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Yoann DUFRESNE
linked reads molecule ordering
Commits
66948796
Commit
66948796
authored
Mar 12, 2019
by
Yoann Dufresne
Browse files
overlapping deconvolution
parent
e8142332
Changes
1
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Inline
Sidebyside
deconvolve.py
View file @
66948796
#!/usr/bin/env python3
import
sys
import
math
import
networkx
as
nx
import
itertools
def
deconvolve
(
G
,
node
):
def
deconvolve
(
G
,
node
,
verbose
=
0
):
neighbors
=
list
(
G
.
neighbors
(
node
))
print
(
"node"
,
node
,
len
(
neighbors
)
,
"neighbors"
)
nei_len
=
len
(
neighbors
)
# Extract neighbors from the graph
G_neighbors
=
nx
.
Graph
(
G
.
subgraph
(
neighbors
))
communities
=
get_communities
(
G_neighbors
,
node
==
"273:597_148"
)
communities
=
get_communities
(
G_neighbors
,
verbose
=
verbose

1
)
# Continue only if something need to be splited.
if
len
(
communities
)
==
1
:
...
...
@@ 30,16 +31,20 @@ def deconvolve(G,node):
# Remove old node
G
.
remove_node
(
node
)
print
(
"splitted into"
,
len
(
communities
),
"parts
\n
"
)
if
verbose
>
0
:
print
(
"node"
,
node
,
nei_len
,
"neighbors"
)
print
(
"splitted into"
,
len
(
communities
),
"parts
\n
"
)
def
get_communities
(
G
,
max_overlap
=
2
,
verbose
=
False
):
def
get_communities
(
G
,
max_overlap
=
1
,
verbose
=
0
):
# Half dgraphs are cliques. So compute max cliques
cliques
=
list
(
nx
.
find_cliques
(
G
))
if
verbose
:
if
verbose
>
0
:
print
(
"clique list"
)
for
clq
in
cliques
:
print
(
clq
,
"
\n
"
)
print
(
clq
)
print
()
candidate_d_graphs
=
[]
...
...
@@ 56,16 +61,17 @@ def get_communities(G, max_overlap=2, verbose=False):
if
val
in
clq2
:
overlap
+=
1
if
overlap
>
max_overlap
:
# print(overlap, "is too high overlap")
continue
# Check for dgraph candidates
d_graph
=
compute_d_graph
(
clq1
,
clq2
,
G
)
d_graph
=
compute_d_graph
(
clq1
,
clq2
,
G
,
verbose
=
verbose

1
)
if
d_graph
!=
None
:
candidate_d_graphs
.
append
(
d_graph
)
# Extract communites from all the possible dgraphes in the neighborood.
# This is a minimal covering d_graph algorithm.
minimal_d_graphes
=
filter_d_graphs
(
candidate_d_graphs
)
minimal_d_graphes
=
filter_d_graphs
(
candidate_d_graphs
,
max_overlap
=
max_overlap
)
# If no community detected, return one big.
if
len
(
minimal_d_graphes
)
==
0
:
...
...
@@ 87,16 +93,34 @@ def get_communities(G, max_overlap=2, verbose=False):
@param G the graph of the neighbors of the central node (not present).
@return A pair of lists that are the 2 halves of the dgraph ordered from the center.
"""
def
compute_d_graph
(
clq1
,
clq2
,
G
,
verbose
=
False
):
def
compute_d_graph
(
clq1
,
clq2
,
G
,
max_diff_size
=
1
,
verbose
=
0
):
# Compute the arities between the cliques
arities1
=
{
name
:
0
for
name
in
clq1
}
arities2
=
{
name
:
0
for
name
in
clq2
}
sum_edges
=
0
# TODO : Remove this part and improve the detection
if
len
(
clq1
)
!=
len
(
clq2
):
return
None
# /TODO
# Limit the number of recursions
if
abs
(
len
(
clq1
)

len
(
clq2
))
>
max_diff_size
:
return
None
# Recursion on the biggest clique to reduce complexity.
smallest
,
largest
=
(
clq1
,
clq2
)
if
len
(
clq2
)
>
len
(
clq1
)
else
(
clq2
,
clq1
)
minimal_weighted_d_graph
=
None
minimal_weight
=
math
.
inf
for
idx
in
range
(
len
(
largest
)):
recur_d_graph
=
compute_d_graph
(
smallest
,
largest
[:
idx
]
+
largest
[
idx
+
1
:],
G
,
verbose
=
verbose
)
if
recur_d_graph
!=
None
and
recur_d_graph
[
2
]
<
minimal_weight
:
minimal_weighted_d_graph
=
recur_d_graph
minimal_weight
=
recur_d_graph
[
2
]
if
verbose
>
0
:
print
(
f
"Recursive calls for:
\n
{
clq1
}
\n
{
clq2
}
\n
"
)
print
(
minimal_weighted_d_graph
,
"
\n
"
)
print
(
"/ Recursive
\n
"
)
return
minimal_weighted_d_graph
min_clq_size
=
min
(
len
(
clq1
),
len
(
clq2
))
...
...
@@ 105,15 +129,15 @@ def compute_d_graph(clq1, clq2, G, verbose=False):
neighbors
=
list
(
G
.
neighbors
(
node1
))
for
node2
in
clq2
:
if
node2
in
neighbors
:
if
node1
==
node2
or
node2
in
neighbors
:
# print(node1, "", node2)
arities1
[
node1
]
+=
1
arities2
[
node2
]
+=
1
sum_edges
+=
1
if
verbose
:
print
(
clq1
,
clq2
)
print
(
arities1
,
arities2
,
"
\n
"
)
#
if verbose:
#
print(clq1, clq2)
#
print(arities1, arities2, "\n")
# Reject if not enought edges
if
sum_edges
<
min_clq_size
*
(
min_clq_size

