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cdata, cmask 


distance between two clusters 


distance matrix as a list of arrays 


clusterid, error, nfound 


clusterid, error, nfound 


arithmetic mean of the 1D array data. 


median value of the 1D array data 


(columnmean, coordinates, pc, eigenvalues) 


clusterid, celldata 


Tree object 





__package__ = None hash(x) 

The clustercentroids routine calculates the cluster centroids, given to which cluster each element belongs. The centroid is defined as either the mean or the median over all elements for each dimension.


This function returns the distance matrix between gene expression data.
Return value: The distance matrix is returned as a list of 1D arrays containing the distance matrix between the gene expression data. The number of columns in each row is equal to the row number. Hence, the first row has zero elements. An example of the return value is: matrix = [[], array([1.]), array([7., 3.]), array([4., 2., 6.])] This corresponds to the distance matrix: [0., 1., 7., 4.] [1., 0., 3., 2.] [7., 3., 0., 6.] [4., 2., 6., 0.]

This function implements kmeans clustering.

This function implements kmedoids clustering.

This function returns the principal component decomposition of the gene expression data.
Return value: This function returns an array containing the mean of each column, the principal components as an nmin x ncolumns array, as well as the coordinates (an nrows x nmin array) of the data along the principal components, and the associated eigenvalues. The principal components, the coordinates, and the eigenvalues are sorted by the magnitude of the eigenvalue, with the largest eigenvalues appearing first. Here, nmin is the smaller of nrows and ncolumns. Adding the column means to the dot product of the coordinates and the principal components,
recreates the data matrix.

This function implements a selforganizing map on a rectangular grid.

This function implements the pairwise single, complete, centroid, and average linkage hierarchical clustering methods.
Either data or distancematrix should be None. If distancematrix==None, the hierarchical clustering solution is calculated from the gene expression data stored in the argument data. If data==None, the hierarchical clustering solution is calculated from the distance matrix instead. Pairwise centroidlinkage clustering can be calculated only from the gene expression data and not from the distance matrix. Pairwise single, maximum, and averagelinkage clustering can be calculated from either the gene expression data or from the distance matrix. Return value: treecluster returns a Tree object describing the hierarchical clustering result. See the description of the Tree class for more information.

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