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Commit 36aa2efc authored by bay9355's avatar bay9355
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Merge branch 'main' of https://gitlab.rrz.uni-hamburg.de/bay2046/cami

parents 8e1bf655 e105b38d
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cami_src/cami_suite.py
+ 110
− 103
View file @ 36aa2efc
...@@ -35,6 +35,7 @@ def list_combinations(lst, k): ...@@ -35,6 +35,7 @@ def list_combinations(lst, k):
assert len(set(combs)) == nof_combs assert len(set(combs)) == nof_combs
return (combs) return (combs)
def initialize_cami(path_to_ppi_file=''): def initialize_cami(path_to_ppi_file=''):
cami_params = {} cami_params = {}
# find homepath aka ~/cami # find homepath aka ~/cami
...@@ -85,12 +86,13 @@ def initialize_cami(path_to_ppi_file=''): ...@@ -85,12 +86,13 @@ def initialize_cami(path_to_ppi_file=''):
seed_lists = {seedname: preprocess.txt2lst(seed_paths[seedname]) for seedname in seed_paths} seed_lists = {seedname: preprocess.txt2lst(seed_paths[seedname]) for seedname in seed_paths}
class cami(): class cami():
""" A module that is used for Active Module identifaction based on a """ A module that is used for Active Module identifaction based on a
consensus approach consensus approach
""" """
def __init__(self, ppi_graph, seed_lst, tool_wrappers, output_dir, uid, home_path, tmp_dir='', config='camiconf', seed_score=10, parallelization=False):
def __init__(self, ppi_graph, seed_lst, tool_wrappers, output_dir, uid, home_path, tmp_dir='', config='camiconf',
seed_score=10, parallelization=False):
"""Instance variables of CAMI """Instance variables of CAMI
:param ppi_graph: The PPI-Graph on which all predictions in CAMI are based of :param ppi_graph: The PPI-Graph on which all predictions in CAMI are based of
...@@ -201,13 +203,15 @@ class cami(): ...@@ -201,13 +203,15 @@ class cami():
ref=set(self.seed_lst), ref=set(self.seed_lst),
ref_id='entrez') ref_id='entrez')
if validation_results['status'] == 'ok': if validation_results['status'] == 'ok':
biodigest.single_validation.save_results(validation_results, f'{result_set}_{self.uid}', self.output_dir) biodigest.single_validation.save_results(validation_results, f'{result_set}_{self.uid}',
self.output_dir)
biodigest.evaluation.d_utils.plotting_utils.create_plots(results=validation_results, biodigest.evaluation.d_utils.plotting_utils.create_plots(results=validation_results,
mode='set-set', mode='set-set',
tar=set(self.result_module_sets[result_set]), tar=set(self.result_module_sets[result_set]),
tar_id='entrez', tar_id='entrez',
out_dir=self.output_dir, out_dir=self.output_dir,
prefix=f'{result_set}_{self.uid}') prefix=f'{result_set}_{self.uid}')
def run_threaded_tool(self, tool, pred_sets): def run_threaded_tool(self, tool, pred_sets):
"""run a tool in one thread and save the results into a dictionary pred_sets """run a tool in one thread and save the results into a dictionary pred_sets
...@@ -296,46 +300,45 @@ class cami(): ...@@ -296,46 +300,45 @@ class cami():
for tool in result_sets: for tool in result_sets:
result_sets[tool] -= set(self.seed_lst) result_sets[tool] -= set(self.seed_lst)
params = {'hub_pentalty': [0, 0.25, 0.5, 0.75, 1.0], 'damping_factor': [0.1, 0.25, 0.5, 0.75], 'confidence_level': [0.2, 0.35, 0.5, 0.75], 'ranking':["trustrank", "betweenness", "harmonic"], 'function':[cami_v2.run_cami, cami_v3.run_cami]}
camis = { camis = {
'cami_v1': {'function': cami_v1.run_cami, 'params': {'consens_threshold': consens_threshold}}, 'cami_v1': {'function': cami_v1.run_cami, 'params': {'consens_threshold': consens_threshold}},
'cami_v2_param1_tr': {'function': cami_v2.run_cami, 'params': { 'cami_v2_param1_tr': {'function': cami_v2.run_cami, 'params': {
'hub_penalty': 0.8, 'damping_factor': 0.5, 'confidence_level': 0.75 'hub_penalty': 0.3, 'damping_factor': 0.7, 'confidence_level': 0.5
}}, }},
'cami_v2_param1_bc': {'function': cami_v2.run_cami, 'params': { 'cami_v2_param1_b': {'function': cami_v2.run_cami, 'params': {
'hub_penalty': 0.8, 'damping_factor': 0.5, 'confidence_level': 0.75, 'ranking': 'betweenness' 'hub_penalty': 0.3, 'ranking': 'betweenness', 'confidence_level': 0.5
}}, }},
'cami_v2_param1_m': {'function': cami_v2.run_cami, 'params': { 'cami_v2_param1_hc': {'function': cami_v2.run_cami, 'params': {
'hub_penalty': 0.8, 'damping_factor': 0.5, 'confidence_level': 0.75, 'ranking': 'must' 'hub_penalty': 0.3,'ranking': 'harmonic', 'confidence_level': 0.5
