graphml or gefx format", file=sys.stderr)
        exit()



48

Yoann Dufresne's avatar
Yoann Dufresne committed
49
50
# ------------- Path Graph -------------

Yoann Dufresne's avatar
Yoann Dufresne committed
51
52
53
54
def mols_from_node(node_name):
    return [int(idx) for idx in node_name.split(":")[1].split(".")[0].split("_")]


55
56
57
58
def parse_udg_qualities(graph):
    """ Compute the quality for the best udgs present in the graph.
        All the node names must be under the format :
        {idx}:{mol1_id}_{mol2_id}_...{molx_id}.other_things_here
59
60
        :param graph: The networkx graph representing the deconvolved graph
        :return: A tuple containing two dictionaries. The first one with theoretical frequencies of each node, the second one with observed frequencies.
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
    """
    dg_per_node = {}

    for node, data in graph.nodes(data=True):
        str_udg = data["udg"]
        central, h1, h2 = str_to_udg_lists(str_udg)

        if central not in dg_per_node:
            dg_per_node[central] = []
        dg_per_node[central].append(data["udg"])

    for node in dg_per_node:
        print(node, dg_per_node[node])
        print(len(dg_per_node))

    return dg_per_node


Yoann Dufresne's avatar
Yoann Dufresne committed
79
def parse_path_graph_frequencies(graph, barcode_graph):
80
81
82
83
    """ Compute appearance frequencies from node names.
        All the node names must be under the format :
        {idx}:{mol1_id}_{mol2_id}_...{molx_id}.other_things_here
        :param graph: The networkx graph representing the deconvolved graph
84
        :param barcode_graph: The barcode graph
85
86
        :return: A tuple containing two dictionaries. The first one with theoretical frequencies of each node, the second one with observed frequencies.
    """
Yoann Dufresne's avatar
Yoann Dufresne committed
87
    # Compute origin nodes formatted as `{idx}:{mol1_id}_{mol2_id}_...`
Yoann Dufresne's avatar
Yoann Dufresne committed
88
    observed_frequencies = {}
Yoann Dufresne's avatar
Yoann Dufresne committed
89
    real_frequencies = {}
90
    origin_node_names = []
Yoann Dufresne's avatar
Yoann Dufresne committed
91
92
    node_per_barcode = {}

Yoann Dufresne's avatar
Yoann Dufresne committed
93
    for node, data in graph.nodes(data=True):
94
95
        parsed = parse_dg_name(graph, node)
        origin_name = parsed[0][1]
Yoann Dufresne's avatar
Yoann Dufresne committed
96
97

        if origin_name not in node_per_barcode:
Yoann Dufresne's avatar
Yoann Dufresne committed
98
99
            node_per_barcode[origin_name] = []
        node_per_barcode[origin_name].append(node)
100
101

        # Count frequency
Yoann Dufresne's avatar
Yoann Dufresne committed
102
        if origin_name not in observed_frequencies:
Yoann Dufresne's avatar
Yoann Dufresne committed
103
            observed_frequencies[origin_name] = 0
104
            origin_node_names.append(origin_name)
Yoann Dufresne's avatar
Yoann Dufresne committed
105
        observed_frequencies[origin_name] += 1
106

Yoann Dufresne's avatar
Yoann Dufresne committed
107
108
    # Theoretical frequencies
    real_frequencies = {node_id: len(node_id.split(":")[1].split("_")) for node_id in barcode_graph.nodes()}
109

Yoann Dufresne's avatar
Yoann Dufresne committed
110
    return real_frequencies, observed_frequencies, node_per_barcode
111
112


Yoann Dufresne's avatar
Yoann Dufresne committed
113
def parse_graph_path(graph):
114
115
116
    """ This function aims to look for direct molecule neighbors.
        If a node has more than 2 direct neighbors, it's not rightly split
    """
Yoann Dufresne's avatar
Yoann Dufresne committed
117
118
119
120
121
    neighborhood = {}

    for node in graph.nodes():
        molecules = mols_from_node(node)
        neighbors = list(graph.neighbors(node))
122

Yoann Dufresne's avatar
Yoann Dufresne committed
123
124
125
126
127
128
129
130
        neighborhood[node] = []
        for mol in molecules:
            for nei in neighbors:
                nei_mols = mols_from_node(nei)
                if mol-1 in nei_mols:
                    neighborhood[node].append(nei)
                if mol+1 in nei_mols:
                    neighborhood[node].append(nei)
131

