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)