}}, }},
'cami_v2_param2_tr': {'function': cami_v2.run_cami, 'params': { 'cami_v2_param2_tr': {'function': cami_v2.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5 'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5
}}, }},
'cami_v2_param2_m': {'function': cami_v2.run_cami, 'params': { 'cami_v2_param2_b': {'function': cami_v2.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'must', 'hub_penalty': 0, 'ranking': 'betweenness', 'confidence_level': 0.5
}}, }},
'cami_v2_param2_bc': {'function': cami_v2.run_cami, 'params': { 'cami_v2_param2_hc': {'function': cami_v2.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5, 'ranking': 'betweenness' 'hub_penalty': 0, 'ranking': 'harmonic', 'confidence_level': 0.5
}}, }},
'cami_v3_param1_tr': {'function': cami_v3.run_cami, 'params': { 'cami_v3_param1_tr': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0.8, 'damping_factor': 0.5, 'confidence_level': 0.75 'hub_penalty': 0.3, 'damping_factor': 0.7, 'confidence_level': 0.5
}}, }},
'cami_v3_param1_bc': {'function': cami_v3.run_cami, 'params': { 'cami_v3_param1_b': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0.8, 'damping_factor': 0.5, 'confidence_level': 0.75, 'ranking': 'betweenness' 'hub_penalty': 0.3, 'ranking': 'betweenness', 'confidence_level': 0.5
}}, }},
'cami_v3_param1_m': {'function': cami_v3.run_cami, 'params': { 'cami_v3_param1_hc': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0.8, 'damping_factor': 0.5, 'confidence_level': 0.75, 'ranking': 'must' 'hub_penalty': 0.3, 'ranking': 'harmonic', 'confidence_level': 0.5
}}, }},
'cami_v3_param2_tr': {'function': cami_v3.run_cami, 'params': { 'cami_v3_param2_tr': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5, 'ranking': 'trustrank' 'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5
}},
'cami_v3_param2_bc': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5, 'ranking': 'betweenness'
}}, }},
'cami_v3_param2_m': {'function': cami_v3.run_cami, 'params': { 'cami_v3_param2_b': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5, 'ranking': 'must' 'hub_penalty': 0, 'ranking': 'betweenness', 'confidence_level': 0.5
}}, }},
'cami_v3_param3_m': {'function': cami_v3.run_cami, 'params': { 'cami_v3_param2_hc': {'function': cami_v3.run_cami, 'params': {
'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5, 'ranking': 'must', 'trees': 15 'hub_penalty': 0, 'ranking': 'harmonic', 'confidence_level': 0.5
}}, }},
} }
...@@ -369,7 +372,8 @@ class cami(): ...@@ -369,7 +372,8 @@ class cami():
for vertex in cami_vlist: for vertex in cami_vlist:
print(f'{gene_name_map[vertex]}\t{cami_scores[vertex]}\t{codes2tools[vertex]}') print(f'{gene_name_map[vertex]}\t{cami_scores[vertex]}\t{codes2tools[vertex]}')
else: else:
print(f'With the {len(seed_genes)} seed genes CAMI ({cami_method_name}) proposes {len(cami_vlist)} genes to add to the Active Module') print(
f'With the {len(seed_genes)} seed genes CAMI ({cami_method_name}) proposes {len(cami_vlist)} genes to add to the Active Module')
# for visualization with nvenn # for visualization with nvenn
self.result_gene_sets[cami_method_name] = set(cami_genes) self.result_gene_sets[cami_method_name] = set(cami_genes)
...@@ -381,8 +385,10 @@ class cami(): ...@@ -381,8 +385,10 @@ class cami():
# add seeds to result sets for drugstone and digest # add seeds to result sets for drugstone and digest
for toolname in self.result_gene_sets: for toolname in self.result_gene_sets:
self.result_module_sets[toolname] = self.result_gene_sets[toolname].union(set([gene_name_map[svertex] for svertex in self.seed_lst])) self.result_module_sets[toolname] = self.result_gene_sets[toolname].union(
print(f'With the {len(seed_genes)} seed genes the module predicted by {toolname} contains {len(self.result_module_sets[toolname])} genes') set([gene_name_map[svertex] for svertex in self.seed_lst]))
print(
f'With the {len(seed_genes)} seed genes the module predicted by {toolname} contains {len(self.result_module_sets[toolname])} genes')
def generate_output(self, cami_method, seed_genes, cami_vlist, cami_vertices, putative_vertices, cami_genes, def generate_output(self, cami_method, seed_genes, cami_vlist, cami_vertices, putative_vertices, cami_genes,
gene_name_map, codes2tools, cami_scores): gene_name_map, codes2tools, cami_scores):
...@@ -390,13 +396,16 @@ class cami(): ...@@ -390,13 +396,16 @@ class cami():
if self.debug: if self.debug:
print('Saving the results...') print('Saving the results...')