Yoann Dufresne's avatar
Yoann Dufresne committed
132
    return neighborhood
133
134


Yoann Dufresne's avatar
Yoann Dufresne committed
135
136
def print_path_summary(frequencies, light_print=False, file_pointer=sys.stdout):
    if file_pointer is None:
Yoann Dufresne's avatar
Yoann Dufresne committed
137
138
        return

Yoann Dufresne's avatar
Yoann Dufresne committed
139
    print("--- Nodes analysis ---", file=file_pointer)
Yoann Dufresne's avatar
Yoann Dufresne committed
140
141
142
    theoretical_frequencies, observed_frequencies, node_per_barcode = frequencies
    for key in theoretical_frequencies:
        obs, the = observed_frequencies[key] if key in observed_frequencies else 0, theoretical_frequencies[key]
Yoann Dufresne's avatar
Yoann Dufresne committed
143
144
145
146
147
        result = f"{key}: {obs}/{the}"

        if file_pointer == sys.stdout:
            result = colored(result, 'green' if obs==the else 'red')

Yoann Dufresne's avatar
Yoann Dufresne committed
148
        if light_print and obs == the:
Yoann Dufresne's avatar
Yoann Dufresne committed
149
150
151
152
153
            continue

        print(result, file=file_pointer)


154
155
    print("--- Global summary ---", file=file_pointer)

Yoann Dufresne's avatar
Yoann Dufresne committed
156
157
    # --- Frequency usage ---
    # Tags
Yoann Dufresne's avatar
Yoann Dufresne committed
158
159
160
    distinct_theoretical_nodes = len(theoretical_frequencies)
    distinct_observed_nodes = len(observed_frequencies)
    print(f"Distinct barcodes: {distinct_observed_nodes}/{distinct_theoretical_nodes}", file=file_pointer)
Yoann Dufresne's avatar
Yoann Dufresne committed
161
    # molecules
Yoann Dufresne's avatar
Yoann Dufresne committed
162
163
164
    cumulative_theoretical_nodes = sum(theoretical_frequencies.values())
    cumulative_observed_nodes = sum(observed_frequencies.values())
    print(f"Molecules: {cumulative_observed_nodes}/{cumulative_theoretical_nodes}", file=file_pointer)
Yoann Dufresne's avatar
Yoann Dufresne committed
165
166
167
    # Wrong splits
    over_split = 0
    under_split = 0
Yoann Dufresne's avatar
Yoann Dufresne committed
168
169
170
171
172
    for barcode in theoretical_frequencies:
        observed = observed_frequencies[barcode] if barcode in observed_frequencies else 0
        theoretic = theoretical_frequencies[barcode]
        over_split += max(observed-theoretic, 0)
        under_split += max(theoretic-observed, 0)
Yoann Dufresne's avatar
Yoann Dufresne committed
173
    print(f"Under/Over splitting: {under_split} - {over_split}")
174
175


176
177
178
179
def print_dgraphs_summary(frequencies, light_print=False):
    pass


Yoann Dufresne's avatar
Yoann Dufresne committed
180
181
# ------------- D2 Graph -------------

182
183
def str_to_udg_lists(s):
    udg = s.replace("]", "").replace(' [', '[')
184
    return udg.split('[')[1:]
185

Rayan  CHIKHI's avatar
Rayan CHIKHI committed
186
187
188
189
# speeds up networkx access to attributes
cached_udg_attr = None
cached_score_attr = None

190
def parse_dg_name(gr, name):
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
191
192
193
194
    global cached_udg_attr, cached_score_attr 
    if cached_udg_attr is None:
        cached_udg_attr = nx.get_node_attributes(gr, 'udg')
    udg = cached_udg_attr[name]
Rayan Chikhi's avatar
Rayan Chikhi committed
195
196
197
198
    res = str_to_udg_lists(udg)
    if len(res) != 3:
        print("parsing problem:",res)
    central, h1, h2 = res
Yoann Dufresne's avatar
Yoann Dufresne committed
199
    
200
    idx = name
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
201
202
203
    if cached_score_attr is None:
        cached_score_attr = nx.get_node_attributes(gr, 'score')
    score = cached_score_attr[name]
Yoann Dufresne's avatar
Yoann Dufresne committed
204
205
206
207
208
209
210
211

