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. Definition at line 386 of file __init__.py. : """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 distancematrix(self, transpose=0, dist='e'):