with open(f'{self.output_dir}/all_predictions_{self.uid}.tsv', 'w') as outputfile: with open(f'{self.output_dir}/all_predictions_{self.uid}.tsv', 'w') as outputfile:
outputfile.write(f'CAMI predictions with {len(self.seed_lst)} of initially {len(self.initial_seed_lst)} seeds: {seed_genes},\n'+ outputfile.write(
f'CAMI predictions with {len(self.seed_lst)} of initially {len(self.initial_seed_lst)} seeds: {seed_genes},\n' +
f'initially: {self.initial_seed_lst}\n') f'initially: {self.initial_seed_lst}\n')
outputfile.write(f'gene\tpredicted_by\tcami_score\tindex_in_graph\tdegree_in_graph\n') outputfile.write(f'gene\tpredicted_by\tcami_score\tindex_in_graph\tdegree_in_graph\n')
all_vertices = cami_vertices.union(putative_vertices) all_vertices = cami_vertices.union(putative_vertices)
for vertex in all_vertices: for vertex in all_vertices:
outputfile.write(f'{gene_name_map[vertex]}\t{codes2tools[vertex]}\t{cami_scores[vertex]}\t{str(vertex)}\t{vertex.out_degree()}\n') outputfile.write(
if self.debug: print(f'saved all predictions by the used tools in: {self.output_dir}/all_predictions_{self.uid}.tsv') f'{gene_name_map[vertex]}\t{codes2tools[vertex]}\t{cami_scores[vertex]}\t{str(vertex)}\t{vertex.out_degree()}\n')
if self.debug: print(
f'saved all predictions by the used tools in: {self.output_dir}/all_predictions_{self.uid}.tsv')
# save the predictions made by cami # save the predictions made by cami
ncbi_url = ('\tncbi_url' if self.ncbi else '') ncbi_url = ('\tncbi_url' if self.ncbi else '')
...@@ -414,7 +423,8 @@ class cami(): ...@@ -414,7 +423,8 @@ class cami():
summary = '' summary = ''
else: else:
url, summary = '', '' url, summary = '', ''
outputfile.write(f'{gene_name_map[vertex]}\t{str(vertex)}\t{cami_scores[vertex]}\t{vertex.out_degree()}{url}{summary}\n') outputfile.write(
f'{gene_name_map[vertex]}\t{str(vertex)}\t{cami_scores[vertex]}\t{vertex.out_degree()}{url}{summary}\n')
# # save the whole module # # save the whole module
# whole_module = [] # whole_module = []
...@@ -429,7 +439,6 @@ class cami(): ...@@ -429,7 +439,6 @@ class cami():
# print(f'saved {cami_method} output in: {cami_method}_output_{self.uid}.tsv') # print(f'saved {cami_method} output in: {cami_method}_output_{self.uid}.tsv')
# print(f'saved the Consensus Active Module by CAMI in: {self.output_dir}/{cami_method}_module_{self.uid}.txt') # print(f'saved the Consensus Active Module by CAMI in: {self.output_dir}/{cami_method}_module_{self.uid}.txt')
# save predicted modules by all other tools # save predicted modules by all other tools
for tool in self.result_module_sets: for tool in self.result_module_sets:
with open(f'{self.output_dir}/{tool}_output_{self.uid}.tsv', 'w') as outputfile: with open(f'{self.output_dir}/{tool}_output_{self.uid}.tsv', 'w') as outputfile:
...@@ -439,7 +448,6 @@ class cami(): ...@@ -439,7 +448,6 @@ class cami():
if self.debug: if self.debug:
print(f'saved {tool} output in: {self.output_dir}/{tool}_output_{self.uid}.tsv') print(f'saved {tool} output in: {self.output_dir}/{tool}_output_{self.uid}.tsv')
def use_nvenn(self): def use_nvenn(self):
"""Create Venn Diagrams via a external tool named degradome. """Create Venn Diagrams via a external tool named degradome.
