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A Record stores the gene expression data and related information contained in a data file following the file format defined for Michael Eisen's Cluster/TreeView program. A Record has the following members: data: a matrix containing the gene expression data mask: a matrix containing only 1's and 0's, denoting which values are present (1) or missing (0). If all elements of mask are one (no missing data), then mask is set to None. geneid: a list containing a unique identifier for each gene (e.g., ORF name) genename: a list containing an additional description for each gene (e.g., gene name) gweight: the weight to be used for each gene when calculating the distance gorder: an array of real numbers indicating the preferred order of the genes in the output file expid: a list containing a unique identifier for each experimental condition eweight: the weight to be used for each experimental condition when calculating the distance eorder: an array of real numbers indication the preferred order in the output file of the experimental conditions uniqid: the string that was used instead of UNIQID in the input file.
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saves the clustering results. The saved files follow the convention for Java TreeView program, which can therefore be used to view the clustering result. Arguments: jobname: The base name of the files to be saved. The filenames are jobname.cdt, jobname.gtr, and jobname.atr for hierarchical clustering, and jobname-K*.cdt, jobname-K*.kgg, jobname-K*.kag for k-means clustering results. geneclusters=None: For hierarchical clustering results, geneclusters is an (ngenes-1 x 2) array that describes the hierarchical clustering result for genes. This array can be calculated by the hierarchical clustering methods implemented in treecluster. For k-means clustering results, geneclusters is a vector containing ngenes integers, describing to which cluster a given gene belongs. This vector can be calculated by kcluster. expclusters=None: For hierarchical clustering results, expclusters is an (nexps-1 x 2) array that describes the hierarchical clustering result for experimental conditions. This array can be calculated by the hierarchical clustering methods implemented in treecluster. For k-means clustering results, expclusters is a vector containing nexps integers, describing to which cluster a given experimental condition belongs. This vector can be calculated by kcluster. |
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