Description: Agglomerative hierarchical clustering of genes/experiments
Author: Joshua Gould, Broad Institute
Cluster analysis is a means of discovering, within a body of data, groups whose members are similar for some property. Clustering of gene expression data is geared toward finding genes that are expressed or not expressed in similar ways under certain conditions.
Given a set of items to be clustered (items can be either genes or samples), agglomerative hierarchical clustering (HC) recursively merges items with other items or with the result of previous merges, according to the distance between each pair of items, with the closest item pairs being merged first. As a result, it produces a tree structure, referred to as dendogram, whose nodes correspond to:
The HierarchicalClustering module produces a CDT file that contains the original data, but reordered to reflect the clustering. Additionally, either a dendrogram or two dendrogram files are created (one for clustering rows and one for clustering columns). The row dendrogram has the extension GTR, while the column dendrogram has the extension ATR. These files describe the order in which nodes were joined during the clustering.
The module includes several preprocessing options. The order of the preprocessing operations is:
Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998;95:14863-14868.
de Hoon MJ, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics. 2004;20:1453-1454.
|input filename *||input data file name - .gct, .res, .pcl|
|column distance measure *||
Distance measure for column (sample) clustering. Options include:
|row distance measure *||
Distance measure for row (gene) clustering. Options include:
|clustering method *||
Hierarchical clustering method to use. Options include:
|log transform||Specifies whether to log-transform the data before clustering. Default: no|
|row center||Specifies whether to center each row (gene) in the data. Centering each row subtracts the row-wise mean or median from the values in each row of data, so that the mean or median value of each row is 0. Default: no|
|row normalize||Specifies whether to normalize each row (gene) in the data. Normalizing each row multiplies all values in each row of data by a scale factor S so that the sum of the squares of the values in each row is 1.0 (a separate S is computed for each row). Default: no|
|column center||Specifies whether to center each column (sample) in the data. Centering each column subtracts the column-wise mean or median from the values in each column of data, so that the mean or median value of each column is 0. Default: no|
|column normalize||Specifies whether to normalize each column (sample) in the data. Normalizing each column multiplies all values in each column of data by a scale factor S so that the sum of the squares of the values in each column is 1.0 (a separate S is computed for each column). Default: no|
|output base name *||Base name for the output files|
* - required
HierarchicalClustering is distributed under the license available at http://rana.lbl.gov/EisenSoftwareSource.htm
|6||2013-03-13||Updated for Java 7|
|2||2005-12-16||Fixes bugs in previous version|