def main():
kmers = ["TGTAT", "CGTAT", "TTAGT", "TCTAT", "TCTAC"]
motif_clust = MotifCluster(kmers)
#result = motif_clust.cluster_by_seq()
#print "Result: ", result
data = np.transpose(np.array(kmers))
print "DATA: "
print data
dist_func = lambda x, y: sw_align(x, y, return_score=True)
linkage_method = "average"
hclust = clustering.hierarchical_clust(np.array(kmers), dist_func,
linkage_method)
def main():
kmers = ["TGTAT", "CGTAT", "TTAGT", "TCTAT", "TCTAC"]
motif_clust = MotifCluster(kmers)
#result = motif_clust.cluster_by_seq()
#print "Result: ", result
data = np.transpose(np.array(kmers))
print "DATA: "
print data
dist_func = lambda x, y: sw_align(x, y, return_score=True)
linkage_method = "average"
hclust = clustering.hierarchical_clust(np.array(kmers),
dist_func,
linkage_method)
def cluster_by_edit(self, kmers, linkage_method):
"""
Cluster sequences by edit distances.
Parameters:
-----------
kmers : flat list of kmers
linkage_method : determines linkage function for hierarchical
clustering ('average', 'single', ...).
"""
# Nest kmers to create matrix for clustering purposes.
kmers = [kmers]
data = np.array(kmers)
hclust = clustering.hierarchical_clust(data, self.edit_dist_func,
linkage_method)
return hclust
def cluster_by_edit(self, kmers, linkage_method):
"""
Cluster sequences by edit distances.
Parameters:
-----------
kmers : flat list of kmers
linkage_method : determines linkage function for hierarchical
clustering ('average', 'single', ...).
"""
# Nest kmers to create matrix for clustering purposes.
kmers = [kmers]
data = np.array(kmers)
hclust = clustering.hierarchical_clust(data,
self.edit_dist_func,
linkage_method)
return hclust