Sends a request via requests to the degradome server. Sends a request via requests to the degradome server.
...@@ -503,4 +511,3 @@ class cami(): ...@@ -503,4 +511,3 @@ class cami():
for seed in removed_seeds: for seed in removed_seeds:
self.ppi_graph.vertex_properties["cami_score"][seed] = 0.0 self.ppi_graph.vertex_properties["cami_score"][seed] = 0.0
return removed_seeds return removed_seeds
cami_src/consensus/cami_v2.py
+ 5
− 2
View file @ 36aa2efc
import sys import sys
from collections import defaultdict from collections import defaultdict
import graph_tool as gt import graph_tool as gt
from utils.networks import trustrank, betweenness, must from utils.networks import trustrank, betweenness, must, closeness
...@@ -38,12 +38,15 @@ def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2t ...@@ -38,12 +38,15 @@ def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2t
if ranking_method == 'trustrank': if ranking_method == 'trustrank':
scores = trustrank(subnet, seed_lst, damping_factor, hub_penalty, weights) scores = trustrank(subnet, seed_lst, damping_factor, hub_penalty, weights)
elif ranking_method == 'betweenness': elif ranking_method == 'betweenness':
scores = betweenness(subnet, seed_lst, hub_penalty, weights) scores = betweenness(subnet, hub_penalty, weights)
elif ranking_method == 'must': elif ranking_method == 'must':
scores = must(subnet, seed_lst, 5, hub_penalty, weights, trees) scores = must(subnet, seed_lst, 5, hub_penalty, weights, trees)
elif ranking_method == 'harmonic':
scores = closeness(subnet, hub_penalty, weights)
putative_scores = scores.a[[int(id) for id in putative_vertices]] putative_scores = scores.a[[int(id) for id in putative_vertices]]
putative_scores.sort() putative_scores.sort()
threshold = putative_scores[int(len(putative_vertices) * (1 - confidence_level))] threshold = putative_scores[int(len(putative_vertices) * (1 - confidence_level))]
for v in putative_vertices: for v in putative_vertices:
if scores.a[int(v)] >= threshold and scores.a[int(v)] > 0: if scores.a[int(v)] >= threshold and scores.a[int(v)] > 0:
......
cami_src/consensus/cami_v3.py
+ 9
− 6
View file @ 36aa2efc
import sys import sys
from collections import defaultdict from collections import defaultdict
from utils.networks import trustrank, betweenness, must from utils.networks import trustrank, betweenness, must, closeness
import graph_tool as gt import graph_tool as gt
# This uses a trustrank algorithm to rank all putative nodes starting from the seeds and only accepts the top 0.X entries # This uses a trustrank algorithm to rank all putative nodes starting from the seeds and only accepts the top 0.X entries
# TODO maybe find a smart way to cutoff automatically? # TODO maybe find a smart way to cutoff automatically?
def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2toolname, tool_code, params): def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2toolname, tool_code, params):
damping_factor = params['damping_factor'] damping_factor = params['damping_factor']
hub_penalty = params['hub_penalty'] hub_penalty = params['hub_penalty']
confidence_level = params['confidence_level'] confidence_level = params.get('confidence_level', 0.5)
weighted = 'weighted' in params and params['weighted'] weighted = 'weighted' in params and params['weighted']
ranking_method = params['ranking'] if 'ranking' in params else 'trustrank' ranking_method = params['ranking'] if 'ranking' in params else 'trustrank'
trees = params.get('trees', 5) trees = params.get('trees', 5)
...@@ -40,9 +41,11 @@ def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2t ...@@ -40,9 +41,11 @@ def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2t
if ranking_method == 'trustrank': if ranking_method == 'trustrank':
scores = trustrank(subnet, seed_lst, damping_factor, hub_penalty, weights) scores = trustrank(subnet, seed_lst, damping_factor, hub_penalty, weights)
elif ranking_method == 'betweenness': elif ranking_method == 'betweenness':
scores = betweenness(subnet, seed_lst, hub_penalty, weights) scores = betweenness(subnet, hub_penalty, weights)
elif ranking_method == 'must': elif ranking_method == 'must':
scores = must(subnet, seed_lst, trees, hub_penalty, weights, tolerance) scores = must(subnet, seed_lst, trees, hub_penalty, weights, tolerance)
elif ranking_method == 'harmonic':
scores = closeness(subnet, hub_penalty, weights)
tool_scores[tool] = scores tool_scores[tool] = scores
putative_score_map = defaultdict(lambda: 0) putative_score_map = defaultdict(lambda: 0)
......
