Moved lots of stuff back to sandbox
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7f1f639ee7
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48047f1395
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@ -8,10 +8,13 @@ from cx_utils import sorted_eig
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import numpy
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eps = numpy.finfo(float).eps.item()
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feps = numpy.finfo(numpy.single).eps.item()
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_array_precision = {'f': 0, 'd': 1, 'F': 0, 'D': 1,'i': 1}
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class NXUTILSException(Exception): pass
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def xgraph_to_graph(G):
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"""Convert an Xgraph to an ordinary graph.
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@ -65,10 +68,11 @@ def get_affinity_matrix(G, data, ids, dist='e', mask=None, weight=None, t=0, out
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try:
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from Bio import Cluster as CLS
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except:
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raise ValueError, "Need installed biopython"
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nVar = len(data)
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nSamp = len(data[data.keys()[0]])
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raise NXUTILSError("Import of Biopython failed")
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n_var = len(data)
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n_samp = len(data[data.keys()[0]])
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X = zeros((nVar, nSamp),dtpye='<f8')
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for i, gene in enumerate(ids): #this shuld be right!!
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X[i,:] = data[gene]
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@ -233,6 +237,7 @@ def assortative_index(G):
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out.append((d[node], nn_d))
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return array(out).T
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def struct_equivalence(G,n1,n2):
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"""Returns the structural equivalence of a node pair. Two nodes
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are structural equal if they share the same neighbors.
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@ -264,146 +269,22 @@ def hamming_distance(n1,n2):
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"""
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pass
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def graph_corrcoeff(G):
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"""Not finnished.
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"""
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A,index = NX.adj_matrix(G,with_labels=True)
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#C = zeros(*A.shape(),'d')
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n = 1.*G.number_of_nodes()
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for node in G.nodes():
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a_j = A[index[node],:] #neighbors
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mean_a = sum(a_j)/n# degree(G)/number_of_nodes()
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var_a = sqrt(sum((a_j - mean_a)**2)/n)
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pass
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def graph_and_data_intersection(data, graph, pathways=None,
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keep_connected=True):
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"""Returns the intersection of keys in two dictionaries.
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NB: keep track of identifer sorting after these dict transforms.
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input:
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data -- dict, keys: gene id, value: measurement profile
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graph -- networkx,base.graph, full graph
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pathways -- dict, keys: pathway name, values: nodes in pathway
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call:
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new_data, new_graph,pathways = graph_and_data_intersection(data,graph,pathways,keep_connected=True)
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def graph_corrcoeff(G, vec=None, nodelist=None, sim='corr'):
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"""Returns the correlation coefficient for each node. The
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correlation coefficient is between the node and its neighbours.
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"""
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new_graph = graph.copy()
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new_data = {}
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new_pathways = {}
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graph_set = set(graph.nodes())
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data_set = set(data.keys())
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intersection = data_set & graph_set
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new_graph.delete_nodes_from(graph_set - data_set) #remove difference
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for k in intersection:
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new_data[k] = data[k]
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if nodelist==None:
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nodelist=G.nodes()
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if vec == None:
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vec = G.degree(nodelist)
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if len(vec)!=len(nodelist):
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raise NXUTILSError("The node value vector is not of same length (%s) as the nodelist(%s)") %(len(vec), len(nodelist))
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if keep_connected:
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max_iter = 0
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sub_graphs = NX.connected_component_subgraphs(new_graph)
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if len(sub_graphs)==0:
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new_graph = sub_graphs[0]
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else:
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new_graph = sub_graphs[0]
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old_data = new_data
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while new_graph.number_of_nodes() != len(new_data) and max_iter<100:
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max_iter+=1
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graph_set = sets.Set(new_graph.nodes())
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data_set = sets.Set(new_data.keys())
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intersection = data_set & graph_set
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new_graph.delete_nodes_from(graph_set - data_set)
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new_data={}
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for k in intersection:
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new_data[k] = old_data[k]
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old_data = new_data.copy()
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new_graph = NX.connected_component_subgraphs(new_graph)[0]
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if pathways!=None:
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for pth,nodes in pathways.items():
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new_pathways[pth] = [node for node in nodes if node in new_graph]
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print "\nSUMMARY (graph_and_data_intersection): "
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print "Number of input variables: %s\n\
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Number nodes in input graph: %s" %(len(data),len(graph))
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print "\nUsing intersection of connected graph and nodes with data values"
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print "Number of variables is now: %s" %len(new_data)
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print "Number of nodes in graph: %s" %new_graph.number_of_nodes()
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if pathways!=None:
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return new_data,new_graph,new_pathways
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else:
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return new_data,new_graph
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def rx_graph_and_data_intersection(graph,node_data,pathways,data,keep_connected=False):
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"""Returns a (connected) reaction graph with present gene expression data.