    # Parse hands
    h1 = h1.split(', ')
    h2 = h2.split(', ')

    return (idx, central, score), h1, h2


212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
def path_to_jumps(path):
    chuncks = []
    prev_start = -1000
    current_molecule = -1000

    for mol, node in path:
        # If there is a gap
        if mol > current_molecule + 1:
            chuncks.append((prev_start, current_molecule))
            prev_start = mol

        current_molecule = mol

    # Add the last piece
    chuncks.append((prev_start, current_molecule))

    del chuncks[0]
    return chuncks


Yoann Dufresne's avatar
Yoann Dufresne committed
232
def print_d2_summary(connected_components, longest_path, coverage_vars=(0, 0), light_print=False):
Yoann Dufresne's avatar
Yoann Dufresne committed
233
234
235
    print("--- Global summary ---")
    print(f"Number of connected components: {len(connected_components)}")
    print(f"Total number of nodes: {sum([len(x) for x in connected_components])}")
Yoann Dufresne's avatar
Yoann Dufresne committed
236
237
    no_singleton = [x for x in connected_components if len(x) > 1]
    print(f"There are {len(no_singleton)} connex components with at least 2 nodes")
Yoann Dufresne's avatar
Yoann Dufresne committed
238
239
240
    print(f"The 5 largest components: {[len(x) for x in connected_components][:5]}")

    print("--- Largest component analysis ---")
241
242
243
244
245
246
247
248
249
    # Get the list of node idx
    path_dg_idx = [int(x[1].split(" ")[0]) for x in longest_path]
    # print("\n".join(longest_path))
    if not light_print:
        print("Longest path for increasing molecule number:")
        print(path_dg_idx)
    print(f"Size of the longest path: {len(longest_path)}")
    print("Jumps in central nodes:")
    print(path_to_jumps(longest_path))
Yoann Dufresne's avatar
Yoann Dufresne committed
250
251
252

    print(f"Number of usable coverage variables: {len(coverage_vars[1])}")
    print(f"Coverage: {len(coverage_vars[0])}/{len(coverage_vars[1])}")
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
    if not light_print:
        print(f"Missing coverage variables:\n{coverage_vars[1]-coverage_vars[0]}")

def _get_distant_neighbors(graph, node, dist):
    neighbors = set()

    to_compute = [node]
    for _ in range(dist):
        next_compute = []
        for node in to_compute:
            for neighbor in graph[node]:
                if neighbor not in neighbors:
                    neighbors.add(neighbor)
                    next_compute.append(neighbor)
        to_compute = next_compute
Yoann Dufresne's avatar
Yoann Dufresne committed
268

269
    return neighbors
Yoann Dufresne's avatar
Yoann Dufresne committed
270

271
def compute_next_nodes(d2_component, max_jumps=0):
272
273
274
275
276
    # First parse dg names
    dg_names = {}
    for node in d2_component.nodes():
        dg_names[node] = parse_dg_name(d2_component,node)

Yoann Dufresne's avatar
Yoann Dufresne committed
277
278
279
280
    next_nodes = {}

    for node in d2_component.nodes():
        # Parse the current node name
281
        head, h1, h2 = dg_names[node]
Yoann Dufresne's avatar
Yoann Dufresne committed
282
283
284
285
        next_nodes[node] = {}

        # Get the current molecule idxs
        molecule_idxs = mols_from_node(head[1])
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
286
287
        #print("node",node,"dg name",dg_names[node],"mol idxs",molecule_idxs)

Yoann Dufresne's avatar
Yoann Dufresne committed
288
289
        for mol_idx in molecule_idxs:
            nexts = []
290
291
            # for neighbor in d2_component[node]:
            for neighbor in _get_distant_neighbors(d2_component, node, max_jumps+1):
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
292
                # nei_head: central node of the neighbor of 'node'
293
                nei_head, _, _ = dg_names[neighbor]
Yoann Dufresne's avatar
Yoann Dufresne committed
294
                nei_mols = mols_from_node(nei_head[1])
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
295
                # only consider neighbor molecules that are strictly bigger than the current molecule idx considered (from 'node')
Yoann Dufresne's avatar
Yoann Dufresne committed
296
297
298
299
300
                nei_mols = [x for x in nei_mols if x > mol_idx]
                
