def pipeline_degree(g, essential_proteins):
thresholds = (1, 2) # TODO
protein_count_total = [] # number of proteins with a degree >= threshold
protein_count_essential = [] # same for essential proteins
degrees = centrality_degree(g)
for threshold in thresholds:
protein_count_total.append(0)
protein_count_essential.append(0)
for vertex, degree in degrees:
if degree >= threshold:
if vertex.attributes()['name'] in essential_proteins:
protein_count_essential[-1] += 1
protein_count_total[-1] += 1
# plotting proportions
plot_stats(
protein_count_total,
protein_count_essential,
thresholds,
)
# hypergeometric test
pvalues = []
for index, threshold in enumerate(thresholds):
protein_count = len(g.vs)
# total number of protein: protein_count,
# subset of proteins: protein_count_total[index]
# total number of essential proteins: len(essential_proteins)
# number of essential proteins in subset: protein_count_essential[index]
pvalues.append(phyper(
protein_count, len(essential_proteins),
protein_count_total[index],
protein_count_essential[index]
))
# plotting p-value evolution
plot_phyper(pvalues, thresholds)
def pipeline_degree(g, essential_proteins, centrality, thresholds):
# thresholds = (1, 2, 5, 8, 12, 20) # TODO
# thresholds = range(1, 20) # TODO
protein_count_total = [] # number of proteins with a degree >= threshold
protein_count_essential = [] # same for essential proteins
# degrees = centrality_degree(g)
degrees = [(v, centrality(v)) for v in g.vs]
for threshold in thresholds:
protein_count_total.append(0)
protein_count_essential.append(0)
for vertex, degree in degrees:
if degree >= threshold:
if vertex.attributes()['name'] in essential_proteins:
protein_count_essential[-1] += 1
protein_count_total[-1] += 1
# plotting proportions
plot_stats(
protein_count_total,
protein_count_essential,
thresholds,
)
# hypergeometric test
pvalues = []
for index, threshold in enumerate(thresholds):
protein_count = len(g.vs)
# total number of protein: protein_count,
# subset of proteins: protein_count_total[index]
# total number of essential proteins: len(essential_proteins)
# number of essential proteins in subset: protein_count_essential[index]
pvalues.append(phyper(
protein_count, len(essential_proteins),
protein_count_total[index],
protein_count_essential[index]
))
# plotting p-value evolution
plot_phyper(pvalues, thresholds)
def figure1Ning():
g = ig.Graph()
g.add_vertex('S. Townsendii')
g.add_vertex('R. Eofficinalis')
g.add_vertex('A. Vera')
g.add_vertex('S. Alterniflora')
g.add_vertex('A. Millefolium')
g.add_edge(0, 1, weight=2.5)
g.add_edge(1, 2, weight=2.0)
g.add_edge(2, 3, weight=1.0)
g.add_edge(3, 4, weight=4.0)
g.add_edge(1, 3, weight=2.5)
g.add_edge(0, 4, weight=5.0)
g.add_edge(0, 3, weight=1.5)
visual_style = {
'vertex_label': g.vs['name'],
'edge_label' : g.es['weight'],
'layout' : g.layout_circle(),
'margin' : 150,
'autocurve' : False,
}
ig.plot(g, **visual_style)
exit()
# plot_stats(
# [3000, 2000, 1000],
# [ 300, 200, 100],
# [ 10, 20, 30],
# )
plot_phyper(
(0.23, 0.42, 0.2),
(3, 4, 8),
)
exit()
plot_dumb_stats()
g = ig.Graph(directed=False)
# i.plot(g)
# print(help(g.add_vertex))
for i in range(1, 6):
g.add_vertex(i, label='node ' + str(i))
# print(help(g.add_edge))
g.es["weight"] = 1.0 # make the graph a weighed one
g[0, 1] = 2.5
g[0, 1] = 2.5
# g[1, 2] = 2.0 # equivalent to g.add_edge(1, 2, weight=2.0)
g.add_edge(1, 2, weight=2.0, color='green')
g[2, 3] = 1.0
g[3, 4] = 4.0
g[0, 4] = 5.0
g[0, 3] = 1.5
g[1, 3] = 2.5
# CENTRALITY
for vertex in g.vs:
print('VERTEX:', vertex)
print('\tdegree:', vertex.degree())
print('\tbetweenness:', vertex.betweenness())
print('\tcloseness:', vertex.closeness())
# PLOT
visual_style = {
'vertex_label': g.vs['label'],
'edge_label' : g.es['weight'],
'layout' : g.layout_circle(),
'margin' : 150,
}
ig.plot(g, **visual_style)
rnn = RkNN(g, k=2)
rnn_visual_style = {
'vertex_label': rnn.vs['label'],
'edge_label' : rnn.es['weight'],
'layout' : rnn.layout_kamada_kawai(),
'margin' : 150,
'autocurve' : False,
}
ig.plot(rnn, **rnn_visual_style)
def figure1Ning():
g = ig.Graph()
g.add_vertex('S. Townsendii')
g.add_vertex('R. Eofficinalis')
g.add_vertex('A. Vera')
g.add_vertex('S. Alterniflora')
g.add_vertex('A. Millefolium')
g.add_edge(0, 1, weight=2.5)
g.add_edge(1, 2, weight=2.0)
g.add_edge(2, 3, weight=1.0)
g.add_edge(3, 4, weight=4.0)
g.add_edge(1, 3, weight=2.5)
g.add_edge(0, 4, weight=5.0)
g.add_edge(0, 3, weight=1.5)
visual_style = {
'vertex_label': g.vs['name'],
'edge_label': g.es['weight'],
'layout': g.layout_circle(),
'margin': 150,
'autocurve': False,
}
ig.plot(g, **visual_style)
exit()
# plot_stats(
# [3000, 2000, 1000],
# [ 300, 200, 100],
# [ 10, 20, 30],
# )
plot_phyper(
(0.23, 0.42, 0.2),
(3, 4, 8),
)
exit()
plot_dumb_stats()
g = ig.Graph(directed=False)
# i.plot(g)
# print(help(g.add_vertex))
for i in range(1, 6):
g.add_vertex(i, label='node ' + str(i))
# print(help(g.add_edge))
g.es["weight"] = 1.0 # make the graph a weighed one
g[0, 1] = 2.5
g[0, 1] = 2.5
# g[1, 2] = 2.0 # equivalent to g.add_edge(1, 2, weight=2.0)
g.add_edge(1, 2, weight=2.0, color='green')
g[2, 3] = 1.0
g[3, 4] = 4.0
g[0, 4] = 5.0
g[0, 3] = 1.5
g[1, 3] = 2.5
# CENTRALITY
for vertex in g.vs:
print('VERTEX:', vertex)
print('\tdegree:', vertex.degree())
print('\tbetweenness:', vertex.betweenness())
print('\tcloseness:', vertex.closeness())
# PLOT
visual_style = {
'vertex_label': g.vs['label'],
'edge_label': g.es['weight'],
'layout': g.layout_circle(),
'margin': 150,
}
ig.plot(g, **visual_style)
rnn = RkNN(g, k=2)
rnn_visual_style = {
'vertex_label': rnn.vs['label'],
'edge_label': rnn.es['weight'],
'layout': rnn.layout_kamada_kawai(),
'margin': 150,
'autocurve': False,
}
ig.plot(rnn, **rnn_visual_style)
