Class: Bio::Pathway
Overview
Bio::Pathway is a general graph object initially constructed by the list of the ((<Bio::Relation>)) objects. The basic concept of the Bio::Pathway object is to store a graph as an adjacency list (in the instance variable @graph), and converting the list into an adjacency matrix by calling to_matrix method on demand. However, in some cases, it is convenient to have the original list of the ((<Bio::Relation>))s, Bio::Pathway object also stores the list (as the instance variable @relations) redundantly.
Note: you can clear the @relations list by calling clear_relations! method to reduce the memory usage, and the content of the @relations can be regenerated from the @graph by to_relations method.
Direct Known Subclasses
Instance Attribute Summary (collapse)

 (Object) graph
readonly
Readonly accessor for the adjacency list of the graph.

 (Object) index
readonly
Readonly accessor for the row/column index (@index) of the adjacency matrix.

 (Object) label
Accessor for the hash of the label assigned to the each node.

 (Object) relations
readonly
Readonly accessor for the internal list of the Bio::Relation objects.
Instance Method Summary (collapse)

 (Object) append(rel, add_rel = true)
Add an Bio::Relation object 'rel' to the @graph and @relations.

 (Object) bellman_ford(root)
BellmanFord method for solving the singlesource shortestpaths problem in the graph in which edge weights can be negative.

 (Object) bfs_shortest_path(node1, node2)
Calculates the shortest path between two nodes by using breadth_first_search method and returns steps and the path as Array.

 (Object) breadth_first_search(root)
(also: #bfs)
Breadth first search solves steps and path to the each node and forms a tree contains all reachable vertices from the root node.

 (Object) clear_relations!
Clear @relations array to reduce the memory usage.

 (Object) clique
Not implemented yet.

 (Object) cliquishness(node)
Returns completeness of the edge density among the surrounded nodes.

 (Object) common_subgraph(graph)
Not implemented yet.

 (Object) delete(rel)
Remove an edge indicated by the Bio::Relation object 'rel' from the.

 (Object) depth_first_search
(also: #dfs)
Depth first search yields much information about the structure of the graph especially on the classification of the edges.

 (Object) dfs_topological_sort
Topological sort of the directed acyclic graphs (“dags”) by using depth_first_search.

 (Object) dijkstra(root)
Dijkstra method to solve the shortest path problem in the weighted graph.

 (Object) directed
Changes the internal state of the graph from 'undirected' to 'directed' and regenerate adjacency list.

 (Boolean) directed?
Returns true or false respond to the internal state of the graph.

 (Object) dump_list
Pretty printer of the adjacency list.

 (Object) dump_matrix(*arg)
Pretty printer of the adjacency matrix.

 (Object) edges
Returns the number of the edges in the graph.

 (Object) floyd_warshall
(also: #floyd)
FloydWardshall alogrithm for solving the allpairs shortestpaths problem on a directed graph G = (V, E).

 (Pathway) initialize(relations, undirected = false)
constructor
Initial graph (adjacency list) generation from the list of Relation.

 (Object) kruskal
Kruskal method for finding minimam spaninng trees.

 (Object) nodes
Returns the number of the nodes in the graph.

 (Object) small_world
Returns frequency of the nodes having same number of edges as hash.

 (Object) subgraph(list = nil)
Select labeled nodes and generate subgraph.

 (Object) to_list
Graph (adjacency list) generation from the Relations.

 (Object) to_matrix(default_value = nil, diagonal_value = nil)
Convert adjacency list to adjacency matrix.

 (Object) to_relations
Reconstruct @relations from the adjacency list @graph.

 (Object) undirected
Changes the internal state of the graph from 'directed' to 'undirected' and regenerate adjacency list.

