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laydi/scripts/lpls/plots_lpls.py

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import pylab
import matplotlib
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import networkx as nx
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import scipy
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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):
""" Correlation loading plot."""
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# background
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if ax==None or drawback==True:
radius = 1
center = (0,0)
c100 = matplotlib.patches.Circle(center,radius=radius,
facecolor='gray',
alpha=.1,
zorder=1)
c50 = matplotlib.patches.Circle(center, radius=radius/2.0,
facecolor='gray',
alpha=.1,
zorder=2)
ax = pylab.gca()
ax.add_patch(c100)
ax.add_patch(c50)
ax.axhline(lw=1.5,color='k')
ax.axvline(lw=1.5,color='k')
# corrloads
ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder)
ax.set_xlim([-1,1])
ax.set_ylim([-1,1])
if expvar!=None:
xstring = "Comp: %d expl.var: %.1f " %(pc1+1, expvar[pc1])
pylab.xlabel(xstring)
ystring = "Comp: %d expl.var.: %.1f " %(pc2+1, expvar[pc2])
pylab.ylabel(ystring)
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if labels!=None:
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assert(len(labels)==R.shape[0])
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 plot_dag(edge_dict, node_color='b', node_size=30,labels=None,nodelist=None,pos=None):
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# networkx does not play well with colon in node names
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clean_edges = {}
for head, neigb in edge_dict.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'))
pos = nx.pydot_layout(G, prog='dot')
G = nx.from_dict_of_lists(edge_dict, nx.DiGraph(name='GO'))
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)
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def plot_ZXcorr(gene_ids, term_ids, gene2go, X, D, scale=True):
""" Plot correlation/covariance between genes as a function of
semantic difference.
input: X (n, p) data matrix
D (p, p) gene-gene sematic similarity matrix
"""
D = scipy.corrcoef(X)
term2ind = dict(enumerate(term_ids))
for i, gene_i in enumerate(gene_ids):
for j, gene_j in enumerate(gene_ids):
if j<i:
r2 = D[i,j]
terms_i = gene2go[gene_i]
terms_j = gene2go[gene_j]
for ti, term in enumerate(term_ids):
if term in terms_i:
pass
def clustering_index(T, Yg):
pass