143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
import pylab
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import matplotlib
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import networkx as nx
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import scipy
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import rpy
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def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None, **kwds):
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""" Correlation loading plot."""
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# background
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if ax==None or drawback==True:
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radius = 1
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center = (0,0)
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c100 = matplotlib.patches.Circle(center,radius=radius,
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facecolor='gray',
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alpha=.1,
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zorder=1)
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c50 = matplotlib.patches.Circle(center, radius=radius/2.0,
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facecolor='gray',
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alpha=.1,
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zorder=2)
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ax = pylab.gca()
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ax.add_patch(c100)
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ax.add_patch(c50)
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ax.axhline(lw=1.5,color='k')
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ax.axvline(lw=1.5,color='k')
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# corrloads
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ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder, **kwds)
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ax.set_xlim([-1,1])
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ax.set_ylim([-1,1])
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if expvar!=None:
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xstring = "Comp: %d expl.var: %.1f " %(pc1+1, expvar[pc1])
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pylab.xlabel(xstring)
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ystring = "Comp: %d expl.var.: %.1f " %(pc2+1, expvar[pc2])
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pylab.ylabel(ystring)
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if labels!=None:
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assert(len(labels)==R.shape[0])
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for name, r in zip(labels, R):
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pylab.text(r[pc1], r[pc2], " " + name)
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#pylab.show()
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def dag(terms, ontology):
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rpy.r.library("GOstats")
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__parents = {'bp' : rpy.r.GOBPPARENTS,
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'mf' : rpy.r.GOMFPARENTS,
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'cc' : rpy.r.GOCCPARENTS}
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gograph = rpy.r.GOGraph(terms, __parents.get(ontology.lower()))
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dag = rpy.r.edges(gograph)
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#setattr(dag, "_ontology", ontology)
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return dag
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def plot_dag(dag, node_color='b', node_size=30,with_labels=False,nodelist=None,pos=None):
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rpy.r.library("GOstats")
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dag_name = "GO-bp"
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# networkx does not play well with colon in node names
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clean_edges = {}
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for head, neigb in dag.items():
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head = head.replace(":", "_")
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nei = [i.replace(":", "_") for i in neigb]
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clean_edges[head] = nei
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if pos==None:
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G = nx.from_dict_of_lists(clean_edges, nx.DiGraph(name=dag_name))
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pos = nx.pydot_layout(G, prog='dot')
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pos_new = {}
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for k, v in pos.items():
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x,y = v
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k = k.replace("_", ":")
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pos_new[k] = (x, -y)
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pos = pos_new
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G = nx.from_dict_of_lists(dag, nx.Graph(name=dag_name))
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if len(node_color)>1:
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assert(len(node_color)==len(nodelist))
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nx.draw_networkx(G,pos, with_labels=with_labels, node_size=node_size, node_color=node_color, nodelist=nodelist)
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return pos
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def plot_ZXcorr(gene_ids, term_ids, gene2go, X, D, scale=True):
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""" Plot correlation/covariance between genes as a function of
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semantic difference.
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input: X (n, p) data matrix
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D (p, p) gene-gene sematic similarity matrix
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"""
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D = scipy.corrcoef(X)
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term2ind = dict(enumerate(term_ids))
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for i, gene_i in enumerate(gene_ids):
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for j, gene_j in enumerate(gene_ids):
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if j<i:
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r2 = D[i,j]
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terms_i = gene2go[gene_i]
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terms_j = gene2go[gene_j]
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for ti, term in enumerate(term_ids):
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if term in terms_i:
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pass
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def clustering_index(T, Yg):
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pass
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def draw_gene(gid, gene_ids, gene2go, Z, tmat, terms, G, pos):
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"""Draw dags with marked go terms and distance to all terms.
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"""
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sub_terms = gene2go[gid]
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sub_index = [i for i, tid in enumerate(terms) if tid in sub_terms]
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node_size = 70.*scipy.ones((len(terms),))
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node_size[sub_index] = 500
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gene_index = [i for i, gene_id in enumerate(gene_ids) if gene_id==gid]
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node_color = Z[:,gene_index].ravel()
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#1/0
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#node_size=200*node_color
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#node_color='g'
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pylab.figure()
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nx.draw_networkx(G, pos, node_color=node_color, node_size=node_size, with_labels=False, nodelist=terms)
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ax = pylab.gca()
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pylab.colorbar(ax.collections[0])
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for tid in sub_index:
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pylab.figure()
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node_color = tmat[tid,:]
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#node_size = 70*scipy.ones((len(terms),))
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node_size = 170*node_color
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node_size[tid] = 500
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nx.draw_networkx(G, pos, node_color=node_color, node_size=node_size, with_labels=False, nodelist=terms)
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pylab.title(terms[tid])
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ax = pylab.gca()
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pylab.colorbar(ax.collections[0])
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pylab.show()
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#nx.show()
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