                # If there are molecule next
                if len(nei_mols) > 0:
                    next_nei_mol = min(nei_mols)
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
301
                    # append to the neighbors of (node,mol_idx) that 'neighbor' if it contains a molecule that's bigger than mol_idx
Yoann Dufresne's avatar
Yoann Dufresne committed
302
303
304
305
                    nexts.append((next_nei_mol, neighbor))

            nexts.sort(key=lambda x: x[0])
            next_nodes[node][mol_idx] = nexts
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
306
            #print("next nodes of node",node,"mol idx",mol_idx,":",next_nodes)
Yoann Dufresne's avatar
Yoann Dufresne committed
307
308
309
310

    return next_nodes


311
312
def compute_longest_increasing_paths(d2_component, max_gap=0):
    next_nodes = compute_next_nodes(d2_component, max_jumps=max_gap)
Yoann Dufresne's avatar
Yoann Dufresne committed
313
314
315
316
317
318

    # Compute the longest path for each node
    longest_paths = {}
    for idx, start_node in enumerate(next_nodes):
        # print(f"{idx}/{len(next_nodes)}")
        for mol_idx in next_nodes[start_node]:
319
            recursive_longest_path(start_node, mol_idx, next_nodes, longest_paths)
Yoann Dufresne's avatar
Yoann Dufresne committed
320

Rayan  CHIKHI's avatar
Rayan CHIKHI committed
321
322
323
324
325
326
    test_node = '5339'
    for mol in longest_paths[test_node]:
        print("investigating node",test_node,"mol",mol,longest_paths[test_node][mol])

    # Get the longest path size,
    # across all barcode graph nodes and all molecules in these barcodes
Yoann Dufresne's avatar
Yoann Dufresne committed
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
    max_len, node_val, mol_idx = 0, None, -1
    for node in longest_paths:
        for mol in longest_paths[node]:
            length, _, _ = longest_paths[node][mol]
            if max_len < length:
                max_len = length
                node_val = node
                mol_idx = mol

    # Backtrack the longest path
    path = backtrack_longest_path(node_val, mol_idx, longest_paths)
    return path


def backtrack_longest_path(node, molecule, longest_paths, path=[]):
342
    if node is None:
Yoann Dufresne's avatar
Yoann Dufresne committed
343
344
        return path

345
    path.append((molecule, node))
Yoann Dufresne's avatar
Yoann Dufresne committed
346
347
348
349
350
351
352
    length, next_node, next_mol = longest_paths[node][molecule]
    return backtrack_longest_path(next_node, next_mol, longest_paths, path)


def recursive_longest_path(current_node, current_molecule, next_nodes, longest_paths):
    # Dynamic programming
    if current_node in longest_paths and current_molecule in longest_paths[current_node]:
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
353
        #print("getting cached result for node",current_node,"mol",current_molecule,longest_paths[current_node][current_molecule])
Yoann Dufresne's avatar
Yoann Dufresne committed
354
355
356
357
        return longest_paths[current_node][current_molecule]

    longest = 0
    longest_next = None
358
    min_mol_idx = float('inf') 
Yoann Dufresne's avatar
Yoann Dufresne committed
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
    # Recursively compute the longest path
    for mol_idx, node in next_nodes[current_node][current_molecule]:
        size, _, _ = recursive_longest_path(node, mol_idx, next_nodes, longest_paths)
        if size + 1 > longest:
            longest = size + 1
            longest_next = node
            min_mol_idx = mol_idx
        # If there is an alternative path with shorter distance
        elif size + 1 == longest and mol_idx < min_mol_idx:
            longest = size + 1
            longest_next = node
            min_mol_idx = mol_idx
    
    # Save the result
    if not current_node in longest_paths:
        longest_paths[current_node] = {}
    longest_paths[current_node][current_molecule] = (longest, longest_next, min_mol_idx)
    return longest_paths[current_node][current_molecule]


379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
# def longest_common_subsequence(barcode_true_path, barcoded_graph):
#     """ Assume that the two graphs have an attribute barcode for each node and a unique node name"""
#     path_nodes = []
#     path_nodes_barcodes = []
#     for node, data in barcode_true_path.nodes(data=True):
#         path_nodes.append(node)
#         path_nodes_barcodes.append(data["barcode"])
#     path_nodes_to_idx = {n: idx for idx, n in enumerate(path_nodes)}
#
#     graph_nodes = []
#     graph_nodes_barcodes = []
#     for node, data in barcoded_graph.nodes(data=True):
#         graph_nodes.append(node)
#         graph_nodes_barcodes.append(data["barcode"])
#     graph_nodes_to_idx = {n: idx for idx, n in enumerate(graph_nodes)}
#
#     dynamic_array = [[0 for _ in range(len(graph_nodes)+1)] for _ in range(len(path_nodes)+1)]
#     for row in range(1, len(path_nodes)+1):
#         path_node = path_nodes[row-1]
#         path_barcode = path_nodes_barcodes[row-1]
#
#         for column in range(1, len(graph_nodes)):
#             graph_node = graph_nodes[column-1]
#             graph_barcode = graph_nodes_barcodes[column-1]
#
#             prev_scores = [dynamic_array[row-1][column]]
#             for neighbor_node in barcoded_graph[graph_node]:
#                 neighbor_idx = graph_nodes_to_idx[neighbor_node]
#                 prev_scores.append(dynamic_array[row-1][neighbor_idx])
#
#             match_point = 1 if path_barcode == graph_barcode else 0
#             dynamic_array[row][column] = max(prev_scores) + match_point