 (Boolean) undirected?
Returns true or false respond to the internal state of the graph.
Constructor Details
 (Pathway) initialize(relations, undirected = false)
Initial graph (adjacency list) generation from the list of Relation.
Generate Bio::Pathway object from the list of Bio::Relation objects. If the second argument is true, undirected graph is generated.
r1 = Bio::Relation.new('a', 'b', 1)
r2 = Bio::Relation.new('a', 'c', 5)
r3 = Bio::Relation.new('b', 'c', 3)
list = [ r1, r2, r3 ]
g = Bio::Pathway.new(list, 'undirected')
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# File 'lib/bio/pathway.rb', line 41 def initialize(relations, undirected = false) @undirected = undirected @relations = relations @graph = {} # adjacency list expression of the graph @index = {} # numbering each node in matrix @label = {} # additional information on each node self.to_list # generate adjacency list end 
Instance Attribute Details
 (Object) graph (readonly)
Readonly accessor for the adjacency list of the graph.
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# File 'lib/bio/pathway.rb', line 54 def graph @graph end 
 (Object) index (readonly)
Readonly accessor for the row/column index (@index) of the adjacency matrix. Contents of the hash @index is created by calling to_matrix method.
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# File 'lib/bio/pathway.rb', line 59 def index @index end 
 (Object) label
Accessor for the hash of the label assigned to the each node. You can label some of the nodes in the graph by passing a hash to the label and select subgraphs which contain labeled nodes only by subgraph method.
hash = { 1 => 'red', 2 => 'green', 5 => 'black' }
g.label = hash
g.label
g.subgraph # => new graph consists of the node 1, 2, 5 only
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# File 'lib/bio/pathway.rb', line 70 def label @label end 
 (Object) relations (readonly)
Readonly accessor for the internal list of the Bio::Relation objects
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# File 'lib/bio/pathway.rb', line 51 def relations @relations end 
Instance Method Details
 (Object) append(rel, add_rel = true)
Add an Bio::Relation object 'rel' to the @graph and @relations. If the second argument is false, @relations is not modified (only useful when genarating @graph from @relations internally).
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# File 'lib/bio/pathway.rb', line 144 def append(rel, add_rel = true) @relations.push(rel) if add_rel if @graph[rel.from].nil? @graph[rel.from] = {} end if @graph[rel.to].nil? @graph[rel.to] = {} end @graph[rel.from][rel.to] = rel.relation @graph[rel.to][rel.from] = rel.relation if @undirected end 
 (Object) bellman_ford(root)
BellmanFord method for solving the singlesource shortestpaths problem in the graph in which edge weights can be negative.
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# File 'lib/bio/pathway.rb', line 592 def bellman_ford(root) distance, predecessor = initialize_single_source(root) (self.nodes  1).times do @graph.each_key do u @graph[u].each do v, w # relaxing procedure of root > 'u' > 'v' if distance[v] > distance[u] + w distance[v] = distance[u] + w predecessor[v] = u end end end end # negative cyclic loop check @graph.each_key do u @graph[u].each do v, w if distance[v] > distance[u] + w return false end end end return distance, predecessor end 
 (Object) bfs_shortest_path(node1, node2)
Calculates the shortest path between two nodes by using breadth_first_search method and returns steps and the path as Array.
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# File 'lib/bio/pathway.rb', line 450 def bfs_shortest_path(node1, node2) distance, route = breadth_first_search(node1) step = distance[node2] node = node2 path = [ node2 ] while node != node1 and route[node] node = route[node] path.unshift(node) end return step, path end 
 (Object) breadth_first_search(root) Also known as: bfs
Breadth first search solves steps and path to the each node and forms a tree contains all reachable vertices from the root node. This method returns the result in 2 hashes  1st one shows the steps from root node and 2nd hash shows the structure of the tree.
The weight of the edges are not considered in this method.
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# File 'lib/bio/pathway.rb', line 419 def breadth_first_search(root) visited = {} distance = {} predecessor = {} visited[root] = true distance[root] = 0 predecessor[root] = nil queue = [ root ] while from = queue.shift next unless @graph[from] @graph[from].each_key do to unless visited[to] visited[to] = true distance[to] = distance[from] + 1 predecessor[to] = from queue.push(to) end end end return distance, predecessor end 
 (Object) clear_relations!
Clear @relations array to reduce the memory usage.
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# File 'lib/bio/pathway.rb', line 114 def clear_relations! @relations.clear end 
