def clusterdistance(self,
index1=0,
index2=0,
method='a',
dist='e',
transpose=0):
"""Calculate the distance between two clusters.
- index1 : 1D array identifying which genes/microarrays belong to the
first cluster. If the cluster contains only one gene, then
index1 can also be written as a single integer.
- index2 : 1D array identifying which genes/microarrays belong to the
second cluster. If the cluster contains only one gene, then
index2 can also be written as a single integer.
- transpose: if equal to 0, genes (rows) are clustered;
if equal to 1, microarrays (columns) are clustered.
- dist : specifies the distance function to be used:
- dist=='e': Euclidean distance
- dist=='b': City Block distance
- dist=='c': Pearson correlation
- dist=='a': absolute value of the correlation
- dist=='u': uncentered correlation
- dist=='x': absolute uncentered correlation
- dist=='s': Spearman's rank correlation
- dist=='k': Kendall's tau
- method : specifies how the distance between two clusters is defined:
- method=='a': the distance between the arithmetic means of the
two clusters
- method=='m': the distance between the medians of the two
clusters
- method=='s': the smallest pairwise distance between members
of the two clusters
- method=='x': the largest pairwise distance between members of
the two clusters
- method=='v': average of the pairwise distances between
members of the clusters
- transpose: if equal to 0: clusters of genes (rows) are considered;
if equal to 1: clusters of microarrays (columns) are considered.
"""
if transpose == 0:
weight = self.eweight
else:
weight = self.gweight
return clusterdistance(self.data, self.mask, weight, index1, index2,
method, dist, transpose)
def clusterdistance(self, index1=0, index2=0, method='a', dist='e',
transpose=0):
"""Calculate the distance between two clusters.
Arguments:
- index1 : 1D array identifying which genes/microarrays belong to the
first cluster. If the cluster contains only one gene, then
index1 can also be written as a single integer.
- index2 : 1D array identifying which genes/microarrays belong to the
second cluster. If the cluster contains only one gene, then
index2 can also be written as a single integer.
- transpose: if equal to 0, genes (rows) are clustered;
if equal to 1, microarrays (columns) are clustered.
- dist : specifies the distance function to be used:
- dist=='e': Euclidean distance
- dist=='b': City Block distance
- dist=='c': Pearson correlation
- dist=='a': absolute value of the correlation
- dist=='u': uncentered correlation
- dist=='x': absolute uncentered correlation
- dist=='s': Spearman's rank correlation
- dist=='k': Kendall's tau
- method : specifies how the distance between two clusters is defined:
- method=='a': the distance between the arithmetic means of the
two clusters
- method=='m': the distance between the medians of the two clusters
- method=='s': the smallest pairwise distance between members of
the two clusters
- method=='x': the largest pairwise distance between members of
the two clusters
- method=='v': average of the pairwise distances between members
of the clusters
- transpose: if equal to 0: clusters of genes (rows) are considered;
if equal to 1: clusters of microarrays (columns) are considered.
"""
if transpose == 0:
weight = self.eweight
else:
weight = self.gweight
return clusterdistance(self.data, self.mask, weight,
index1, index2, method, dist, transpose)