Updates

scripts/lpls/plots_lpls.py

@@ -2,8 +2,9 @@ import pylab
 import matplotlib
 import networkx as nx
 import scipy
+import rpy
 
-def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None):
+def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None, **kwds):
     """ Correlation loading plot."""
 
     # background
@@ -25,7 +26,7 @@ def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,draw
         ax.axvline(lw=1.5,color='k')
 
     # corrloads
-    ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder)
+    ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder, **kwds)
     ax.set_xlim([-1,1])
     ax.set_ylim([-1,1])
     if expvar!=None:
@@ -39,24 +40,45 @@ def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,draw
             pylab.text(r[pc1], r[pc2], "  " + name)
     #pylab.show()
 
-def plot_dag(edge_dict, node_color='b', node_size=30,labels=None,nodelist=None,pos=None):
+
+def dag(terms, ontology):
+    rpy.r.library("GOstats")
+    __parents = {'bp' : rpy.r.GOBPPARENTS,
+                 'mf' : rpy.r.GOMFPARENTS,
+                 'cc' : rpy.r.GOCCPARENTS}
+    gograph = rpy.r.GOGraph(terms, __parents.get(ontology))
+    dag = rpy.r.edges(gograph)
+    #setattr(dag, "_ontology", ontology)
+    return dag
+
+def plot_dag(dag, node_color='b', node_size=30,with_labels=False,nodelist=None,pos=None):
+
+    rpy.r.library("GOstats")
+    
+    dag_name = "GO-bp" 
     # networkx does not play well with colon in node names
     clean_edges = {}
-    for head, neigb in edge_dict.items():
+    for head, neigb in dag.items():
         head = head.replace(":", "_")
         nei = [i.replace(":", "_") for i in neigb]
         clean_edges[head] = nei
     if pos==None:
-        G = nx.from_dict_of_lists(clean_edges, nx.DiGraph(name='GO'))
+        G = nx.from_dict_of_lists(clean_edges, nx.DiGraph(name=dag_name))
         pos = nx.pydot_layout(G, prog='dot')
-    G = nx.from_dict_of_lists(edge_dict, nx.DiGraph(name='GO'))
+        pos_new = {}
+        for k, v in pos.items():
+            x,y = v
+            k = k.replace("_", ":")
+            pos_new[k] = (x, -y) 
+        pos = pos_new
+    
+    G = nx.from_dict_of_lists(dag, nx.Graph(name=dag_name))
     if len(node_color)>1:
         assert(len(node_color)==len(nodelist))
-    if labels!=None:
-        with_labels=True
         
     nx.draw_networkx(G,pos, with_labels=with_labels, node_size=node_size, node_color=node_color, nodelist=nodelist)
-    
+    return pos
+
 
 def plot_ZXcorr(gene_ids, term_ids, gene2go, X, D, scale=True):
     """ Plot correlation/covariance between genes as a function of
@@ -80,6 +102,39 @@ def plot_ZXcorr(gene_ids, term_ids, gene2go, X, D, scale=True):
 
 def clustering_index(T, Yg):
     pass
+
+def draw_gene(gid, gene_ids, gene2go, Z, tmat, terms, G, pos):
+    """Draw dags with marked go terms and distance to all terms.
+    """
+    sub_terms = gene2go[gid]
+    sub_index = [i for i, tid in enumerate(terms) if tid in sub_terms]
+    node_size = 70.*scipy.ones((len(terms),))
+    node_size[sub_index] = 500
+    gene_index = [i for i, gene_id in enumerate(gene_ids) if gene_id==gid]
+    node_color = Z[:,gene_index].ravel()
+    #1/0
+    #node_size=200*node_color
+    #node_color='g'
+    pylab.figure()
+    nx.draw_networkx(G, pos, node_color=node_color, node_size=node_size, with_labels=False, nodelist=terms)
+    ax = pylab.gca()
+    pylab.colorbar(ax.collections[0])
+
+    for tid in sub_index:
+        pylab.figure()
+        node_color = tmat[tid,:]
+        #node_size = 70*scipy.ones((len(terms),))
+        node_size = 170*node_color
+        node_size[tid] = 500
+        nx.draw_networkx(G, pos, node_color=node_color, node_size=node_size, with_labels=False, nodelist=terms)
+        pylab.title(terms[tid])
+        ax = pylab.gca()
+        pylab.colorbar(ax.collections[0])
+    pylab.show()
+    #nx.show()
+    
+    
+