 (Object) clique
Not implemented yet.
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# File 'lib/bio/pathway.rb', line 370 def clique raise NotImplementedError end 
 (Object) cliquishness(node)
Returns completeness of the edge density among the surrounded nodes.
Calculates the value of cliquishness around the 'node'. This value indicates completeness of the edge density among the surrounded nodes.
Note: cliquishness (clustering coefficient) for a directed graph is also calculated. Reference: en.wikipedia.org/wiki/Clustering_coefficient
Note: Cliquishness (clustering coefficient) for a node that has only one neighbor node is undefined. Currently, it returns NaN, but the behavior may be changed in the future.
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# File 'lib/bio/pathway.rb', line 388 def cliquishness(node) neighbors = @graph[node].keys sg = subgraph(neighbors) if sg.graph.size != 0 edges = sg.edges nodes = neighbors.size complete = (nodes * (nodes  1)) return edges.quo(complete) else return 0.0 end end 
 (Object) common_subgraph(graph)
Not implemented yet.
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# File 'lib/bio/pathway.rb', line 364 def common_subgraph(graph) raise NotImplementedError end 
 (Object) delete(rel)
Remove an edge indicated by the Bio::Relation object 'rel' from the
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# File 'lib/bio/pathway.rb', line 158 def delete(rel) @relations.delete_if do x x === rel end @graph[rel.from].delete(rel.to) @graph[rel.to].delete(rel.from) if @undirected end 
 (Object) depth_first_search Also known as: dfs
Depth first search yields much information about the structure of the graph especially on the classification of the edges. This method returns 5 hashes  1st one shows the timestamps of each node containing the first discoverd time and the search finished time in an array. The 2nd, 3rd, 4th, and 5th hashes contain 'tree edges', 'back edges', 'cross edges', 'forward edges' respectively.
If $DEBUG is true (e.g. ruby d), this method prints the progression of the search.
The weight of the edges are not considered in this method.
Note: The result of this method depends on the order of Hash#each (and each_key, etc.), which may be variable with Ruby version and Ruby interpreter variations (JRuby, etc.). For a workaround to remove such dependency, you can use @index to set order of Hash keys. Note that this bahavior might be changed in the future.
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# File 'lib/bio/pathway.rb', line 481 def depth_first_search visited = {} = {} tree_edges = {} back_edges = {} cross_edges = {} forward_edges = {} count = 0 # begin workaround removing depencency to order of Hash#each if @index.empty? then preference_of_nodes = nil else preference_of_nodes = {}.merge(@index) i = preference_of_nodes.values.max @graph.each_key do node0 preference_of_nodes[node0] = (i += 1) end end # end workaround removing depencency to order of Hash#each dfs_visit = Proc.new { from visited[from] = true [from] = [count += 1] ary = @graph[from].keys # begin workaround removing depencency to order of Hash#each if preference_of_nodes then ary = ary.sort_by { node0 preference_of_nodes[node0] } end # end workaround removing depencency to order of Hash#each ary.each do to if visited[to] if [to].size > 1 if [from].first < [to].first # forward edge (black) p "#{from} > #{to} : forward edge" if $DEBUG forward_edges[from] = to else # cross edge (black) p "#{from} > #{to} : cross edge" if $DEBUG cross_edges[from] = to end else # back edge (gray) p "#{from} > #{to} : back edge" if $DEBUG back_edges[from] = to end else # tree edge (white) p "#{from} > #{to} : tree edge" if $DEBUG tree_edges[to] = from dfs_visit.call(to) end end [from].push(count += 1) } ary = @graph.keys # begin workaround removing depencency to order of Hash#each if preference_of_nodes then ary = ary.sort_by { node0 preference_of_nodes[node0] } end # end workaround removing depencency to order of Hash#each ary.each do node unless visited[node] dfs_visit.call(node) end end return , tree_edges, back_edges, cross_edges, forward_edges end 
 (Object) dfs_topological_sort
Topological sort of the directed acyclic graphs (“dags”) by using depth_first_search.
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# File 'lib/bio/pathway.rb', line 558 def dfs_topological_sort # sorted by finished time reversely and collect node names only , = self.depth_first_search .sort {a,b b[1][1] <=> a[1][1]}.collect {x x.first } end 
 (Object) dijkstra(root)
Dijkstra method to solve the shortest path problem in the weighted graph.