Yoann Dufresne's avatar
Yoann Dufresne committed
413
414
415
416
def compute_covered_variables(graph, path):
    path_nodes = set()
    for mol, node_name in path:
        path_nodes.add(node_name)
417

Yoann Dufresne's avatar
Yoann Dufresne committed
418
419
420
421
422
423
424
    used_vars = set()
    total_vars = set()
    for node, data in graph.nodes(data=True):
        vars = data["barcode_edges"].split(" ")
        total_vars.update(vars)
        if node in path_nodes:
            used_vars.update(vars)
425

Yoann Dufresne's avatar
Yoann Dufresne committed
426
    return used_vars, total_vars
427

428
# returns True iff there exist x in mol1 such that there exists y in mol2 and |x-y| <= some_value
429
430
431
432
433
434
435
436
437
438
439
def nearby_udg_molecules(mols1, mols2):
    for x in mols1:
        for y in mols2:
            if abs(x-y) <= 5:
                return True
    return False

def verify_graph_edges(d2_component):
    udg_molecules_dict=dict()
    for node in d2_component.nodes():
        # Parse the current node name
440
        head, c1, c2 = parse_dg_name(d2_component,node)
441
442

        # Construct the molecule(s) that this udg really 'reflects'
443
444
445
446
447
        # i.e. the udg has a central node and two cliques
        # that central node is the result of merging of several molecules
        # ideally, only one of those molecules is connected to the molecules of the cliques
        # (there could be more than one though; in that case the udg is 'ambiguous')
        # udg_molecules aims to reflect the molecule(s) underlying this udg
448
449
450
451
        udg_molecules = set()

        # Get the current molecule idxs of central node
        molecule_idxs = mols_from_node(head[1])
452
        # print("mol idxs", molecule_idxs)
453
454

        # Examine molecule idx's of cliques to see which are close to the central node
455
        # rationale: c1/c2 contain nearby molecule id's
456
457
458
459
460
        for mol_idx in molecule_idxs:
            nexts = []
            for c in [c1,c2]:
                for c_node in c:
                    nei_mols = mols_from_node(c_node.split()[0])
461
                    nei_mols = [x for x in nei_mols if x > mol_idx]  # fixme: also look at the <= molecules for more robustness
462
463
464
465
466
467
468
469
                    
                    # If there are molecule next
                    if len(nei_mols) > 0:
                        next_nei_mol = min(nei_mols)
                        nexts.append((next_nei_mol))

            nexts.sort(key=lambda x: x)
            quality = sum([1.0/x if mol_idx+x in nexts else 0 for x in range(1,6)]) / sum([1.0/x for x in range(1,6)])
470
            if quality > 0.6:  # eyeballed but still arbitrary
471
                udg_molecules.add(mol_idx)
472
            # print("mol",mol_idx,molecule_idxs,"quality",quality,"nexts",nexts)
473
474
475
476
477
478
       
        udg_molecules_dict[head[0]]=udg_molecules

    # Then: annotate edges as to whether they're real (their udg_molecule(s) are nearby) or fake
    for n1, n2 , data in d2_component.edges(data=True):
        # Parse the current node name
479
        head, c1, c2 = parse_dg_name(d2_component,n1)
480
481
        node_udg_molecules = udg_molecules_dict[head[0]]
        
482
        n_head, n_c1, n_c2 = parse_dg_name(d2_component,n2)
483
484
485
486
487
488
489
        neighbor_udg_molecules = udg_molecules_dict[n_head[0]]
       
        if nearby_udg_molecules(node_udg_molecules, neighbor_udg_molecules):
            color = 'green'
        else:
            color = 'red'
        data['color'] = color
490
        # print("edge",node_udg_molecules,neighbor_udg_molecules,color)
491
492
493
494
    