cami_src/example_run.py
+ 1
− 1
View file @ 36aa2efc
...@@ -9,5 +9,5 @@ chdir((sys.argv[0].rsplit('/', 1))[0]) ...@@ -9,5 +9,5 @@ chdir((sys.argv[0].rsplit('/', 1))[0])
networkfile = "../data/input/networks/example_network.tsv" networkfile = "../data/input/networks/example_network.tsv"
seedfile = "../data/input/seeds/example_seeds.txt" seedfile = "../data/input/seeds/example_seeds.txt"
identifier = "example_run" identifier = "example_run"
command = f'./cami.py -n {networkfile} -s {seedfile} -id {identifier} -img -d -p --f;' command = f'./cami.py -n {networkfile} -s {seedfile} -id {identifier} -img -p --f;'
subprocess.call(command, shell=True) subprocess.call(command, shell=True)
\ No newline at end of file
cami_src/utils/networks.py
+ 29
− 18
View file @ 36aa2efc
...@@ -9,6 +9,7 @@ import graph_tool.topology as gtt ...@@ -9,6 +9,7 @@ import graph_tool.topology as gtt
import graph_tool.util as gtu import graph_tool.util as gtu
import itertools as it import itertools as it
def edge_weights(g, base_weigths, hub_penalty, inverse=False): def edge_weights(g, base_weigths, hub_penalty, inverse=False):
avdeg = gts.vertex_average(g, "total")[0] avdeg = gts.vertex_average(g, "total")[0]
weights = g.new_edge_property("double", val=avdeg) weights = g.new_edge_property("double", val=avdeg)
...@@ -124,6 +125,7 @@ def steiner_tree(g, seeds, seed_map, weights, non_zero_hub_penalty): ...@@ -124,6 +125,7 @@ def steiner_tree(g, seeds, seed_map, weights, non_zero_hub_penalty):
return g2 return g2
def find_bridges(g): def find_bridges(g):
r"""Finds all bridges in a graph.""" r"""Finds all bridges in a graph."""
global __time global __time
...@@ -139,6 +141,7 @@ def find_bridges(g): ...@@ -139,6 +141,7 @@ def find_bridges(g):
__dfs_find_bridges(g, node, visited, disc, low, parent, is_bridge) __dfs_find_bridges(g, node, visited, disc, low, parent, is_bridge)
return is_bridge return is_bridge
def __dfs_find_bridges(g, node, visited, disc, low, parent, is_bridge): def __dfs_find_bridges(g, node, visited, disc, low, parent, is_bridge):
visited[node] = True visited[node] = True
global __time global __time
...@@ -246,19 +249,27 @@ def trustrank(g, seed_ids, damping_factor, hub_penalty, weights=None): ...@@ -246,19 +249,27 @@ def trustrank(g, seed_ids, damping_factor, hub_penalty, weights=None):
return scores return scores
def betweenness(g, seed_ids, hub_penalty, weights=None): def betweenness(g, hub_penalty, weights=None):
if gt.openmp_enabled(): if gt.openmp_enabled():
gt.openmp_set_num_threads(6) gt.openmp_set_num_threads(6)
weights = edge_weights(g, weights, hub_penalty, inverse=True) weights = edge_weights(g, weights, hub_penalty, inverse=True)
scores = g.new_vertex_property("float") # Call graph-tool to compute TrustRank.
all_pairs = [(source, target) for source in seed_ids for target in seed_ids if source < target] # trust = g.new_vertex_property("double")
for source, target in all_pairs: scores, _ = gtc.betweenness(g, weight=weights)
local_scores = g.new_vertex_property("float") # Compute and return the results.
num_paths = 0.0
for path in gtt.all_shortest_paths(g, source, target, weights=weights):
local_scores.a[path[1:-1]] += 1
num_paths += 1
if num_paths > 0:
local_scores.a /= num_paths
scores.a += local_scores.a
return scores return scores
def closeness(g, hub_penalty, weights=None):
if gt.openmp_enabled():
gt.openmp_set_num_threads(6)
weights = edge_weights(g, weights, hub_penalty, inverse=True)
# Call graph-tool to compute TrustRank.
# trust = g.new_vertex_property("double")
scores = gtc.closeness(g, weight=weights, harmonic=True)
# Compute and return the results.
return scores
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