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# File 'lib/bio/pathway.rb', line 566 def dijkstra(root) distance, predecessor = initialize_single_source(root) @graph[root].each do k, v distance[k] = v predecessor[k] = root end queue = distance.dup queue.delete(root) while queue.size != 0 min = queue.min {a, b a[1] <=> b[1]} u = min[0] # extranct a node having minimal distance @graph[u].each do k, v # relaxing procedure of root > 'u' > 'k' if distance[k] > distance[u] + v distance[k] = distance[u] + v predecessor[k] = u end end queue.delete(u) end return distance, predecessor end 
 (Object) directed
Changes the internal state of the graph from 'undirected' to 'directed' and regenerate adjacency list. The undirected graph can be converted to directed graph, however, the edge between two nodes will be simply doubled to both ends.
Note: this method can not be used without the list of the Bio::Relation objects (internally stored in @relations variable). Thus if you already called clear_relations! method, call to_relations first.
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# File 'lib/bio/pathway.rb', line 92 def directed if undirected? @undirected = false self.to_list end end 
 (Boolean) directed?
Returns true or false respond to the internal state of the graph.
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# File 'lib/bio/pathway.rb', line 74 def directed? @undirected ? false : true end 
 (Object) dump_list
Pretty printer of the adjacency list.
Useful when you want to check the internal state of the adjacency list (for debug purpose etc.) easily.
The result of this method depends on the order of Hash#each (and each_key, etc.), which may be variable with Ruby version and Ruby interpreter variations (JRuby, etc.). For a workaround to remove such dependency, you can use @index to set order of Hash keys. Note that this behavior might be changed in the future.
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# File 'lib/bio/pathway.rb', line 293 def dump_list # begin workaround removing depencency to order of Hash#each if @index.empty? then pref = nil enum = @graph else pref = {}.merge(@index) i = pref.values.max @graph.each_key do node pref[node] = (i += 1) end graph_to_a = @graph.to_a graph_to_a.sort! { x, y pref[x[0]] <=> pref[y[0]] } enum = graph_to_a end # end workaround removing depencency to order of Hash#each list = "" enum.each do from, hash list << "#{from} => " # begin workaround removing depencency to order of Hash#each if pref then ary = hash.to_a ary.sort! { x,y pref[x[0]] <=> pref[y[0]] } hash = ary end # end workaround removing depencency to order of Hash#each a = [] hash.each do to, relation a.push("#{to} (#{relation})") end list << a.join(", ") + "\n" end list end 
 (Object) dump_matrix(*arg)
Pretty printer of the adjacency matrix.
The dump_matrix method accepts the same arguments as to_matrix. Useful when you want to check the internal state of the matrix (for debug purpose etc.) easily.
This method internally calls to_matrix method. Read documents of to_matrix for important informations.
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# File 'lib/bio/pathway.rb', line 274 def dump_matrix(*arg) matrix = self.to_matrix(*arg) sorted = @index.sort {a,b a[1] <=> b[1]} "[# " + sorted.collect{x x[0]}.join(", ") + "\n" + matrix.to_a.collect{row ' ' + row.inspect}.join(",\n") + "\n]" end 
 (Object) edges
Returns the number of the edges in the graph.
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# File 'lib/bio/pathway.rb', line 172 def edges edges = 0 @graph.each_value do v edges += v.size end edges end 
 (Object) floyd_warshall Also known as: floyd
FloydWardshall alogrithm for solving the allpairs shortestpaths problem on a directed graph G = (V, E).
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# File 'lib/bio/pathway.rb', line 619 def floyd_warshall inf = 1 / 0.0 m = self.to_matrix(inf, 0) d = m.dup n = self.nodes for k in 0 .. n  1 do for i in 0 .. n  1 do for j in 0 .. n  1 do if d[i, j] > d[i, k] + d[k, j] d[i, j] = d[i, k] + d[k, j] end end end end return d end 
 (Object) kruskal
Kruskal method for finding minimam spaninng trees
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# File 'lib/bio/pathway.rb', line 641 def kruskal # initialize rel = self.to_relations.sort{a, b a <=> b} index = [] for i in 0 .. (rel.size  1) do for j in (i + 1) .. (rel.size  1) do if rel[i] == rel[j] index << j end end end index.sort{x, y y<=>x}.each do idx rel[idx, 1] = [] end mst = [] seen = Hash.new() @graph.each_key do x seen[x] = nil end i = 1 # initialize end rel.each do r if seen[r.node[0]] == nil seen[r.node[0]] = 0 end if seen[r.node[1]] == nil seen[r.node[1]] = 0 end if seen[r.node[0]] == seen[r.node[1]] && seen[r.node[0]] == 0 mst << r seen[r.node[0]] = i seen[r.node[1]] = i elsif seen[r.node[0]] != seen[r.node[1]] mst << r v1 = seen[r.node[0]].dup v2 = seen[r.node[1]].dup seen.each do k, v if v == v1  v == v2 seen[k] = i end end end i += 1 end return Pathway.new(mst) end 
 (Object) nodes
Returns the number of the nodes in the graph.
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# File 'lib/bio/pathway.rb', line 167 def nodes @graph.keys.length end 
 (Object) small_world