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keep_connected==True:
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When a node (gene) is not present in our expression data, the node
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is deleted and all neighbors are connected with edge weight=0.5
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if the are not already neigbors.
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input:
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data -- dict, keys: gene id, value: measurement profile
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graph -- networkx.xbase.xgraph, full wieghted graph
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node_data -- dict, keys: rx id, value: set of gene_ids
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pathways -- dict, keys: pathway name, values: lidt of nodes in pathway
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"""
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# We do not connect the full graph ... may be performed by using the reference graph?
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graph = NX.connected_component_subgraphs(graph)[0] #largest connected component
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new_graph = graph.copy()
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new_data = {}
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new_node_data = node_data.copy()
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new_pathways = {}
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genes_in_graph=set()
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genes_in_data = set(data.keys())
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rx_in_graph = set(new_graph.nodes())
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# genes in graph nodes (rx_nodes)
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for rx in rx_in_graph:
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genes_in_graph.update(set(new_node_data.get(rx)))
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keep_genes = genes_in_data.intersection(genes_in_graph) #both in graph and data
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#update node data
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for rx,genes in node_data.items(): # delete node data of nodes not present in graph
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genes = set(genes)
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genes.intersection_update(keep_genes) #remove genes if they are not in inters.
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if len(genes)==0 or rx not in rx_in_graph: #no gene data or not in graph
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print "removing: " + str(rx)
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del new_node_data[rx]
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rx_in_data= set(new_node_data.keys())
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rx_intersection = rx_in_data.intersection(rx_in_graph)
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for gene in keep_genes:
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new_data[gene] = data.get(gene)
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# update pathways nodes
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for pth,genes in pathways.items():
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if genes:
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genes = set(genes)
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genes.intersection_update(keep_genes) # gene needs to have data
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else:
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pass
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new_pathways[pth] = genes
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bad_nodes = rx_in_graph.difference(rx_in_data) #in graph but no data
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if keep_connected==True:
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dummy = new_graph.copy()
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for rx in bad_nodes:
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dummy.delete_node(rx)
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if len(NX.connected_component_subgraphs(dummy))>1:
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nghbrs = new_graph.neighbors(rx)
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for i in nghbrs:
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for j in nghbrs:
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if i!=j:
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if not new_graph.has_edge(i,j):
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new_graph.add_edge(i,j,0.5)
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#update graph
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new_graph.delete_nodes_from(list(bad_nodes))
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return new_graph,new_node_data,new_pathways,new_data
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A = NX.ad_matrix(G, nodelist=nodelist)
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for i, node in enumerate(nodelist):
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nei_i = A[i,:]==1
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vec_i = vec[nei_i]
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def weighted_laplacian(G,with_labels=False):
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"""Return standard Laplacian of graph from a weighted adjacency matrix."""
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@ -418,10 +299,10 @@ def weighted_laplacian(G,with_labels=False):
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else:
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return L
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def subnetworks(G, T2):
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def grow_subnetworks(G, T2):
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"""Return the highest scoring (T2-test) subgraph og G.
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Use simulated annealing to identify highly scoring subgraphs.
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Use simulated annealing to identify highly grow subgraphs.
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ref: -- Ideker et.al (Bioinformatics 18, 2002)
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-- Patil and Nielsen (PNAS 2006)
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