CS446/546 - Class Session 13 - similarity and hierarchical clustering

In this class session we are going to hierachically cluster (based on Sorensen-Dice similarity) vertices in a directed graph from a landmark paper on human gene regulation (Neph et al., Cell, volume 150, pages 1274-1286, 2012; see PDF on Canvas)

Using Pandas read_csv, read in the ifle shared/neph_gene_network.txt, which has two columns of text (first column is the regulator gene, second column is the target gene), into a data frame. The file has no header and is tab-delimited. Assign the column names of the dataframe to be regulator and target, respectively.

Let's load the Python packages that we will need for this exercise



In [19]:

    
import pandas
import igraph
import numpy
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy
import scipy.spatial.distance

Using pandas.read_csv, read the file shared/neph_gene_network.txt; name the two columns of the resulting data frame, regulator and target.



In [3]:

    
edge_list_neph = pandas.read_csv("shared/neph_gene_network.txt",
                                 sep="\t", 
                                 names=["regulator","target"])

Load the edge-list data into an undirected igraph.Graph object neph_graph, using igraph.Graph.TupleList.



In [4]:

    
neph_graph = igraph.Graph.TupleList(edge_list_neph.values.tolist(), directed=False)
neph_graph.summary()









    Out[4]:





'IGRAPH UN-- 538 47945 -- \n+ attr: name (v)'

Using the igraph Graph.similarity_dice() method, compute a similarity matrix and assign it to name S



In [5]:

    
S = neph_graph.similarity_dice()

Using the numpy.matrix constructor, compute a distance matrix (1-S) and assign to object D



In [11]:

    
D = 1 - numpy.matrix(S)

Use scipy.spatial.distance.squareform to make a vector-form distance vector from the square-form distance matrix D; call the resulting object vD



In [29]:

    
vD = scipy.spatial.distance.squareform(D)

Using scipy.cluster.hierarchy.linkage on vD (with method="average"), perform hierarchical agglomerative clustering. Assign the resulting object to name hc



In [30]:

    
hc = scipy.cluster.hierarchy.linkage(vD, method="average")

Plot a dendrogram using scipy.cluster.hierarchy.dendrogram



In [31]:

    
plt.figure()
scipy.cluster.hierarchy.dendrogram(hc)
plt.show()