Returns frequency of the nodes having same number of edges as hash
Calculates the frequency of the nodes having the same number of edges and returns the value as Hash.
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# File 'lib/bio/pathway.rb', line 405 def small_world freq = Hash.new(0) @graph.each_value do v freq[v.size] += 1 end return freq end 
 (Object) subgraph(list = nil)
Select labeled nodes and generate subgraph
This method select some nodes and returns new Bio::Pathway object consists of selected nodes only. If the list of the nodes (as Array) is assigned as the argument, use the list to select the nodes from the graph. If no argument is assigned, internal property of the graph @label is used to select the nodes.
hash = { 'a' => 'secret', 'b' => 'important', 'c' => 'important' }
g.label = hash
g.subgraph
list = [ 'a', 'b', 'c' ]
g.subgraph(list)
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# File 'lib/bio/pathway.rb', line 343 def subgraph(list = nil) if list @label.clear list.each do node @label[node] = true end end sub_graph = Pathway.new([], @undirected) @graph.each do from, hash next unless @label[from] sub_graph.graph[from] = {} hash.each do to, relation next unless @label[to] sub_graph.append(Relation.new(from, to, relation)) end end return sub_graph end 
 (Object) to_list
Graph (adjacency list) generation from the Relations
Generate the adjcancecy list @graph from @relations (called by initialize and in some other cases when @relations has been changed).
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# File 'lib/bio/pathway.rb', line 134 def to_list @graph.clear @relations.each do rel append(rel, false) # append to @graph without push to @relations end end 
 (Object) to_matrix(default_value = nil, diagonal_value = nil)
Convert adjacency list to adjacency matrix
Returns the adjacency matrix expression of the graph as a Matrix object. If the first argument was assigned, the matrix will be filled with the given value. The second argument indicates the value of the diagonal constituents of the matrix besides the above.
The result of this method depends on the order of Hash#each (and each_key, etc.), which may be variable with Ruby version and Ruby interpreter variations (JRuby, etc.). For a workaround to remove such dependency, you can use @index to set order of Hash keys. Note that this behavior might be changed in the future. Be careful that @index is overwritten by this method.
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# File 'lib/bio/pathway.rb', line 196 def to_matrix(default_value = nil, diagonal_value = nil) # # Note: following code only fills the outer Array with the reference # to the same inner Array object. # # matrix = Array.new(nodes, Array.new(nodes)) # # so create a new Array object for each row as follows: #++ matrix = Array.new nodes.times do matrix.push(Array.new(nodes, default_value)) end if diagonal_value nodes.times do i matrix[i][i] = diagonal_value end end # assign index number if @index.empty? then # assign index number for each node @graph.keys.each_with_index do k, i @index[k] = i end else # begin workaround removing depencency to order of Hash#each # assign index number from the preset @index indices = @index.to_a indices.sort! { i0, i1 i0[1] <=> i1[1] } indices.collect! { i0 i0[0] } @index.clear v = 0 indices.each do k, i if @graph[k] and !@index[k] then @index[k] = v; v += 1 end end @graph.each_key do k unless @index[k] then @index[k] = v; v += 1 end end # end workaround removing depencency to order of Hash#each end if @relations.empty? # only used after clear_relations! @graph.each do from, hash hash.each do to, relation x = @index[from] y = @index[to] matrix[x][y] = relation end end else @relations.each do rel x = @index[rel.from] y = @index[rel.to] matrix[x][y] = rel.relation matrix[y][x] = rel.relation if @undirected end end Matrix[*matrix] end 
 (Object) to_relations
Reconstruct @relations from the adjacency list @graph.
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# File 'lib/bio/pathway.rb', line 119 def to_relations @relations.clear @graph.each_key do from @graph[from].each do to, w @relations << Relation.new(from, to, w) end end return @relations end 
 (Object) undirected
Changes the internal state of the graph from 'directed' to 'undirected' and regenerate adjacency list.
Note: this method can not be used without the list of the Bio::Relation objects (internally stored in @relations variable). Thus if you already called clear_relations! method, call to_relations first.
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# File 'lib/bio/pathway.rb', line 106 def undirected if directed? @undirected = true self.to_list end end 
 (Boolean) undirected?
Returns true or false respond to the internal state of the graph.
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# File 'lib/bio/pathway.rb', line 79 def undirected? @undirected ? true : false end 