    # also, annotate nodes by their putative molecule found
    for n, data in d2_component.nodes(data=True):
        # Parse the current node name
495
        head, c1, c2 = parse_dg_name(d2_component,n)
496
497
498
        node_udg_molecules = udg_molecules_dict[head[0]]
        data['udg_molecule']= '_'.join(list(map(str,node_udg_molecules)))
        
499
500
501
502
503
504
505
506
    # aggressive: delete nodes which have either no found udg_molecule, or two udg_molecules
    # turns out it's not a good strategy as the nodes with two udg_molecules are important to connect portions of graph
    # but what if we magically keep those where the two adjacent molecules are close together
    if True:
        d2_component = d2_component.copy()
        nodes_to_remove = []
        for n, data in d2_component.nodes(data=True):
            # Parse the current node name
507
            head, c1, c2 = parse_dg_name(d2_component,n)
508
509
510
            if "_" in data['udg_molecule'] or data['udg_molecule'] == '':
                if "_" in data['udg_molecule']:
                    m1, m2 = list(map(int,data['udg_molecule'].split("_")))
511
                    if abs(m2-m1) < 30: continue  # don't remove that kind of nodes
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
                nodes_to_remove += [n]
        d2_component.remove_nodes_from(nodes_to_remove)
        print("removed",len(nodes_to_remove),"bad nodes")

    # aggressive: delete red edges
    if True:
        d2_component = d2_component.copy()
        edges_to_remove = []
        for n1, n2, data in d2_component.edges(data=True):
            if data['color'] == 'red':
                edges_to_remove += [(n1,n2)]
        d2_component.remove_edges_from(edges_to_remove)
        print("removed",len(edges_to_remove),"bad edges")


    return d2_component
528

529
530
531
532
def main():
    args = parse_args()
    graph = load_graph(args.filename)

Yoann Dufresne's avatar
Yoann Dufresne committed
533
    if args.type == "path":
Yoann Dufresne's avatar
Yoann Dufresne committed
534
        barcode_graph = load_graph(args.barcode_graph)
Rayan  CHIKHI's avatar
Rayan CHIKHI committed
535
536
537
538
539
540
541
542
543
544
        if len(list(nx.connected_components(graph))) != 1:
            exit("when running evaluate.py --type path, the graph should have a single connected component (it's supposed to be a path, after all)")

        # compute LIS over the path
        longest_path = compute_longest_increasing_paths(graph)
        print("--- Largest component analysis ---")
        print(f"Size of the longest path: {len(longest_path)}")
        #print("Jumps in central nodes:") # what does this do?
        #print(path_to_jumps(longest_path))
        return
Yoann Dufresne's avatar
Yoann Dufresne committed
545

Rayan  CHIKHI's avatar
Rayan CHIKHI committed
546
547
548
        # get over/under counted molecules
        print("--- Under/over molecule counts ---")
        frequencies = parse_path_graph_frequencies(graph, barcode_graph)
Yoann Dufresne's avatar
Yoann Dufresne committed
549
        print_path_summary(frequencies, light_print=args.light_print)
550
551
552
    elif args.type == "dgraphs":
        udg_per_node = parse_udg_qualities(graph)
        # print(udg_per_node)
Yoann Dufresne's avatar
Yoann Dufresne committed
553
554
555
556
557
    elif args.type == "d2":
        components = list(nx.connected_components(graph))
        components.sort(key=lambda x: -len(x))

        component = graph.subgraph(components[0])
558
        longest_path = compute_longest_increasing_paths(component, max_gap=args.max_gap)
Yoann Dufresne's avatar
Yoann Dufresne committed
559
560
        vars = compute_covered_variables(graph, longest_path)
        print_d2_summary(components, longest_path, coverage_vars=vars, light_print=args.light_print)
561
562
563
564
565
566
567
568
569
    
    # added by Rayan
    # example:
    # python evaluate.py --type d2-2annotate ~/Dropbox/cnrs/projects/10x-barcodes/yoann_to_cedric_ilp/d2_graph.gexf
    elif args.type == "d2-2annotate":
        components = list(nx.connected_components(graph))
        components.sort(key=lambda x: -len(x))
        component = graph.subgraph(components[0])
 
570
        component = verify_graph_edges(component)
571

Yoann Dufresne's avatar
Yoann Dufresne committed
572
573
574
575
        extension = args.filename.split('.')[-1]
        base_filename = '.'.join(args.filename.split('.')[:-1])
        save_graph(component, base_filename+".verified."+extension)

576
577
578

if __name__ == "__main__":
    main()