1
)
/
2
:
...
...
@@ 127,40 +151,63 @@ def compute_d_graph(clq1, clq2, G, verbose=False):
lst1
=
[
key
for
key
,
value
in
sorted
(
arities1
.
items
(),
key
=
lambda
tup
:

tup
[
1
])]
lst2
=
[
key
for
key
,
value
in
sorted
(
arities2
.
items
(),
key
=
lambda
tup
:

tup
[
1
])]
if
verbose
:
if
verbose
>
0
:
print
(
min_clq_size
)
print
(
lst1
,
"
\n
"
,
lst2
,
"
\n
"
)
# Return the 2 halves of the dgraph
return
lst1
,
lst2
return
lst1
,
lst2
,
sum_edges
""" Filter the candiates regarding their compatibilities
"""
def
filter_d_graphs
(
candidates
):
# Count for each node the number of their apparition
counts
=
{}
def
filter_d_graphs
(
candidates
,
max_overlap
=
0
):
# Count for each node the number of their apparition (regarding the half overlap)
selected
=
{}
counts_by_size
=
[{}
for
_
in
range
(
max_overlap
+
1
)]
sorted_d_graphs
=
[[]
for
_
in
range
(
max_overlap
+
1
)]
for
d_graph
in
candidates
:
# Compute intersection of the two halves
common_length
=
len
(
set
(
d_graph
[
0
])
&
set
(
d_graph
[
1
]))
sorted_d_graphs
[
common_length
].
append
(
d_graph
)
# Count occurences
for
node
in
itertools
.
chain
(
d_graph
[
0
],
d_graph
[
1
]):
if
not
node
in
counts
:
counts
[
node
]
=
0
counts
[
node
]
+=
1
if
not
node
in
counts_by_size
[
common_length
]:
counts_by_size
[
common_length
][
node
]
=
0
counts_by_size
[
common_length
][
node
]
+=
1
selected
[
node
]
=
False
# take d_graphes with nodes that appears only once
filtered
=
[]
selected
=
{
node
:
False
for
node
in
counts
.
keys
()}
for
d_graph
in
candidates
:
for
node
in
itertools
.
chain
(
d_graph
[
0
],
d_graph
[
1
]):
if
counts
[
node
]
==
1
:
# Add the d_graph to the selection
filtered
.
append
(
d_graph
)
# register selection of the nodes
for
node
in
itertools
.
chain
(
d_graph
[
0
],
d_graph
[
1
]):
selected
[
node
]
=
True
# Over for this dgraph
for
overlap_size
in
range
(
max_overlap
+
1
):
# Look for d_graphs with overlapping halves first, then 1 node, ...
for
d_graph
in
sorted_d_graphs
[
overlap_size
]:
common_length
=
len
(
set
(
d_graph
[
0
])
&
set
(
d_graph
[
1
]))
for
node
in
itertools
.
chain
(
d_graph
[
0
],
d_graph
[
1
]):
# Count appearance
total_count
=
0
for
length
in
range
(
overlap_size
+
1
):
total_count
+=
counts_by_size
[
common_length
][
node
]
if
node
in
counts_by_size
[
common_length
]
else
0
# Add dgraph
if
total_count
==
1
:
# Add the d_graph to the selection
filtered
.
append
(
d_graph
)
# register selection of the nodes
for
node
in
itertools
.
chain
(
d_graph
[
0
],
d_graph
[
1
]):
selected
[
node
]
=
True
# Over for this dgraph
break
# Stop if all nodes are selected
over
=
True
for
val
in
selected
.
values
():
if
not
val
:
over
=
False
break
if
over
:
break
# TODO : improve performances when there are no uniq solution
for
val
in
selected
.
values
():
...
...
@@ 184,7 +231,7 @@ def main():
# Deconvolve
g_nodes
=
list
(
G
.
nodes
())
for
node
in
g_nodes
:
deconvolve
(
G
,
node
)
deconvolve
(
G
,
node
,
verbose
=
1
)
# if (node=="273:597_148") else 0
)
# exit()
print
(
len
(
g_nodes
),
">"
,
len
(
list
(
G
.
nodes
())))
...
...
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